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Related Concept Videos

Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
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.
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...

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Errors as a Means of Reducing Impulsive Food Choice
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Published on: June 5, 2016

The error in total error reduction.

James E Witnauer1, Gonzalo P Urcelay2, Ralph R Miller3

  • 1Department of Psychology, State University of New York at Brockport, USA.

Neurobiology of Learning and Memory
|July 30, 2013
PubMed
Summary
This summary is machine-generated.

Most learning models assume total error reduction (TER), but this study shows local error reduction (LER) better explains associative learning. The LER model provides a superior fit to existing data, suggesting a re-evaluation of the TER assumption in learning research.

Keywords:
Associative learningConditioned inhibitionCue competitionLocal error reductionPavlovian conditioningTotal error reductionWithin-compound associations

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Computational Psychology

Background:

  • Current associative learning models often rely on the total error reduction (TER) principle.
  • TER posits learning is proportional to the overall discrepancy between predicted and actual outcomes across all cues.
  • This principle is foundational in connectionist and neural network models and linked to dopamine neuron activity.

Purpose of the Study:

  • To compare the predictive accuracy of total error reduction (TER) models against a local error reduction (LER) model.
  • To determine which model better explains associative learning phenomena based on existing data.
  • To challenge the prevailing TER assumption in learning theories.

Main Methods:

  • Employed a computational modeling approach.
  • Developed and tested several TER models of associative learning.
  • Developed and tested an alternative LER model where learning is specific to individual cue discrepancies.

Main Results:

  • The local error reduction (LER) model demonstrated a superior fit to the reviewed data compared to TER models.
  • This indicates that learning about individual cues, rather than the total error, is a more accurate predictor of learning outcomes.
  • The findings challenge the universal applicability of the TER framework.

Conclusions:

  • The local error reduction (LER) model offers a more accurate account of associative learning than traditional total error reduction (TER) models.
  • The study suggests that the acceptance of the TER assumption in learning should be reconsidered.
  • Future research should explore LER mechanisms in various learning paradigms.