Base-rate inverse and base-rate neglect in categorization: A test of the elimination hypothesis

Previous research investigating base rate neglect as a bias in human information processing has focused on isolated individuals. This study complements this research by showing that in settings of interacting individuals, especially in settings of social learning, where individuals can learn from one another, base rate neglect can increase a population's welfare. This study further supports the research arguing that a population with members biased by neglecting base rates does not need to perform worse than a population with unbiased members. Adapting the model of social learning suggested by Bikhchandani, Hirshleifer and Welch (The Journal of Political Economy100 (1992) 992–1026) and including base rates that differ from generic cases such as 50–50, conditions are identified that make underweighting base rate information increasing the population's welfare. The base rate neglect can start a social learning process that otherwise had not been started and thus base rate neglect can generate positive externalities improving a population's welfare.

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Model-based fMRI reveals dissimilarity processes underlying base rate neglect

eLife ◽

10.7554/elife.36395 ◽

2018 ◽

Vol 7 ◽

Cited By ~ 2

Author(s):

Sean R O'Bryan ◽

Darrell A Worthy ◽

Evan J Livesey ◽

Tyler Davis

Keyword(s):

Computational Models ◽

Human Subjects ◽

Base Rate ◽

Imaging Data ◽

Multivoxel Pattern Analysis ◽

Activation Patterns ◽

Model Based ◽

Base Rate Neglect ◽

Rate Information ◽

Rare Category

Extensive evidence suggests that people use base rate information inconsistently in decision making. A classic example is the inverse base rate effect (IBRE), whereby participants classify ambiguous stimuli sharing features of both common and rare categories as members of the rare category. Computational models of the IBRE have either posited that it arises from associative similarity-based mechanisms or dissimilarity-based processes that may depend upon higher-level inference. Here we develop a hybrid model, which posits that similarity- and dissimilarity-based evidence both contribute to the IBRE, and test it using functional magnetic resonance imaging data collected from human subjects completing an IBRE task. Consistent with our model, multivoxel pattern analysis reveals that activation patterns on ambiguous test trials contain information consistent with dissimilarity-based processing. Further, trial-by-trial activation in left rostrolateral prefrontal cortex tracks model-based predictions for dissimilarity-based processing, consistent with theories positing a role for higher-level symbolic processing in the IBRE.

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Expanding the Weighted Updating Model

10.31235/osf.io/ctjf6 ◽

2016 ◽

Cited By ~ 1

Author(s):

Jesse Aaron Zinn

Keyword(s):

Cognitive Biases ◽

Bayesian Updating ◽

Irrelevant Information ◽

Base Rate ◽

Order Effects ◽

Availability Heuristic ◽

Base Rate Neglect ◽

Attribution Bias

This work casts light upon a pair of restrictions inherent to the basic weighted updating model, which is a generalization of Bayesian updating that allows for biased learning. Relaxing the restrictions allows for the study of individuals who discriminate between observations or who treat information in a dynamically inconsistent manner. These generalizations augment the set of cognitive biases that can be studied using new versions of the weighted updating model to include the availability heuristic, order effects, self-attribution bias, and base-rate neglect in light of irrelevant information.

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