scholarly journals Base rate neglect and neural computations for subjective weight in decision under uncertainty

2020 ◽  
Vol 117 (29) ◽  
pp. 16908-16919
Author(s):  
Yun-Yen Yang ◽  
Shih-Wei Wu
Keyword(s):  
Orbitofrontal Cortex ◽  
Relative Sensitivity ◽  
Base Rate ◽  
Neural Mechanisms ◽  
Decision Under Uncertainty ◽  
Base Rate Neglect ◽  
Subjective Weight ◽  
Reward Probability ◽  
Neural Computations ◽  
The Individual

Base rate neglect, an important bias in estimating probability of uncertain events, describes humans’ tendency to underweight base rate (prior) relative to individuating information (likelihood). However, the neural mechanisms that give rise to this bias remain elusive. In this study, subjects chose between uncertain prospects where estimating reward probability was essential. We found that when the variability of prior and likelihood information about reward probability were systematically manipulated, prior variability significantly affected the degree to which subjects underweight the base rate of reward probability. Activity in the orbitofrontal cortex, medial prefrontal cortex, and putamen represented the relative subjective weight that reflected such bias. Further, sensitivity to likelihood relative to prior variability in the putamen correlated with individuals’ overall tendency to underweight base rate. These findings suggest that in combining prior and likelihood, relative sensitivity to information variability and subjective-weight computations critically contribute to the individual heterogeneity in base rate neglect.

scholarly journals Base-rate neglect and neural computations for subjective weight in probabilistic inference

2019 ◽  
Author(s):  
Yun-Yen Yang ◽  
Shih-Wei Wu
Keyword(s):  
Prefrontal Cortex ◽  
Medial Prefrontal Cortex ◽  
Orbitofrontal Cortex ◽  
Base Rate ◽  
Policy Implications ◽  
Sources Of Information ◽  
Base Rate Neglect ◽  
Subjective Weight ◽  
Subjective Value ◽  
The Brain

AbstractHumans show systematic biases when estimating probability of uncertain events. Base-rate neglect is a well-known bias that describes the tendency to underweight information from the past relative to the present. In this study, we characterized base-rate neglect at the computational and neural implementation levels. At the computational level, we established that base-rate neglect arises from insufficient adjustment to weighting prior information in response to changes in prior variability. At the neural implementation level, we found that orbitofrontal cortex (OFC) and medial prefrontal cortex (mPFC) represent subjective weighting of information that reflects base-rate neglect. Critically, both subjective-weight and subjective-value signals that guide choice were found in mPFC. However, subjective-weight signals preceded subjective-value signals. These results indicate that when facing multiple sources of information, estimation bias such as base-rate neglect arises from information weighting computed in OFC and mPFC, which directly contributes to subjective-value computations that guide decisions under uncertainty.Significance StatementFacing uncertainty, estimating the probability of different potential outcomes carries significant weight in affecting how we act and decide. Decades of research show that humans are prone to giving biased estimation but it remains elusive how these biases arise in the brain. We focus on base-rate neglect, a well-known bias in probability estimation and find that it is tightly associated with activity in the medial prefrontal cortex and orbitofrontal cortex. These regions represent the degree to which human participants weigh different sources of information, suggesting that base-rate neglect arises from information-weighting computations in the brain. As technology provides us the opportunity to seek and gather information at an ever-increasing pace, understanding information-weighting and its biases also carry important policy implications.

scholarly journals Base rate neglect and conservatism in probabilistic reasoning: Insights from eliciting full distributions

2021 ◽  
Author(s):  
Piers Howe ◽  
Andrew Perfors ◽  
Bradley Walker ◽  
Yoshihisa Kashima ◽  
Nicolas Fay
Keyword(s):  
Probabilistic Reasoning ◽  
Single Point ◽  
Base Rate ◽  
Substantial Variation ◽  
Systematic Relationship ◽  
Base Rate Neglect ◽  
Point Estimates ◽  
The Individual ◽  
First Time ◽  
New Evidence

Bayesian statistics offers a normative description for how a person should combine their original beliefs (i.e., their priors) in light of new evidence (i.e., the likelihood). Previous research suggests that people tend to under-weight both their prior (base rate neglect) and the likelihood (conservatism), although this varies by individual and situation. Yet this work generally elicits people's knowledge as single point estimates (e.g., x has 5% probability of occurring) rather than as a full distribution. Here we demonstrate the utility of eliciting and fitting full distributions when studying these questions. Across three experiments, we found substantial variation in the extent to which people showed base rate neglect and conservatism, which our method allowed us to measure for the first time simultaneously at the level of the individual. We found that while most people tended to disregard the base rate, they did so less when the prior was made explicit. Although many individuals were conservative, there was no apparent systematic relationship between base rate neglect and conservatism within individuals. We suggest that this method shows great potential for studying human probabilistic reasoning.

BASE RATE NEGLECT FOR THE WEALTH OF INTERACTING PEOPLE

2010 ◽  
Vol 13 (05) ◽  
pp. 607-619 ◽  
Author(s):  
DIEMO URBIG
Keyword(s):  
Information Processing ◽  
Social Learning ◽  
Learning Process ◽  
Base Rate ◽  
Human Information Processing ◽  
Base Rates ◽  
Base Rate Neglect ◽  
Positive Externalities ◽  
Rate Information

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.

scholarly journals Model-based fMRI reveals dissimilarity processes underlying base rate neglect

eLife ◽  
2018 ◽  
Vol 7 ◽  
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.

scholarly journals Expanding the Weighted Updating Model

2016 ◽  
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.

American base‐rate neglect: It is not the math, but the context

2020 ◽  
Vol 34 (1) ◽  
pp. 116-130
Author(s):  
Shali Wu ◽  
Clifton Emery
Keyword(s):  
Base Rate ◽  
Base Rate Neglect
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