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Intuition in insight and nonsight problem solving
Abstract
People’s metacognitions, both before and during problem solving, may be of importance in motivating and guiding problem-solving
behavior. These metacognitions could also be diagnostic for distinguishing among different classes of problems, each perhaps
controlled by different cognitive processes. In the present experiments, intuitions on classic insight problems were compared
with those on noninsight and algebra problems. The findings were as follows: (1) subjective feeling of knowing predicted performance
on algebra problems but not on insight problems; (2) subjects’ expectations of performance greatly exceeded their actual performance,
especially on insight problems; (3) normative predictions provided a better estimate of individual performance than did subjects’
own predictions, especially on the insight problems; and, most importantly, (4) the patterns-of-warmth ratings, which reflect
subjects’ feelings of approaching solution, differed for insight and noninsight problems. Algebra problems and noninsight
problems showed a more incremental pattern over the course of solving than did insight problems. In general, then, the data
indicated that noninsight problems were open to accurate predictions of performance, whereas insight problems were opaque
to such predictions. Also, the phenomenology of insight-problem solution was characterized by a sudden, unforeseen flash of
illumination. We propose that the difference in phenomenology accompanying insight and noninsight problem solving, as empirically
demonstrated here, be used to define insight.
... The first refers to the set of processes involved in the processing, representation and resolution of the problem; the second, to the monitoring and control of cognitive processes; while the last one, to the emotional disposition of who solves the aforementioned problem. This model agrees with literature that suggests that the relationship between metacognition and SP occurs especially on complex problems that require a wide deployment of cognitive resources (i.e., non-insight problems), and not so on insight problems, in which the solution emerges spontaneously in consciousness (Metcalfe and Wiebe, 1987). In this way, metacognition would play an active and continuous role in the conscious administration of the cognitive resources used during the resolution of a problem (Stuyck et al., 2022). ...
Metacognitive ability has been described as an important predictor of several processes involved in learning, including problem-solving. Although this relationship is fairly documented, little is known about the mechanisms that could modulate it. We decided to evaluate the impact of self-knowledge on problem-solving and, in addition, we inspected whether emotional (self-reported anxiety) and interpersonal (attitudes towards social interdependence) variables could affect the relationship between metacognition and problem-solving. We tested a sample of 32 undergraduate students and used behavioural tasks and self-report questionnaires. Contrary to the literature, we found no significant relationship between metacognition and problem-solving performance, nor a significant moderating effect when including emotional and interpersonal variables in the model. In contrast, we observed a significant moderating model combining metacognition, self-reported anxiety and attitudes towards social interdependence. It was found that participants with high metacognition reported attitudes unfavourable towards interdependence when they felt high anxiety. These results suggest that already anxious individuals with high metacognition would prefer to work alone rather than with others, as a coping mechanism against further anxiety derived from cooperation. We hypothesise that in anxiogenic contexts, metacognition is used as a tool to compare possible threats with one's own skills and act accordingly, in order to maximise one's own performance. Further studies are needed to understand how metacognition works in contexts adverse to learning.
... In such context, a link between insight and unconscious processing has been established (Bowden et al., 2005;Salvi et al., 2015). A second widely accepted trait of insight is a feeling of certainty in solvers (Burton, 1999;Fischbein, 1987;Liljedahl, 2005;Metcalfe & Wiebe, 1987); indeed, despite the debate on whether insight forcefully leads to a right answer (Laukkonena et al., 2020), it is certain that whether correct or incorrect, insight is always followed by a pronounced feeling of certainty (Danek & Wiley, 2017;Palma & Cosmelli, 2008). A third trait of insight is the fluency of resolution it brings, dissolving strain and leading to fluent solutions (Topolinski & Reber, 2010). ...
The present study aims to describe and characterize the insights of elementary school students during the collaborative solving of mathematics problems. A total of 10 elementary school 60-min math classes were analyzed, with each session documented by videotaping the interaction of groups of 3 to 6 children. The study included 41 students in total (14 boys and 27 girls aged from 7 to 10). Group interaction was analyzed by taking spontaneous insights as queues. The results distinguish full insight—where students suddenly discover the answer to the problem—from partial insight—where students realize they have been following an incorrect procedure. Data shows that most insight takes place during interaction and not individually. An in-depth description of such insights shows a junction between the semantic content in verbal explanations and proto-symbolic content involved in gestures. The described insights also show in situ emergence of joint rhythmicity among participants. Results suggest that it is possible to clearly identify insight in spontaneous interaction among school children.
... Studies of insight properties usually focus on two dimensions: cognitive process and individual characteristics (Ash and Wiley, 2006;Ovington et al., 2016;Ogawa et al., 2018). Previous studies, treating insight as a problem's solution process, used specialized tasks to induce insight and reveal the cognitive mechanisms under insightful solutions (Metcalfe and Wiebe, 1987;Knoblich et al., 1999;Kounios and Beeman, 2009). Overall, these studies measured episodic (state) insight. ...
Studies on the neural correlates of episodic insight have made significant progress in the past decades. However, the neural mechanisms underlying dispositional insight are largely unknown. In the present study, we recruited forty-four young, healthy adults and performed several analyses to reveal the neural mechanisms of dispositional insight. Firstly, a voxel-based morphometry (VBM) technique was used to explore the structural brain mechanisms of dispositional insight. We found that dispositional insight was significantly and negatively correlated with the regional gray matter volume (rGMV) in the left thalamus (TLM.L), right temporoparietal junction (TPJ.R), and left dorsal medial prefrontal cortex (DMPFC.L). Secondly, we performed a seed-based resting-state functional connectivity (RSFC) analysis to complement the findings of VBM analysis further. The brain regions of TLM.L, DMPFC.L, and TPJ.R were selected as seed regions. We found that dispositional insight was associated with altered RSFC between the DMPFC.L and bilateral TPJ, between the TPJ.R and left dorsolateral prefrontal cortex, left ventrolateral prefrontal cortex, DMPFC.L, TPJ.L, right insula, and right cerebellum. Finally, a mediation analysis found that the personality of neuroticism partially mediated the relationship between the brain region of TLM.L and dispositional insight. These findings imply that dispositional insight has a specific functional and structural neural mechanism. The personality of neuroticism may play a pivotal role in the processes of dispositional insight.
... There remain some intelligent behaviors that systematic probing may still struggle to help measure or explain as the process of understanding unfolds. Consider the sudden ability to solve an insight problem (Metcalfe, 1986;Metcalfe and Wiebe, 1987). People are generally unable to articulate how they are trying to reason through or solve a problem prior to insight. ...
How do we gauge understanding? Tests of understanding, such as Turing's imitation game, are numerous; yet, attempts to achieve a state of understanding are not satisfactory assessments. Intelligent agents designed to pass one test of understanding often fall short of others. Rather than approaching understanding as a system state, in this paper, we argue that understanding is a process that changes over time and experience. The only window into the process is through the lens of natural language. Usefully, failures of understanding reveal breakdowns in the process. We propose a set of natural language-based probes that can be used to map the degree of understanding a human or intelligent system has achieved through combinations of successes and failures.
Existing theories and frameworks generally have regarded creativity as inhering in a person, a task, a situation, or a combination of 2 of these 3 elements. After reviewing these approaches, and frameworks that are based on the interaction of more than 2 components, we propose a Person × Task × Situation synergistic paradigm, according to which creativity is dynamic and can be fully understood only as an interaction of all 3 of the elements. Building on the strengths of existing frameworks, our P × T × S proposal highlights: (a) the need to include the other 2 elements as moderators, regardless of which element is the central or starting point of the analysis, (b) the idea that the extent to which each element influences the others and the degree of overlap among elements can vary, and (c) the dynamic aspect of creativity, associated with changes in different persons across the lifespan, for different creative tasks, and for different situations. In addition to contributing an integrative theoretical account, this framework has significant, pragmatic implications for both the assessment and the development of creativity. We end with a call for both researchers and practitioners who test for or teach for creativity to specify the range of persons, tasks, and situations to which their assessments or training generalize.
In this paper, we develop and apply the concept of ‘insight discovery’ as a key competence for transdisciplinary research and learning. To address complex societal and environmental problems facing the world today, a particular expertise that can identify new connections between diverse knowledge fields is needed in order to integrate diverse perspectives from a wide range of stakeholders and develop novel solutions. The capacity for “insight discovery” means becoming aware of personal mental representations of the world and being able to shape and integrate perspectives different from one’s own. Based on experiences and empirical observations within the scope of an educational programme for Masters students, PhD candidates and post-doctoral researchers, we suggest that insights are the outcome of a learning process influenced by the collective and environment in which they are conceived, rather than instant moments of individual brilliance. The process which we describe, named the insight discovery process (IDP), is made up of five aspects. Within a group setting, a person begins with an “original mental model”, experiences an “insight trigger”, processes new information within the “liminal space”, “formulates an insight” and eventually forms an “adapted mental model”. There is a potential for incorporating such process as a fundamental competence for transdisciplinary curricula in undergraduate and graduate programmes by cultivating specific practices and safe learning environments, focused on the enquiry, exchange and integration of diverse perspectives.
Процесс инсайтного решения неоднороден и имеет скрытую динамику, которую следует учитывать при анализе полученных в результате исследования данных. Классические методы исследования инсайта, ориентированные только на результативные показатели или вербальные отчёты, не являются оптимальными для исследования процессуальной составляющей. Методы, изучающие процесс косвенно через связанные процессы (загрузка рабочей памяти, физиологические процессы, поведенческие паттерны), выглядят наиболее перспективными. Анализ поведения испытуемого позволяет изучать инсайт без вмешательства в процесс решения задач.
В ходе теоретического анализа роли антиципации и ожиданий в инсайтном решении задач было выявлено, что поиск инсайтного решения представляет собой направленный процесс, обусловленный как прошлым опытом, так и образом цели или критерием приближения к ней. Выделены два типа антиципации: антиципация от материала основана на ограничении возможных исходов благодаря исполнению автоматизированных процессов, антиципация от задачи основана на построении репрезентации задачи, включающей в себя представление об ожидаемом результате.
We respect Herbert A. Simon as an established leader of empirical and logical analysis in the human sciences while we happily think of him as also the loner; of course he works with many colleagues but none can match him. He has been writing fruitfully and steadily for four decades in many fields, among them psychology, logic, decision theory, economics, computer science, management, production engineering, information and control theory, operations research, confirmation theory, and we must have omitted several. With all of them, he is at once the technical scientist and the philosophical critic and analyst. When writing of decisions and actions, he is at the interface of philosophy of science, decision theory, philosophy of the specific social sciences, and inventory theory (itself, for him, at the interface of economic theory, production engineering and information theory). When writing on causality, he is at the interface of methodology, metaphysics, logic and philosophy of physics, systems theory, and so on. Not that the interdisciplinary is his orthodoxy; we are delighted that he has chosen to include in this book both his early and little-appreciated treatment of straightforward philosophy of physics - the axioms of Newtonian mechanics, and also his fine papers on pure confirmation theory.
Reflecting important new research developments of the past eight years as well as classic theories of problem solving, this book provides a balanced survey of the higher cognitive processes, human thinking, problem solving, and learning. Divided into four parts, the book discusses associationism and Gestalt theory before introducing current research and theories of induction and deduction in part two. Part three considers recent cognitive theories and discusses information-processing analysis, outlining techniques for analyzing cognition into processes, strategies, and knowledge structures. Finally, the implications and applications of findings in cognitive psychology are examined, with emphasis on applications such as teaching creative problem solving and intelligence measurement. The Second Edition updates research and includes new chapters on everyday thinking, expert problem solving, and analogical reasoning to reflect the growing interest in problem solving within specific domains and in real situations. New and classic problem solving exercises are used liberally throughout the text to illustrate concepts and to enhance the reader's active involvement in the process of problem solving.
To study productive thinking where it is most conspicuous in great achievements is certainly a temptation, and without a doubt, important information about the genesis of productive thought could be found in biographical material. A problem arises when a living creature has a goal but does not know how this goal is to be reached. Whenever one cannot go from the given situation to the desired situation simply by action, then there has to be recourse to thinking. The subjects ( S s), who were mostly students of universities or of colleges, were given various thinking problems, with the request that they think aloud. This instruction, "Think aloud", is not identical with the instruction to introspect which has been common in experiments on thought-processes. While the introspecter makes himself as thinking the object of his attention, the subject who is thinking aloud remains immediately directed to the problem, so to speak allowing his activity to become verbal. It is the shift of function of the components of a complex mathematical pattern—a shift which must so often occur if a certain structure is to be recognized in a given pattern—it is this restructuration, more precisely: this transformation of function within a system, which causes more or less difficulty for thinking, as one individual or another tries to find a mathematical proof.