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Related Concept Videos

Toxicity Testing in Animals01:23

Toxicity Testing in Animals

Toxicity tests in animals are grounded on two main assumptions: first, the effects observed in laboratory animals can be extrapolated to humans, especially when adjusted for body surface area; second, high-dose exposure in animals is essential to identify potential human hazards from lower doses. This is based on the quantal dose-response concept, which faces the challenge of extrapolating results from relatively few test animals to much larger human populations. For example, a 0.01% incidence...

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Related Experiment Video

Updated: Jun 12, 2026

Assessment of Social Transmission of Food Preferences Behaviors
04:56

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Published on: January 25, 2018

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Advancing preference testing in humans and animals.

Dana Pfefferle1,2, Steven R Talbot3, Pia Kahnau4

  • 1Welfare and Cognition Group, Cognitive Neuroscience Laboratory, German Primate Center-Leibniz Institute for Primate Research, Kellnerweg 4, 37077, Göttingen, Germany.

Behavior Research Methods
|June 6, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using multiple binary comparisons to scale preferences, not just rank them. This approach efficiently quantifies the relative value of options for animals, optimizing welfare assessments.

Keywords:
Binary choiceHumanMicePreferenceRankingRatingRhesus macaqueScalingWelfare

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Area of Science:

  • Behavioral science
  • Animal welfare
  • Quantitative psychology

Background:

  • Preference testing ranks options but doesn't show preference strength.
  • Simultaneous presentation of options causes mutual influence, increasing with more options.
  • Existing methods fail to reveal the relative strengths of preference or worth value.

Purpose of the Study:

  • To develop and validate a method for scaling preferences using multiple binary comparisons.
  • To quantitatively assess the relative value of options presented to animals.
  • To optimize preference testing for applications like animal welfare assessment.

Main Methods:

  • Utilized multiple binary choice tests to scale preferences among many options.
  • Analyzed human image preference data with known valence scores for validation.
  • Developed an approach to incorporate additional option choices into existing rank orders via simulations.
  • Introduced consensus error and intransitivity ratio for assessing scaled ranking confidence.

Main Results:

  • Demonstrated that multiple binary comparisons can scale preferences, revealing worth value.
  • Successfully applied the approach to assess valence in mice and rhesus macaques.
  • Showed that simulations can reduce the number of animal experiments needed.
  • Developed quality measures to assess and improve scaled ranking confidence.

Conclusions:

  • The novel approach enables efficient and quantitative assessment of relative option values in animals.
  • This method optimizes preference testing, particularly for welfare assessment.
  • The developed software, an R package named "simsalRbim", is publicly available.