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Related Concept Videos

Serial Position Effect01:03

Serial Position Effect

The serial position effect is a cognitive phenomenon where individuals are more likely to recall the first and last items in a list compared to those in the middle. This effect is divided into the primacy effect and the recency effect. The primacy effect is observed when the initial items in a list are remembered better. This occurs because these items are rehearsed more frequently or receive more elaborative processing, allowing them to be encoded into long-term memory more effectively. For...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

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.
To conduct the sign test, we first calculate the differences in value between...
Framing Effects03:26

Framing Effects

Information is everywhere and its presentation—such as how and when items are presented—can impact our perceptions and decisions surrounding the info. This broad concept umbrellas framing effects—influences that occur due to the way information is framed in its appearance, whether it’s purely the order or the specific wording of a message. Let’s take a look at numerous ways in which two versions of something can objectively say the same thing, yet we respond in different ways based on the...

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Related Experiment Video

Updated: Jun 9, 2026

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

Analysis and interpretation of serial position data.

Andrew Olson1, Cristina Romani, Alfonso Caramazza

  • 1Department of Psychology, University of Birmingham, Birmingham, UK. a.l.o.olson@bham.ac.uk

Cognitive Neuropsychology
|August 26, 2010
PubMed
Summary
This summary is machine-generated.

Normalized serial position curves can obscure critical distinctions in cognitive research. Presenting raw data and using binomial regression are recommended for accurate analysis of sequence representation in memory and language.

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Area of Science:

  • Cognitive Science
  • Neuropsychology
  • Cognitive Neuroscience

Background:

  • Serial position representation is crucial in language, memory, and motor control.
  • Normalized serial position data are commonly used in neuropsychology, with recent improvements by Machtynger and Shallice (2009).
  • Length effects and U-shaped curves are criteria for identifying working memory deficits.

Purpose of the Study:

  • To illustrate issues in relating serial position data to cognitive theories.
  • To demonstrate the limitations of normalized data for making theoretical distinctions.
  • To propose alternative methods for analyzing serial position effects.

Main Methods:

  • Simulations and analyses of serial position data.
  • Comparison of normalized versus raw data presentation.
  • Application of binomial regression for theoretical analysis.

Main Results:

  • Normalized serial position data often make critical theoretical distinctions difficult to discern.
  • Raw data curves for different sequence lengths are more informative.
  • Binomial regression effectively addresses specific questions about length, position, and shape effects.

Conclusions:

  • Raw serial position curves are superior for theoretical clarity.
  • Binomial regression offers a robust method for analyzing sequence representation.
  • Rethinking normalization practices is necessary for advancing working memory and cognitive theories.