Modeling the Aha! Moment: A Computational Mechanism for Restructuring and Incubation in Creative Problem Solving

Zhao, Cheng; Ray, Laura E.; Nguyen, Ha Thi The; Kralik, Jerald D.

doi:10.2316/p.2013.807-025

Cited by 3 publications

(5 citation statements)

References 13 publications

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“…Although humans clearly separate themselves from other species with respect to creative problem-solving ability, the mechanisms by which we access previously nonapparent problem components when faced with challenging problems may be shared. For the 9-dot problem, for example, abstraction and a subsequent cascade may be involved in which the problem-solvers realize they can utilize any point on the page (abstraction from 9 dots to the x, y plane), which could subsequently lead to consideration of longer lines beyond the dots themselves, leading ultimately to the solution (Cheng et al, 2013; Kralik, Mao, Cheng, & Ray, 2016). Hence, we believe our study shows how comparative research can provide insights and impetus for further investigation of the specific mechanisms underlying human high-level cognition.…”

Section: Discussionmentioning

confidence: 99%

“…For example, to find a new path to a restaurant one normally considers the most obvious means of transportation (e.g., walk, subway, car), and will take the most direct route available; these solutions are in turn bound by the factors of cost, availability, and intended effort. In contrast, creative problem-solving entails consideration of nonapparent problem components, which at first pass means those outside the scope of the original problem representation: i.e., those not as salient, directly relevant, or learned from experience (Cheng, Ray, Nguyen, & Kralik, 2013; Kralik, Mao, Cheng, & Ray, 2016; Kralik, Shi, & El-Shroa, 2016; Smith & Ward, 2012). A classic example with humans is the 9-dot problem, in which nine dots are displayed in a 3 × 3 square matrix, and the participant must connect all nine dots by drawing only four lines without lifting the pen/pencil (Cheng et al, 2013; Maier, 1930; van Steenburgh et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, creative problem-solving entails consideration of nonapparent problem components, which at first pass means those outside the scope of the original problem representation: i.e., those not as salient, directly relevant, or learned from experience (Cheng, Ray, Nguyen, & Kralik, 2013; Kralik, Mao, Cheng, & Ray, 2016; Kralik, Shi, & El-Shroa, 2016; Smith & Ward, 2012). A classic example with humans is the 9-dot problem, in which nine dots are displayed in a 3 × 3 square matrix, and the participant must connect all nine dots by drawing only four lines without lifting the pen/pencil (Cheng et al, 2013; Maier, 1930; van Steenburgh et al, 2012). In this case, the highly salient dots and most direct lines that begin and end at the dots define the apparent problem formulation, whereas a solution can only be found when one realizes that the lines can extend past the dots ( nonapparent formulation).…”

Section: Introductionmentioning

confidence: 99%

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Abstraction promotes creative problem-solving in rhesus monkeys

et al. 2018

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Abstraction allows us to discern regularities beyond the specific instances we encounter. It also promotes creative problem-solving by enabling us to consider unconventional problem solutions. However, the mechanisms by which this occurs are not well understood. Because it is often difficult to isolate human high-level cognitive processes, we utilized a nonhuman primate model, in which rhesus monkeys appear to use similar processes to consider an unconventional solution to the difficult reverse-reward problem: i.e., given the choice between a better and worse food option they must select the worse one to receive the better one. After solving this problem with only one specific example-one vs. four half-peanuts-three of four monkeys immediately transferred to novel cases: novel quantities, food items, non-food items, and to the choice between a larger, but inferior vegetable and a smaller, but superior food item (either grape or marshmallow), in which they selected the inferior vegetable to receive the superior option. Thus, we show that nonhuman animals have the capacity to comprehend abstract non-perceptual features, to infer them from one specific case, and to use them to override the natural preference to select the superior option. Critically, we also found that three monkeys had a large learning and performance advantage over the fourth monkey who showed less generalization from the original one and four half-peanuts. This difference suggests that abstraction promoted problem-solving via cascading activation from the two food item options to the relation between them, thus providing access to an initially nonapparent problem solution.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Abstraction promotes creative problem-solving in rhesus monkeys

et al. 2018

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“…The algorithm, however, readily extends to cases in which the problem solver must generate the solution internally, which we have done with the classic 9-dot insight problem with humans (Cheng et al, 2013;van Steenburgh et al, 2012). The problem requires the participant to connect all nine dots by drawing only four lines without lifting the pen/pencil.…”

Section: Scaling To More Sophisticated Types Of Creative Problem Solvingmentioning

confidence: 99%

“…The problem is very difficult for humans because we continue to attempt the apparent, efficient solution in which lines are drawn that begin and end at dots; yet the nonapparent solution requires the problem solver to draw lines that extend beyond the dots. In Cheng et al (2013), we modeled the solution as a nonapparent action being added to the action set.…”

Section: Scaling To More Sophisticated Types Of Creative Problem Solvingmentioning

confidence: 99%

Modeling incubation and restructuring for creative problem solving in robots

Kralik

Mao

Zhao

et al. 2016

Robotics and Autonomous Systems

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Creativity represents the pinnacle of higher-level cognition, but exactly how it is achieved remains poorly understood, especially when simultaneously facing the opposing challenge of intractable complexity. The aims of the current study were (a) to examine how the brain may achieve the dual goals of creativity and complexity reduction, and (b) to begin developing higher-level cognition and creativity in robots. We addressed these aims by (a) modeling an example of insight problem solving and comparing it to empirical data, and (b) testing the model on a robot platform. Unlike other models, we propose a single mechanism for both creative problem solving and complexity reduction. Focusing on creativity, the computational mechanism leads to insightful problem solving by restructuring an internal belief representation based on evidence collected during an incubation period. Because insightful problem solving has been examined closely in nonhuman primates, providing detailed quantitative datasets lacking in humans, we tested the model by comparing simulations to insightful problem solving by rhesus monkeys. Results show that the proposed model accounts for both the discontinuous three stage problem-solving patterns and the spontaneous generalization to novel cases observed with the monkeys. To test the model in a physical environment, we implemented it in a vision-equipped robot, and the model solved the same insight problem from camera percepts. Our model shows how the creative brain may address the dual challenges of complex environmentsfinding unprecedented opportunity hidden within potentially intractable complexityand suggests that both challenges may be met by a single underlying computational mechanism.

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