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Causal reasoning with mental models

Overview of attention for article published in Frontiers in Human Neuroscience, October 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

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148 Mendeley
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Title
Causal reasoning with mental models
Published in
Frontiers in Human Neuroscience, October 2014
DOI 10.3389/fnhum.2014.00849
Pubmed ID
Authors

Sangeet S. Khemlani, Aron K. Barbey, Philip N. Johnson-Laird

Abstract

This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex.

X Demographics

X Demographics

The data shown below were collected from the profiles of 11 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 148 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 <1%
Spain 1 <1%
United States 1 <1%
France 1 <1%
Unknown 144 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 18%
Researcher 23 16%
Student > Master 23 16%
Student > Bachelor 12 8%
Student > Doctoral Student 10 7%
Other 28 19%
Unknown 25 17%
Readers by discipline Count As %
Psychology 35 24%
Computer Science 14 9%
Social Sciences 10 7%
Business, Management and Accounting 10 7%
Linguistics 7 5%
Other 37 25%
Unknown 35 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 29 October 2015.
All research outputs
#5,834,778
of 24,072,790 outputs
Outputs from Frontiers in Human Neuroscience
#2,249
of 7,414 outputs
Outputs of similar age
#60,727
of 264,830 outputs
Outputs of similar age from Frontiers in Human Neuroscience
#88
of 237 outputs
Altmetric has tracked 24,072,790 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,414 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.8. This one has gotten more attention than average, scoring higher than 69% 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 264,830 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 237 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.