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Most theoretical accounts of non-instrumental information seeking suggest that the magnitude of rewards has a direct influence on the attractiveness of the information. Specifically, the magnitude of rewards is assumed to be proportional to the strength of information seeking (or avoidant) behaviour. In a series of experiments using numerical and pictorial stimuli, we explore the extent to which observed information seeking behaviour tracks these predictions. Our findings indicate a robust independence of information seeking from outcome magnitude and valence with preferences for information largely remaining constant across different reward valence and magnitudes. We discuss these results in the context of current computational models with suggestions for future theoretical and empirical work.

People’s desire to seek or avoid information is not only influenced by the possible outcomes of an event, but the probability of those particular outcomes occurring. There are competing explanations however as to how and why people’s desire for non-instrumental information is affected by factors including expected value, probability of outcome, and a unique formulation of outcome uncertainty. Over two experiments, we find that people’s preference for non-instrumental information is positively correlated with probability when the outcome is positive (i.e., winning money) and negatively correlated when the outcome is negative (i.e., losing money). Furthermore, at the aggregate level, we find the probability of an outcome to be a better predictor of information preference than the expected value of the event or its outcome uncertainty.

People tend to place value on information even when it doesnot affect the outcome of a decision. Two competingaccounts offer explanations for such non-instrumentalinformation seeking. One account foregrounds the role ofanticipation and the other focusses on uncertainty aversion.Both accounts make similar predictions for short cue-outcome delays and when outcomes are positively valenced,but they differ in their explanation of information preferenceat long delays with negative outcomes. We present a seriesof experiments involving both primary and secondaryreinforcers that pit these accounts against each other. Theresults indicate a consistent preference for non-instrumentalinformation even at long cue-outcome delays and noevidence for information avoidance with negative outcomes.This pattern appears to provide more support for theuncertainty-aversion account than one based on anticipation.

The categorization literature often considers two types of cat-egories as equivalent: (a) standard categories and (b) negationcategories. For example, category learning studies typicallyconflate learning categories A and B with learning categoriesA and NOT A. This study represents the first attempt at de-lineating these two separate types of generated categories. Wespecifically test for differences in the distributional structure ofgenerated categories, demonstrating that categories identifiedas not what was known are larger and wider-spread comparedto categories that were identified with a specific label. We alsoobserve consistency in distributional structure across multiplegenerated categories, replicating and extending previous find-ings. These results are discussed in the context of providing afoundation for future modeling work.