Abstract
Learning and generalization in spatial domains is often thought to rely on a "cognitive map", representing relationships between spatial locations. Recent research suggests that this same neural machinery is also recruited for reasoning about more abstract, conceptual forms of knowledge. Yet, to what extent do spatial and conceptual reasoning share common computational principles, and what are the implications for behavior? Using a within-subject design we studied how participants used spatial or conceptual distances to generalize and search for correlated rewards in successive multi-armed bandit tasks. Participant behavior indicated sensitivity to both spatial and conceptual distance, and was best captured using a Bayesian model of generalization that formalized distance-dependent generalization and uncertainty-guided exploration as a Gaussian Process regression with a radial basis function kernel. The same Gaussian Process model best captured human search decisions and judgments in both domains, and could simulate realistic learning curves, where we found equivalent levels of generalization in spatial and conceptual tasks. At the same time, we also find characteristic differences between domains. Relative to the spatial domain, participants showed reduced levels of uncertainty-directed exploration and increased levels of random exploration in the conceptual domain. Participants also displayed a one-directional transfer effect, where experience in the spatial task boosted performance in the conceptual task, but not vice versa. While confidence judgments indicated that participants were sensitive to the uncertainty of their knowledge in both tasks, they did not or could not leverage their estimates of uncertainty to guide exploration in the conceptual task. These results support the notion that value-guided learning and generalization recruit cognitive-map dependent computational mechanisms in spatial and conceptual domains. Yet both behavioral and model-based analyses suggest domain specific differences in how these representations map onto actions.
Highlights
The conceptual task was represented using Gabor patches, where a single patch was displayed on the screen and the arrow keys changed the tilt and stripe frequency, providing a non-spatial domain where similarities are relatively well defined
The strength of reward correlations were manipulated between subjects, with one half assigned to smooth environments and the other assigned to rough environments
We investigated to what extent learning and searching for rewards are governed by similar computational principles in spatial and conceptual domains
Summary
We remember things in terms of places [1,2,3], describe the world using spatial metaphors [4, 5], and commonly use concepts like “space” or “distance” in mathematical descriptions of abstract phenomena In line with these observations, previous theories have argued that reasoning about abstract conceptual information follows the same computational principles as spatial reasoning [6,7,8]. One important implication of these accounts is that reinforcement learning [14] in nonspatial domains may rely on a map-like organization of information, supported by the computation of distances or similarities between experiences These representations of distance facilitate generalization, allowing for predictions about novel stimuli based on their similarity to previous experiences. We ask to what extent does the search for rewards depend on the same distance-dependent generalization across two different domains—one defined by spatial location and another by abstract features of a Gabor patch—despite potential differences in how the stimuli and their similarities may be processed?
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