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A formal model of interpersonal inference

Overview of attention for article published in Frontiers in Human Neuroscience, March 2014
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

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1 news outlet
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10 X users
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1 Google+ user

Citations

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45 Dimensions

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118 Mendeley
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Title
A formal model of interpersonal inference
Published in
Frontiers in Human Neuroscience, March 2014
DOI 10.3389/fnhum.2014.00160
Pubmed ID
Authors

Michael Moutoussis, Nelson J. Trujillo-Barreto, Wael El-Deredy, Raymond J. Dolan, Karl J. Friston

Abstract

Introduction: We propose that active Bayesian inference-a general framework for decision-making-can equally be applied to interpersonal exchanges. Social cognition, however, entails special challenges. We address these challenges through a novel formulation of a formal model and demonstrate its psychological significance. Method: We review relevant literature, especially with regards to interpersonal representations, formulate a mathematical model and present a simulation study. The model accommodates normative models from utility theory and places them within the broader setting of Bayesian inference. Crucially, we endow people's prior beliefs, into which utilities are absorbed, with preferences of self and others. The simulation illustrates the model's dynamics and furnishes elementary predictions of the theory. Results: (1) Because beliefs about self and others inform both the desirability and plausibility of outcomes, in this framework interpersonal representations become beliefs that have to be actively inferred. This inference, akin to "mentalizing" in the psychological literature, is based upon the outcomes of interpersonal exchanges. (2) We show how some well-known social-psychological phenomena (e.g., self-serving biases) can be explained in terms of active interpersonal inference. (3) Mentalizing naturally entails Bayesian updating of how people value social outcomes. Crucially this includes inference about one's own qualities and preferences. Conclusion: We inaugurate a Bayes optimal framework for modeling intersubject variability in mentalizing during interpersonal exchanges. Here, interpersonal representations are endowed with explicit functional and affective properties. We suggest the active inference framework lends itself to the study of psychiatric conditions where mentalizing is distorted.

X Demographics

X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 118 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Japan 1 <1%
Unknown 116 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 21%
Researcher 15 13%
Student > Master 14 12%
Student > Postgraduate 12 10%
Student > Doctoral Student 8 7%
Other 24 20%
Unknown 20 17%
Readers by discipline Count As %
Psychology 40 34%
Neuroscience 9 8%
Agricultural and Biological Sciences 9 8%
Business, Management and Accounting 8 7%
Social Sciences 6 5%
Other 24 20%
Unknown 22 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 24 October 2019.
All research outputs
#2,181,539
of 24,874,764 outputs
Outputs from Frontiers in Human Neuroscience
#1,015
of 7,576 outputs
Outputs of similar age
#21,577
of 229,873 outputs
Outputs of similar age from Frontiers in Human Neuroscience
#35
of 145 outputs
Altmetric has tracked 24,874,764 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,576 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.9. This one has done well, scoring higher than 86% 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 229,873 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 90% of its contemporaries.
We're also able to compare this research output to 145 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.