↓ Skip to main content

Logical fallacies in animal model research

Overview of attention for article published in Behavioral and Brain Functions, February 2017
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#42 of 419)
  • High Attention Score compared to outputs of the same age (91st percentile)

Mentioned by

blogs
1 blog
twitter
16 X users
facebook
2 Facebook pages
f1000
1 research highlight platform

Citations

dimensions_citation
40 Dimensions

Readers on

mendeley
190 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Logical fallacies in animal model research
Published in
Behavioral and Brain Functions, February 2017
DOI 10.1186/s12993-017-0121-8
Pubmed ID
Authors

Espen A. Sjoberg

Abstract

Animal models of human behavioural deficits involve conducting experiments on animals with the hope of gaining new knowledge that can be applied to humans. This paper aims to address risks, biases, and fallacies associated with drawing conclusions when conducting experiments on animals, with focus on animal models of mental illness. Researchers using animal models are susceptible to a fallacy known as false analogy, where inferences based on assumptions of similarities between animals and humans can potentially lead to an incorrect conclusion. There is also a risk of false positive results when evaluating the validity of a putative animal model, particularly if the experiment is not conducted double-blind. It is further argued that animal model experiments are reconstructions of human experiments, and not replications per se, because the animals cannot follow instructions. This leads to an experimental setup that is altered to accommodate the animals, and typically involves a smaller sample size than a human experiment. Researchers on animal models of human behaviour should increase focus on mechanistic validity in order to ensure that the underlying causal mechanisms driving the behaviour are the same, as relying on face validity makes the model susceptible to logical fallacies and a higher risk of Type 1 errors. We discuss measures to reduce bias and risk of making logical fallacies in animal research, and provide a guideline that researchers can follow to increase the rigour of their experiments.

X Demographics

X Demographics

The data shown below were collected from the profiles of 16 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 190 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
United States 1 <1%
Unknown 188 99%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 30 16%
Student > Master 26 14%
Student > Ph. D. Student 25 13%
Researcher 16 8%
Professor 9 5%
Other 31 16%
Unknown 53 28%
Readers by discipline Count As %
Psychology 27 14%
Neuroscience 21 11%
Agricultural and Biological Sciences 21 11%
Medicine and Dentistry 17 9%
Biochemistry, Genetics and Molecular Biology 10 5%
Other 29 15%
Unknown 65 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 21. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 March 2022.
All research outputs
#1,829,627
of 25,654,806 outputs
Outputs from Behavioral and Brain Functions
#42
of 419 outputs
Outputs of similar age
#40,219
of 450,613 outputs
Outputs of similar age from Behavioral and Brain Functions
#1
of 4 outputs
Altmetric has tracked 25,654,806 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 419 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.8. This one has done well, scoring higher than 89% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 450,613 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them