Psychologists have long been fascinated with understanding the nature of Aha! moments, moments when we transition from not knowing to suddenly realizing the solution to a problem. In this work, we present a theoretical framework that explains when and why we experience Aha! moments. Our theory posits that during problem-solving, in addition to solving the problem, people also maintain a meta-cognitive model of their ability to solve the problem as well as a prediction about the time it would take them to solve that problem. Aha! moments arise when we experience a positive error in this meta-cognitive prediction, i.e. when we solve a problem much faster than we expected to solve it. We posit that this meta-cognitive error is analogous to a positive reward prediction error thereby explaining why we feel so good after an Aha! moment. A large-scale pre-registered experiment on anagram solving supports this theory, showing that people's time prediction errors are strongly correlated with their ratings of an Aha! experience while solving anagrams. A second experiment provides further evidence to our theory by demonstrating a causal link between time prediction errors and the Aha! experience. These results highlight the importance of meta-cognitive prediction errors and deepen our understanding of human meta-reasoning.
Signage systems are critical for communicating spatial information during wayfinding among a plethora of noise in the environment. A proper signage system can improve wayfinding performance and user experience by reducing the perceived complexity of the environment. However, previous models of sign-based wayfinding do not incorporate realistic noise or quantify the reduction in perceived complexity from the use of signage. Drawing upon concepts from information theory, we propose and validate a new agent-signage interaction model that quantifies available wayfinding information from signs for wayfinding. We conducted two online crowd-sourcing experiments to compute the distribution of a sign's visibility and an agent's decision-making confidence as a function of observation angle and viewing distance. We then validated this model using a virtual reality (VR) experiment with trajectories from human participants. The crowd-sourcing experiments provided a distribution of decision-making entropy (conditioned on visibility) that can be applied to any sign/environment. From the VR experiment, a training dataset of 30 trajectories was used to refine our model, and the remaining test dataset of 10 trajectories was compared with agent behavior using dynamic time warping (DTW) distance. The results revealed a reduction of 38.76% in DTW distance between the average trajectories before and after refinement. Our refined agentsignage interaction model provides realistic predictions of human wayfinding behavior using signs. These findings represent a first step towards modeling human wayfinding behavior in complex real environments in a manner that can incorporate several additional random variables (e.g., environment layout). Keywords Cognitive agent • Information theory • Entropy • Artificial intelligence • Agent-based model • Signage system Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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