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Related Concept Videos

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Perception is influenced by perceptual set, context, motivation, and emotion. Perceptual set, or perceptual expectancy, refers to the tendency to perceive things in a particular way, influenced by previous experiences and expectations. This phenomenon affects the interpretation of stimuli, creating a set of mental tendencies and assumptions that impact sensory perceptions of sound, taste, touch, and sight.
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First impressions play a crucial role in social perception, shaping how individuals assess others in professional, academic, and interpersonal contexts. Psychological research highlights the significance of cognitive biases, such as the primacy and recency effects, which influence how people interpret and recall information.The Primacy Effect and Cognitive AnchoringThe primacy effect describes the tendency for initial information to impact judgment disproportionately. When individuals encounter...
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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Related Experiment Video

Updated: Apr 16, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
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Individual differences in attention influence perceptual decision making.

Michael D Nunez1, Ramesh Srinivasan2, Joachim Vandekerckhove3

  • 1Department of Cognitive Sciences, University of California, Irvine Irvine, CA, USA.

Frontiers in Psychology
|March 13, 2015
PubMed
Summary

Individual differences in decision-making stem from variations in evidence accumulation and non-decision times. Attentional control, measured via electroencephalography (EEG), explains these cognitive differences, improving behavioral predictions.

Keywords:
Phase-lockingdiffusion modelselectroencephalography (EEG)hierarchical Bayesian modelingindividual differencesperceptual decision makingsteady-state visual evoked potential (SSVEP)

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience

Background:

  • Sequential sampling models explain reaction time (RT) and accuracy in decision tasks.
  • Individual variability in these models is linked to cognitive processes.

Purpose of the Study:

  • Investigate sources of individual differences in a novel perceptual decision-making task.
  • Link cognitive variables to neural measures of attention using electroencephalography (EEG).

Main Methods:

  • Applied a diffusion model to reaction time and accuracy data.
  • Measured attentional differences using phase-locking indices (PLIs) of steady-state visual evoked potentials (SSVEPs).
  • Integrated cognitive and neural data within a hierarchical Bayesian framework.

Main Results:

  • Individual differences attributed to evidence accumulation rates, variability, and non-decision times.
  • Attentional differences, specifically suppressing SSVEP responses to noise, correlated with faster evidence accumulation and shorter non-decision times.
  • Models incorporating neural data improved prediction of out-of-sample participant behavior.

Conclusions:

  • Hierarchical Bayesian modeling effectively links neural and cognitive processes in decision-making.
  • Attentional control, reflected in SSVEP phase-locking, is a key determinant of individual differences in perceptual decision tasks.
  • Integrating physiological data enhances the predictive power of cognitive models for individual behavior.