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The samples of evidence we use to make inferences in everyday and formal settings are often subject to selection biases. Two property induction experiments examined group and individual sensitivity to one type of selection bias: sampling frames - causal constraints that only allow certain types of instances to be sampled. Group data from both experiments indicated that people were sensitive to the effects of such frames, showing narrower generalization when sample instances were selected because they shared a target property (property sampling) than when instances were sampled because they belonged to a particular group (category sampling). Group generalization patterns conformed to the predictions of a Bayesian model of property induction that incorporates a selective sampling mechanism. In each experiment, however, there was considerable individual variation, with a nontrivial minority showing little sensitivity to sampling frames. Experiment 2 examined correlates of frames sensitivity. A composite measure of working memory capacity predicted individual sensitivity to sampling frames. These results have important implications for current debates about people's ability to factor sample selection mechanisms into their inferences and for the development of formal models of inductive inference.

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.

Standard theories suggest that humans should seek information only when it can help them make better decisions. However, recent work suggests that people choose to seek information even when it cannot influence the outcome of a choice. Across three experiments, we examined how this preference for non‐instrumental information was related to the risk, regret, and rejoice associated with different choices. Experiment 1 examined how risk preference informed the appetite for non‐instrumental information and tested how risk and information preference in a gamble‐task related to the desire for knowledge across a range of hypothetical real‐world scenarios. In Experiment 2, we tested how risk, operationalized as variance, related to non‐instrumental information seeking when allowing participants to mentally simulate the potential outcomes of gambles. In Experiment 3, we provided explicit feedback about forgone options, intending to make the potential for regret or rejoice more salient. Taken together, our results show a consistent appetite for information that was robust to changes across all experimental manipulations. We found some evidence of a positive correlation between the desire for knowledge and the level of anticipated regret (Experiment 1), but overall, our data appear more consistent with the idea that non‐instrumental information seeking is driven by a general aversion to uncertainty than by an attempt to regulate specific future emotions. [ABSTRACT FROM AUTHOR]