Modeling incubation and restructuring for creative problem solving in robots

Abstract

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.

Abstraction promotes creative problem-solving in rhesus monkeys

“…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).…”

“…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). Thus, in creative problem-solving, an inadequate formulation of the problem based on apparent factors must be replaced by considering ‘outside-the-box’ components (Kralik, Mao, Cheng, & Ray, 2016; Kralik, Shi, & El-Shroa, 2016; Smith & Ward, 2012; van Steenburgh et al, 2012).…”

“…First, when choosing between a larger and smaller quantity most subjects inhibit the selection of the larger quantity relatively quickly; however, instead of selecting the smaller quantity, they switch to a side bias (e.g., repeatedly selecting the left option), thus reaching an extended impasse prior to spontaneously solving the problem (Chudasama, Kralik, & Murray, 2007; Murray et al, 2005). This extended impasse and spontaneous problem-solving suggests additional confusion with the task that is eventually overcome (Kralik, Mao, Cheng, & Ray, 2016; Murray et al, 2005). Second, to test the issue of self-control directly, Kralik (2005) first posed an even simpler version of the reverse-reward problem to cotton-top tamarins ( Saguinus oedipus ), a New World monkey, in which when given a choice between 1 and 3 food items, selecting the 1-item option received the 3 item-option, but selecting 3 received nothing (Fig.…”

“…Additional studies have also shown that in multiple cases where problem-solving difficulties have been assumed to reflect lower-level (e.g., Pavlovian) influences, they may more accurately resemble cognitive illusions that reflect the constraints/biases of a simple problem-solving system rather than affective-driven prepotent responses (Kralik, Shi, & El-Shroa, 2016; Santos, Ericson, & Hauser, 1999; Wallis, Dias, Robbins, & Roberts, 2001). Indeed, the discontinuous reverse-reward learning curve for rhesus monkeys suggests that, after the extended side-bias impasse, the spontaneous solving of the problem does not occur via simple gradual strengthening over trials, but rather, some change that provides access to the previously inaccessible nonapparent solution (Chudasama et al, 2007; Kralik, Mao, Cheng, & Ray, 2016; Murray et al, 2005). In this light, reverse-reward problem-solving by nonhumans provides a model to study the mechanisms used to find nonapparent solutions, ones that may be shared by people to access remote possibilities that lead to creative solutions.…”

“…In this light, reverse-reward problem-solving by nonhumans provides a model to study the mechanisms used to find nonapparent solutions, ones that may be shared by people to access remote possibilities that lead to creative solutions. It has in fact been theorized that the ability to solve nonapparent problems may be the key functional advance with the evolution of granular prefrontal cortex in primates (i.e., lateral and frontal polar cortex) (Kralik, Mao, Cheng, & Ray, 2016; Kralik, Shi, & El-Shroa, 2016; Passingham & Wise, 2012; Preuss, 1995; Striedter, 2005; Wise, 2008).…”