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Measures of Central Tendency02:16

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The "center" of a data set is also a way of describing location. The two most widely used measures of the "center" of the data are the mean (average) and the median. The words "mean" and "average" are often used interchangeably. The substitution of one word for the other is common practice. The technical term is "arithmetic mean" and "average" is technically a center location. However, in practice among non-statisticians,...
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Descriptive statistics describe or summarize relevant characteristics of a sample and aid in the analysis of data of interest. When analyzing large quantities of data and developing an inference, one needs to identify a value representative of the entire data set. Characteristics such as central tendency, extreme values, range of measurements, or the most repeated value can help better understand the data.
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Measures of central tendency are tools used in biostatistics to identify the average or center of a dataset. They offer a single representative value for understanding and summarizing data distribution.
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Long-term memory is a relatively permanent type of memory, capable of storing vast amounts of information over extended periods. Its storage capacity is generally considered unlimited.
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Central tendency refers to the central point or typical value of a dataset. It summarizes the data set with a single value that represents the center of its distribution. The three main measures of central tendency are:
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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
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Central tendency representation and exemplar matching in visual short-term memory.

Chad Dubé1

  • 1University of South Florida, Tampa, FL, USA. chaddube@usf.edu.

Memory & Cognition
|March 5, 2019
PubMed
Summary

This study introduces a new model for visual short-term memory (VSTM) that includes central tendency representations, challenging the generalized context model (GCM) which excludes prototypes. The new model better explains Sternberg scanning data, suggesting prototype models warrant reconsideration in VSTM research.

Keywords:
CategorizationMemory modelsRecognitionShort-term memory

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Area of Science:

  • Cognitive Psychology
  • Neuroscience
  • Human Memory

Background:

  • The generalized context model (GCM) is a prominent theory in memory research that excludes prototypes.
  • The visual short-term memory (VSTM) literature frequently reports prototype effects, which are not accounted for by the standard GCM.
  • Sternberg scanning tasks are commonly used to investigate VSTM, and recent extensions of the GCM (like EBRW) have been applied to this paradigm.

Purpose of the Study:

  • To review evidence for obligatory prototype influence in VSTM, particularly in Sternberg scanning tasks.
  • To propose and evaluate a new computational model of VSTM that incorporates central tendency representations.
  • To compare the predictive power of the new model against the GCM using unpublished Sternberg scanning data.

Main Methods:

  • Literature review of prototype effects in VSTM and Sternberg scanning.
  • Development of a novel computational model incorporating central tendency representations.
  • Model comparison using prediction and postdiction on existing Sternberg scanning data.

Main Results:

  • The GCM requires post hoc modifications to explain the observed Sternberg scanning data patterns.
  • The proposed central tendency representation model accurately predicts the patterns in the data without ad-hoc adjustments.
  • Existing GCM extensions do not fully capture the nuances of prototype effects in VSTM.

Conclusions:

  • The findings suggest that prototype models, or models incorporating central tendency representations, may be more suitable for explaining VSTM phenomena than current GCM extensions.
  • While the new model is acknowledged as imperfect, it highlights the potential importance of central tendency representations in VSTM.
  • A re-evaluation of prototype models within the VSTM literature is recommended based on these modeling results.