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Related Concept Videos

Confirmation Biases01:31

Confirmation Biases

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The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
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Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
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Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Correspondence bias, also referred to as the fundamental attribution error, describes the tendency to attribute another person’s behavior to internal characteristics rather than situational influences. This cognitive bias leads individuals to overlook external factors that may be influencing actions, thereby fostering potentially inaccurate assessments of others’ intentions and dispositions.Empirical Evidence for Correspondence BiasResearch has consistently demonstrated the...
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Related Experiment Video

Updated: Feb 3, 2026

Online Repetitive Transcranial Magnetic Stimulation of Dorsomedial and Dorsolateral Prefrontal Cortex in Cognition Decision Making, and Cognitive Dissonance
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A Biased Bayesian Inference for Decision-Making and Cognitive Control.

Kaosu Matsumori1,2, Yasuharu Koike3, Kenji Matsumoto1

  • 1Tamagawa University Brain Science Institute, Machida, Tokyo, Japan.

Frontiers in Neuroscience
|October 30, 2018
PubMed
Summary

This study introduces exponentially-biased Bayesian inference to explain suboptimal decision-making. It models biases in probability judgments and explores neural mechanisms for cognitive control of these biases.

Keywords:
cognitive controlcomputational psychiatrygain modulationparameter estimationprobabilistic population codesprobability judgmentsub-optimalitytwo-alternative forced choice

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Area of Science:

  • Cognitive Neuroscience
  • Decision-Making Science
  • Computational Psychiatry

Background:

  • Classical decision-making models assume Bayes-optimality.
  • Sub-optimality and biases in decision-making are actively debated.
  • Existing models do not fully capture observed biases.

Purpose of the Study:

  • To propose a unified framework for decision-making biases.
  • To model various decision-making and probability judgments using biased Bayesian inference.
  • To investigate the neural underpinnings and cognitive control of decision biases.

Main Methods:

  • Developed an exponentially-biased Bayesian inference model.
  • Mapped parameter estimation methods in a two-dimensional bias space (prior and likelihood).
  • Proposed a neural implementation using neural integrators and population coding.

Main Results:

  • The proposed model synthesizes diverse decision-making biases.
  • Parameter estimation methods were systematically analyzed within the bias space.
  • A neural mechanism involving synaptic weight changes was outlined.

Conclusions:

  • Exponentially-biased Bayesian inference provides a robust framework for understanding decision biases.
  • Neural implementations suggest specific mechanisms for biased processing.
  • Cognitive control mechanisms may dynamically regulate these biases.