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Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

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Related Experiment Video

Updated: Jun 5, 2026

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

Published on: May 10, 2012

Motion discrimination under uncertainty and ambiguity.

Joke P Kalisvaart¹, Igor Klaver, Jeroen Goossens

¹Department of Biophysics UMC, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands. j.kalisvaart@donders.ru.nl

Journal of Vision

|January 27, 2011

Summary

This summary is machine-generated.

This study reveals how the brain makes decisions with visual motion, finding that ambiguous stimuli like binocular rivalry slow down reaction times compared to unambiguous stimuli.

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Area of Science:

Cognitive Neuroscience
Computational Neuroscience
Visual Perception

Background:

Visual motion discrimination accuracy and speed depend on motion strength.
Diffusion models explain evidence accumulation for unambiguous decisions.
Bistable vision theories propose competitive interactions for ambiguous stimuli.

Purpose of the Study:

Investigate how the brain decides under conditions of uncertainty versus ambiguity.
Reconcile discrepancies between existing models for decision-making.
Examine motion discrimination at stimulus onset under both unambiguous and ambiguous conditions.

Main Methods:

Experiment 1: Assessed speed and accuracy with unambiguous motion patterns (identical vs. uncorrelated dot locations).
Experiments 2 & 3: Investigated reaction times under binocular rivalry conditions.
Compared results to diffusion and rivalry models.

Main Results:

Unambiguous motion discrimination showed similar speed and accuracy, supporting a race model.
Reaction times significantly increased under binocular rivalry.
The increase in reaction times under rivalry could not be explained by motion transparency alone, indicating competitive interactions.

Conclusions:

Competitive rivalry interactions play a crucial role in resolving visual ambiguity.
A combined diffusion-rivalry model can explain decision-making across both ambiguous and unambiguous visual motion conditions.
Highlights a unified framework for understanding visual decision-making under varying levels of stimulus certainty.