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Related Concept Videos

Accuracy and Precision01:52

Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.  Highly accurate measurements...
Accuracy and Precision01:52

Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.  Highly accurate measurements...
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...

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Related Experiment Video

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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Attention is more than prediction precision.

Howard Bowman1, Marco Filetti, Brad Wyble

  • 1Centre for Cognitive Neuroscience and Cognitive Systems, and School of Computing, University of Kent at Canterbury, Kent CT2 7NF, United Kingdom. H.Bowman@kent.ac.uk

The Behavioral and Brain Sciences
|May 14, 2013
PubMed
Summary

Attention models in predictive coding may be incomplete. Event-related potential data suggests frequent, predicted stimuli also elicit strong responses, challenging current attention theories.

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Area of Science:

  • Cognitive Neuroscience
  • Computational Psychiatry

Background:

  • Predictive coding frameworks explain attention by weighting prediction error with precision.
  • Existing models struggle to account for neural responses to predicted stimuli.

Purpose of the Study:

  • To critically evaluate the predictive coding model of attention.
  • To propose an extension that accounts for evoked responses to predicted stimuli.

Main Methods:

  • Theoretical analysis of predictive coding models.
  • Interpretation of event-related potential (ERP) data.

Main Results:

  • Weighting prediction error by precision does not fully explain attention.
  • Frequently presented, predicted target stimuli evoke significant ERPs.
  • This suggests attention mechanisms beyond error minimization are at play.

Conclusions:

  • The predictive coding model of attention requires refinement.
  • Future models must incorporate mechanisms that explain responses to predicted stimuli, not just prediction errors.