Rochelle Kaper^1,2,3, Megan A.K. Peters^1,2,3,4,5,6; ¹Department of Cognitive Sciences, University of California Irvine, ²Center for Theoretical Behavioral Sciences, University of California Irvine, ³Center for the Neurobiology of Learning & Memory, University of California Irvine, ⁴Department of Logic & Philosophy of Science, University of California Irvine, ⁵Department of Experimental Psychology, University College London, ⁶Program in Brain, Mind, & Consciousness, Canadian Institute for Advanced Research

Our daily lives afford the ability to construct internal models of multiple spatiotemporal patterns without feedback, in order to predict incoming information and generalize to novel contexts; this process is called visual structure learning. Along with prediction errors, metacognitive error signals guide learning in the absence of external feedback. It is unknown how metacognitive capacity may change over time throughout learning, and how such dynamics would affect learning across structures and stimuli differing in abstractness under uncertainty. To address these gaps, we developed a novel probabilistic visual structure learning paradigm where we probed the dynamics and representation of learning and metacognition under uncertainty. Participants learned probabilistic co-occurrences of item pairs (AA’, BB’…) and a probabilistic sequence of those co-occurrences (AA’→BB’...). After each learning phase where participants viewed probabilistic streams of item pairs, participants completed tests of explicit structure learning (forced-choice accuracy) and metacognitive capacity (accuracy-confidence correspondence). Participants completed 2 sessions in counterbalanced order: one with low-level perceptual fractals (n=56), and the other with familiar line drawings (n=57). Mixed effects linear models revealed significantly higher sequence accuracy compared to co-occurrences for fractals, and vice versa for line drawings. We also observed significant metacognitive capacity increases across learning for fractals but not line drawings, and metacognitive capacity was higher for sequence structure despite greater task accuracy in fractals. We modeled the representation of hierarchical structure information using analytical successor representations, revealing a potential tradeoff between metacognitive capacity, representation, and usage of sequential information in learning the co-occurrences that differ among stimuli. Our results reveal distinct learning rates, metacognitive capacity dynamics, and representations of hierarchical information across structures and stimuli varying in abstractness and familiarity, motivating novel task designs that can assess how different stimuli and structures alter how hierarchical information is represented and varying metacognitive capacity dynamics throughout learning.

Acknowledgements: Funding Source: Canadian Institute for Advanced Research / Research Corporation for Science Advancement