The idea of a “cognitive map” was originally developed to ex-plain planning and generalization in spatial domains througha representation of inferred relationships between experiences.Recently, new research has suggested similar principles mayalso govern the representation of more abstract, conceptualknowledge in the brain. We test whether the search for rewardsin conceptual spaces follows similar computational principlesas in spatial environments. Using a within-subject design, par-ticipants searched for both spatially and conceptually corre-lated rewards in multi-armed bandit tasks. We use a GaussianProcess model combining generalization with an optimisticsampling strategy to capture human search decisions and judg-ments in both domains, and to simulate human-level perfor-mance when specified with participant parameter estimates. Inline with the notion of a domain-general generalization mecha-nism, parameter estimates correlate across spatial and concep-tual search, yet some differences also emerged, with partici-pants generalizing less and exploiting more in the conceptualdomain.

How do humans generalise to make better decisions? Previous work has investigated this question using reward-guided decision-making tasks with low-dimensional and artificial stimuli. In this paper, we extend this work by presenting participants with a naturalistic decision-making task, in which options were images of real-world objects and the underlying reward function was based on one of their latent dimensions. Even though participants received no explicit instruction about object features, they quickly learned to do the task and generalised to unseen objects. To understand how they accomplished this, we tested a range of computational models and found that human behaviour is overall best explained by a linear model but that participants' strategies changed during the experiment. Lastly, we showed that combining pixel-based representations extracted from convolutional neural networks with the original latent dimensions further improved our models. Taken together, our study offers new insights into human decision making under naturalistic settings.