Working Wonders? Investigating insight with magic tricks

We propose a new approach to differentiate between insight and noninsight problem solving, by introducing magic tricks as problem solving domain. We argue that magic tricks are ideally suited to investigate representational change, the key mechanism that yields sudden insight into the solution of a problem, because in order to gain insight into the magicians’ secret method, observers must overcome implicit constraints and thus change their problem representation. In Experiment 1, 50 participants were exposed to 34 different magic tricks, asking them to find out how the trick was accomplished. Upon solving a trick, participants indicated if they had reached the solution either with or without insight. Insight was reported in 41.1% of solutions. The new task domain revealed differences in solution accuracy, time course and solution confidence with insight solutions being more likely to be true, reached earlier, and obtaining higher confidence ratings. In Experiment 2, we explored which role self-imposed constraints actually play in magic tricks. 62 participants were presented with 12 magic tricks. One group received verbal cues, providing solution relevant information without giving the solution away. The control group received no informative cue. Experiment 2 showed that participants’ constraints were suggestible to verbal cues, resulting in higher solution rates. Thus, magic tricks provide more detailed information about the differences between insightful and noninsightful problem solving, and the underlying mechanisms that are necessary to have an insight.     

The Importance of the Default Mode Network in Creativity—A Structural MRI Study

Anecdotal reports as well as behavioral studies have suggested that creative performance benefits from unconscious processes. So far, however, little is known about how creative ideas arise from the brain. In the current study, we aimed to investigate the neural correlates of creativity by means of structural MRI research. Given that unconscious and less controlled processes are important in creative thinking, structural brain research may find a positive correlation between well‐established creativity measures and cortical thickness in brain structures of the default mode network (i.e., the counterpart of the cognitive control network). Individuals performed the Alternative Uses task by which an individual's cognitive flexibility and the average uniqueness and average creativity of a participant's ideas were assessed. We computed optimized voxel‐based‐morphometry (VBM) to explore the correlation between inter‐individual differences in creativity and inter‐individual differences in gray matter volume. For all creativity measures, a positive correlation was found between creative performance and gray matter volume of the default mode network. These findings support the idea that the default mode network is important in creativity, and provide neurostructural support for the idea that unconscious forms of information processing are important in creativity. Theoretical and practical implications are discussed.     

Discovering the Sequential Structure of Thought

Multi‐voxel pattern recognition techniques combined with Hidden Markov models can be used to discover the mental states that people go through in performing a task. The combined method identifies both the mental states and how their durations vary with experimental conditions. We apply this method to a task where participants solve novel mathematical problems. We identify four states in the solution of these problems: Encoding, Planning, Solving, and Respond. The method allows us to interpret what participants are doing on individual problem‐solving trials. The duration of the planning state varies on a trial‐to‐trial basis with novelty of the problem. The duration of solution stage similarly varies with the amount of computation needed to produce a solution once a plan is devised. The response stage similarly varies with the complexity of the answer produced. In addition, we identified a number of effects that ran counter to a prior model of the task. Thus, we were able to decompose the overall problem‐solving time into estimates of its components and in way that serves to guide theory.     

Productive Failure in Learning Math

When learning a new math concept, should learners be first taught the concept and its associated procedures and then solve problems, or solve problems first even if it leads to failure and then be taught the concept and the procedures? Two randomized‐controlled studies found that both methods lead to high levels of procedural knowledge. However, students who engaged in problem solving before being taught demonstrated significantly greater conceptual understanding and ability to transfer to novel problems than those who were taught first. The second study further showed that when given an opportunity to learn from the failed problem‐solving attempts of their peers, students outperformed those who were taught first, but not those who engaged in problem solving first. Process findings showed that the number of student‐generated solutions significantly predicted learning outcomes. These results challenge the conventional practice of direct instruction to teach new math concepts and procedures, and propose the possibility of learning from one's own failed problem‐solving attempts or those of others before receiving instruction as alternatives for better math learning.     

An Examination of the Personality Constructs Underlying Dimensions of Creative Problem‐Solving Style

This study investigated the personality facets that underpin the construct of problem‐solving style, particularly when approaching more creative kinds of problem‐solving. Cattell's Sixteen Personality Factors Questionnaire and VIEW—An Assessment of Problem Solving Style were administered to 165 students from the Norwegian Business School. We explored relationships through correlational and regression analysis. Personality profiles were derived for each of VIEW's three dimensions and were in generally expected directions. Those with an Explorer preference were more imaginative and idea oriented, open to change, unconventional, freethinking, and flexible than Developers. Those with a Developer preference were more practical and solution oriented, more traditional, rule conscious, conservative, and respecting of traditional ideas. Those with an External preference were more group oriented, affiliative, socially bold, warm, and attentive to others than those with an Internal preference. Those with a more Task‐oriented preference were more impersonal, detached, utilitarian, and tough minded than those with Person‐oriented preference. We outlined implications and suggestions for further research.     

Divergent thinking in the grasslands: thinking about object function in the context of a grassland survival scenario elicits more alternate uses than control scenarios

The survival processing effect is a recall advantage for information processed in the context of a grassland survival scenario. The current studies build upon previous research suggesting the effect is due to elaborative encoding and functional thinking. In two experiments, participants completed the “alternate uses test” under five conditions: baseline, grassland survival, Ebola survival, moving to a new home and planning a bank heist. Experiment 1 stimuli were everyday objects. Experiment 2 stimuli were functionally ambiguous “mystery” objects. Number of generated uses was highest in the baseline, but the grassland scenario was consistently highest of the schematic conditions. Recall data lend support to the mnemonic superiority of the grassland condition. Results suggest that grassland scenarios place fewer attenuating constraints on divergent thinking. It is suggested that the survival processing effect might be usefully conceptualised as an effect of creatively thinking about object function in response to broadly defined problems.     

The Aha! moment: Is insight a different form of problem solving?

In everyday life, we mainly solve problems with a conscious solution search (non-insight). However, sometimes a perplexing problem is resolved by a quantum leap in understanding. This phenomenon is known as the Aha! experience (insight). Although insight has a distinct phenomenological and behavioral signature, its driving mechanism remains debated. Weisberg (2015) proposed an integrated theory of insight arguing that insight, like non-insight, mainly depends on conscious, cognitive operations with restructuring as a distinguishing feature of insight. However, only if those operations lead to an impasse, insight is achieved through unconscious processes. We assessed some of the premises of this theory by asking participants (N = 42) to solve 70 word puzzles (CRAT) that can either be solved with insight or non-insight. For each puzzle, participants indicated word puzzle difficulty, solution confidence, solution suddenness, and the experiences of impasse and restructuring. As expected, participants reported higher suddenness of and confidence in insight solutions than non-insightful ones. Surprisingly, we could not corroborate the otherwise consistently reported higher solution accuracy and faster solution speed for insight. Crucially, as suggested by the integrated theory of insight, impasse was not a prerequisite for insight to occur. Although restructuring, indeed, preceded insight solutions more often, it seemed a more general problem-solving skill also applied for non-insight solutions. Moreover, early on, participants reported an increased experience of problem difficulty for puzzles later solved with insight. This ability to report on the solution search of insight demonstrates that, as proposed by the theory, insight involves conscious, cognitive operations.     

Problem‐Solving Phase Transitions During Team Collaboration

Multiple theories of problem‐solving hypothesize that there are distinct qualitative phases exhibited during effective problem‐solving. However, limited research has attempted to identify when transitions between phases occur. We integrate theory on collaborative problem‐solving (CPS) with dynamical systems theory suggesting that when a system is undergoing a phase transition it should exhibit a peak in entropy and that entropy levels should also relate to team performance. Communications from 40 teams that collaborated on a complex problem were coded for occurrence of problem‐solving processes. We applied a sliding window entropy technique to each team's communications and specified criteria for (a) identifying data points that qualify as peaks and (b) determining which peaks were robust. We used multilevel modeling, and provide a qualitative example, to evaluate whether phases exhibit distinct distributions of communication processes. We also tested whether there was a relationship between entropy values at transition points and CPS performance. We found that a proportion of entropy peaks was robust and that the relative occurrence of communication codes varied significantly across phases. Peaks in entropy thus corresponded to qualitative shifts in teams’ CPS communications, providing empirical evidence that teams exhibit phase transitions during CPS. Also, lower average levels of entropy at the phase transition points predicted better CPS performance. We specify future directions to improve understanding of phase transitions during CPS, and collaborative cognition, more broadly.     

The Power of a “Maverick” in Collaborative Problem Solving: An Experimental Investigation of Individual Perspective‐Taking Within a Group

Integrating different perspectives is a sophisticated strategy for developing constructive interactions in collaborative problem solving. However, cognitive aspects such as individuals’ knowledge and bias often obscure group consensus and produce conflict. This study investigated collaborative problem solving, focusing on a group member interacting with another member having a different perspective (a “maverick”). It was predicted that mavericks might mitigate disadvantages and facilitate perspective taking during problem solving. Thus, 344 university students participated in two laboratory‐based experiments by engaging in a simple rule‐discovery task that raised conflicts among perspectives. They interacted with virtual partners whose conversations were controlled by multiple conversational agents. Results show that when participants interacted with a maverick during the task, they were able to take others’ perspectives and integrate different perspectives to solve the problem. Moreover, when participants interacted in groups with a positive mood, groups with a maverick outperformed groups having several perspectives.     

Strategic demonstration of problem solutions by groups: The effects of member preferences,confidence, and learning goals

Research indicates that groups perform best, and their members learn the most, when they solve problems with demonstrably correct solutions. These outcomes are often attributed to correct members demonstrating to incorrect members how to solve such problems. However, because few studies have directly observed group interaction (Moreland, Swanenburg, Flagg, & Fetterman, 2010), the extent to which correct members actually demonstrate problem solutions remains unclear. Assuming that groups are strategic and desire to solve problems both accurately and efficiently, we predicted that initially correct minorities would be more likely than initially correct majorities to demonstrate problem solutions. Results from two studies support this prediction, in the form of member behavioral intentions (Study 1) and observed group interaction processes (Study 2). Study 1 also highlights the role of confidence in this effect, while Study 2 reveals that demonstration is overall more likely when groups have a goal that encourages member learning.