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Related Concept Videos

Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
Theory of Attribution II: Kelley's Covariation Theory01:29

Theory of Attribution II: Kelley's Covariation Theory

Attribution theory plays a crucial role in social psychology, helping to explain how individuals interpret the causes of behavior. One prominent model within this field is Harold Kelley's covariation theory, which provides a systematic approach to determining whether internal traits or external circumstances drive a person's actions. The model posits that individuals rely on three key types of information—consensus, consistency, and distinctiveness—to make these judgments.Consensus: Comparing...
Group Design02:01

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...
Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the population that is...
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...

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Related Experiment Video

Updated: May 16, 2026

Online Repetitive Transcranial Magnetic Stimulation of Dorsomedial and Dorsolateral Prefrontal Cortex in Cognition Decision Making, and Cognitive Dissonance
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Robust versus optimal strategies for two-alternative forced choice tasks.

M Zacksenhouse1, R Bogacz, P Holmes

  • 1Faculty of Mechanical Engineering, Technion -Israel Institute of Technology, Haifa 32000, Israel.

Journal of Mathematical Psychology
|November 28, 2012
PubMed
Summary

Most individuals do not optimize decision-making speed and accuracy. Robust strategies, like maximin, better explain the performance of 70% of subjects facing response-to-stimulus interval uncertainty.

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Last Updated: May 16, 2026

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Area of Science:

  • Cognitive psychology
  • Decision-making neuroscience
  • Computational modeling

Background:

  • The drift-diffusion model predicts optimal speed-accuracy tradeoffs maximizing reward rate.
  • Behavioral data show most subjects deviate from this predicted optimal performance.

Purpose of the Study:

  • Investigate if robust strategies, rather than optimal ones, explain suboptimal performance under uncertainty.
  • Compare maximin and robust-satisficing strategies against optimal performance models.

Main Methods:

  • Developed theoretical performance curves for maximin and robust-satisficing strategies.
  • Compared these curves to empirical data from two-alternative forced-choice tasks.
  • Analyzed performance under uncertainties in response-to-stimulus interval and signal-to-noise ratio.

Main Results:

  • Maximin strategy curves align well with data for the 70% of subjects not achieving optimality.
  • Maximin strategy is a better fit than robust-satisficing or accuracy-focused optimal curves for response-to-stimulus interval uncertainty.
  • Neither maximin nor robust-satisficing adequately explain data for signal-to-noise ratio uncertainties.

Conclusions:

  • Robust strategies, particularly maximin, offer a more plausible explanation for observed decision-making behavior under uncertainty.
  • Findings suggest individuals may prioritize guaranteed performance over maximizing reward rate in uncertain environments.
  • Further research is needed to explore decision-making under various types of uncertainty.