problem solving experiment in psychology research paper

The Process of Problem Solving

• Editor's Choice
• Experimental Psychology
• Problem Solving

In a 2013 article published in the Journal of Cognitive Psychology , Ngar Yin Louis Lee (Chinese University of Hong Kong) and APS William James Fellow Philip N. Johnson-Laird (Princeton University) examined the ways people develop strategies to solve related problems. In a series of three experiments, the researchers asked participants to solve series of matchstick problems.

In matchstick problems, participants are presented with an array of joined squares. Each square in the array is comprised of separate pieces. Participants are asked to remove a certain number of pieces from the array while still maintaining a specific number of intact squares. Matchstick problems are considered to be fairly sophisticated, as there is generally more than one solution, several different tactics can be used to complete the task, and the types of tactics that are appropriate can change depending on the configuration of the array.

Louis Lee and Johnson-Laird began by examining what influences the tactics people use when they are first confronted with the matchstick problem. They found that initial problem-solving tactics were constrained by perceptual features of the array, with participants solving symmetrical problems and problems with salient solutions faster. Participants frequently used tactics that involved symmetry and salience even when other solutions that did not involve these features existed.

To examine how problem solving develops over time, the researchers had participants solve a series of matchstick problems while verbalizing their problem-solving thought process. The findings from this second experiment showed that people tend to go through two different stages when solving a series of problems.

People begin their problem-solving process in a generative manner during which they explore various tactics — some successful and some not. Then they use their experience to narrow down their choices of tactics, focusing on those that are the most successful. The point at which people begin to rely on this newfound tactical knowledge to create their strategic moves indicates a shift into a more evaluative stage of problem solving.

In the third and last experiment, participants completed a set of matchstick problems that could be solved using similar tactics and then solved several problems that required the use of novel tactics.  The researchers found that participants often had trouble leaving their set of successful tactics behind and shifting to new strategies.

From the three studies, the researchers concluded that when people tackle a problem, their initial moves may be constrained by perceptual components of the problem. As they try out different tactics, they hone in and settle on the ones that are most efficient; however, this deduced knowledge can in turn come to constrain players’ generation of moves — something that can make it difficult to switch to new tactics when required.

These findings help expand our understanding of the role of reasoning and deduction in problem solving and of the processes involved in the shift from less to more effective problem-solving strategies.

Reference Louis Lee, N. Y., Johnson-Laird, P. N. (2013). Strategic changes in problem solving. Journal of Cognitive Psychology, 25 , 165–173. doi: 10.1080/20445911.2012.719021

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• Review Article
• Published: 14 December 2023

Restructuring processes and Aha! experiences in insight problem solving

• Jennifer Wiley   ORCID: orcid.org/0000-0002-2590-7392 1 &
• Amory H. Danek   ORCID: orcid.org/0000-0002-2849-8774 2  

Nature Reviews Psychology volume  3 ,  pages 42–55 ( 2024 ) Cite this article

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Insightful solution processes represent cases of problem solving in which the emergence of a new interpretation allows for an abrupt shift from bewilderment to clarity. One approach to researching insight problem solving emphasizes cognitive restructuring of the problem representation as a defining feature of the insightful solution process. By contrast, another approach emphasizes phenomenological Aha! experiences. In this Review, we summarize both approaches, considering the restructuring processes involved in finding a solution and the Aha! experiences that might accompany solutions. We then consider the extent to which Aha! experiences co-occur with restructuring, and the critical observation that sometimes they do not. We conclude by proposing avenues for future research that combine the methodologies used to study restructuring and Aha! experiences to better understand the cognitive and phenomenological underpinnings of insight problem solving and the connections between them.

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The authors thank I. K. Ash, P. J. Cushen, T. George, A. F. Jarosz, T. S. Miller and S. Ohlsson for discussion on these topics.

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Wiley, J., Danek, A.H. Restructuring processes and Aha! experiences in insight problem solving. Nat Rev Psychol 3 , 42–55 (2024). https://doi.org/10.1038/s44159-023-00257-x

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Real World Problem-Solving

Real world problem-solving (RWPS) is what we do every day. It requires flexibility, resilience, resourcefulness, and a certain degree of creativity. A crucial feature of RWPS is that it involves continuous interaction with the environment during the problem-solving process. In this process, the environment can be seen as not only a source of inspiration for new ideas but also as a tool to facilitate creative thinking. The cognitive neuroscience literature in creativity and problem-solving is extensive, but it has largely focused on neural networks that are active when subjects are not focused on the outside world, i.e., not using their environment. In this paper, I attempt to combine the relevant literature on creativity and problem-solving with the scattered and nascent work in perceptually-driven learning from the environment. I present my synthesis as a potential new theory for real world problem-solving and map out its hypothesized neural basis. I outline some testable predictions made by the model and provide some considerations and ideas for experimental paradigms that could be used to evaluate the model more thoroughly.

1. Introduction

In the Apollo 13 space mission, astronauts together with ground control had to overcome several challenges to bring the team safely back to Earth (Lovell and Kluger, 2006 ). One of these challenges was controlling carbon dioxide levels onboard the space craft: “For 2 days straight [they] had worked on how to jury-rig the Odysseys canisters to the Aquarius's life support system. Now, using materials known to be available onboard the spacecraft—a sock, a plastic bag, the cover of a flight manual, lots of duct tape, and so on—the crew assembled a strange contraption and taped it into place. Carbon dioxide levels immediately began to fall into the safe range” (Team, 1970 ; Cass, 2005 ).

The success of Apollo 13's recovery from failure is often cited as a glowing example of human resourcefulness and inventiveness alongside more well-known inventions and innovations over the course of human history. However, this sort of inventive capability is not restricted to a few creative geniuses, but an ability present in all of us, and exemplified in the following mundane example. Consider a situation when your only suit is covered in lint and you do not own a lint remover. You see a roll of duct tape, and being resourceful you reason that it might be a good substitute. You then solve the problem of lint removal by peeling a full turn's worth of tape and re-attaching it backwards onto the roll to expose the sticky side all around the roll. By rolling it over your suit, you can now pick up all the lint.

In both these examples (historic as well as everyday), we see evidence for our innate ability to problem-solve in the real world. Solving real world problems in real time given constraints posed by one's environment are crucial for survival. At the core of this skill is our mental capability to get out of “sticky situations” or impasses, i.e., difficulties that appear unexpectedly as impassable roadblocks to solving the problem at hand. But, what are the cognitive processes that enable a problem solver to overcome such impasses and arrive at a solution, or at least a set of promising next steps?

A central aspect of this type of real world problem solving, is the role played by the solver's surrounding environment during the problem-solving process. Is it possible that interaction with one's environment can facilitate creative thinking? The answer to this question seems somewhat obvious when one considers the most famous anecdotal account of creative problem solving, namely that of Archimedes of Syracuse. During a bath, he found a novel way to check if the King's crown contained non-gold impurities. The story has traditionally been associated with the so-called “Eureka moment,” the sudden affective experience when a solution to a particularly thorny problem emerges. In this paper, I want to temporarily turn our attention away from the specific “aha!” experience itself and take particular note that Archimedes made this discovery, not with his eyes closed at a desk, but in a real-world context of a bath 1 . The bath was not only a passive, relaxing environment for Archimedes, but also a specific source of inspiration. Indeed it was his noticing the displacement of water that gave him a specific methodology for measuring the purity of the crown; by comparing how much water a solid gold bar of the same weight would displace as compared with the crown. This sort of continuous environmental interaction was present when the Apollo 13 engineers discovered their life-saving solution, and when you solved the suit-lint-removal problem with duct tape.

The neural mechanisms underlying problem-solving have been extensively studied in the literature, and there is general agreement about the key functional networks and nodes involved in various stages of problem-solving. In addition, there has been a great deal of work in studying the neural basis for creativity and insight problem solving, which is associated with the sudden emergence of solutions. However, in the context of problem-solving, creativity, and insight have been researched as largely an internal process without much interaction with and influence from the external environment (Wegbreit et al., 2012 ; Abraham, 2013 ; Kounios and Beeman, 2014 ) 2 . Thus, there are open questions of what role the environment plays during real world problem-solving (RWPS) and how the brain enables the assimilation of novel items during these external interactions.

In this paper, I synthesize the literature on problem-solving, creativity and insight, and particularly focus on how the environment can inform RWPS. I explore three environmentally-informed mechanisms that could play a critical role: (1) partial-cue driven context-shifting, (2) heuristic prototyping and learning novel associations, and (3) learning novel physical inferences. I begin first with some intuitions about real world problem solving, that might help ground this discussion and providing some key distinctions from more traditional problem solving research. Then, I turn to a review of the relevant literature on problem-solving, creativity, and insight first, before discussing the three above-mentioned environmentally-driven mechanisms. I conclude with a potential new model and map out its hypothesized neural basis.

2. Problem solving, creativity, and insight

2.1. what is real world problem-solving.

Archimedes was embodied in the real world when he found his solution. In fact, the real world helped him solve the problem. Whether or not these sorts of historic accounts of creative inspiration are accurate 3 , they do correlate with some of our own key intuitions about how problem solving occurs “in the wild.” Real world problem solving (RWPS) is different from those that occur in a classroom or in a laboratory during an experiment. They are often dynamic and discontinuous, accompanied by many starts and stops. Solvers are never working on just one problem. Instead, they are simultaneously juggling several problems of varying difficulties and alternating their attention between them. Real world problems are typically ill-defined, and even when they are well-defined, often have open-ended solutions. Coupled with that is the added aspect of uncertainty associated with the solver's problem solving strategies. As introduced earlier, an important dimension of RWPS is the continuous interaction between the solver and their environment. During these interactions, the solver might be inspired or arrive at an “aha!” moment. However, more often than not, the solver experiences dozens of minor discovery events— “hmmm, interesting…” or “wait, what?…” moments. Like discovery events, there's typically never one singular impasse or distraction event. The solver must iterate through the problem solving process experiencing and managing these sorts of intervening events (including impasses and discoveries). In summary, RWPS is quite messy and involves a tight interplay between problem solving, creativity, and insight. Next, I explore each of these processes in more detail and explicate a possible role of memory, attention, conflict management and perception.

2.2. Analytical problem-solving

In psychology and neuroscience, problem-solving broadly refers to the inferential steps taken by an agent 4 that leads from a given state of affairs to a desired goal state (Barbey and Barsalou, 2009 ). The agent does not immediately know how this goal can be reached and must perform some mental operations (i.e., thinking) to determine a solution (Duncker, 1945 ).

The problem solving literature divides problems based on clarity (well-defined vs. ill-defined) or on the underlying cognitive processes (analytical, memory retrieval, and insight) (Sprugnoli et al., 2017 ). While memory retrieval is an important process, I consider it as a sub-process to problem solving more generally. I first focus on analytical problem-solving process, which typically involves problem-representation and encoding, and the process of forming and executing a solution plan (Robertson, 2016 ).

2.2.1. Problem definition and representation

An important initial phase of problem-solving involves defining the problem and forming a representation in the working memory. During this phase, components of the prefrontal cortex (PFC), default mode network (DMN), and the dorsal anterior cingulate cortex (dACC) have been found to be activated. If the problem is familiar and well-structured, top-down executive control mechanisms are engaged and the left prefrontal cortex including the frontopolar, dorso-lateral (dlPFC), and ventro-lateral (vlPFC) are activated (Barbey and Barsalou, 2009 ). The DMN along with the various structures in the medial temporal lobe (MTL) including the hippocampus (HF), parahippocampal cortex, perirhinal and entorhinal cortices are also believed to have limited involvement, especially in episodic memory retrieval activities during this phase (Beaty et al., 2016 ). The problem representation requires encoding problem information for which certain visual and parietal areas are also involved, although the extent of their involvement is less clear (Anderson and Fincham, 2014 ; Anderson et al., 2014 ).

2.2.1.1. Working memory

An important aspect of problem representation is the engagement and use of working memory (WM). The WM allows for the maintenance of relevant problem information and description in the mind (Gazzaley and Nobre, 2012 ). Research has shown that WM tasks consistently recruit the dlPFC and left inferior frontal cortex (IC) for encoding an manipulating information; dACC for error detection and performance adjustment; and vlPFC and the anterior insula (AI) for retrieving, selecting information and inhibitory control (Chung and Weyandt, 2014 ; Fang et al., 2016 ).

2.2.1.2. Representation

While we generally have a sense for the brain regions that are functionally influential in problem definition, less is known about how exactly events are represented within these regions. One theory for how events are represented in the PFC is the structured event complex theory (SEC), in which components of the event knowledge are represented by increasingly higher-order convergence zones localized within the PFC, akin to the convergence zones (from posterior to anterior) that integrate sensory information in the brain (Barbey et al., 2009 ). Under this theory, different zones in the PFC (left vs. right, anterior vs. posterior, lateral vs. medial, and dorsal vs. ventral) represent different aspects of the information contained in the events (e.g., number of events to be integrated together, the complexity of the event, whether planning, and action is needed). Other studies have also suggested the CEN's role in tasks requiring cognitive flexibility, and functions to switch thinking modes, levels of abstraction of thought and consider multiple concepts simultaneously (Miyake et al., 2000 ).

Thus, when the problem is well-structured, problem representation is largely an executive control activity coordinated by the PFC in which problem information from memory populates WM in a potentially structured representation. Once the problem is defined and encoded, planning and execution of a solution can begin.

2.2.2. Planning

The central executive network (CEN), particularly the PFC, is largely involved in plan formation and in plan execution. Planning is the process of generating a strategy to advance from the current state to a goal state. This in turn involves retrieving a suitable solution strategy from memory and then coordinating its execution.

2.2.2.1. Plan formation

The dlPFC supports sequential planning and plan formation, which includes the generation of hypothesis and construction of plan steps (Barbey and Barsalou, 2009 ). Interestingly, the vlPFC and the angular gyrus (AG), implicated in a variety of functions including memory retrieval, are also involved in plan formation (Anderson et al., 2014 ). Indeed, the AG together with the regions in the MTL (including the HF) and several other regions form a what is known as the “core” network. The core network is believed to be activated when recalling past experiences, imagining fictitious, and future events and navigating large-scale spaces (Summerfield et al., 2010 ), all key functions for generating plan hypotheses. A recent study suggests that the AG is critical to both episodic simulation, representation, and episodic memory (Thakral et al., 2017 ). One possibility for how plans are formulated could involve a dynamic process of retrieving an optimal strategy from memory. Research has shown significant interaction between striatal and frontal regions (Scimeca and Badre, 2012 ; Horner et al., 2015 ). The striatum is believed to play a key role in declarative memory retrieval, and specifically helping retrieve optimal (or previously rewarded) memories (Scimeca and Badre, 2012 ). Relevant to planning and plan formation, Scimeca & Badre have suggested that the striatum plays two important roles: (1) in mapping acquired value/utility to action selection, and thereby helping plan formation, and (2) modulation and re-encoding of actions and other plan parameters. Different types of problems require different sets of specialized knowledge. For example, the knowledge needed to solve mathematical problems might be quite different (albeit overlapping) from the knowledge needed to select appropriate tools in the environment.

Thus far, I have discussed planning and problem representation as being domain-independent, which has allowed me to outline key areas of the PFC, MTL, and other regions relevant to all problem-solving. However, some types of problems require domain-specific knowledge for which other regions might need to be recruited. For example, when planning for tool-use, the superior parietal lobe (SPL), supramarginal gyrus (SMG), anterior inferior parietal lobe (AIPL), and certain portions of the temporal and occipital lobe involved in visual and spatial integration have been found to be recruited (Brandi et al., 2014 ). It is believed that domain-specific information stored in these regions is recovered and used for planning.

2.2.2.2. Plan execution

Once a solution plan has been recruited from memory and suitably tuned for the problem on hand, the left-rostral PFC, caudate nucleus (CN), and bilateral posterior parietal cortices (PPC) are responsible for translating the plan into executable form (Stocco et al., 2012 ). The PPC stores and maintains “mental template” of the executable form. Hemispherical division of labor is particularly relevant in planning where it was shown that when planning to solve a Tower of Hanoi (block moving) problem, the right PFC is involved in plan construction whereas the left PFC is involved in controlling processes necessary to supervise the execution of the plan (Newman and Green, 2015 ). On a separate note and not the focus of this paper, plan execution and problem-solving can require the recruitment of affective and motivational processing in order to supply the agent with the resolve to solve problems, and the vmPFC has been found to be involved in coordinating this process (Barbey and Barsalou, 2009 ).

2.3. Creativity

During the gestalt movement in the 1930s, Maier noted that “most instances of “real” problem solving involves creative thinking” (Maier, 1930 ). Maier performed several experiments to study mental fixation and insight problem solving. This close tie between insight and creativity continues to be a recurring theme, one that will be central to the current discussion. If creativity and insight are linked to RWPS as noted by Maier, then it is reasonable to turn to the creativity and insight literature for understanding the role played by the environment. A large portion of the creativity literature has focused on viewing creativity as an internal process, one in which the solvers attention is directed inwards, and toward internal stimuli, to facilitate the generation of novel ideas and associations in memory (Beaty et al., 2016 ). Focusing on imagination, a number of researchers have looked at blinking, eye fixation, closing eyes, and looking nowhere behavior and suggested that there is a shift of attention from external to internal stimuli during creative problem solving (Salvi and Bowden, 2016 ). The idea is that shutting down external stimuli reduces cognitive load and focuses attention internally. Other experiments studying sleep behavior have also noted the beneficial role of internal stimuli in problem solving. The notion of ideas popping into ones consciousness, suddenly, during a shower is highly intuitive for many and researchers have attempted to study this phenomena through the lens of incubation, and unconscious thought that is internally-driven. There have been several theories and counter-theories proposed to account specifically for the cognitive processes underlying incubation (Ritter and Dijksterhuis, 2014 ; Gilhooly, 2016 ), but none of these theories specifically address the role of the external environment.

The neuroscience of creativity has also been extensively studied and I do not focus on an exhaustive literature review in this paper (a nice review can be found in Sawyer, 2011 ). From a problem-solving perspective, it has been found that unlike well-structured problems, ill-structured problems activate the right dlPFC. Most of the past work on creativity and creative problem-solving has focused on exploring memory structures and performing internally-directed searches. Creative idea generation has primarily been viewed as internally directed attention (Jauk et al., 2012 ; Benedek et al., 2016 ) and a primary mechanism involved is divergent thinking , which is the ability to produce a variety of responses in a given situation (Guilford, 1962 ). Divergent thinking is generally thought to involve interactions between the DMN, CEN, and the salience network (Yoruk and Runco, 2014 ; Heinonen et al., 2016 ). One psychological model of creative cognition is the Geneplore model that considers two major phases of generation (memory retrieval and mental synthesis) and exploration (conceptual interpretation and functional inference) (Finke et al., 1992 ; Boccia et al., 2015 ). It has been suggested that the associative mode of processing to generate new creative association is supported by the DMN, which includes the medial PFC, posterior cingulate cortex (PCC), tempororparietal juntion (TPJ), MTL, and IPC (Beaty et al., 2014 , 2016 ).

That said, the creativity literature is not completely devoid of acknowledging the role of the environment. In fact, it is quite the opposite. Researchers have looked closely at the role played by externally provided hints from the time of the early gestalt psychologists and through to present day studies (Öllinger et al., 2017 ). In addition to studying how hints can help problem solving, researchers have also looked at how directed action can influence subsequent problem solving—e.g., swinging arms prior to solving the two-string puzzle, which requires swinging the string (Thomas and Lleras, 2009 ). There have also been numerous studies looking at how certain external perceptual cues are correlated with creativity measures. Vohs et al. suggested that untidiness in the environment and the increased number of potential distractions helps with creativity (Vohs et al., 2013 ). Certain colors such as blue have been shown to help with creativity and attention to detail (Mehta and Zhu, 2009 ). Even environmental illumination, or lack thereof, have been shown to promote creativity (Steidle and Werth, 2013 ). However, it is important to note that while these and the substantial body of similar literature show the relationship of the environment to creative problem solving, they do not specifically account for the cognitive processes underlying the RWPS when external stimuli are received.

2.4. Insight problem solving

Analytical problem solving is believed to involve deliberate and conscious processing that advances step by step, allowing solvers to be able to explain exactly how they solved it. Inability to solve these problems is often associated with lack of required prior knowledge, which if provided, immediately makes the solution tractable. Insight, on the other hand, is believed to involve a sudden and unexpected emergence of an obvious solution or strategy sometimes accompanied by an affective aha! experience. Solvers find it difficult to consciously explain how they generated a solution in a sequential manner. That said, research has shown that having an aha! moment is neither necessary nor sufficient to insight and vice versa (Danek et al., 2016 ). Generally, it is believed that insight solvers acquire a full and deep understanding of the problem when they have solved it (Chu and Macgregor, 2011 ). There has been an active debate in the problem solving community about whether insight is something special. Some have argued that it is not, and that there are no special or spontaneous processes, but simply a good old-fashioned search of a large problem space (Kaplan and Simon, 1990 ; MacGregor et al., 2001 ; Ash and Wiley, 2006 ; Fleck, 2008 ). Others have argued that insight is special and suggested that it is likely a different process (Duncker, 1945 ; Metcalfe, 1986 ; Kounios and Beeman, 2014 ). This debate lead to two theories for insight problem solving. MacGregor et al. proposed the Criterion for Satisfactory Progress Theory (CSPT), which is based on Newell and Simons original notion of problem solving as being a heuristic search through the problem space (MacGregor et al., 2001 ). The key aspect of CSPT is that the solver is continually monitoring their progress with some set of criteria. Impasses arise when there is a criterion failure, at which point the solver tries non-maximal but promising states. The representational change theory (RCT) proposed by Ohlsson et al., on the other hand, suggests that impasses occur when the goal state is not reachable from an initial problem representation (which may have been generated through unconscious spreading activation) (Ohlsson, 1992 ). In order to overcome an impasse, the solver needs to restructure the problem representation, which they can do by (1) elaboration (noticing new features of a problem), (2) re-encoding fixing mistaken or incomplete representations of the problem, and by (3) changing constraints. Changing constraints is believed to involve two sub-processes of constraint relaxation and chunk-decomposition.

The current position is that these two theories do not compete with each other, but instead complement each other by addressing different stages of problem solving: pre- and post-impasse. Along these lines, Ollinger et al. proposed an extended RCT (eRCT) in which revising the search space and using heuristics was suggested as being a dynamic and iterative and recursive process that involves repeated instances of search, impasse and representational change (Öllinger et al., 2014 , 2017 ). Under this theory, a solver first forms a problem representation and begins searching for solutions, presumably using analytical problem solving processes as described earlier. When a solution cannot be found, the solver encounters an impasse, at which point the solver must restructure or change the problem representation and once again search for a solution. The model combines both analytical problem solving (through heuristic searches, hill climbing and progress monitoring), and creative mechanisms of constraint relaxation and chunk decomposition to enable restructuring.

Ollingers model appears to comprehensively account for both analytical and insight problem solving and, therefore, could be a strong candidate to model RWPS. However, while compelling, it is nevertheless an insufficient model of RWPS for many reasons, of which two are particularly significant for the current paper. First, the model does explicitly address mechanisms by which external stimuli might be assimilated. Second, the model is not sufficiently flexible to account for other events (beyond impasse) occurring during problem solving, such as distraction, mind-wandering and the like.

So, where does this leave us? I have shown the interplay between problem solving, creativity and insight. In particular, using Ollinger's proposal, I have suggested (maybe not quite explicitly up until now) that RWPS involves some degree of analytical problem solving as well as the post-impasse more creative modes of problem restructuring. I have also suggested that this model might need to be extended for RWPS along two dimensions. First, events such as impasses might just be an instance of a larger class of events that intervene during problem solving. Thus, there needs to be an accounting of the cognitive mechanisms that are potentially influenced by impasses and these other intervening events. It is possible that these sorts of events are crucial and trigger a switch in attentional focus, which in turn facilitates switching between different problem solving modes. Second, we need to consider when and how externally-triggered stimuli from the solver's environment can influence the problem solving process. I detail three different mechanisms by which external knowledge might influence problem solving. I address each of these ideas in more detail in the next two sections.

3. Event-triggered mode switching during problem-solving

3.1. impasse.

When solving certain types of problems, the agent might encounter an impasse, i.e., some block in its ability to solve the problem (Sprugnoli et al., 2017 ). The impasse may arise because the problem may have been ill-defined to begin with causing incomplete and unduly constrained representations to have been formed. Alternatively, impasses can occur when suitable solution strategies cannot be retrieved from memory or fail on execution. In certain instances, the solution strategies may not exist and may need to be generated from scratch. Regardless of the reason, an impasse is an interruption in the problem solving process; one that was running conflict-free up until the point when a seemingly unresolvable issue or an error in the predicted solution path was encountered. Seen as a conflict encountered in the problem-solving process it activates the anterior cingulate cortex (ACC). It is believed that the ACC not only helps detect the conflict, but also switch modes from one of “exploitation” (planning) to “exploration” (search) (Quilodran et al., 2008 ; Tang et al., 2012 ), and monitors progress during resolution (Chu and Macgregor, 2011 ). Some mode switching duties are also found to be shared with the AI (the ACC's partner in the salience network), however, it is unclear exactly the extent of this function-sharing.

Even though it is debatable if impasses are a necessary component of insight, they are still important as they provide a starting point for the creativity (Sprugnoli et al., 2017 ). Indeed, it is possible that around the moment of impasse, the AI and ACC together, as part of the salience network play a crucial role in switching thought modes from analytical planning mode to creative search and discovery mode. In the latter mode, various creative mechanisms might be activated allowing for a solution plan to emerge. Sowden et al. and many others have suggested that the salience network is potentially a candidate neurobiological mechanism for shifting between thinking processes, more generally (Sowden et al., 2015 ). When discussing various dual-process models as they relate to creative cognition, Sowden et al. have even noted that the ACC activation could be useful marker to identify shifting as participants work creative problems.

3.2. Defocused attention

As noted earlier, in the presence of an impasse there is a shift from an exploitative (analytical) thinking mode to an exploratory (creative) thinking mode. This shift impacts several networks including, for example, the attention network. It is believed attention can switch between a focused mode and a defocused mode. Focused attention facilitates analytic thought by constraining activation such that items are considered in a compact form that is amenable to complex mental operations. In the defocused mode, agents expand their attention allowing new associations to be considered. Sowden et al. ( 2015 ) note that the mechanism responsible for adjustments in cognitive control may be linked to the mechanisms responsible for attentional focus. The generally agreed position is that during generative thinking, unconscious cognitive processes activated through defocused attention are more prevalent, whereas during exploratory thinking, controlled cognition activated by focused attention becomes more prevalent (Kaufman, 2011 ; Sowden et al., 2015 ).

Defocused attention allows agents to not only process different aspects of a situation, but to also activate additional neural structures in long term memory and find new associations (Mendelsohn, 1976 ; Yoruk and Runco, 2014 ). It is believed that cognitive material attended to and cued by positive affective state results in defocused attention, allowing for more complex cognitive contexts and therefore a greater range of interpretation and integration of information (Isen et al., 1987 ). High attentional levels are commonly considered a typical feature of highly creative subjects (Sprugnoli et al., 2017 ).

4. Role of the environment

In much of the past work the focus has been on treating creativity as largely an internal process engaging the DMN to assist in making novel connections in memory. The suggestion has been that “individual needs to suppress external stimuli and concentrate on the inner creative process during idea generation” (Heinonen et al., 2016 ). These ideas can then function as seeds for testing and problem-solving. While true of many creative acts, this characterization does not capture how creative ideas arise in many real-world creative problems. In these types of problems, the agent is functioning and interacting with its environment before, during and after problem-solving. It is natural then to expect that stimuli from the environment might play a role in problem-solving. More specifically, it can be expected that through passive and active involvement with the environment, the agent is (1) able to trigger an unrelated, but potentially useful memory relevant for problem-solving, (2) make novel connections between two events in memory with the environmental cue serving as the missing link, and (3) incorporate a completely novel information from events occuring in the environment directly into the problem-solving process. I explore potential neural mechanisms for these three types of environmentally informed creative cognition, which I hypothesize are enabled by defocused attention.

4.1. Partial cues trigger relevant memories through context-shifting

I have previously discussed the interaction between the MTL and PFC in helping select task-relevant and critical memories for problem-solving. It is well-known that pattern completion is an important function of the MTL and one that enables memory retrieval. Complementary Learning Theory (CLS) and its recently updated version suggest that the MTL and related structures support initial storage as well as retrieval of item and context-specific information (Kumaran et al., 2016 ). According to CLS theory, the dentate gyrus (DG) and the CA3 regions of the HF are critical to selecting neural activity patterns that correspond to particular experiences (Kumaran et al., 2016 ). These patterns might be distinct even if experiences are similar and are stabilized through increases in connection strengths between the DG and CA3. Crucially, because of the connection strengths, reactivation of part of the pattern can activate the rest of it (i.e., pattern completion). Kumaran et al. have further noted that if consistent with existing knowledge, these new experiences can be quickly replayed and interleaved into structured representations that form part of the semantic memory.

Cues in the environment provided by these experiences hold partial information about past stimuli or events and this partial information converges in the MTL. CLS accounts for how these cues might serve to reactivate partial patterns, thereby triggering pattern completion. When attention is defocused I hypothesize that (1) previously unnoticed partial cues are considered, and (2) previously noticed partial cues are decomposed to produce previously unnoticed sub-cues, which in turn are considered. Zabelina et al. ( 2016 ) have shown that real-world creativity and creative achievement is associated with “leaky attention,” i.e., attention that allows for irrelevant information to be noticed. In two experiments they systematically explored the relationship between two notions of creativity— divergent thinking and real-world creative achievement—and the use of attention. They found that attentional use is associated in different ways for each of the two notions of creativity. While divergent thinking was associated with flexible attention, it does not appear to be leaky. Instead, selective focus and inhibition components of attention were likely facilitating successful performance on divergent thinking tasks. On the other hand, real-world creative achievement was linked to leaky attention. RWPS involves elements of both divergent thinking and of real-world creative achievement, thus I would expect some amount of attentional leaks to be part of the problem solving process.

Thus, it might be the case that a new set of cues or sub-cues “leak” in and activate memories that may not have been previously considered. These cues serve to reactivate a diverse set of patterns that then enable accessing a wide range of memories. Some of these memories are extra-contextual, in that they consider the newly noticed cues in several contexts. For example, when unable to find a screwdriver, we might consider using a coin. It is possible that defocused attention allows us to consider the coin's edge as being a potentially relevant cue that triggers uses for the thin edge outside of its current context in a coin. The new cues (or contexts) may allow new associations to emerge with cues stored in memory, which can occur during incubation. Objects and contexts are integrated into memory automatically into a blended representation and changing contexts disrupts this recognition (Hayes et al., 2007 ; Gabora, 2016 ). Cue-triggered context shifting allows an agent to break-apart a memory representation, which can then facilitate problem-solving in new ways.

4.2. Heuristic prototyping facilitates novel associations

It has long been the case that many scientific innovations have been inspired by events in nature and the surrounding environment. As noted earlier, Archimedes realized the relationship between the volume of an irregularly shaped object and the volume of water it displaced. This is an example of heuristic prototyping where the problem-solver notices an event in the environment, which then triggers the automatic activation of a heuristic prototype and the formation of novel associations (between the function of the prototype and the problem) which they can then use to solve the problem (Luo et al., 2013 ). Although still in its relative infancy, there has been some recent research into the neural basis for heuristic prototyping. Heuristic prototype has generally been defined as an enlightening prototype event with a similar element to the current problem and is often composed of a feature and a function (Hao et al., 2013 ). For example, in designing a faster and more efficient submarine hull, a heuristic prototype might be a shark's skin, while an unrelated prototype might be a fisheye camera (Dandan et al., 2013 ).

Research has shown that activating the feature function of the right heuristic prototype and linking it by way of semantic similarity to the required function of the problem was the key mechanism people used to solve several scienitific insight problems (Yang et al., 2016 ). A key region activated during heuristic prototyping is the dlPFC and it is believed to be generally responsible for encoding the events into memory and may play an important role in selecting and retrieving the matched unsolved technical problem from memory (Dandan et al., 2013 ). It is also believed that the precuneus plays a role in automatic retrieval of heuristic information allowing the heuristic prototype and the problem to combine (Luo et al., 2013 ). In addition to semantic processing, certain aspects of visual imagery have also been implicated in heuristic prototyping leading to the suggestion of the involvement of Broadman's area BA 19 in the occipital cortex.

There is some degree of overlap between the notions of heuristic prototyping and analogical transfer (the mapping of relations from one domain to another). Analogical transfer is believed to activate regions in the left medial fronto-parietal system (dlPFC and the PPC) (Barbey and Barsalou, 2009 ). I suggest here that analogical reasoning is largely an internally-guided process that is aided by heuristic prototyping which is an externally-guided process. One possible way this could work is if heuristic prototyping mechanisms help locate the relevant memory with which to then subsequently analogize.

4.3. Making physical inferences to acquire novel information

The agent might also be able to learn novel facts about their environment through passive observation as well as active experimentation. There has been some research into the neural basis for causal reasoning (Barbey and Barsalou, 2009 ; Operskalski and Barbey, 2016 ), but beyond its generally distributed nature, we do not know too much more. Beyond abstract causal reasoning, some studies looked into the cortical regions that are activated when people watch and predict physical events unfolding in real-time and in the real-world (Fischer et al., 2016 ). It was found that certain regions were associated with representing types of physical concepts, with the left intraparietal sulcus (IPS) and left middle frontal gyrus (MFG) shown to play a role in attributing causality when viewing colliding objects (Mason and Just, 2013 ). The parahippocampus (PHC) was associated with linking causal theory to observed data and the TPJ was involved in visualizing movement of objects and actions in space (Mason and Just, 2013 ).

5. Proposed theory

I noted earlier that Ollinger's model for insight problem solving, while serving as a good candidate for RWPS, requires extension. In this section, I propose a candidate model that includes some necessary extensions to Ollinger's framework. I begin by laying out some preliminary notions that underlie the proposed model.

5.1. Dual attentional modes

I propose that the attention-switching mechanism described earlier is at the heart of RWPS and enables two modes of operation: focused and defocused mode. In the focused mode, the problem representation is more or less fixed, and problem solving proceeds in a focused and goal directed manner through search, planning, and execution mechanisms. In the defocused mode, problem solving is not necessarily goal directed, but attempts to generate ideas, driven by both internal and external items.

At first glance, these modes might seem similar to convergent and divergent thinking modes postulated by numerous others to account for creative problem solving. Divergent thinking allows for the generation of new ideas and convergent thinking allows for verification and selection of generated ideas. So, it might seem that focused mode and convergent thinking are similar and likewise divergent and defocused mode. They are, however, quite different. The modes relate less to idea generation and verification, and more to the specific mechanisms that are operating with regard to a particular problem at a particular moment in time. Convergent and divergent processes may be occurring during both defocused and focused modes. Some degree of divergent processes may be used to search and identify specific solution strategies in focused mode. Also, there might be some degree of convergent idea verification occuring in defocused mode as candidate items are evaluated for their fit with the problem and goal. Thus, convergent and divergent thinking are one amongst many mechanisms that are utilized in focused and defocused mode. Each of these two modes has to do with degree of attention placed on a particular problem.

There have been numerous dual-process and dual-systems models of cognition proposed over the years. To address criticisms raised against these models and to unify some of the terminology, Evans & Stanovich proposed a dual-process model comprising Type 1 and Type 2 thought (Evans and Stanovich, 2013 ; Sowden et al., 2015 ). Type 1 processes are those that are believed to be autonomous and do not require working memory. Type 2 processes, on the other hand, are believed to require working memory and are cognitively decoupled to prevent real-world representations from becoming confused with mental simulations (Sowden et al., 2015 ). While acknowledging various other attributes that are often used to describe dual process models (e.g., fast/slow, associative/rule-based, automatic/controlled), Evans & Stanovich note that these attributes are merely frequent correlates and not defining characteristics of Type 1 or Type 2 processes. The proposed dual attentional modes share some similarities with the Evans & Stanovich Type 1 and 2 models. Specifically, Type 2 processes might occur in focused attentional mode in the proposed model as they typically involve the working memory and certain amount of analytical thought and planning. Similarly, Type 1 processes are likely engaged in defocused attentional mode as there are notions of associative and generative thinking that might be facilitated when attention has been defocused. The crucial difference between the proposed model and other dual-process models is that the dividing line between focused and defocused attentional modes is the degree of openness to internal and external stimuli (by various networks and functional units in the brain) when problem solving. Many dual process models were designed to classify the “type” of thinking process or a form of cognitive processing. In some sense, the “processes” in dual process theories are characterized by the type of mechanism of operation or the type of output they produced. Here, I instead characterize and differentiate the modes of thinking by the receptivity of different functional units in the brain to input during problem solving.

This, however, raises a different question of the relationship between these attentional modes and conscious vs. unconscious thinking. It is clear that both the conscious and unconscious are involved in problem solving, as well as in RWPS. Here, I claim that a problem being handled is, at any given point in time, in either a focused mode or in a defocused mode. When in the focused mode, problem solving primarily proceeds in a manner that is available for conscious deliberation. More specifically, problem space elements and representations are tightly managed and plans and strategies are available in the working memory and consciously accessible. There are, however, secondary unconscious operations in the focused modes that includes targeted memory retrieval and heuristic-based searches. In the defocused mode, the problem is primarily managed in an unconscious way. The problem space elements are broken apart and loosely managed by various mechanisms that do not allow for conscious deliberation. That said, it is possible that some problem parameters remain accessible. For example, it is possible that certain goal information is still maintained consciously. It is also possible that indexes to all the problems being considered by the solver are maintained and available to conscious awareness.

5.2. RWPS model

Returning to Ollinger's model for insight problem solving, it now becomes readily apparent how this model can be modified to incorporate environmental effects as well as generalizing the notion of intervening events beyond that of impasses. I propose a theory for RWPS that begins with standard analytical problem-solving process (See Figures ​ Figures1, 1 , ​ ,2 2 ).

Summary of neural activations during focused problem-solving (Left) and defocused problem-solving (Right) . During defocused problem-solving, the salience network (insula and ACC) coordinates the switching of several networks into a defocused attention mode that permits the reception of a more varied set of stimuli and interpretations via both the internally-guided networks (default mode network DMN) and externally guided networks (Attention). PFC, prefrontal cortex; ACC, anterior cingulate cortex; PCC, posterior cingulate cortex; IPC, inferior parietal cortex; PPC, posterior parietal cortex; IPS, intra-parietal sulcus; TPJ, temporoparietal junction; MTL, medial temporal lobe; FEF, frontal eye field.

Proposed Model for Real World Problem Solving (RWPS). The corresponding neural correlates are shown in italics. During problem-solving, an initial problem representation is formed based on prior knowledge and available perceptual information. The problem-solving then proceeds in a focused, goal-directed mode until the goal is achieved or a defocusing event (e.g., impasse or distraction) occurs. During focused mode operation, the solver interacts with the environment in directed manner, executing focused plans, and allowing for predicted items to be activated by the environment. When a defocusing event occurs, the problem-solving then switches into a defocused mode until a focusing event (e.g., discovery) occurs. In defocused mode, the solver performs actions unrelated to the problem (or is inactive) and is receptive to a set of environmental triggers that activate novel aspects using the three mechanisms discussed in this paper. When a focusing event occurs, the diffused problem elements cohere into a restructured representation and problem-solving returns into a focused mode.

5.2.1. Focused problem solving mode

Initially, both prior knowledge and perceptual entities help guide the creation of problem representations in working memory. Prior optimal or rewarding solution strategies are obtained from LTM and encoded in the working memory as well. This process is largely analytical and the solver interacts with their environment through focused plan or idea execution, targeted observation of prescribed entities, and estimating prediction error of these known entities. More specifically, when a problem is presented, the problem representations are activated and populated into working memory in the PFC, possibly in structured representations along convergence zones. The PFC along with the Striatum and the MTL together attempt at retrieving an optimal or previously rewarded solution strategy from long term memory. If successfully retrieved, the solution strategy is encoded into the PPC as a mental template, which then guides relevant motor control regions to execute the plan.

5.2.2. Defocusing event-triggered mode switching

The search and solve strategy then proceeds analytically until a “defocusing event” is encountered. The salience network (AI and ACC) monitor for conflicts and attempt to detect any such events in the problem-solving process. As long as no conflicts are detected, the salience network focuses on recruiting networks to achieve goals and suppresses the DMN (Beaty et al., 2016 ). If the plan execution or retrieval of the solution strategy fails, then a defocusing event is detected and the salience network performs mode switching. The salience network dynamically switches from the focused problem-solving mode to a defocused problem-solving mode (Menon, 2015 ). Ollinger's current model does not account for other defocusing events beyond an impasse, but it is not inconceivable that there could be other such events triggered by external stimuli (e.g., distraction or an affective event) or by internal stimuli (e.g., mind wandering).

5.2.3. Defocused problem solving mode

In defocused mode, the problem is operated on by mechanisms that allow for the generation and testing of novel ideas. Several large-scale brain networks are recruited to explore and generate new ideas. The search for novel ideas is facilitated by generally defocused attention, which in turn allows for creative idea generation from both internal as well as external sources. The salience network switches operations from defocused event detection to focused event or discovery detection, whereby for example, environmental events or ideas that are deemed interesting can be detected. During this idea exploration phase, internally, the DMN is no longer suppressed and attempts to generate new ideas for problem-solving. It is known that the IPC is involved in the generation of new ideas (Benedek et al., 2014 ) and together with the PPC in coupling different information together (Simone Sandkühler, 2008 ; Stocco et al., 2012 ). Beaty et al. ( 2016 ) have proposed that even this internal idea-generation process can be goal directed, thereby allowing for a closer working relationship between the CEN and the DMN. They point to neuroimaging evidence that support the possibility that the executive control network (comprising the lateral prefrontal and inferior parietal regions) can constrain and direct the DMN in its process of generating ideas to meet task-specific goals via top down monitoring and executive control (Beaty et al., 2016 ). The control network is believed to maintain an “internal train of thought” by keeping the task goal activated, thereby allowing for strategic and goal-congruent searches for ideas. Moreover, they suggest that the extent of CEN involvement in the DMN idea-generation may depend on the extent to which the creative task is constrained. In the RWPS setting, I would suspect that the internal search for creative solutions is not entirely unconstrained, even in the defocused mode. Instead, the solver is working on a specified problem and thus, must maintain the problem-thread while searching for solutions. Moreover, self-generated ideas must be evaluated against the problem parameters and thereby might need some top-down processing. This would suggest that in such circumstances, we would expect to see an increased involvement of the CEN in constraining the DMN.

On the external front, several mechanisms are operating in this defocused mode. Of particular note are the dorsal attention network, composed of the visual cortex (V), IPS and the frontal eye field (FEF) along with the precuneus and the caudate nucleus allow for partial cues to be considered. The MTL receives synthesized cue and contextual information and populates the WM in the PFC with a potentially expanded set of information that might be relevant for problem-solving. The precuneus, dlPFC and PPC together trigger the activation and use of a heuristic prototype based on an event in the environment. The caudate nucleus facilitates information routing between the PFC and PPC and is involved in learning and skill acquisition.

5.2.4. Focusing event-triggered mode switching

The problem's life in this defocused mode continues until a focusing event occurs, which could be triggered by either external (e.g., notification of impending deadline, discovery of a novel property in the environment) or internal items (e.g., goal completion, discovery of novel association or updated relevancy of a previously irrelevant item). As noted earlier, an internal train of thought may be maintained that facilitates top-down evaluation of ideas and tracking of these triggers (Beaty et al., 2016 ). The salience network switches various networks back to the focused problem-solving mode, but not without the potential for problem restructuring. As noted earlier, problem space elements are maintained somewhat loosely in the defocused mode. Thus, upon a focusing event, a set or subset of these elements cohere into a tight (restructured) representation suitable for focused mode problem solving. The process then repeats itself until the goal has been achieved.

5.3. Model predictions

5.3.1. single-mode operation.

The proposed RWPS model provides several interesting hypotheses, which I discuss next. First, the model assumes that any given problem being worked on is in one mode or another, but not both. Thus, the model predicts that there cannot be focused plan execution on a problem that is in defocused mode. The corollary prediction is that novel perceptual cues (as those discussed in section 4) cannot help the solver when in focused mode. The corollary prediction, presumably has some support from the inattentional blindness literature. Inattentional blindness is when perceptual cues are not noticed during a task (e.g., counting the number of basketball passes between several people, but not noticing a gorilla in the scene) (Simons and Chabris, 1999 ). It is possible that during focused problem solving, that external and internally generated novel ideas are simply not considered for problem solving. I am not claiming that these perceptual cues are always ignored, but that they are not considered within the problem. Sometimes external cues (like distracting occurrences) can serve as defocusing events, but the model predicts that the actual content of these cues are not themselves useful for solving the specific problem at hand.

When comparing dual-process models Sowden et al. ( 2015 ) discuss shifting from one type of thinking to another and explore how this shift relates to creativity. In this regard, they weigh the pros and cons of serial vs. parallel shifts. In dual-process models that suggest serial shifts, it is necessary to disengage one type of thought prior to engaging the other or to shift along a continuum. Whereas, in models that suggest parallel shifts, each of the thinking types can operate in parallel. Per this construction, the proposed RWPS model is serial, however, not quite in the same sense. As noted earlier, the RWPS model is not a dual-process model in the same sense as other dual process model. Instead, here, the thrust is on when the brain is receptive or not receptive to certain kinds of internal and external stimuli that can influence problem solving. Thus, while the modes may be serial with respect to a certain problem, it does not preclude the possibility of serial and parallel thinking processes that might be involved within these modes.

5.3.2. Event-driven transitions

The model requires an event (defocusing or focusing) to transition from one mode to another. After all why else would a problem that is successfully being resolved in the focused mode (toward completion) need to necessarily be transferred to defocused mode? These events are interpreted as conflicts in the brain and therefore the mode-switching is enabled by the saliency network and the ACC. Thus, the model predicts that there can be no transition from one mode to another without an event. This is a bit circular, as an event is really what triggers the transition in the first place. But, here I am suggesting that an external or internal cue triggered event is what drives the transition, and that transitions cannot happen organically without such an event. In some sense, the argument is that the transition is discontinuous, rather than a smooth one. Mind-wandering is good example of when we might drift into defocused mode, which I suggest is an example of an internally driven event caused by an alternative thought that takes attention away from the problem.

A model assumption underlying RWPS is that events such as impasses have a similar effect to other events such as distraction or mind wandering. Thus, it is crucial to be able to establish that there exists of class of such events and they have a shared effect on RWPS, which is to switch attentional modes.

5.3.3. Focused mode completion

The model also predicts that problems cannot be solved (i.e., completed) within the defocused mode. A problem can be considered solved when a goal is reached. However, if a goal is reached and a problem is completed in the defocused mode, then there must have not been any converging event or coherence of problem elements. While it is possible that the solver arbitrarily arrived at the goal in a diffused problem space and without conscious awareness of completing the task or even any converging event or problem recompiling, it appears somewhat unlikely. It is true that there are many tasks that we complete without actively thinking about it. We do not think about what foot to place in front of another while walking, but this is not an instance of problem solving. Instead, this is an instance of unconscious task completion.

5.3.4. Restructuring required

The model predicts that a problem cannot return to a focused mode without some amount of restructuring. That is, once defocused, the problem is essentially never the same again. The problem elements begin interacting with other internally and externally-generated items, which in turn become absorbed into the problem representation. This prediction can potentially be tested by establishing some preliminary knowledge, and then showing one group of subjects the same knowledge as before, while showing the another group of subjects different stimuli. If the model's predictions hold, the problem representation will be restructured in some way for both groups.

There are numerous other such predictions, which are beyond the scope of this paper. One of the biggest challenges then becomes evaluating the model to set up suitable experiments aimed at testing the predictions and falsifying the theory, which I address next.

6. Experimental challenges and paradigms

One of challenges in evaluating the RWPS is that real world factors cannot realistically be accounted for and sufficiently controlled within a laboratory environment. So, how can one controllably test the various predictions and model assumptions of “real world” problem solving, especially given that by definition RWPS involves the external environment and unconscious processing? At the expense of ecological validity, much of insight problem solving research has employed an experimental paradigm that involves providing participants single instances of suitably difficult problems as stimuli and observing various physiological, neurological and behavioral measures. In addition, through verbal protocols, experimenters have been able to capture subjective accounts and problem solving processes that are available to the participants' conscious. These experiments have been made more sophisticated through the use of timed-hints and/or distractions. One challenge with this paradigm has been the selection of a suitable set of appropriately difficult problems. The classic insight problems (e.g., Nine-dot, eight-coin) can be quite difficult, requiring complicated problem solving processes, and also might not generalize to other problems or real world problems. Some in the insight research community have moved in the direction of verbal tasks (e.g., riddles, anagrams, matchstick rebus, remote associates tasks, and compound remote associates tasks). Unfortunately, these puzzles, while providing a great degree of controllability and repeatability, are even less realistic. These problems are not entirely congruent with the kinds of problems that humans are solving every day.

The other challenge with insight experiments is the selection of appropriate performance and process tracking measures. Most commonly, insight researchers use measures such as time to solution, probability of finding solution, and the like for performance measures. For process tracking, verbal protocols, coded solution attempts, and eye tracking are increasingly common. In neuroscientific studies of insight various neurological measures using functional magnetic resonance imaging (fMRI), electroencephalography (EEGs), transcranial direct current stimulation (tDCS), and transcranial magnetic stimulation (tMS) are popular and allow for spatially and temporally localizing an insight event.

Thus, the challenge for RWPS is two-fold: (1) selection of stimuli (real world problems) that are generalizable, and (2) selection of measures (or a set of measures) that can capture key aspects of the problem solving process. Unfortunately, these two challenges are somewhat at odds with each other. While fMRI and various neuroscientific measures can capture the problem solving process in real time, it is practically difficult to provide participants a realistic scenario while they are laying flat on their back in an fMRI machine and allowed to move nothing more than a finger. To begin addressing this conundrum, I suggest returning to object manipulation problems (not all that different from those originally introduced by Maier and Duncker nearly a century ago), but using modern computing and user-interface technologies.

One pseudo-realistic approach is to generate challenging object manipulation problems in Virtual Reality (VR). VR has been used to describe 3-D environment displays that allows participants to interact with artificially projected, but experientially realistic scenarios. It has been suggested that virtual environments (VE) invoke the same cognitive modules as real equivalent environmental experience (Foreman, 2010 ). Crucially, since VE's can be scaled and designed as desired, they provide a unique opportunity to study pseudo-RWPS. However, a VR-based research approach has its limitations, one of which is that it is nearly impossible to track participant progress through a virtual problem using popular neuroscientific measures such as fMRI because of the limited mobility of connected participants.

Most of the studies cited in this paper utilized an fMRI-based approach in conjunction with a verbal or visual task involving problem-solving or creative thinking. Very few, if any, studies involved the use physical manipulation, and those physical manipulations were restricted to limited finger movements. Thus, another pseudo-realistic approach is allowing subjects to teleoperate robotic arms and legs from inside the fMRI machine. This paradigm has seen limited usage in psychology and robotics, in studies focused on Human-Robot interaction (Loth et al., 2015 ). It could be an invaluable tool in studying real-time dynamic problem-solving through the control of a robotic arm. In this paradigm a problem solving task involving physical manipulation is presented to the subject via the cameras of a robot. The subject (in an fMRI) can push buttons to operate the robot and interact with its environment. While the subjects are not themselves moving, they can still manipulate objects in the real world. What makes this paradigm all the more interesting is that the subject's manipulation-capabilities can be systematically controlled. Thus, for a particular problem, different robotic perceptual and manipulation capabilities can be exposed, allowing researchers to study solver-problem dynamics in a new way. For example, even simple manipulation problems (e.g., re-arranging and stacking blocks on a table) can be turned into challenging problems when the robotic movements are restricted. Here, the problem space restrictions are imposed not necessarily on the underlying problem, but on the solver's own capabilities. Problems of this nature, given their simple structure, may enable studying everyday practical creativity without the burden of devising complex creative puzzles. Crucial to note, both these pseudo-realistic paradigms proposed demonstrate a tight interplay between the solver's own capabilities and their environment.

7. Conclusion

While the neural basis for problem-solving, creativity and insight have been studied extensively in the past, there is still a lack of understanding of the role of the environment in informing the problem-solving process. Current research has primarily focused on internally-guided mental processes for idea generation and evaluation. However, the type of real world problem-solving (RWPS) that is often considered a hallmark of human intelligence has involved both a dynamic interaction with the environment and the ability to handle intervening and interrupting events. In this paper, I have attempted to synthesize the literature into a unified theory of RWPS, with a specific focus on ways in which the environment can help problem-solve and the key neural networks involved in processing and utilizing relevant and useful environmental information. Understanding the neural basis for RWPS will allow us to be better situated to solve difficult problems. Moreover, for researchers in computer science and artificial intelligence, clues into the neural underpinnings of the computations taking place during creative RWPS, can inform the design the next generation of helper and exploration robots which need these capabilities in order to be resourceful and resilient in the open-world.

Author contributions

The author confirms being the sole contributor of this work and approved it for publication.

Conflict of interest statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

I am indebted to Professor Matthias Scheutz, Professor Elizabeth Race, Professor Ayanna Thomas, and Professor. Shaun Patel for providing guidance with the research and the manuscript. I am also grateful for the facilities provided by Tufts University, Medford, MA, USA.

1 My intention is not to ignore the benefits of a concentrated internal thought process which likely occurred as well, but merely to acknowledge the possibility that the environment might have also helped.

2 The research in insight does extensively use “hints” which are, arguably, a form of external influence. But these hints are highly targeted and might not be available in this explicit form when solving problems in the real world.

3 The accuracy of these accounts has been placed in doubt. They often are recounted years later, with inaccuracies, and embellished for dramatic effect.

4 I use the term “agent” to refer to the problem-solver. The term agent is more general than “creature” or “person” or “you" and is intentionally selected to broadly reference humans, animals as well as artificial agents. I also selectively use the term “solver.”

Funding. The research for this Hypothesis/Theory Article was funded by the authors private means. Publication costs will be covered by my institution: Tufts University, Medford, MA, USA.

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Comparing online and lab methods in a problem-solving experiment

• Published: May 2008
• Volume 40 , pages 428–434, ( 2008 )

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• Frédéric Dandurand 1 ,
• Thomas R. Shultz 1 &
• Kristine H. Onishi 1  

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Online experiments have recently become very popular, and—in comparison with traditional lab experiments— they may have several advantages, such as reduced demand characteristics, automation, and generalizability of results to wider populations (Birnbaum, 2004; Reips, 2000, 2002a, 2002b). We replicated Dandurand, Bowen, and Shultz’s (2004) lab-based problem-solving experiment as an Internet experiment. Consistent with previous results, we found that participants who watched demonstrations of successful problem-solving sessions or who read instructions outperformed those who were told only that they solved problems correctly or not. Online participants were less accurate than lab participants, but there was no interaction with learning condition. Thus, we conclude that online and Internet results are consistent. Disadvantages included high dropout rate for online participants; however, combining the online experiment with the department subject pool worked well.

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Dandurand, F., Shultz, T.R. & Onishi, K.H. Comparing online and lab methods in a problem-solving experiment. Behavior Research Methods 40 , 428–434 (2008). https://doi.org/10.3758/BRM.40.2.428

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EDITORIAL article

Editorial: novel approaches for studying creativity in problem-solving and artistic performance.

• 1 School of Psychology and Wellbeing, University of Buckingham, Buckingham, United Kingdom
• 2 Department of Psychology, Universität Heidelberg, Heidelberg, Germany
• 3 School of Psychology, Politics and Sociology, Christchurch Canterbury University, Canterbury, United Kingdom
• 4 Department of Psychology, Macquarie University, Sydney, NSW, Australia

Editorial on the Research Topic Novel Approaches for Studying Creativity in Problem-Solving and Artistic Performance

Introduction

Creativity can be observed across multiple domains of human behavior including problem solving, artistic and athletic engagement, scientific reasoning, decision making, business and marketing, leadership styles, and social interactions. It has a long history of research in many disciplines, and involves a variety of conceptual and methodological approaches. However, given its multi-faceted character, and the multidisciplinary (though not necessarily interdisciplinary) nature of creativity research, it is perhaps unsurprising that such research has tended to examine discrete areas of study, thereby adopting a focused approach that lacks opportunity for cross-fertilization. It is therefore important to encourage interdisciplinary discourse and novel methodological approaches to investigating all aspects of creativity. This can best be achieved by sharing and integrating research ideas, methods, and findings across multiple domains and disciplines, including but not restricted to psychology, neuroscience, philosophy, linguistics, medicine, education, and performance science.

The aim of this Research Topic is to showcase recent creativity research involving new methodological approaches across a range of creativity domains and academic disciplines. Broadly speaking, we see three ways by which such novel methodological approaches can develop. Firstly, adopting technologies such as brain stimulation and EEG allow researchers to investigate creativity in new ways, and new digital research platforms allow researchers to more easily access domain-specific online populations. Secondly, traditional methodologies, already shown to be effective in one field of creativity research, can be employed to investigate hitherto neglected creativity domains. Thirdly, taking advantage of the interdisciplinary nature of creativity research, we can interrogate one domain of creative performance using research perspectives from another, such as viewing medicine as a performance science akin to music ( Kneebone, 2016 ) or investigating insight moments with magic tricks ( Danek et al., 2014 ). This novel juxtaposition of methods from multiple domains and disciplines allows new research questions to be addressed. These three ways of developing novel methodological approaches thus involve: the development of novel methods; the novel application of tried-and-tested methods; and the novel combination of previously separate methodologies.

The Research Topic contains 27 articles (20 Original Research articles, one Case Report, one Review, and five methodological or theoretical contributions). Twelve address questions of creative cognition, covering insight, divergent thinking, and problem solving. Eleven articles investigate creative arts and artistic performance, with a further four addressing other aspects of creativity. Given the focus of the Research Topic, we have decided to address the articles in terms of their methodological approaches, rather than the type of creativity under investigation. Indeed, we hope to encourage the development and ultimately the wider application of those methodological approaches described herein to any aspect or domain of creativity.

Tracking the Process: Physiological Approaches

In line with the increasing pace of technological advancement, several articles utilize physiological techniques to measure and manipulate the creative process, including the electroencephalogram (EEG), and transcranial current stimulation, both direct (tDCS) and alternating (tACS). Dolan et al. employ EEG in both music performers and selected audience members during prepared and improvised renditions of the same piece of classical music, demonstrating what they call an “improvisatory state of mind.” Truelove-Hill et al. measure resting-state EEG in their investigation of the effects of near-future and far-future priming on insight and analytical problem-solving. Di Bernardi Luft et al. use both EEG and tACS in their case study of a professional visual artist with exceptionally vivid spontaneous visual imagery during meditation sessions. They demonstrate increased occipital gamma oscillations during visual imagery, and an effect of alpha tACS on the contents of the artist's images. In another study of musical creativity, Anic et al. investigate the effects of both excitatory and inhibitory tDCS over the left hemisphere primary motor cortex (M1) of pianists who were improvising with their right hands: improvisations under excitatory tDCS were rated as significantly more creative, demonstrating the role of M1 in musical creativity.

Various other articles employ process-tracing methods to probe the creative process. Carey et al. investigate dance in a novel way, using pupillometry (a metric of mental effort) to demonstrate greater pupil dilation in novice, rather than intermediate, dancers as they performed or imagined dance movements. Jankowska et al. use both eye-tracking and think-aloud (verbal protocol) analyses whilst adults completed a creative drawing task, demonstrating methodological synergy between both types of process-tracing and various psychometric measures of drawing creativity. Spiridonov et al. , Loesche et al. , and Dolan et al. all track physical movement during various creative acts. Spiridonov et al. examine the classic 9-dot problem by tracking the position and movement of the solver's index finger on a tablet, and demonstrate specific patterns of motor behavior characterizing the differences between unsuccessful and successful solvers. Similarly, Loesche et al. investigate the chronology of insight moments in a novel insight eliciting task, “Dira,” by tracking the position of the mouse cursor, allowing them to better pinpoint the moment when solutions emerge. Finally, Dolan et al. investigate musical creativity in ensemble playing in various ways, including continuous 3D tracking of the musicians' movement. This enables them to explore movement pattern differences between improvised and prepared renditions, as well as demonstrate, for instance, that the flutist and pianist correlated their fast movements significantly more in an improvised rendition than a classically prepared one.

The Time-Course of Creativity

One common theme, found in 10 articles, is the study of temporal or chronometric aspects of the creative and associated processes. Three articles involving process-tracing, focusing particularly on moment-to-moment aspects of the creative process, have already been mentioned ( Loesche et al. , Spiridonov et al. , and Dolan et al. ). Hass and Beatty directly compare performance on the Alternative Uses Task (AUT) and Consequences Task, showing that both approximate well to an exponential cumulative response time model; they also provide an explanation for why later responses are generally rated as more creative than earlier ones, known as the serial order effect. Kizilirmak et al. measure feelings of warmth (FoW) ratings for Compound Remote Associate Tasks as a function of task difficulty, whether it was successfully solved, and whether the solution (if it occurred) was an example of insight; they demonstrate that FoW ratings increase more abruptly for trials solved with compared to without an insight experience. Kupers et al. measure moment-to-moment ratings of novelty and appropriateness in their study of children's creativity using a novel coding framework. Botella et al. explore the stages of the creative artistic process, which they propose differs from both the creative process and the artistic process, by interviewing visual graphic arts students, integrating their findings into Creative process Report Diaries.

Rather than focusing on the creative process itself, three articles measure the time-course of associated processes. Wang et al. explore the temporal structure of semantic associations in an association chain task and its relationship to divergent thinking. Korovkin et al. use a dual-task procedure to track the temporal dynamics of working memory involvement throughout both insight and non-insight problem-solving experiences. Truelove-Hill et al. investigate the effects of a priming procedure on creative problem-solving by asking problem-solvers to think about the near vs. distant future in order to differentially impact their cognitive style, in accordance with construal level theory. They then apply growth-curve analysis in a novel way to uncover the time-course of these transient priming effects.

Promoting and Measuring Creativity: Psychometric Approaches

Several articles describe novel approaches to promote, track or measure creativity. Three articles propose novel methods for inducing insight. Friedlander and Fine posit a new protocol for eliciting insight moments, that of cryptic crossword solving, drawing parallels between certain cryptic clue mechanisms and problem types already found in the insight literature, such as rebus puzzles, remote associate problems, anagrams, and jokes. Such an approach could be instrumental in exploring individual differences in insight ability, and identifying insight experts. In order to investigate multiple instances of both positive (Aha!) and negative (Uh-oh!) insight experiences, Hill and Kemp use the well-known adversarial game of Connect 4, asking participants to label each move as insight or search (either positive or negative) and collecting concomitant phenomenological ratings. Loesche et al. have developed a new game, “Dira,” based on the existing game “Dixit,” in which participants must find a connection between a short sentence and one of six visual images. However, only the image (or text) over which the mouse is hovering is clearly visible: this allows real-time process-tracing via mouse movements, and provides information about relevant metacognitive and behavioral mechanisms, such as the intensity of the insight moment.

Other cognitive methods applied to creativity research in the current articles include: the use of verbal protocol analysis to probe metacognitive and self-regulation mechanisms together with eye-movement measures during a creative drawing task ( Jankovska et al. ); the measurement of feelings of warmth during insight and non-insight puzzle solving ( Kizilirmak et al. ); and the application of the classic dual-task paradigm to investigate the effect of working memory load on solving insight and non-insight problems ( Korovkin et al. ). Camic et al. also describe the potential utility, for those with dementia, of Visual Thinking Strategies (VTS), an arts-based facilitated learning methodology involving moderated group discussions, permitting individuals to create meaning through viewing visual art.

Two articles probe novel and interesting causal relationships between creativity and other cognitive activities or processes. Having a broad attentional scope has previously been shown to enhance creativity, but Wronska et al. demonstrate the reverse relationship, that divergent thinking can broaden visual attention on a subsequent visual scanning task and enhance peripheral target recognition. Osowiecka and Kolanczyk show that silently reading poetry can both increase and decrease divergent thinking performance, depending on the type of poetic metaphors, the poetic narration style, and individual differences in long-term exposure to poetry.

Several articles explore novel psychometric methods for measuring and otherwise quantifying aspects of creativity. Threadgold et al. present a newly validated normative pool of 84 rebus puzzles freely available for future use in problem-solving and insight studies. Kupers et al. propose a micro-level domain-general systematic coding framework for measuring novelty and appropriateness of creative products on a continual basis. Kershaw et al. apply a novel originality scoring method, the Decision Tree for Originality Assessment in Design (DTOAD), to creative ideation within engineering design. Clements et al. adapt Amabile's Consensual Assessment Technique (CAT; Amabile, 1982 ; Cseh and Jeffries, 2019 ) for online use so as to have a broader reach, by which they investigate the effects of varying levels of dance expertise and experience on ratings of choreographic creativity. Loesche et al. 's exploration of the chronometry of insight moments and Threadgold et al. 's construction of a normative database of rebus puzzles both treat the strength of the Eureka experience as a continuum rather than a dichotomous all-or-none phenomenon, which has generally been a more common approach; similarly, some articles, including Hill and Kemp , and Loesche et al. , consider phenomenological correlates of the insight moment as continua.

Technological and Methodological Advances

In addition to the studies using tDCS, tACS, and EEG already mentioned, two articles in particular employ methods novel to creativity research to increase the reach of their studies. For their direct comparison of the AUT and the Consequences Task, Hass and Beatty's participants were recruited from Amazon Mechanical Turk (MTurk) using psiTurk, an open-access web-app which interfaces with MTurk, allowing online experimental control and response collection. In their study of choreographic creativity, Clements et al. use an online version of the CAT together with a snowball sampling technique in which participants could rate as few or as many as they wished out of 23 randomly ordered short videos: this yielded 2153 individual ratings from 850 raters.

Camic et al. advocate the use of wearable technology for measuring psychophysiological changes on a continuous basis during creative behaviors, particularly where it is important that such data collection is unobtrusive, for instance in persons with dementia. Wearable technology such as wristbands can record 3D position using accelerometers, as well as physiological indices of arousal and stress including heart rate, heart rate variability, skin conductance, and skin temperature. Finally, in their Perspective article, Gobet and Sala advocate the use of methods in Artificial Intelligence (AI), which they argue are less susceptible to mental set issues, in both the design of new experiments and the generation of new theory in relation to the study of creativity.

Investigating Creative People and Populations

Several articles focus more on the creative person, by studying either specific (and sometimes less-studied) populations, or interpersonal aspects of teamwork, ensemble, and co-creativity. Hogan et al. investigate budding fashion designers on a reality television programme in which they are tasked with designing garments. The authors analyze the designers' thinking dispositions using qualitative analysis of the programme transcripts in terms of the 8 Studio Habits of Mind. In a multi-institutional wide-ranging Conceptual Analysis article, Camic et al. explore how we can conceptualize and understand artistic creativity in the dementias, a population easily and undeservedly overlooked in creativity studies. An interesting aspect of the article is their discussion of co-creativity, which focuses on shared processes. Hocking , too, addresses co-creativity, in his dyadic case study of the subjective experience of a professional artist as seen through the eyes of a psychological researcher and thus artistic collaborator, using Interpretative Phenomenological Analysis (IPA). Another case study of an artist ( Di Bernardi Luft et al. ) employs neuroimaging to investigate spontaneous vivid visual imagery, central to this artist's creativity. Though still focusing on the creative process, Kupers et al. present two case studies specifically investigating children's creativity, exemplified by two empirical examples, a music composition task and the solving of a physics problem: their coding framework will no doubt also be applicable to adults (and to other domains of creativity).

Other articles addressed questions of interpersonal interaction with reference to teamwork and ensemble. Reiter-Palmon and Murugavel demonstrate the utility of problem construction in teams by studying the social and cognitive processes involved. Both Bishop and Dolan et al. investigate aspects of ensemble playing and collaborative processes in music performance. Bishop reviews recent literature on collaborative musical creativity, in terms of how ensembles achieve creative spontaneity, through the lenses of embodied music cognition, emergence, and group flow. Dolan et al. explore synchrony of movement in ensemble music performers as a function of the level of improvisation.

Multidisciplinary, Interdisciplinary, and Blended Methodological Approaches

As noted in the introduction to this editorial, one of the main drivers of this Research Topic is that of fostering interdisciplinary cross-fertilization. Two articles explicitly use such a multidisciplinary approach. Wang et al. combine approaches from computational linguistics, complex systems, and creativity research in their investigation of the relationship between semantic association and divergent thinking tasks. Camic et al. 's article about artistic creativity in the dementias is the culmination of a 2-year interdisciplinary study involving research psychologists and neurologists, artists, and media professionals.

Certain articles, although focusing more on a single discipline (often psychology), use a blended approach of multiple methods, some comparing different methodologies directly, such as Hass and Beatty's comparison of the AUT and the Consequences Task. Dolan et al. , in their study of an improvisatory approach to performing classical music, measure various performance-related parameters, post-performance ratings from both performers and audience members, EEG signals again from both performers and selected audience, and 3D motion tracking of the performers' movements. This broad range of measures enables them to demonstrate convergent evidence for differences between improvised and prepared musical performances. Jankowska et al. integrate psychometric, eye-tracking, and verbal protocol analysis in their study of creative drawing. Finally, Carey et al. combine measures of motor imagery, dance performance, and pupillometry to investigate dancers' learning of dance moves.

The Future of Creativity Research

Given the breadth of creativity research, investigating as it does at least the creator, the creative process, the creative product, and environmental influences on creativity ( Rhodes, 1961 ; Abdulla and Cramond, 2017 ), it is important to integrate research ideas, methods, and findings across diverse disciplines. The 27 articles in this Research Topic present a broad picture of contemporary creativity research across multiple disciplines and domains. Separately and together they present a range of novel approaches for studying all aspects of creativity which we hope will encourage further interdisciplinary cross-fertilization. Creativity research is clearly thriving, and through the methodological creativity of developing innovative research methods and approaches, we are in a strong position to advance our understanding of creativity in all its forms.

Author Contributions

PF wrote the first draft of this editorial, and all authors equally contributed to the revisions.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abdulla, A. M., and Cramond, B. (2017). After six decades of systematic study of creativity: what do teachers need to know about what it is and how it is measured? Roeper. Rev. 39, 9–23. doi: 10.1080/02783193.2016.1247398

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Keywords: creativity, problem solving, artistic performance, methodology, novel approach

Citation: Fine PA, Danek AH, Friedlander KJ, Hocking I and Thompson WF (2019) Editorial: Novel Approaches for Studying Creativity in Problem-Solving and Artistic Performance. Front. Psychol. 10:2059. doi: 10.3389/fpsyg.2019.02059

Received: 01 August 2019; Accepted: 23 August 2019; Published: 18 September 2019.

Edited and reviewed by: Aaron Williamon , Royal College of Music, United Kingdom

Copyright © 2019 Fine, Danek, Friedlander, Hocking and Thompson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Philip A. Fine, philip.fine@buckingham.ac.uk

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Research Article

Self-Affirmation Improves Problem-Solving under Stress

* E-mail: [email protected]

Affiliation Department of Psychology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America

Affiliation Department of Psychology, University of California Los Angeles, Los Angeles, California, United States of America

Affiliation Division of Cancer Control and Population Sciences, NCI, Bethesda, Maryland, United States of America

Affiliation Department of Psychology, University of Sheffield, Sheffield, United Kingdom

Affiliation Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America

• J. David Creswell, 
• Janine M. Dutcher, 
• William M. P. Klein, 
• Peter R. Harris, 
• John M. Levine

• Published: May 1, 2013
• https://doi.org/10.1371/journal.pone.0062593
• Reader Comments

High levels of acute and chronic stress are known to impair problem-solving and creativity on a broad range of tasks. Despite this evidence, we know little about protective factors for mitigating the deleterious effects of stress on problem-solving. Building on previous research showing that self-affirmation can buffer stress, we tested whether an experimental manipulation of self-affirmation improves problem-solving performance in chronically stressed participants. Eighty undergraduates indicated their perceived chronic stress over the previous month and were randomly assigned to either a self-affirmation or control condition. They then completed 30 difficult remote associate problem-solving items under time pressure in front of an evaluator. Results showed that self-affirmation improved problem-solving performance in underperforming chronically stressed individuals. This research suggests a novel means for boosting problem-solving under stress and may have important implications for understanding how self-affirmation boosts academic achievement in school settings.

Citation: Creswell JD, Dutcher JM, Klein WMP, Harris PR, Levine JM (2013) Self-Affirmation Improves Problem-Solving under Stress. PLoS ONE 8(5): e62593. https://doi.org/10.1371/journal.pone.0062593

Editor: José César Perales, Universidad de Granada, Spain

Received: September 28, 2012; Accepted: March 26, 2013; Published: May 1, 2013

Copyright: © 2013 Creswell et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This research was supported by the National Science Foundation under Grant #924387 and the Pittsburgh Life Sciences Greenhouse Opportunity Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Acute and chronic stress have been shown to disrupt problem-solving and creativity [1] . For example, acutely stressful contexts, such as completing problem-solving tasks under negative social evaluation, have been shown to impair performance on a variety of tasks, such as anagrams and remote associate problems [2] , [3] . Feeling chronically stressed produces similar performance impairments. For example, Liston and colleagues found that participants who reported high levels of stress over the previous month demonstrated impaired attention-shifting performance compared to participants who reported low levels of stress [4] , [5] . Moreover, these stress-induced performance impairments were reversed when the high-stress participants completed the tasks after a one-month low stress period [4] . Although this body of research provides supportive evidence indicating that acute and chronic stressors can impair problem solving, little is currently known about stress management approaches for mitigating the effects of stress on problem solving.

An emerging body of research suggests that self-affirmation may be one such effective stress management approach. Self-affirmation theory posits that the goal of the self is to protect one’s self-image when threatened and that one way to do this is through affirmation of valued sources of self-worth [6] , [7] . In order to manipulate self-affirmation, experimental studies commonly have participants rank-order personal values (e.g., politics, relations with friends/family), and then participants in the self-affirmation condition are asked to respond to questions or complete a short essay on why their #1 ranked value is important (control participants complete a similar activity about why a lower ranked value might be important to someone else) [8] . As a result, participants in the self-affirmation condition have an opportunity to affirm a valued self-domain or characteristic [6] , [8] . Studies using this experimental approach have found that self-affirmation can buffer threats to the self in variety of domains [6] , with several recent studies showing that self-affirmation can buffer stress responses to laboratory stressors [9] , [10] and naturalistic academic stressors [11] . Collectively, this work suggests that if self-affirmation can reduce stress, it may also promote problem-solving performance under high stress conditions, although no previous studies have tested the effects of self-affirmation manipulations on actual problem-solving performance [12] – [16] .

In the present study, we test whether a brief self-affirmation can buffer the negative impacts of chronic stress on problem-solving. Specifically, we used a well-known measure of problem-solving and creativity (the Remote Associates Task (RAT)) [17] – [20] to test three hypotheses. First, we tested whether chronic stress is related to poorer problem-solving performance. Second, we tested whether self-affirmation improves problem-solving. Third, we tested whether these two main effects are qualified by a chronic stress × self-affirmation interaction, such that self-affirmation will improve problem-solving in chronically stressed participants, whom are likely to have impaired problem-solving, compared to participants who are low in chronic stress.

Ethics Statement

This research was approved by the Carnegie Mellon University Institutional Review Board.

Participants

Eighty students from two urban universities in Pittsburgh participated for course credit or $20. We excluded seven participants who did not follow instructions (N = 5) or who did not rate academic performance as important to them (N = 2). The sample thus consisted of 73 students (34 females; 39 males) who ranged in age from 18 to 34, with an average age of 21 (SD = 2.4). Given this broad age range and the marginally significantly association between age and overall RAT performance ( r  = −.21, p  = .07), we controlled for age in all analyses. The ethnic composition of the sample was predominantly Caucasian (55%), followed by Asian-American (16.5%), Other (12%), African-American (9.5%), mixed-race (5.5%), and Latino/Hispanic (1.5%). The sample had similar levels of chronic stress ( M =  16.6, SD  = 7.1, Range = 1–34) to normed US samples of individuals under 25 years of age (M = 16.8) [21] . Ethnicity (Caucasian vs. all others) and gender (male vs. female) did not moderate any of the primary study results (see Tables 1 and 2 ).

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Participants provided written informed consent and then completed an experiment ostensibly about intelligence and performance. Participants were informed that a trained evaluator would administer the performance task. Prior to completing the RAT and while the evaluator was ostensibly preparing to administer the test, participants were asked if they would be willing to complete a questionnaire and writing activity that was being piloted for an unrelated experiment on personal values (all agreed). Participants were randomly assigned either to the self-affirmation or control condition. In both cases, they rated 11 values (i.e., art, business, friends/family) in order of personal importance. Next, they wrote about their first ranked value and why it was important to them (self-affirmation condition) or their ninth ranked value and why it might be important to others (control condition) [12] . Following the self-affirmation writing task, as a manipulation check, participants were asked to respond to two items assessing how important the value they wrote about was, using a 6-point response scale (1 = Strongly Disagree to 6 = Strongly Agree). Items were, “This value has influenced my life” and “This value is an important part of who I am” (study α = .96). Participants then completed a state mood adjective checklist assessing state positive mood (5 items: proud, content, joyful, love, and grateful; study α = .84) and state negative mood (3 items: sad, angry, scared; study α = .65) (PANAS-X; [22] , [23] ).

Participants’ heart rate and mean arterial pressure were measured at 2-minute intervals using an automatic sphygmomanometer and inflatable cuff on their left arm (Dinamap Carescape V100, General Electric Company, Finland) during three different periods: an eight-minute baseline period, followed by the RAT (about 9 minutes), and a five-minute recovery period. All readings in each period were averaged. Heart rate was included because it is a useful indirect marker for task engagement [24] , [25] , which may be affected by our self-affirmation manipulation. Mean arterial blood pressure was collected to measure cardiovascular reactivity to the laboratory challenge task.

The experimenter was blind to participant condition, and a separate RAT evaluator (also blind to condition) administered the 30-item RAT performance task. 144 RAT items have been normed for difficulty [17] , and pilot testing indicated that our undergraduate sample population can solve all easy RAT items. We thus selected 30 challenging RAT items ranging in difficulty from moderately to extremely difficult (the items are available in Table S1 ). For each RAT item, participants saw three words on a computer screen (e.g., flake, mobile, cone) and were asked to generate a fourth word (e.g., snow) that when combined with each of the three stimulus words results in a common word pair used in everyday English language (e.g., snowflake, snow mobile, snow cone). They were given 12 seconds to provide an answer verbally. The evaluator provided veridical verbal performance feedback (incorrect, correct) after each response and recorded each response. In order to create performance pressure, the evaluator provided evaluative feedback three times during the 30 RAT trials (“I need you to try harder”).

After completing the performance task, the evaluator left the room and the experimenter re-entered and indicated that the participant was to rest quietly (5 minute recovery period). Participants then completed individual difference measures, including the 10-item Perceived Stress Scale [26] to assess perceived stress over the last month (all items were summed to form a composite index of chronic stress, study α = .87). To reduce potential confounding effects, we administered these measures at the end of the experimental session because previous studies indicate that completing individual difference measures at the beginning of an experimental session may act as an affirmation manipulation (i.e., they have carry-over effects) [27] . We had no reason to expect that the experimental task would bias participants’ responses when self-reporting their chronic stress levels over the past month, and a one-way ANCOVA indicated that the self-affirmation manipulation did not affect perceived stress over the last month ( F (1, 72) = .95, p  = .22, η 2  = .01). After completing individual difference measures, participants were debriefed, compensated, and excused.

Data Analysis

All descriptive statistics, ANCOVA, and multiple regression analyses were conducted using SPSS 19.0 (IBM, Armonk, New York). All predictor variables were mean-centered prior to being entered in multiple regression equations. Our experimental manipulation of self-affirmation was dummy coded (self-affirmation = 1, control = 0). Correct responses on the RAT were summed across the 30 trials to form an overall composite RAT problem-solving performance score. As described above, age was included as a covariate in all analyses (except the preliminary chi-square analyses described below).

Preliminary Analyses

It is possible that there may have been significant differences in how participants ranked their #1 value across study conditions, which could indicate a failure of randomization. To test whether there were differences in the selected #1 ranked value between study conditions, chi-square analyses were conducted to test for condition differences (self-affirmation vs. control, low vs. high chronic stress) on which value participants’ ranked #1 ( Table 3 provides frequencies of #1 ranked values across conditions). Consistent with previous studies [28] , approximately 50% of participants selected “Relations with Friends and Family” as their #1 ranked value. Importantly, there was no main effect for either self-affirmation condition (χ 2 (8) = 6.36, p =  .61) or chronic stress level (χ 2 (8) = 6.50, p  = .59) on the #1 ranked value. Moreover, the self-affirmation × chronic stress interaction for the #1 ranked value was not significant (χ 2 (8) = 3.03, p  = .93). In sum, there was no evidence that self-affirmation condition or chronic stress level affected participants’ selection of their top-ranked value.

https://doi.org/10.1371/journal.pone.0062593.t003

As expected, self-affirmation and control participants wrote about different values during the writing activity (χ 2 (10) = 33.7, p<.001; see Table 4 ), such that participants in the control condition wrote about a ninth-ranked value that was different from the first-ranked value in the self-affirmation condition. As shown in Table 4 and noted above, approximately half the self-affirmation condition participants wrote about relations with friends and family, whereas control condition participants wrote about a heterogeneous set of values. We had no reason to believe that chronic stress would influence choice of value. Consistent with this expectation, there was not a main effect for either chronic stress level (χ 2 (10) = 11.08, p  = .35) nor a self-affirmation condition × stress level interaction (χ 2 (10) = 10.6, p =  .39).

https://doi.org/10.1371/journal.pone.0062593.t004

As a manipulation check, we compared the ratings that participants in different conditions made about their value writing activity immediately after completing the writing activity. A one-way ANCOVA confirmed that the self-affirmation group ( M  = 22.97, SD  = 1.38) rated the value as significantly more important than did the control group ( M =  15.13, SD  = 3.69), F (1, 71) = 142.6, p <.001, η 2  = .671, indicating success of the value-affirmation manipulation.

We also conducted an ANCOVA comparing the total number of words written in the affirmation and control essays to determine if self-affirmation participants were more engaged in the writing task and thus wrote longer essays. Although self-affirmation condition participants wrote somewhat longer essays on average ( M  = 68.79 words, SD  = 25.9) than did control condition participants ( M  = 60.34, SD  = 26.9), this difference was not statistically significant ( F (1,72) = 1.63, p  = .21). Moreover, chronic stress level was not associated with the number of words written in the self-affirmation essays ( F (1, 72) = 1.13, p  = .35). There was also no interaction between self-affirmation condition and chronic stress level on number of words written ( F (1,72) = 1.30, p  = .26). It is also worth noting that word count was not correlated with RAT problem-solving performance ( r  = .14, p  = .23), and including word count as a covariate did not appreciably change our primary problem-solving results (word count was not further pursued as a variable of interest).

Self-Affirmation, Stress, and Problem-Solving Performance

To test our primary hypotheses, we conducted a multiple regression analysis with condition (self-affirmation vs. control), perceived stress over the last month, and their interaction predicting RAT score. Consistent with hypotheses, we observed a significant main effect of chronic stress on RAT performance ( β  = −.45, t (72) = −2.75, p  = .008), such that participants with higher stress in the last month had lower problem-solving performance. Moreover, we observed a significant main effect for self-affirmation condition, ( β  = .31, t (72) = 2.88, p  = .005), such that affirmed participants performed significantly better on the RAT task than control participants ( Figure 1 ). Consistent with our self-affirmation stress buffering hypothesis, these main effects were qualified by a significant chronic stress × self-affirmation interaction on RAT problem-solving performance ( β  = .35, t (72) = 2.09, p  = .041). As shown in Figure 1 , self-affirmation (compared to the control condition) improved the RAT problem solving performance of underperforming high chronic stress individuals, but had a minimal impact on the performance of participants low in chronic stress. Moreover, as depicted in Figure 1 , this stress buffering effect of self-affirmation improved the problem-solving performance of high stress individuals to a level comparable to individuals low in stress.

Low and high stress groups (as measured by the Perceived Stress Scale) were determined by median split for visual presentation. Error bars reflect standard errors of the mean.

https://doi.org/10.1371/journal.pone.0062593.g001

Testing the Positive Affect and Task Engagement Accounts of Problem-Solving

Previous studies indicate that positive affect boosts problem-solving performance [29] , [30] , so we tested the possibility that the self-affirmation activity was a positive affect induction, and that positive affect engendered by self-affirmation explained the problem-solving effects. Consistent with other reports [28] , we found that the self-affirmation group had higher state positive affect compared to the control group (as determined by multiple regression controlling for age: β = . 51, t (69) = 4.79, p <.001.) We also tested negative affect using the same approach, but there was not a significant main effect for self-affirmation condition ( β  = −.12, t (71) = −1.06, p  = .29) or a stress × self-affirmation interaction ( β  = −.02, t (71) = −.90, p  = .37) on state negative affect. However, there was not a self-affirmation × chronic stress interaction on positive affect ( β  = .19, t (69) = 1.19, p  = .24). Given that self-affirmation increased state positive affect, we conducted mediation analyses (following procedure described in [31] ) testing whether state positive affect mediated the impact of self-affirmation on problem-solving. In the first step of the mediation analysis, self-affirmation increased positive affect (as described above). The second step in testing mediation consists of evaluating whether the mediating variable (positive affect) predicts the outcome variable (problem-solving performance) when entered simultaneously with the predictor variable (self-affirmation condition). This second analysis revealed that positive affect was not a significant predictor of RAT performance when it was entered as a simultaneous predictor variable with the self-affirmation condition variable ( β  = −.07, t (71) = −.54, p  = .59). Thus we did not find supporting evidence for positive affect as a mediator for the self-affirmation main effect or the chronic stress × self-affirmation interaction on problem-solving performance.

As noted earlier, previous research suggests that heart rate is a useful indirect marker for task engagement [24] , [25] . To test whether there was differential task engagement in the self-affirmation and control conditions using this physiological measure, we conducted a repeated measures ANCOVA to assess change in heart rate over time between conditions (In order to run a parallel ANCOVA analyses as our primary analysis, the heart rate and mean arterial pressure analyses were run with the chronic stress variable entered as a two-level between subjects variable (low vs. high stress), as determined by median split). Although participants showed an overall significant heart rate increase from baseline ( M  = 68.50, SE  = 1.03) to the RAT problem solving period ( M  = 76.44, SE  = 1.31) ( paired-samples t (69) = −9.26, p  = <.001), there were no significant main effect or interactive effects of conditions on heart rate change. Specifically, we did not observe a significant main effect for self-affirmation condition ( F (1, 67) = .36 p  = .55, η 2  = .01) or chronic stress ( F (1,66) = .09, p  = .77, η 2  = .001). Notably, we also did not observe a significant self-affirmation condition × time interaction ( F (2, 67) = .43 p  = .65, η 2  = .01) or a condition × time × chronic stress interaction ( F (2, 67) = 1.15 p  = .32, η 2  = .03) ( Figure 2 ), indicating that there were no differential effects of self-affirmation (or the self-affirmation × chronic stress interaction) on heart rate. Collectively, these findings do not provide support for a differential task engagement explanation of our performance findings. Instead, our data indicate that participants across conditions were similarly engaged in the problem-solving task.

Panel A depicts the results for participants low in chronic stress, and Panel B depicts the results for participants high in chronic stress, as determined by median split. Error bars reflect standard errors of the mean.

https://doi.org/10.1371/journal.pone.0062593.g002

We also assessed the impact of our self-affirmation manipulation on mean arterial blood pressure responses during the RAT problem-solving period. Like heart rate, participants showed an overall significant mean arterial pressure increase from baseline ( M  = 79.71, SE  = .86) to the RAT problem solving period ( M  = 89.05, SE  = 1.08) ( paired-samples t (69) = −12.12, p <.001), but we did not observe significant main effects of self-affirmation ( F (1,67) = 2.21, p  = .14, η 2  = .03) or chronic stress ( F (1,66) = .32, p  = .57, η 2  = .01). Similarly, the self-affirmation condition × time ( F (2, 64) = .13, p  = .88, η 2  = .004) and condition × time × chronic stress ( F (2, 64) = 1.53 p  = .23, η 2  = .05) interactions were not significant ( Figure 3 ). These heart rate and mean arterial blood pressure results are in accord with our previous work showing that self-affirmation does not appreciably alter heart rate or blood pressure responses to acute stress-challenge tasks [9] . Importantly, the changes in heart rate and blood pressure reaffirm that the RAT task was stressful for participants.

https://doi.org/10.1371/journal.pone.0062593.g003

The present study provides the first evidence that self-affirmation can protect against the deleterious effects of stress on problem-solving performance. Specifically, we show that chronically stressed individuals have impaired problem-solving performance and that self-affirmation can boost problem-solving performance under pressure. Notably, these effects were qualified by a significant chronic stress by self-affirmation interaction, such that self-affirmation improved problem-solving performance in underperforming chronically stressed individuals. These findings have important implications for self-affirmation research and educational interventions. First, although we have shown in several studies that self-affirmation can reduce acute stress experiences [9] – [11] , previous research has not tested whether self-affirmation can be protective in the context of chronic (or ongoing) stressors. Moreover, until now it has been unclear whether the stress buffering benefits of self-affirmation translate into improved performance outcomes on actual problem solving tasks. Our present study suggests that a brief self-affirmation activity is sufficient to buffer the negative effects of chronic stress on task performance and can improve the ability to problem solve in a flexible manner during high stress periods [3] , [32] . It is important to note that the task used in the present study (RAT) is a common measure of creativity performance and insight [18] , [33] , and hence our study suggests that self-affirmation may increase creativity and insight in stressed individuals [16] , [34] .

Second, our study suggests that self-affirmation may be effective at boosting performance in academic tasks requiring associative processing and creativity, particularly for students who experience stress on such tasks [34] . Thus, our findings identify a potential mechanism by which a self-affirmation intervention at the beginning of a school term can improve at-risk students’ academic achievement, reducing achievement disparities between African Americans and European Americans and between women and men in science [12] – [15] .

Finally, two limitations of our study should be mentioned. It is possible that the stress buffering effects of self-affirmation on problem-solving performance that we obtained are specific to evaluative performance settings, since all of our participants completed difficult RAT items under time pressure in front of a critical evaluator. (We note that the problem-solving task we used produced significant cardiovascular stress reactivity (see Figures 2 & 3 ), comparable to other well-known psychosocial stress-challenge tasks [35] .) Future studies should therefore experimentally test whether social evaluation is a necessary condition for self-affirmation problem-solving effects. Another limitation of our study is that we measured chronic stress using a self-report measure, and this measure was collected at the end of our study session (although there were no experimental (self-affirmation manipulation) effects on chronic stress scores). We elected to use this procedure given that completing individual difference measures may have carry-over effects if completed immediately prior to self-affirmation activities [27] . Future studies using other measures for assessing chronic stress (e.g., selecting chronically stressed vs. matched control groups) [4] would therefore be useful.

The present research contributes to a broader effort at understanding how stress management approaches can facilitate problem-solving performance under stress. Despite many studies showing that acute and chronic stressors can impair problem-solving [1] , [2] , [4] , we know little about stress management and coping approaches for buffering stress during problem-solving [36] . Our work suggests that self-affirmation may be a relatively easy-to-use strategy for mitigating stress and improving problem-solving performance in evaluative settings. It will be important for future studies to consider the mechanisms linking self-affirmation with improved problem solving. We show here that our self-affirmation effects are unlikely to be explained by changes in positive affect or task engagement. The fact that we did not see any differential effects of self-affirmation on a physiological measure of task engagement (heart rate) also suggests that these effects are not driven by changes in persistence or motivation [32] . A more likely possibility, to be tested by future research, is that self-affirmation facilitates a more open and flexible attentional stance (e.g., [16] ), which increases working memory availability [37] , [38] for problem-solving in evaluative contexts.

Conclusions

The present study builds on previous research showing that self-affirmation has stress protective effects in performance settings [9] , [12] , [13] , [15] , providing an initial indication that self-affirmation can buffer the effects of chronic stress on actual problem-solving in performance settings.

Supporting Information

Remote Associate items used in the present study.

https://doi.org/10.1371/journal.pone.0062593.s001

Acknowledgments

This dataset is available upon request ( [email protected] ).

Author Contributions

Conceived and designed the experiments: JDC JMD WMPK PRH JML. Performed the experiments: JMD. Analyzed the data: JDC JMD. Contributed reagents/materials/analysis tools: JDC. Wrote the paper: JDC JMD WMPK PRH JML.

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Experimental Psychology Research Paper Topics

This page provides a comprehensive list of experimental psychology research paper topics , tailored specifically for students aiming to explore and understand the intricacies of human psychological processes through empirical research. Experimental psychology serves as a cornerstone of psychological science, employing rigorous scientific methods to investigate and interpret the vast complexities of human behavior and mental functions. Through carefully designed experiments, researchers can isolate variables and establish causal relationships, paving the way for advancements in our understanding of perception, cognition, emotion, and other psychological phenomena. By delving into these topics, students will gain valuable insights into the experimental designs, methodologies, and ethical considerations that define this vibrant field. This resource is designed to inspire and facilitate impactful research endeavors, equipping students with the knowledge to contribute significantly to the expansion and refinement of psychological science.

100 Experimental Psychology Research Paper Topics

Experimental psychology stands as a pivotal branch of psychology that applies scientific methods to investigate and unravel the mechanisms behind human thought and behavior. This field allows researchers to design experiments that precisely manipulate variables to observe their effects on subjects, thereby providing clear, causal links between psychological phenomena. The selection of the right experimental psychology research paper topics is not merely academic—it is foundational to advancing our understanding of human psychology. By choosing insightful and challenging topics, students can push the boundaries of what is known and contribute valuable new insights to the scientific community.

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• The effects of color on mood and perception.
• Sensory deprivation and its impact on cognitive functions.
• The role of attention in perceptual processing.
• Multisensory integration and its effects on human perception.
• Perceptual illusions and what they reveal about the human brain.
• The influence of aging on sensory acuity.
• Cross-cultural differences in sensory perceptions.
• The impact of technology on visual and auditory perception.
• Neuropsychological insights into taste and smell.
• The perception of pain: mechanisms and modifiers.
• The impact of sleep on memory consolidation.
• Neuroplasticity and memory: how experiences rewire the brain.
• The effects of stress on memory retrieval.
• Comparative analysis of short-term and long-term memory.
• The role of repetition and spacing in learning effectiveness.
• Memory enhancement techniques: cognitive and pharmacological approaches.
• The reliability of eyewitness memory in different environments.
• Age-related differences in learning capacity and memory retention.
• The use of virtual reality in memory recall experiments.
• False memories: their creation and implications.
• Cognitive biases that influence decision making.
• The role of emotion in rational decision-making processes.
• The impact of cognitive overload on decision quality.
• Differences in decision making between genders.
• The effect of social influence on decision-making accuracy.
• Decision fatigue: causes and consequences.
• The use of heuristics in complex decision-making.
• Neurological underpinnings of spontaneous versus planned decisions.
• The role of intuition in cognitive processing.
• The impact of aging on decision-making abilities.
• The physiological basis of emotional responses.
• Emotional regulation and its effects on mental health.
• The impact of culture on emotional expression and recognition.
• The role of emotions in moral judgment.
• Emotional contagion in groups and crowds.
• The effects of music and art on emotional states.
• Gender differences in emotional processing.
• The relationship between emotional responses and psychopathologies.
• The development of emotional intelligence over the lifespan.
• Measuring emotions: methodologies and technologies.
• The influence of group dynamics on individual behavior.
• Conformity and obedience: experiments and explanations.
• The effects of social exclusion on psychological health.
• The role of social media in shaping public opinions.
• Stereotypes and prejudice: their formation and impacts.
• Altruism and prosocial behavior in controlled experiments.
• The psychology of persuasion and its mechanisms.
• Social loafing vs. social facilitation in work and sports.
• The impact of first impressions on subsequent interactions.
• Leadership styles and their psychological effects on group performance.
• The stages of cognitive development in children.
• The impact of parental styles on child behavior.
• Adolescence: risk factors and psychological resilience.
• Developmental disorders: early detection and intervention strategies.
• The role of play in social and cognitive development.
• Aging and cognitive decline: preventive strategies.
• Lifespan psychology: changes in aspirations and motivations.
• The effects of early educational interventions on developmental outcomes.
• The influence of genetics vs. environment in developmental trajectories.
• Social development and peer influences during childhood and adolescence.
• Brain injuries and their impact on personality and behavior.
• Neurological bases of addiction and substance abuse.
• The effects of neurological diseases on family dynamics.
• Cognitive rehabilitation techniques for stroke survivors.
• The relationship between brain structure and cognitive functions.
• Neuroethics: the implications of brain research.
• The use of neuroimaging to study thought processes.
• The impact of diet and physical health on neurological health.
• Sleep disorders and their psychological effects.
• The role of mirror neurons in empathy and learning.
• Conditioning and learning: classical and operant approaches.
• The effects of reinforcement schedules on behavior modification.
• Behavioral theories in marketing and consumer behavior.
• Animal models in behavioral research: ethics and insights.
• The use of behavior therapy techniques for psychological disorders.
• The psychology of habits: formation, maintenance, and change.
• The role of behavioral factors in obesity and other health issues.
• Behavioral genetics: separating nature from nurture.
• The impact of environmental factors on behavior.
• Behavioral adaptations to climate change and environmental stresses.
• Language acquisition in children and adults.
• The cognitive processes involved in reading and writing.
• The relationship between language and thought.
• Language disorders: dyslexia, aphasia, and others.
• The impact of bilingualism on cognitive development.
• Speech perception and processing mechanisms.
• The neuroanatomy of language production and comprehension.
• Social interactions and language use.
• The evolution of language: theories and evidence.
• Artificial intelligence and natural language processing.
• The psychological impact of chronic illness on individuals and families.
• The effectiveness of psychological interventions in physical health care.
• Stress and its effects on physical health.
• The role of psychology in pain management.
• Behavioral risk factors for heart disease and other illnesses.
• The impact of patient-practitioner communication on health outcomes.
• Psychological aspects of reproductive health.
• The role of motivation in health behavior change.
• Health disparities: the impact of socioeconomic status and race.
• Psychoneuroimmunology: the link between mental states and immune response.

The breadth and depth of experimental psychology research paper topics provide a robust platform for students to explore and contribute to various facets of psychological science. These topics not only allow students to apply scientific methodologies to real-world psychological issues but also offer opportunities to innovate and enhance the understanding of human behavior. Students are encouraged to delve deeply into these experimental psychology research paper topics, as doing so will enable them to produce significant scholarly work that has the potential to influence theoretical frameworks and practical applications in psychology.

The Range of Experimental Psychology Research Paper Topics

Research Methods in Experimental Psychology

One of the core components of experimental psychology is its focus on methodological rigor and precision. The common research methodologies used in experimental psychology include controlled experiments, observational studies, and case studies, each serving different but complementary purposes. In controlled experiments, variables are manipulated in a controlled environment to observe causation and effect, making it possible to draw conclusions about how different factors influence psychological outcomes.

The importance of experimental design, controls, and variables cannot be overstated in this context. Good experimental design ensures that the results are attributable solely to the manipulated variables, not to external factors. Controls help isolate the effects of interest by holding constant other potential influences, thereby increasing the validity of the experiment. A discussion of these elements highlights their role in minimizing biases and errors, thus enhancing the reliability and applicability of the research findings.

Analyzing case studies of successful experimental setups further illustrates these points. For instance, classic experiments in social psychology, such as the Stanford prison experiment or Milgram’s obedience study, though controversial, have provided deep insights into human social behavior and conformity. These case studies not only show effective experimental design but also underscore the ethical considerations and psychological impacts associated with experimental psychology.

Innovative Areas in Experimental Research

Experimental psychology continually evolves as new technologies and theoretical approaches emerge. Cutting-edge research areas within this field include neuropsychology, cognitive robotics, and virtual reality applications, each pushing the boundaries of traditional experimental methods. These innovations allow for more precise measurements and the simulation of complex psychological processes in controlled environments.

Emerging technologies like eye-tracking devices, EEG, and fMRI have revolutionized the way experiments are conducted in experimental psychology. These tools offer unprecedented views into the neural underpinnings of cognition and behavior, allowing for more detailed and accurate predictions about how these processes operate under various conditions. Additionally, the integration of experimental psychology with fields like genetics, neuroscience, and information technology facilitates interdisciplinary research that enriches our understanding of cognitive and behavioral sciences.

Ethical Considerations in Experimental Research

Ethical considerations form a significant pillar of research in experimental psychology. Because experimental methods often involve manipulating variables to observe effects on real participants, ethical guidelines are crucial to ensure the safety and well-being of subjects. Discussions on ethical issues in experimental psychology include considerations about informed consent, deception, and the potential psychological harm that could arise from participation in studies.

Exploring the guidelines and regulations that govern experimental research helps safeguard the interests of participants and maintain public trust in psychological research. For example, the APA’s ethical guidelines mandate that experiments involving humans or animals must adhere to strict ethical standards to minimize harm and discomfort. Case studies highlighting ethical dilemmas in past research, such as the ethical controversies surrounding the aforementioned Stanford prison experiment, serve as important learning tools for current and future psychologists to understand and navigate the complex ethical landscape of experimental research.

Reflecting on the breadth of experimental psychology research paper topics offers a window into the discipline’s vast potential to influence myriad aspects of modern life, from education and health to technology and beyond. The insights gained from rigorous experimental research provide a foundation for practical applications that improve psychological interventions, educational programs, and therapeutic practices, enhancing the quality of life across various settings. As experimental psychology continues to evolve, the fusion of innovative research methods, ethical consideration, and interdisciplinary collaboration holds the promise to further advance psychological science and its applications, ensuring its relevance and impact well into the future.

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7.3 Problem-Solving

Learning objectives.

By the end of this section, you will be able to:

• Describe problem solving strategies
• Define algorithm and heuristic
• Explain some common roadblocks to effective problem solving

   People face problems every day—usually, multiple problems throughout the day. Sometimes these problems are straightforward: To double a recipe for pizza dough, for example, all that is required is that each ingredient in the recipe be doubled. Sometimes, however, the problems we encounter are more complex. For example, say you have a work deadline, and you must mail a printed copy of a report to your supervisor by the end of the business day. The report is time-sensitive and must be sent overnight. You finished the report last night, but your printer will not work today. What should you do? First, you need to identify the problem and then apply a strategy for solving the problem.

The study of human and animal problem solving processes has provided much insight toward the understanding of our conscious experience and led to advancements in computer science and artificial intelligence. Essentially much of cognitive science today represents studies of how we consciously and unconsciously make decisions and solve problems. For instance, when encountered with a large amount of information, how do we go about making decisions about the most efficient way of sorting and analyzing all the information in order to find what you are looking for as in visual search paradigms in cognitive psychology. Or in a situation where a piece of machinery is not working properly, how do we go about organizing how to address the issue and understand what the cause of the problem might be. How do we sort the procedures that will be needed and focus attention on what is important in order to solve problems efficiently. Within this section we will discuss some of these issues and examine processes related to human, animal and computer problem solving.

PROBLEM-SOLVING STRATEGIES

   When people are presented with a problem—whether it is a complex mathematical problem or a broken printer, how do you solve it? Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution.

Problems themselves can be classified into two different categories known as ill-defined and well-defined problems (Schacter, 2009). Ill-defined problems represent issues that do not have clear goals, solution paths, or expected solutions whereas well-defined problems have specific goals, clearly defined solutions, and clear expected solutions. Problem solving often incorporates pragmatics (logical reasoning) and semantics (interpretation of meanings behind the problem), and also in many cases require abstract thinking and creativity in order to find novel solutions. Within psychology, problem solving refers to a motivational drive for reading a definite “goal” from a present situation or condition that is either not moving toward that goal, is distant from it, or requires more complex logical analysis for finding a missing description of conditions or steps toward that goal. Processes relating to problem solving include problem finding also known as problem analysis, problem shaping where the organization of the problem occurs, generating alternative strategies, implementation of attempted solutions, and verification of the selected solution. Various methods of studying problem solving exist within the field of psychology including introspection, behavior analysis and behaviorism, simulation, computer modeling, and experimentation.

A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them (table below). For example, a well-known strategy is trial and error. The old adage, “If at first you don’t succeed, try, try again” describes trial and error. In terms of your broken printer, you could try checking the ink levels, and if that doesn’t work, you could check to make sure the paper tray isn’t jammed. Or maybe the printer isn’t actually connected to your laptop. When using trial and error, you would continue to try different solutions until you solved your problem. Although trial and error is not typically one of the most time-efficient strategies, it is a commonly used one.

   Another type of strategy is an algorithm. An algorithm is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed. Algorithms are used frequently in our everyday lives, especially in computer science. When you run a search on the Internet, search engines like Google use algorithms to decide which entries will appear first in your list of results. Facebook also uses algorithms to decide which posts to display on your newsfeed. Can you identify other situations in which algorithms are used?

A heuristic is another type of problem solving strategy. While an algorithm must be followed exactly to produce a correct result, a heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. A “rule of thumb” is an example of a heuristic. Such a rule saves the person time and energy when making a decision, but despite its time-saving characteristics, it is not always the best method for making a rational decision. Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

• When one is faced with too much information
• When the time to make a decision is limited
• When the decision to be made is unimportant
• When there is access to very little information to use in making the decision
• When an appropriate heuristic happens to come to mind in the same moment

Working backwards is a useful heuristic in which you begin solving the problem by focusing on the end result. Consider this example: You live in Washington, D.C. and have been invited to a wedding at 4 PM on Saturday in Philadelphia. Knowing that Interstate 95 tends to back up any day of the week, you need to plan your route and time your departure accordingly. If you want to be at the wedding service by 3:30 PM, and it takes 2.5 hours to get to Philadelphia without traffic, what time should you leave your house? You use the working backwards heuristic to plan the events of your day on a regular basis, probably without even thinking about it.

Another useful heuristic is the practice of accomplishing a large goal or task by breaking it into a series of smaller steps. Students often use this common method to complete a large research project or long essay for school. For example, students typically brainstorm, develop a thesis or main topic, research the chosen topic, organize their information into an outline, write a rough draft, revise and edit the rough draft, develop a final draft, organize the references list, and proofread their work before turning in the project. The large task becomes less overwhelming when it is broken down into a series of small steps.

Further problem solving strategies have been identified (listed below) that incorporate flexible and creative thinking in order to reach solutions efficiently.

Additional Problem Solving Strategies :

• Abstraction – refers to solving the problem within a model of the situation before applying it to reality.
• Analogy – is using a solution that solves a similar problem.
• Brainstorming – refers to collecting an analyzing a large amount of solutions, especially within a group of people, to combine the solutions and developing them until an optimal solution is reached.
• Divide and conquer – breaking down large complex problems into smaller more manageable problems.
• Hypothesis testing – method used in experimentation where an assumption about what would happen in response to manipulating an independent variable is made, and analysis of the affects of the manipulation are made and compared to the original hypothesis.
• Lateral thinking – approaching problems indirectly and creatively by viewing the problem in a new and unusual light.
• Means-ends analysis – choosing and analyzing an action at a series of smaller steps to move closer to the goal.
• Method of focal objects – putting seemingly non-matching characteristics of different procedures together to make something new that will get you closer to the goal.
• Morphological analysis – analyzing the outputs of and interactions of many pieces that together make up a whole system.
• Proof – trying to prove that a problem cannot be solved. Where the proof fails becomes the starting point or solving the problem.
• Reduction – adapting the problem to be as similar problems where a solution exists.
• Research – using existing knowledge or solutions to similar problems to solve the problem.
• Root cause analysis – trying to identify the cause of the problem.

The strategies listed above outline a short summary of methods we use in working toward solutions and also demonstrate how the mind works when being faced with barriers preventing goals to be reached.

One example of means-end analysis can be found by using the Tower of Hanoi paradigm . This paradigm can be modeled as a word problems as demonstrated by the Missionary-Cannibal Problem :

Missionary-Cannibal Problem

Three missionaries and three cannibals are on one side of a river and need to cross to the other side. The only means of crossing is a boat, and the boat can only hold two people at a time. Your goal is to devise a set of moves that will transport all six of the people across the river, being in mind the following constraint: The number of cannibals can never exceed the number of missionaries in any location. Remember that someone will have to also row that boat back across each time.

Hint : At one point in your solution, you will have to send more people back to the original side than you just sent to the destination.

The actual Tower of Hanoi problem consists of three rods sitting vertically on a base with a number of disks of different sizes that can slide onto any rod. The puzzle starts with the disks in a neat stack in ascending order of size on one rod, the smallest at the top making a conical shape. The objective of the puzzle is to move the entire stack to another rod obeying the following rules:

• 1. Only one disk can be moved at a time.
• 2. Each move consists of taking the upper disk from one of the stacks and placing it on top of another stack or on an empty rod.
• 3. No disc may be placed on top of a smaller disk.

  Figure 7.02. Steps for solving the Tower of Hanoi in the minimum number of moves when there are 3 disks.

Figure 7.03. Graphical representation of nodes (circles) and moves (lines) of Tower of Hanoi.

The Tower of Hanoi is a frequently used psychological technique to study problem solving and procedure analysis. A variation of the Tower of Hanoi known as the Tower of London has been developed which has been an important tool in the neuropsychological diagnosis of executive function disorders and their treatment.

GESTALT PSYCHOLOGY AND PROBLEM SOLVING

As you may recall from the sensation and perception chapter, Gestalt psychology describes whole patterns, forms and configurations of perception and cognition such as closure, good continuation, and figure-ground. In addition to patterns of perception, Wolfgang Kohler, a German Gestalt psychologist traveled to the Spanish island of Tenerife in order to study animals behavior and problem solving in the anthropoid ape.

As an interesting side note to Kohler’s studies of chimp problem solving, Dr. Ronald Ley, professor of psychology at State University of New York provides evidence in his book A Whisper of Espionage  (1990) suggesting that while collecting data for what would later be his book  The Mentality of Apes (1925) on Tenerife in the Canary Islands between 1914 and 1920, Kohler was additionally an active spy for the German government alerting Germany to ships that were sailing around the Canary Islands. Ley suggests his investigations in England, Germany and elsewhere in Europe confirm that Kohler had served in the German military by building, maintaining and operating a concealed radio that contributed to Germany’s war effort acting as a strategic outpost in the Canary Islands that could monitor naval military activity approaching the north African coast.

While trapped on the island over the course of World War 1, Kohler applied Gestalt principles to animal perception in order to understand how they solve problems. He recognized that the apes on the islands also perceive relations between stimuli and the environment in Gestalt patterns and understand these patterns as wholes as opposed to pieces that make up a whole. Kohler based his theories of animal intelligence on the ability to understand relations between stimuli, and spent much of his time while trapped on the island investigation what he described as  insight , the sudden perception of useful or proper relations. In order to study insight in animals, Kohler would present problems to chimpanzee’s by hanging some banana’s or some kind of food so it was suspended higher than the apes could reach. Within the room, Kohler would arrange a variety of boxes, sticks or other tools the chimpanzees could use by combining in patterns or organizing in a way that would allow them to obtain the food (Kohler & Winter, 1925).

While viewing the chimpanzee’s, Kohler noticed one chimp that was more efficient at solving problems than some of the others. The chimp, named Sultan, was able to use long poles to reach through bars and organize objects in specific patterns to obtain food or other desirables that were originally out of reach. In order to study insight within these chimps, Kohler would remove objects from the room to systematically make the food more difficult to obtain. As the story goes, after removing many of the objects Sultan was used to using to obtain the food, he sat down ad sulked for a while, and then suddenly got up going over to two poles lying on the ground. Without hesitation Sultan put one pole inside the end of the other creating a longer pole that he could use to obtain the food demonstrating an ideal example of what Kohler described as insight. In another situation, Sultan discovered how to stand on a box to reach a banana that was suspended from the rafters illustrating Sultan’s perception of relations and the importance of insight in problem solving.

Grande (another chimp in the group studied by Kohler) builds a three-box structure to reach the bananas, while Sultan watches from the ground.  Insight , sometimes referred to as an “Ah-ha” experience, was the term Kohler used for the sudden perception of useful relations among objects during problem solving (Kohler, 1927; Radvansky & Ashcraft, 2013).

Solving puzzles.

   Problem-solving abilities can improve with practice. Many people challenge themselves every day with puzzles and other mental exercises to sharpen their problem-solving skills. Sudoku puzzles appear daily in most newspapers. Typically, a sudoku puzzle is a 9×9 grid. The simple sudoku below (see figure) is a 4×4 grid. To solve the puzzle, fill in the empty boxes with a single digit: 1, 2, 3, or 4. Here are the rules: The numbers must total 10 in each bolded box, each row, and each column; however, each digit can only appear once in a bolded box, row, and column. Time yourself as you solve this puzzle and compare your time with a classmate.

How long did it take you to solve this sudoku puzzle? (You can see the answer at the end of this section.)

   Here is another popular type of puzzle (figure below) that challenges your spatial reasoning skills. Connect all nine dots with four connecting straight lines without lifting your pencil from the paper:

Did you figure it out? (The answer is at the end of this section.) Once you understand how to crack this puzzle, you won’t forget.

   Take a look at the “Puzzling Scales” logic puzzle below (figure below). Sam Loyd, a well-known puzzle master, created and refined countless puzzles throughout his lifetime (Cyclopedia of Puzzles, n.d.).

What steps did you take to solve this puzzle? You can read the solution at the end of this section.

Pitfalls to problem solving.

   Not all problems are successfully solved, however. What challenges stop us from successfully solving a problem? Albert Einstein once said, “Insanity is doing the same thing over and over again and expecting a different result.” Imagine a person in a room that has four doorways. One doorway that has always been open in the past is now locked. The person, accustomed to exiting the room by that particular doorway, keeps trying to get out through the same doorway even though the other three doorways are open. The person is stuck—but she just needs to go to another doorway, instead of trying to get out through the locked doorway. A mental set is where you persist in approaching a problem in a way that has worked in the past but is clearly not working now.

Functional fixedness is a type of mental set where you cannot perceive an object being used for something other than what it was designed for. During the Apollo 13 mission to the moon, NASA engineers at Mission Control had to overcome functional fixedness to save the lives of the astronauts aboard the spacecraft. An explosion in a module of the spacecraft damaged multiple systems. The astronauts were in danger of being poisoned by rising levels of carbon dioxide because of problems with the carbon dioxide filters. The engineers found a way for the astronauts to use spare plastic bags, tape, and air hoses to create a makeshift air filter, which saved the lives of the astronauts.

   Researchers have investigated whether functional fixedness is affected by culture. In one experiment, individuals from the Shuar group in Ecuador were asked to use an object for a purpose other than that for which the object was originally intended. For example, the participants were told a story about a bear and a rabbit that were separated by a river and asked to select among various objects, including a spoon, a cup, erasers, and so on, to help the animals. The spoon was the only object long enough to span the imaginary river, but if the spoon was presented in a way that reflected its normal usage, it took participants longer to choose the spoon to solve the problem. (German & Barrett, 2005). The researchers wanted to know if exposure to highly specialized tools, as occurs with individuals in industrialized nations, affects their ability to transcend functional fixedness. It was determined that functional fixedness is experienced in both industrialized and nonindustrialized cultures (German & Barrett, 2005).

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. Sometimes, however, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000 home? Why would the realtor show you the run-down houses and the nice house? The realtor may be challenging your anchoring bias. An anchoring bias occurs when you focus on one piece of information when making a decision or solving a problem. In this case, you’re so focused on the amount of money you are willing to spend that you may not recognize what kinds of houses are available at that price point.

The confirmation bias is the tendency to focus on information that confirms your existing beliefs. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Representative bias describes a faulty way of thinking, in which you unintentionally stereotype someone or something; for example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

Finally, the availability heuristic is a heuristic in which you make a decision based on an example, information, or recent experience that is that readily available to you, even though it may not be the best example to inform your decision . Biases tend to “preserve that which is already established—to maintain our preexisting knowledge, beliefs, attitudes, and hypotheses” (Aronson, 1995; Kahneman, 2011). These biases are summarized in the table below.

Were you able to determine how many marbles are needed to balance the scales in the figure below? You need nine. Were you able to solve the problems in the figures above? Here are the answers.

   Many different strategies exist for solving problems. Typical strategies include trial and error, applying algorithms, and using heuristics. To solve a large, complicated problem, it often helps to break the problem into smaller steps that can be accomplished individually, leading to an overall solution. Roadblocks to problem solving include a mental set, functional fixedness, and various biases that can cloud decision making skills.

References:

Openstax Psychology text by Kathryn Dumper, William Jenkins, Arlene Lacombe, Marilyn Lovett and Marion Perlmutter licensed under CC BY v4.0. https://openstax.org/details/books/psychology

Review Questions:

1. A specific formula for solving a problem is called ________.

a. an algorithm

b. a heuristic

c. a mental set

d. trial and error

2. Solving the Tower of Hanoi problem tends to utilize a  ________ strategy of problem solving.

a. divide and conquer

b. means-end analysis

d. experiment

3. A mental shortcut in the form of a general problem-solving framework is called ________.

4. Which type of bias involves becoming fixated on a single trait of a problem?

a. anchoring bias

b. confirmation bias

c. representative bias

d. availability bias

5. Which type of bias involves relying on a false stereotype to make a decision?

6. Wolfgang Kohler analyzed behavior of chimpanzees by applying Gestalt principles to describe ________.

a. social adjustment

b. student load payment options

c. emotional learning

d. insight learning

7. ________ is a type of mental set where you cannot perceive an object being used for something other than what it was designed for.

a. functional fixedness

c. working memory

Critical Thinking Questions:

1. What is functional fixedness and how can overcoming it help you solve problems?

2. How does an algorithm save you time and energy when solving a problem?

Personal Application Question:

1. Which type of bias do you recognize in your own decision making processes? How has this bias affected how you’ve made decisions in the past and how can you use your awareness of it to improve your decisions making skills in the future?

anchoring bias

availability heuristic

confirmation bias

functional fixedness

hindsight bias

problem-solving strategy

representative bias

trial and error

working backwards

Answers to Exercises

algorithm:  problem-solving strategy characterized by a specific set of instructions

anchoring bias:  faulty heuristic in which you fixate on a single aspect of a problem to find a solution

availability heuristic:  faulty heuristic in which you make a decision based on information readily available to you

confirmation bias:  faulty heuristic in which you focus on information that confirms your beliefs

functional fixedness:  inability to see an object as useful for any other use other than the one for which it was intended

heuristic:  mental shortcut that saves time when solving a problem

hindsight bias:  belief that the event just experienced was predictable, even though it really wasn’t

mental set:  continually using an old solution to a problem without results

problem-solving strategy:  method for solving problems

representative bias:  faulty heuristic in which you stereotype someone or something without a valid basis for your judgment

trial and error:  problem-solving strategy in which multiple solutions are attempted until the correct one is found

working backwards:  heuristic in which you begin to solve a problem by focusing on the end result

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• Published: 27 September 2024

Brief psychological intervention for suicide prevention based on problem-solving applied in different formats to people over 50 years old: protocol for a randomized controlled trial

• Fernando L. Vázquez 1 ,
• Ángela J. Torres 2 ,
• Vanessa Blanco 3 ,
• Queila Bouza 1 ,
• Patricia Otero 4 ,
• Elena Andrade 5 ,
• Miguel Á. Simón 4 ,
• Ana M. Bueno 4 ,
• Manuel Arrojo 6 ,
• Mario Páramo 6 &
• Alba Fernández 1  

BMC Psychiatry volume  24 , Article number:  628 ( 2024 ) Cite this article

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Suicide is a major public health problem, especially among individuals over 50 years old. Despite the suitability of this life stage for prevention, research on the efficacy of psychological interventions is scarce and methodologically limited, affecting their clinical utility and efficacy. Brief, flexible interventions that can be applied both in-person and remotely are needed. This study aims to evaluate the efficacy of a brief problem-solving-based suicide prevention program applied through various modalities to individuals over 50 years old.

A randomized controlled trial will be conducted. A sample of 212 adults aged 50 or older with suicidal ideation will be randomly assigned to a problem-solving-based psychological intervention administered face-to-face (PSPI-P; n  = 53), by telephone multiconference (PSPI-M; n  = 53), via a smartphone app (PSPI-A; n  = 53), or to a usual care control group (UCCG; n  = 53). The intervention will be delivered in 7 sessions or modules of 90 min each. Blind trained evaluators will conduct assessments at pre-intervention, post-intervention, and follow-ups at 3, 6, and 12 months. The primary outcome will be suicidal ideation evaluated using the Suicidal Ideation Scale (SSI) and the Columbia Suicide Severity Rating Scale (C-SSRS). Secondary outcomes will include hopelessness, anxiety and depression symptoms, reasons for living, impulsivity, problem-solving skills, social support, anger syndrome, gratitude, personality, dropouts, treatment adherence, and satisfaction with the intervention.

This study will provide evidence of the efficacy of a brief problem-solving-based intervention for suicide prevention in individuals over 50 years old, administered face-to-face, by telephone multiconference, and via a smartphone app. If results are favorable, it will indicate that an effective, accessible, clinically and socially useful suicide prevention intervention has been developed for affected individuals, families, and communities.

Trial registration

ClinicalTrials.gov NCT06338904. Registered April 1, 2024.

Peer Review reports

Suicidal behavior is a complex, multifaceted phenomenon manifesting through a spectrum of self-destructive thoughts and behaviors, ranging from suicidal ideation to the consummation of the act, including planning and attempting suicide [ 1 ].

This phenomenon represents a serious public health issue, accounting for more than 700,000 annual deaths worldwide [ 2 ]. It is estimated that for every completed suicide, 20 people attempt suicide [ 1 ], and about 11.3% of the general population experiences suicidal ideation each year [ 3 ]. Furthermore, each suicide affects an average of six people, including family and close friends [ 1 ], constituting a tragedy with lasting impacts on families and communities.

One of the most vulnerable populations globally is individuals over 50 years old [ 4 , 5 ]. In 2021, the suicide mortality rates for people aged 50 to 74 and those over 75 were 14.7 and 29.0 per 100,000 inhabitants, respectively [ 4 ]. These figures exceed the global average (9 per 100,000) and the rates observed in younger age groups [ 4 ]. Factors frequently contributing to these suicide rates in this age group include major depressive disorder (MDD) and depressive symptoms, specific life stressors (e.g., medical conditions, functional disability, bereavement, or legal and financial issues), and social disconnection, which are well-supported by research in their relation to suicidal behaviors [ 6 , 7 , 8 , 9 , 10 , 11 ].

Additionally, people in these age groups show fewer warning signs and choose more lethal methods of suicide [ 12 , 13 ]. Furthermore, it is anticipated that these data will worsen in the coming years as the number of individuals in this age range increases due to population aging [ 14 ]. On the other hand, from the age of 50 onwards, a stage of life opens up that is ideal for preventive actions, since, according to Levinson [ 15 ], there is a transition in adult life that represents an important opportunity to modify and improve life structure.

Therefore, it is necessary to develop strategies to prevent suicide risk in this population. Previous studies evaluating the efficacy of psychological interventions for suicide prevention in this age group through randomized controlled trials (RCTs) have shown positive results. For instance, Kiosses et al. [ 16 ] compared the efficacy of problem-adaptation therapy and supportive therapy in 39 older adults with MDD and cognitive impairment, finding comparable reductions in suicidal ideation over 12 weeks following both interventions. Zhang et al. [ 17 ] evaluated the efficacy of a resilience-focused program in 68 institutionalized older adults with suicidal ideation against a waitlist control group, noting a decrease in suicidal thoughts post-intervention and at one-month follow-up.

Despite these promising results, the amount of research in this field is insufficient and methodologically and clinically limited. The interventions were not developed based on a theoretical model; were applied to small, specific samples limiting external validity; and did not include long-term follow-ups. Additionally, treatments were delivered individually in person, requiring substantial resources and time, increasing costs and limiting efficiency, utility, and accessibility. One potential solution could be implementing interventions using information and communication technologies (ICTs). The use of ICTs (e.g., telephone multiconferencing, apps) increases the possibilities for dissemination and access to populations in need. Bringing interventions to these age groups through ICTs could reduce accessibility barriers (e.g., lack of services in rural areas, transportation issues, or stigmatization).

Considering all these issues, developing brief, simple interventions applicable practically and flexibly in both face-to-face and remote modalities is essential. Problem-solving therapy (PST) [ 18 ], based on the problem-solving model of D’Zurilla and Nezu [ 19 ], stands out among these. It has been described as pragmatic, transdiagnostic, effective, and easy to learn [ 20 ] and has proven effective in numerous contexts and with various problems [ 18 , 20 ], including suicide [ 21 , 22 , 23 ]. In relation to this issue, Gustavson et al. [ 22 ] conducted an RCT to evaluate the efficacy of PST compared to supportive therapy in reducing suicidal ideation in adults with MDD and executive dysfunction. Participants receiving PST showed significantly greater reductions in suicidal ideation post-intervention and at 36-week follow-up. Unützer et al. [ 23 ] conducted an RCT to examine the long-term effects of the IMPACT program, which included PST, on suicidal ideation in older adults with MDD, finding it effective in reducing suicidal thoughts during, after the intervention, and at 18 and 24-month follow-ups. Choi et al. [ 21 ] introduced the use of ICTs in PST implementation by conducting an RCT to evaluate the efficacy of PST delivered in person and via videoconference compared to telephone support in homebound low-income older adults with depressive symptoms. Results showed that videoconference PST was more effective than in-person PST and telephone support in reducing suicidal ideation at 36-week follow-up. However, the three studies were designed with the aim of reducing depressive symptoms, with suicidal ideation being a secondary outcome evaluated through a single item. Additionally, the applicability of these results is limited by the characteristics of the samples used [ 21 , 22 ] or by the nonspecific role of PST within the intervention [ 23 ].

Another significant limitation is that among existing prevention levels, indicated prevention is crucial to address this phenomenon. According to the US Institute of Medicine [IOM] [ 24 ], indicated prevention strategies target individuals who already show signs and symptoms indicating a predisposition to develop a mental disorder but do not yet meet diagnostic criteria (for suicide, individuals showing signs of suicidal behavior or a condition placing them at very high risk, such as a recent suicide attempt) [ 1 ]. Despite its importance, only Zhang et al. [ 17 ] used an indicated prevention perspective among all reviewed studies [ 16 , 17 , 21 , 22 , 23 ].

This study aims to: (1) evaluate the efficacy of a brief problem-solving-based psychological intervention for indicated suicide risk prevention administered face-to-face, by telephone multiconference, or via a smartphone app to individuals aged 50 or older compared to a usual care control group; and (2) examine the mediators and moderators of change in suicidal ideation. The main hypothesis is that the three experimental conditions, compared to the usual care control condition, will significantly reduce suicidal ideation at post-intervention and 3, 6, and 12-month follow-ups. Additionally, it is expected that other clinical variables (hopelessness, anxiety and depression symptoms, impulsivity, anger, gratitude) will improve. We also anticipate that problem-solving skills will mediate the relationship between treatment and the reduction in suicidal ideation; and that sociodemographic, family-related variables, personal history, suicide risk factors, reasons for living, social support, personality, adherence, and satisfaction with the intervention will moderate treatment effects.

A randomized controlled trial (RCT) will be conducted. The present trial protocol follows the recommendations for clinical trial protocols from the SPIRIT Declaration [ 25 ] (see checklist in Additional file 1) and its update [ 26 ]. The RCT will adhere to the CONSORT guidelines extension for psychological and social interventions CONSORT-SPI 2018 [ 27 ]. Participants will be assigned to four groups: (a) a problem-solving-based psychological intervention administered in-person (PSPI-P; experimental group 1); (b) a problem-solving-based psychological intervention administered via telephone multiconference (PSPI-M; experimental group 2); (c) a problem-solving-based psychological intervention administered via a smartphone app (PSPI-A; experimental group 3); or (d) a control group receiving usual care (UCCG; usual care control group).

The study stages are shown in Fig.  1 . There will be five measurement points across the four groups: pre-intervention (T1), post-intervention (T2), and follow-ups at 3, 6, and 12 months (T3, T4, and T5, respectively). After baseline evaluation (pre-intervention), eligible subjects will be selected, and interventions administered. Post-intervention evaluation and three follow-ups (at 3, 6, and 12 months) will be conducted. To minimize participant loss and optimize protocol compliance and follow-up, recommended strategies will be employed [ 28 ], such as making the intervention simple, scheduling comfortable and pleasant sessions, conducting non-invasive, useful, and interesting evaluations, encouraging participants to continue with the trial, and recovering lost participants during follow-up.

SPIRIT Figure. Phases of a randomized controlled trial

Participants

Recruitment.

Participants will be recruited by the Research Group on Mental Health and Psychopathology (GRISAMP) at the University of Santiago de Compostela (USC) from individuals over 50 years old attending health centers of the Galician Health Service (SERGAS) in the Autonomous Community of Galicia, Spain. Galicia is a region in northwest Spain covering an area of 29,575 km 2 with a population of 2,693,451 [ 29 ] and is the second most aged community in Spain [ 30 ].

After referral by health professionals, potential participants will be contacted by phone to schedule the initial evaluation (T1). During the in-person evaluation session at USC facilities, project details will be explained, and those interested in continuing will be asked to sign an informed consent form. Evaluators will then collect sociodemographic data, conduct a clinical interview, and assess suicidal ideation to ensure eligibility criteria are met. Participants meeting these criteria will complete the remaining questionnaires.

Eligibility criteria

Inclusion criteria include: (a) being at least 50 years old; (b) residing in the Autonomous Community of Galicia; and (c) presenting suicidal ideation as indicated by scores above 6 on the Suicidal Ideation Scale (SSI) [ 31 ]. Exclusion criteria include: (a) severe mental or medical disorders (e.g., severe major depressive disorder, bipolar disorder, schizophrenia, severe cognitive impairment, dissociative disorders, substance dependence, acute suicide risk); (b) having started psychological or psychopharmacological treatment in the two months prior to the study or participating in another suicide prevention-related study; (c) lacking an appropriate device to participate (smartphone with internet connection), sufficient Spanish language proficiency, or having sensory or physical problems preventing participation; or (d) planning to move out of the Autonomous Community of Galicia in the next 18 months.

Randomization

Eligible patients will be randomly assigned with equal probability (1:1; random allocation) to one of the four study conditions after initial evaluation (T1). The Spanish version of the automated OxMaR system [ 32 ] will be used for randomization, allowing group assignment and ensuring concealment of the randomization sequence. This will be communicated to researchers through sealed envelopes numbered per participant with instructions for use in numerical order. Due to the nature of the interventions, blinding participants to their assigned group will not be possible.

Sample size

Given the heterogeneity of the types of interventions and the results of existing studies, a conservative estimation of the sample size is necessary. In two previous works [ 22 , 33 ], according to the procedure for interpreting the magnitude of Odds Ratios described by Chen et al. [ 34 ], moderate to large effect sizes were reported for suicidal ideation, suicidal orientation, and depressive symptoms for the problem-solving groups compared to the comparison groups. Taking a moderate effect size (Cohen's d = 0.50) as a reference, assuming a two-tailed test, an α of 0.05, and a power (1—β) of 0.80, a sample size of 42 participants per group is required. Additionally, anticipating a sample loss of approximately 25%, similar to that reported by Fox et al. [ 35 ], it is necessary to recruit a minimum of 53 participants in each group, resulting in a final sample of 212 subjects.

The trial will comply with the principles of the Declaration of Helsinki and the Spanish Organic Law 3/2018 on the Protection of Personal Data and guarantee of digital rights [ 36 ]. It has been approved by the Bioethics Committee of the University of Santiago de Compostela (Code USC 52/2023). Participation will be entirely voluntary without financial or other incentives, and all participants must provide written informed consent.

If a therapist detects an acute suicide risk in a participant, they will be immediately referred to the appropriate health services for psychological and psychiatric treatment for ethical reasons. This participant will exit the intervention due to the initiation of new psychiatric or psychological treatment (exclusion criterion b); their data will be retained for statistical analysis.

Interventions

A standardized intervention protocol for each experimental condition will be developed to increase internal validity. Participants in the PSPI-P group will receive the intervention in person at USC facilities; those in the PSPI-M group will receive it via telephone multiconference; and those in the PSPI-A group will receive it via a smartphone app. The UCCG group will receive usual care.

The three experimental conditions will consist of 7 sessions/modules, each lasting 90 min, conducted once a week, and will include between-session tasks to practice skills in real life. Task completion will be recorded by therapists in the PSPI-P and PSPI-M groups, and by the participants themselves through the app in the PSPI-A group.

In the PSPI-P and PSPI-M groups, the intervention will be administered by psychologists (with master's or doctoral training) who will be previously trained through approximately 60 h of theoretical-practical seminars and role-playing exercises by three professionals with 20 to 35 years of experience in cognitive-behavioral therapies. To control for therapist effects on treatment outcomes in this study, as the same therapy is used across the different experimental groups, varying only in the delivery format, a "crossed therapist" design will be used: the three therapists will participate in the administration of the therapy.

Following the training and prior to conducting the randomized controlled trial, a pilot study will be carried out in which each therapist will apply the intervention to approximately 15 subjects to review the acceptability of the material and refine the intervention. The concordance of the scores among therapists will be checked by calculating kappa indices. Once the pilot experience has been analyzed, the sample recruitment for the randomized controlled trial will be conducted, following the already described procedure. In the PSPI-P and PSPI-M groups, sessions will be recorded, and the professionals responsible for training the therapists will supervise their work weekly, evaluate their adherence to the intervention manuals, their application skills, and also provide weekly supervision to the therapists.

Problem-Solving-Based Psychological Intervention Administered In-Person (PSPI-P)

This group will receive a problem-solving-based psychological intervention developed from the problem-solving model [ 19 ]. The main component of the intervention will focus on teaching participants problem-solving skills to effectively cope with adverse circumstances currently in their lives. The program will also include other empirically supported techniques derived from cognitive-behavioral therapies [ 37 ] and positive psychology [ 38 ].

On the other hand, previous research by the research team will also be taken as a reference (e.g., with people of similar ages to those in the present study, the application of therapies in both face-to-face and remote modalities, or problem-solving therapy); in particular, the indicated depression prevention program in group format [ 39 ], which has demonstrated its effectiveness in non-professional caregivers with subclinical depression in reducing depressive symptoms, emotional distress, burden, and preventing the onset of new episodes of clinical depression both in the short term [ 40 ] and long term [ 41 ], with the results on depressive symptoms maintained at the 8-year follow-up [ 42 ]; and also the indicated suicide prevention group intervention using problem-solving, developed by our research group, which has demonstrated its effectiveness in adolescents in Brazil [ 33 ].

The intervention will be delivered in a group format across 7 sessions, each lasting approximately 90 min, once a week. All sessions will have a similar structure: beginning with a recap of the previous session and review of homework (from the second session), followed by the introduction of key concepts, training in various skills and techniques, and the assignment of homework (see Table  1 ). If a participant misses a session or does not progress as expected, an individual phone call will be made to inquire about their situation, discuss reasons for lack of progress, and encourage continuation without active intervention.

Regarding the session content, in Session 1, an approach will be taken to the concept of suicidal ideation and behavior, their prevalence, and associated factors. In this line, the relationship between suicidal ideation and behaviors and adverse events as a precipitating factor will be presented, as well as coping strategies and problem-solving skills as protective factors. Additionally, the problem-solving model that underpins the main component of the intervention will be explained. Participants will be asked to identify their main current problems. A behavioral contract will also be drawn up, and participants will be trained in mood monitoring techniques, deep breathing, and self-reinforcement. Session 2 will focus on developing a personalized crisis response plan, which will include identifying symptoms and warning signs, cognitive and behavioral emotional regulation strategies, emergency contacts, and arguments against suicidal thoughts. Moreover, cognitive reframing will be conducted to promote active problem-solving. In Session 3, starting from the problems identified by the patients, this session will delve into problem definition, goal setting, and generating alternative solutions. Also, a plan for enjoyable activities will be proposed. During Session 4, decision-making and planning the implementation of the chosen solution will be addressed. In addition, a guided mindfulness practice will be included as a complementary activity. In Session 5, the consequences of implementing the chosen solution will be evaluated, identifying possible obstacles and benefits, and the problem-solving process will be repeated with another problem. Furthermore, participants will be instructed in the identification and reformulation of irrational thoughts. In Session 6, the results obtained after applying the second solution will be evaluated, the process will be repeated with a third problem, and participants will be instructed in a gratitude practice. Finally, in Session 7, the effects of implementing the third chosen solution will be evaluated, and all the skills and techniques learned throughout the intervention will be compiled. To prevent potential relapses, participants will be guided in creating a life project, identifying goals, obstacles, and available resources for coping.

Problem-Solving-Based Psychological Intervention Administered via Telephone Multiconference (PSPI-M)

Participants in this group will receive the previously described intervention in a telephone multiconference group format with the same duration, content, and structure. Adaptations will include small adjustments related to switching from in-person to telephone multiconference format: managing a telephone waiting system, using telephone communication skills (e.g., smiling at the beginning of the call, greeting and identifying oneself, especially courteous speaking, speaking slowly and clearly) [ 43 ]; adding a group rule for participants to state their name each time they speak; abbreviating explanations related to program content; and providing written support materials (a summary brochure for each session with key content and tasks to complete between sessions) by email or postal mail to participants' homes.

Problem-Solving-Based Psychological Intervention Administered via Smartphone App (PSPI-A)

Participants in this group will receive the intervention described earlier, adapted for delivery via a smartphone app with equivalent duration, content, and structure. All intervention components will be maintained, although the program will be adapted for intuitive design and usability. Content will be summarized and simplified for easy comprehension and engagement, using attractive animations and transitions combined with a pleasant and colorful design, enriched with videos and audios to explain the techniques. The app will include a feedback mechanism allowing users to record tasks and receive information on their progress.

Usual Care Control Group (UCCG)

Participants in the control group will receive usual care. Standard care will include individual and/or group psychotherapy and/or psychiatric medication as determined by the health professionals participants are attending at the time of the study, whether in public or private settings. Choosing usual care as the control group allows for comparison of the interventions in the experimental groups PSPI-P, PSPI-M, and PSPI-A with the current standard of treatment, ensuring clinical relevance, ethics, and generalization of the results.

Outcome measures

Participants will be evaluated at pre-intervention (T1), post-intervention (T2), and at follow-ups at 3 (T3), 6 (T4), and 12 months (T5) with the instruments listed in Table  2 . Evaluations will be conducted in-person by independent psychologists who are trained and blind to the study’s objectives, hypotheses, interventions, and participants' group assignments. Evaluators' training will be provided by two experts with 30 years of experience in assessment, consisting of 35 h of theoretical-practical seminars and role-playing on the assessment instruments used.

Sociodemographic and clinical characteristics

To collect information on sociodemographic variables, family-related factors, personal history, and current suicide risk following the criteria recommended in the Clinical Practice Guideline for the Prevention and Treatment of Suicidal Behavior [ 44 ], a structured interviewer-administered questionnaire will be used. It will include information on sex, age, marital status, living arrangements, rural/urban setting, educational level, main activity, monthly family income, characteristics of suicidal ideation (e.g., possible plans, access to lethal methods, intent to die, previous suicide attempts); present/past risk factors (e.g., risk factors related to psychological and psychiatric problems, family history of suicidal behavior and mental disorders, history of physical abuse or sexual abuse); and health and psychosocial stressors (e.g., presence of chronic illness, financial problems).

Presence of mental disorders

To detect mental disorders in subjects, the MINI International Neuropsychiatric Interview [ 45 ] version 7.0.2 will be used. This structured interview with 120 questions explores the main mental disorders of Axis 1 of DSM-5, showing adequate reliability ( k  = 0.50–0.90), sensitivity between 17%-92%, and specificity between 75%-100%.

Primary outcomes

Suicidal ideation will be the primary outcome of the study. The Suicidal Ideation Scale (SSI) [ 31 ], a 19-item semi-structured scale with internal consistency (Kuder-Richardson 20 [KR-20]) of 0.89 and inter-rater reliability (k) of 0.83, will be used. Complementarily, the Columbia Suicide Severity Rating Scale (C-SSRS) [ 46 ], a semi-structured interview assessing the severity of suicidal ideation and behavior over the past month, will be administered. It has good convergent and discriminant validity and high sensitivity (100.0%) and specificity (99.4%) for classifying suicidal behavior; the ideation intensity subscale showed a Cronbach's alpha of 0.73-0.95.

Secondary outcomes

Hopelessness.

Hopelessness will be assessed with the Beck Hopelessness Scale (HS) [ 47 ], a 20-item self-administered instrument with an internal consistency (KR-20) of 0.93.

Anxiety and depression symptoms

The presence of anxiety and depressive symptoms will be assessed with the General Health Questionnaire (GHQ-12) [ 48 ], a 12-item self-administered questionnaire for screening psychiatric morbidity (non-psychotic) with an internal consistency (Cronbach's alpha) of 0.86 for those under 65 years old and 0.90 for those 65 and older.

Reasons for not attempting suicide

Deterrent reasons for suicidal thoughts will be assessed through the Reasons for Living Inventory (RFL) [ 49 ]. This is a 48-item self-administered instrument with six subscales, with internal consistencies (Cronbach's alphas) ranging from 0.72 to 0.89.

Impulsivity

The Barratt Impulsiveness Scale (BIS-11) [ 50 ] will be used to assess impulsivity. This 30-item self-administered instrument has internal consistencies (Cronbach's alphas) ranging from 0.79 to 0.82.

Problem-solving skills

Coping and problem-solving skills will be assessed with the Revised Social Problem-Solving Inventory (SPSI-R) [ 51 ], a 52-item inventory with internal consistencies (Cronbach's alpha) ranging from 0.68 to 0.92.

Social support

The Duke-UNC Functional Social Support Questionnaire (Duke-UNC-11) [ 52 ] will be used to assess perceived social support. This is an 11-item questionnaire with an internal consistency (Cronbach's alpha) of 0.90.

Anger syndrome

Anger syndrome will be assessed with the Clinical Anger Scale (CAS) [ 53 ], a 21-item self-administered instrument with an internal consistency (Cronbach's alpha) of 0.94.

Gratitude will be assessed with the Gratitude Questionnaire [ 54 ], a 6-item self-administered instrument with an internal consistency of 0.82.

Personality

Personality will be assessed with the 10-item short version of the Big Five Inventory (BFI-10) [ 55 ], a self-administered instrument with internal consistencies ranging from 0.70 to 0.90.

Dropouts and treatment adherence

Throughout the study, information on dropouts will be recorded. Treatment adherence will be evaluated by recording the number of sessions attended/modules completed by each participant (in the app), and the number of between-session tasks completed.

Satisfaction with the intervention

The Client Satisfaction Questionnaire (CSQ-8) [ 56 ] will be used to assess participants' satisfaction with the intervention. This scale consists of 8 self-administered items and has an internal consistency (Cronbach's alpha) of 0.80.

Data management

Personal and clinical data of the participants will be coded and stored separately. Participants' files will be organized numerically and kept for 5 years after the study concludes. Data will be entered into a database without including personally identifiable information. Range and consistency checks will be performed with the data already recorded in the database. All information related to the study data will be stored in locked cabinets. Only the researchers will have access to the study data through a password system. A backup of the original database will be made every 15 days. All study reports and publications will be written in a way that ensures no participant can be identified.

Data analysis

The statistical package SPSS for Windows (version 29.0) and R (version 4.4.1) [ 57 ] will be used for data analysis. All analyses will be conducted according to the intention-to-treat principle. If participants drop out of the study or there are missing data for other reasons (e.g., incomplete questionnaires), the missing values will be imputed using multiple imputation [ 58 ]. The imputation will be based on predictors of the outcome, including auxiliary variables (sex, age, marital status, living arrangements, rural/urban setting, education level, main activity, monthly family income, characteristics of suicidal ideation, present/past risk factors, health and psychosocial stressors, hopelessness, anxiety and depression symptoms, reasons for living, impulsivity, problem-solving skills, social support, anger, gratitude, and personality), using 10 imputations through chained equations.

To analyze the effect of the intervention on the primary outcome variable (suicidal ideation) and secondary outcomes (hopelessness, anxiety and depression symptoms, impulsivity, anger syndrome, gratitude, and personality) at post-intervention and follow-ups at 3, 6, and 12 months, Linear Mixed Models [ 59 ] will be used. In the post hoc comparisons, Bonferroni correction will be applied (comparisons between times, between groups, and for the time x group interaction). The effect size will be calculated using Cohen's d, interpreting values d  < 0.5 as small, d  = 0.5–0.79 as medium, and d  ≥ 0.8 as large [ 60 ].

For the evaluation of the clinical significance of the effects of the three interventions, the JT method [ 61 ] will be followed, which involves two complementary procedures: calculating the Reliable Change Index (RCI) and analyzing the clinical significance of these changes. The RCI will be calculated using the formula RCI = post – pre / SEdiff. The index SEdiff = standard error of the difference, obtained from the formula: SEdiff = SD1√2√1-r, where: SD1 = standard deviation (group or individual); r = reliability index of the measurement instrument (Cronbach's alpha). An RCI > 1.96 is defined as a positive reliable change; RCI < -1.96 is a negative reliable change; and RCI values between -1.96 and 1.96 are defined as no change. The clinical significance criterion is operationally defined as a cutoff point (c) beyond which the post-intervention score of the subject falls within the distribution of the functional population, and therefore closer to the mean of the functional population than the dysfunctional one. It is calculated using the formula: c = ( SD0 M1  +  SD1 M0 ) / SD0  +  SD1 , where: SD0  =  SD1  = pre-intervention standard deviation (experimental or control group) or the general population; M0  = mean of the functional general population; M1  = pre-intervention mean (experimental and control group).

Moderation and mediation analyses will be conducted for the IPSP-P, IPSP-M, and IPSP-A groups. To improve interpretation, variables may be centered, according to the recommendations of Kraemer and Blasey [ 62 ]. The impact of potential moderators on the change in suicidal ideation between pre and post-intervention and between pre-intervention and the 12-month follow-up will be analyzed using linear regression analysis. To evaluate the effect of the potential moderator, the model proposed by Baron and Kenny [ 63 ] will be applied, whose general formulation, adapted to the case of a treatment variable with four categories, can be expressed as:

This formulation considers the effect on suicidal ideation (Y) of the different treatment forms X (represented by the comparison of each group Ti against the control) depending on the level of the moderator variable (W). Each term TiW represents the interaction between treatment and the moderator variable. Potential moderators are baseline values of sociodemographic, family-related variables, personal history, current suicide risk factors, reasons for living, social support, personality, adherence, and satisfaction with the intervention. If any of the regression coefficients for the TiW products is significantly different from zero, it implies that the effect of X on Y depends on W [ 64 ].

To analyze whether problem-solving skills act as mediating variables for changes in suicidal ideation, the differences in suicidal ideation between pre and post-intervention will be used as the dependent variable (Y), the intervention as the independent variable (X), and the difference in problem-solving skills between pre and post-intervention as a potential mediator (M). A simple mediation analysis will be performed without covariates and without interaction. The direct effect (c'i) and the total effect (ci) of the treatment levels (Ti) on suicidal ideation (Y) through the change in problem-solving skills (M) will be estimated. The primary interest will be in the indirect effect, represented as aib, equivalent to the difference between the total relative effect of each treatment and the direct relative effect: aib = ci – c'i. An equation expressing the effect ai of each treatment level and the effect b is:

M = iM + a1T1 + a2T2 + a3T3 + eM (association between the independent variable and the mediating variable, ai);

Y = iY + c'1T1 + c'2T2 + c'3T3 + bM + eY (association between the mediating variable and the dependent variable controlling for the independent variable, b).

The direct effect would be expressed as Y = iY + c1T1 + c2T2 + c3T3 + eY (association between the independent variable and the dependent variable, c).

Inference on these effects will be made by calculating 95% confidence intervals using the bootstrap method, based on a minimum of 5000 samples [ 65 ]. Mediation will be considered present when any of the indirect relative effects is significantly different from zero [ 66 ].

Acceptability and satisfaction with the interventions will be described through frequency distributions. The percentage of participants who drop out of the study will be considered. Adherence to the interventions will be studied through the number of sessions attended/modules completed and the record of tasks performed between sessions. Additionally, the level of satisfaction with each intervention (measured with CSQ-8) will be described through frequency analysis and descriptive statistics. Supervised classification/regression trees will be used to identify which variables and to what extent they help predict dropout, adherence, and satisfaction level.

An independent Data Monitoring Committee (DMC) will be established, separate from the study organizers. The steering committee, led by the principal investigator, will adhere to the principles of good clinical practice, including quality control of the clinical protocol, data management, and organization of team meetings. An annual report will be provided in strict confidentiality to the DMC on the progress of the trial.

A pilot study will be conducted to evaluate the feasibility of the protocol, interventions, and instruments. Any significant modification of the protocol that may impact the study's execution, potential benefit, or patient safety, including significant changes in study design, patient population, sample sizes, or study procedures, will require a formal amendment to the protocol. This amendment will be approved by the Bioethics Committee before implementation.

Additionally, an interim analysis will be conducted after the pilot study and on the primary objective when 50% of the patients have been randomized and completed follow-ups. The interim analysis will be performed by an independent statistician. The statistician will report to the independent DMC, which will have unblinded access to all data and will discuss the interim analysis results with the steering committee in a joint meeting. The steering committee will decide on the continuation of the trial and report to the Bioethics Committee.

Exit strategy

An exit plan will be established for two specific scenarios: first, if a participant decides to leave the trial early, a voluntary phone call will be made to gather their reasons using a questionnaire. The project team will ensure that the exit is managed appropriately and that the participant feels satisfied with the conclusion. Second, at the end of the study period, which includes up to 14 weeks of intervention and 12 months of follow-up, participants will be clearly informed about the transition and closure of the study.

In this study, the efficacy of a brief psychological intervention administered face-to-face, via telephone multiconference, and through a smartphone app for the indicated prevention of suicide in people aged 50 and over will be evaluated. The main component of the intervention will be adapted from the problem-solving model by D'Zurilla and Nezu [ 19 ]. Considering the results of previous studies that evaluated the efficacy of problem-solving therapy (PST), both in face-to-face format [ 22 , 23 ] and remote format [ 21 ], and the available evidence on digital cognitive-behavioral interventions [ 67 ], we expect to find a significant reduction in suicidal ideation in the three intervention groups compared to the control group.

The development of this intervention follows the clinical practice guidelines of NICE [ 68 ], which recommend the use of structured, person-centered psychological interventions based on cognitive-behavioral therapy; as well as the collaborative development of a safety plan in an accessible format. The adaptability and ease of learning of PST confer it the potential to overcome barriers identified in the implementation of suicide prevention interventions, such as the lack of adequacy to participants' needs and resources or perceived complexity [ 69 ].

Additionally, this study proposes the implementation of ICTs in its administration, through telephone multiconference and a smartphone application. According to the World Health Assembly Resolution on Digital Health [ 70 ], digital health interventions have the capacity to contribute to advancing universal health coverage, one of the Sustainable Development Goals (SDGs). They allow addressing challenges in health systems such as geographic inaccessibility, delays in service delivery, or patient costs; improving coverage, quality, and affordability of care, and facilitating progress towards universal coverage [ 71 ]. The use of ICTs in the implementation of our intervention entails the intrinsic advantages of digitalization, such as anonymity, increased accessibility, or cost-efficiency [ 71 , 72 ]. Furthermore, administration through group telephone multiconference facilitates social interaction with people going through similar experiences and has the potential to create communities and support networks. On the other hand, the use of an application adds additional benefits such as dissemination at any time and place (without waiting or appointments), the possibility of reviewing materials at the patient's own pace, and personalizing the content or accessing psychological tools [ 73 ]. Additionally, given the importance of user engagement to achieve therapeutic outcomes in this format [ 74 ], strategies to improve adherence are proposed, such as support, personalized feedback, or reminders [ 75 , 76 ].

The strengths of this clinical trial include the specification of the prevention level and the corresponding selection of participants, the prior estimation of sample size, a randomized controlled design with allocation concealment, and the implementation of an intervention based on a theoretical model that has demonstrated its efficacy in previous research [ 21 , 22 , 23 ]. Additionally, the intervention will be manualized, therapist adherence to the protocol will be evaluated, and the results will be analyzed by trained professionals who will be blinded to the study conditions, with a follow-up period of 12 months. Validated instruments will be used to evaluate the outcomes. The prevalence of suicidal ideation will be measured using the Suicidal Ideation Scale (SSI) [ 31 ] and the risk of suicide using the Columbia-Suicide Severity Rating Scale (C-SSRS) [ 46 ]; both tools have demonstrated high correlation and concurrent validity [ 77 ], ensuring consistency and accuracy in the assessment of suicidality. The use of these internationally recognized and validated instruments increases the methodological robustness of our research and facilitates the comparison of our results. Moreover, the study will be conducted in a community context, ensuring a high generalizability of the findings.

In conclusion, this research is pioneering in developing a brief, versatile, and efficient intervention, applied in innovative formats that increase its accessibility, to prevent suicide in at-risk individuals over 50 years old. Its efficacy will be evaluated through a randomized controlled trial, addressing the methodological limitations observed in the literature. Its novelty and methodological quality could have a significant scientific impact. Furthermore, given the relevance of the issue under study, if its efficacy is proven, it would have enormous clinical utility and social impact, helping to mitigate the psychological, social, and economic repercussions on affected individuals, families, and communities.

Status of the trial

Active, not recruiting.

Availability of data and materials

The results of the study will be communicated through publications, which will include data supporting the findings. The dataset used will be available upon prior request to the principal author.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Big Five Inventory (short form with 10 items)

Barrat Impulsiveness Scale

Clinical Anger Scale

Consolidated Standars of Reporting Trials

Client Satisfaction Questionnaire

Columbia Suicide Severity Rating Scale

DUKE-UNC Functional Social Support Questionnaire

Randomized controlled trial

Usual care control group

General Health Questionnaire

The Gratitude Questionnaire

Hopelessness Scale

Institute for Health Metrics and Evaluation

Improving Mood Promoting Access to Collaborative Care Treatment

Problem-solving-based psychological intervention administered via smartphone app

Problem-solving-based psychological intervention administered via telephone multiconference

Problem-solving-based psychological intervention administered in-person

Mini International Neuropsychiatric Interview

World Health Organization

Reasons for Living Inventory

Standard Protocol Items: Recommendations for Interventional Trials

Revised Social Problem-Solving Inventory

Suicidal Ideation Scale

Major depressive disorder

Information and Communication Technologies

Problem Solving Therapy

Research Group on Mental Health and Psychopathology

Galician Health Service

Instituto Nacional de Estadística [National Statistics Institute]

Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition

Reliable Change Index

Data Monitoring Committee

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Acknowledgements

Our thanks to the spin-off of the University of Santiago de Compostela “Xuntos. Psychological and Psychiatric Care”.

Protocol version: 1.1 (26 July 2024).

Recruitment status: Pending.

Project PID2022-141225OB-I00 funded by the Ministry of Science, Innovation and Universities / State Investigation Agency, MICIU/AEI /10.13039/501100011033 and FEDER, EU. The protocol has undergone peer review as part of the funding process. This funding source had no role in the design of this study and will not have any role during its execution, analyses, interpretation of the data, or decision to submit results.

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Fernando L. Vázquez, Queila Bouza & Alba Fernández

Department of Psychiatry, Radiology, Public Health, Nursing and Medicine, University of Santiago de Compostela, Santiago de Compostela, Spain

Ángela J. Torres

Department of Evolutionary and Educational Psychology, University of Santiago de Compostela, Santiago de Compostela, Spain

Vanessa Blanco

Department of Psychology, University of A Coruña, A Coruña, Spain

Patricia Otero, Miguel Á. Simón & Ana M. Bueno

Department of Social Psychology, Basic Psychology and Methodology, University of Santiago de Compostela, Santiago de Compostela, Spain

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Contributions

FV: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing. AT: Conceptualization, Funding acquisition, Project administration, Writing – review & editing. VB: Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing. QB: Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing. EA: Conceptualization, Formal analysis, Methodology, Writing – review & editing. PO: Conceptualization, Investigation, Methodology, Writing – review & editing. MS: Conceptualization, Writing – review & editing. AB: Conceptualization, Writing –review & editing. MA: Conceptualization, Writing – review & editing. MP: Conceptualization, Writing – review & editing. AF: Writing – review & editing.

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Correspondence to Fernando L. Vázquez .

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Human rights and the dignity of the study participants will be ensured in accordance with the principles of the Declaration of Helsinki. The study procedures have received approval from the Bioethics Committee of the University of Santiago de Compostela (Spain). Confidentiality for all participants will be guaranteed, and they will be required to provide written informed consent to participate in the study, according to the form approved by the Bioethics Committee of the University of Santiago de Compostela (Spain). Reference number: 52/2023.

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12888_2024_6076_moesm1_esm.doc.

Additional file 1. SPIRIT 2013 Checklist: Recommended items to address in a clinical trial protocol and related documents.

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Vázquez, F.L., Torres, Á.J., Blanco, V. et al. Brief psychological intervention for suicide prevention based on problem-solving applied in different formats to people over 50 years old: protocol for a randomized controlled trial. BMC Psychiatry 24 , 628 (2024). https://doi.org/10.1186/s12888-024-06076-5

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Received : 03 August 2024

Accepted : 11 September 2024

Published : 27 September 2024

DOI : https://doi.org/10.1186/s12888-024-06076-5

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• Over 50 years old
• Problem-solving
• Suicidal ideation

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