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Related Concept Videos

Perceptual Constancy01:12

Perceptual Constancy

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Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
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Design Example: Strain Gauge Bridge or Wheatstone Bridge01:15

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The utilization of strain gauges as transducers for converting mechanical strain into electrical signals is a common practice in various engineering applications. These strain gauges are frequently integrated into Wheatstone bridge circuits to accurately measure parameters such as force or pressure. Within this context, each element within the circuit exhibits a resistance that undergoes subtle variations when subjected to mechanical strain. The primary objective is to convert minuscule...
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Decision Making01:20

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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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Related Experiment Video

Updated: Feb 7, 2026

Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues
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Bridging Neural and Computational Viewpoints on Perceptual Decision-Making.

Redmond G O'Connell1, Michael N Shadlen2, KongFatt Wong-Lin3

  • 1Trinity College Institute of Neuroscience and School of Psychology, Trinity College Dublin, Ireland.

Trends in Neurosciences
|July 16, 2018
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Summary

Sequential sampling models explain perceptual decision-making by accumulating evidence. New research explores neural signals to test and refine these computational models, addressing challenges in data interpretation.

Keywords:
computational modellinglateral intraparietal area (LIP)perceptual decision-makingsequential sampling

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Decision Science

Background:

  • Sequential sampling models are a leading framework for understanding perceptual decision-making.
  • These models, while sharing core principles, exhibit diverse mechanisms leading to similar behavioral predictions.
  • Identifying neural correlates of decision computations is crucial for refining these models.

Purpose of the Study:

  • To review recent advances in using neural signals to test assumptions of sequential sampling models.
  • To discuss the conceptual and methodological challenges in inferring decision computations from neural data.

Main Methods:

  • Review of recent empirical studies linking neural activity to decision-making models.
  • Analysis of conceptual frameworks for interpreting neural data in the context of computational models.

Main Results:

  • Neural signals offer promising avenues for empirically testing and refining sequential sampling models.
  • Significant conceptual and methodological challenges remain in precisely inferring decision computations from complex neural data.

Conclusions:

  • Integrating neural data with computational models advances the study of decision-making.
  • Further methodological development is needed to overcome challenges in neural data interpretation for refining decision models.