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Insight is commonly viewed as originating from the restructuring of a mental representation. Distributed cognition frameworks such as the Systemic Thinking Model (SysTM, Vallée-Tourangeau and Vallée-Tourangeau, Cognition beyond the brain: interactivity and human thinking, pp 133-154, 2017), however, assumes that information processing can be transformed when it is distributed across mental and material resources. The experiments reported here investigated whether interactivity enhanced incubation effects with the cheap necklace problem. Participants attempted to solve the problem in a low-interactivity condition with pen and paper or in a high-interactivity condition with a set of metal chains. Performance was substantially better in a task environment that fostered a higher degree of interactivity at Time 1. There was evidence of an incubation effect as participants significantly improved in performance after a 2-week gap, particularly in the high-interactivity condition. Experiment 2 showed that the context within which people can enact their thinking following incubation is key to improve problem-solving performance. When the problem presentation changed after a 2-week gap (low interactivity to high interactivity or high interactivity to low interactivity), performance only improved for those who worked on a highly interactive task at Time 2. Taken together, these findings underscore the importance of adopting a systemic perspective when investigating incubation effects in problem solving.

Two experiments examined whether different task ecologies influenced insight problem solving. The 17 animals problem was employed, a pure insight problem. Its initial formulation encourages the application of a direct arithmetic solution, but its solution requires the spatial arrangement of sets involving some degree of overlap. Participants were randomly allocated to either a tablet condition where they could use a stylus and an electronic tablet to sketch a solution or a model building condition where participants were given material with which to build enclosures and figurines. In both experiments, participants were much more likely to develop a working solution in the model building condition. The difference in performance elicited by different task ecologies was unrelated to individual differences in working memory, actively open-minded thinking, or need for cognition (Experiment 1), although individual differences in creativity were correlated with problem solving success in Experiment 2. The discussion focuses on the implications of these findings for the prevailing metatheoretical commitment to methodological individualism that places the individual as the ontological locus of cognition., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Outside the psychologist's laboratory, thinking proceeds on the basis of a great deal of interaction with artefacts that are recruited to augment problem-solving skills. The role of interactivity in problem solving was investigated using a river-crossing problem. In Experiment 1A, participants completed the same problem twice, once in a low interactivity condition, and once in a high interactivity condition (with order counterbalanced across participants). Learning, as gauged in terms of latency to completion, was much more pronounced when the high interactivity condition was experienced second. When participants first completed the task in the high interactivity condition, transfer to the low interactivity condition during the second attempt was limited; Experiment 1B replicated this pattern of results. Participants thus showed greater facility to transfer their experience of completing the problem from a low to a high interactivity condition. Experiment 2 was designed to determine the amount of learning in a low and high interactivity condition; in this experiment participants completed the problem twice, but level of interactivity was manipulated between subjects. Learning was evident in both the low and high interactivity groups, but latency per move was significantly faster in the high interactivity group, in both presentations. So-called problem isomorphs instantiated in different task ecologies draw upon different skills and abilities; a distributed cognition analysis may provide a fruitful perspective on learning and transfer.

Successful statistical reasoning emerges from a dynamic system including: a cognitive agent, material artifacts with their actions possibilities, and the thoughts and actions that are realized while reasoning takes place. Five experiments provide evidence that enabling the physical manipulation of the problem information (through the use of playing cards) substantially improves statistical reasoning, without training or instruction, not only with natural frequency statements (Experiment 1) but also with single-event probability statements (Experiment 2). Improved statistical reasoning was not simply a matter of making all sets and subsets explicit in the pack of cards (Experiment 3), it was not merely due to the discrete and countable layout resulting from the cards manipulation, and it was not mediated by participants' level of engagement with the task (Experiment 5). The positive effect of an increased manipulability of the problem information on participants' reasoning performance was generalizable both over problems whose numeric properties did not map perfectly onto the cards and over different types of cards (Experiment 4). A systematic analysis of participants' behaviors revealed that manipulating cards improved performance when reasoners spent more time actively changing the presentation layout "in the world" as opposed to when they spent more time passively pointing at cards, seemingly attempting to solve the problem "in their head." Although they often go unnoticed, the action possibilities of the material artifacts available and the actions that are realized on those artifacts are constitutive of successful statistical reasoning, even in adults who have ostensibly reached cognitive maturity., ((c) 2015 APA, all rights reserved).)

Participants in cognitive psychology experiments on reasoning and problem solving are commonly sequestered: Efforts are made to impoverish the physical context in which the problem is presented, decoupling people from the richer and modifiable environment that naturally instantiates it outside the lab. Sensemaking activities are constrained, but this conforms to the strong internalist and individualist commitments implicit to these research efforts: Cognition reflects internal computations and the scientists' toils must focus on the individual and what she is thinking, decoupled from the world. We contrast this position with one that identifies cognition as the product of a cognitive system that is configured and enacted by, minimally, an agent and the world in which she is embedded. We review work on the psychology of hypothesis testing and problem solving and argue that refocusing research efforts on the dynamic agentenvironment couplings that generate cognitive products -- such as a problem representation, a hypothesis or a problem solution -- offers a much richer set of methodological opportunities to unveil how people actually think outside the cognitive psychologist's laboratory. We conclude by exploring the ontological implications of a systemic perspective on cognition. [ABSTRACT FROM AUTHOR]

In the wild, thinking demonstrably uses interactive processes that draw on a wide range of external resources, spanning multiple time scales. As Malafouris (2015, p. 361) puts it, "cognition is not a within property; it is an in-between process". Interactive processes configure extended systems within which each human agent is embedded. Yet much research on higher cognition, such as problem solving, reflects an implicit but deep commitment to methodological individualism that casts the agent as the ontological locus of cognition, and largely dictates the nature of the research enterprise. Thus, tasks to measure capacities and gauge reasoning performance are designed in a manner that reduces or eliminates the possibility of interacting with the problem presentation; if thinking takes place in the head, there is no need or reason to engineer procedures wherein agents can interact with the task's physical constituents. Conversely, a methodological interactivism forces one to acknowledge the participative yet not all-encompassing role of capacities such as working memory and thinking dispositions; it also encourages the granular mapping of the cognitive ecosystem from which new ideas emerge. To adopt an interactivist perspective is thus to focus on the cognitive resources of the extended system inviting a careful description of how these resources are dynamically configured over time and space to promote the development of new ideas in problem solving. In turn, a systemic perspective encourages the development of interventions that promote cognitive performance through the optimisation of systemic rather than individualist cognitive resources. [ABSTRACT FROM AUTHOR]