the anonymous reviewers for helpful discussions and comments. A preliminary version of the model and its evaluation (Experiment 1) was presented at the 39th Annual Meeting of the Cognitive Science Society and appeared in the conference proceedings. This work has been updated and extended for inclusion in the current article. RATIONAL ANALYSIS OF CURIOSITY 2 AbstractCuriosity is considered to be the essence of science and an integral component of cognition. What prompts curiosity in a learner? Previous theoretical accounts of curiosity remain dividednovelty-based theories propose that new and highly uncertain stimuli pique curiosity whereas complexity-based theories propose that stimuli with an intermediate degree of uncertainty stimulate curiosity. In this article, we present a rational analysis of curiosity by considering the computational problem underlying curiosity, which allows us to model these distinct accounts of curiosity in a common framework. Our approach posits that a rational agent should explore stimuli that maximally increase the usefulness of its knowledge and that curiosity is the mechanism by which humans approximate this rational behavior. Critically, our analysis show that the causal structure of the environment can determine whether curiosity is driven by either highly uncertain or moderately uncertain stimuli. This suggests that previous theories need not be in contention but are special cases of a more general account of curiosity. Experimental results confirm our predictions and demonstrate that our theory explains a wide range of findings about human curiosity, including its subjectivity and malleability.

Recent studies on image memorability have shed light on what distinguishes the memorability of different images and the intrinsic and extrinsic properties that make those images memorable. However, a clear understanding of the memorability of specific objects inside an image remains elusive. In this paper, we provide the first attempt to answer the question: what exactly is remembered about an image? We augment both the images and object segmentations from the PASCAL-S dataset with ground truth memorability scores and shed light on the various factors andproperties that make an object memorable (or forgettable) to humans. We analyze various visual factors that may influence object memorability (e.g. color, visual saliency, and object categories). We also study the correlation between object and image memorability and find that image memorability is greatly affected by the memorability of its most memorable object. Lastly, we explore the effectiveness of deep learning and other computational approaches in predicting object memorability in images. Our efforts offer a deeper understanding of memorability in general thereby opening up avenues for a wide variety of applications.

Curiosity is considered to be the essence of science and an integral component of cognition. What prompts curiosity in a learner? Previous theoretical accounts of curiosity remain divided – novelty-based theories propose that new and highly uncertain stimuli pique curiosity whereas complexity-based theories propose that stimuli with an intermediate degree of uncertainty stimulate curiosity. In this article, we present a rational analysis of curiosity by considering the computational problem underlying curiosity, which allows us to model these distinct accounts of curiosity in a common framework. Our approach posits that a rational agent should explore stimuli that maximally increase the usefulness of its knowledge and that curiosity is the mechanism by which humans approximate this rational behavior. Critically, our analysis shows that the causal structure of the environment can determine whether curiosity is driven by either highly uncertain or moderately uncertain stimuli. This suggests that previous theories need not be in contention but are special cases of a more general account of curiosity. Experimental results confirm our predictions and demonstrate that our theory explains a wide range of findings about human curiosity, including its subjectivity and malleability.

Recent work in machine learning has demonstrated the benefits of providing artificial agents with a sense of curiosity -a form of intrinsic reward that supports exploration. Two strategies have emerged for defining these rewards: favoring novelty and pursuing prediction errors. Psychological theories of curiosity have also emphasized these two factors. We show how these two literatures can be connected by understanding the function of curiosity, which requires thinking about the abstract computational problem that both humans and machines face as they explore their world.

Our actions and decisions are regularly influenced by the social environment around us. Can social cues be leveraged to induce curiosity and affect subsequent behavior? Across two experiments, we show that curiosity is contagious: The social environment can influence people's curiosity about the answers to scientific questions. Participants were presented with everyday questions about science from a popular on‐line forum, and these were shown with a high or low number of up‐votes as a social cue to popularity. Participants indicated their curiosity about the answers, and they were given an opportunity to reveal a subset of those answers. Participants reported greater curiosity about the answers to questions when the questions were presented with a high (vs. low) number of up‐votes, and they were also more likely to choose to reveal the answers to questions with a high (vs. low) number of up‐votes. These effects were partially mediated by surprise and by the inferred usefulness of knowledge, with a more dramatic effect of low up‐votes in reducing curiosity than of high up‐votes in boosting curiosity. Taken together, these results highlight the important role social information plays in shaping our curiosity.