↓ Skip to main content

Representing, Running, and Revising Mental Models: A Computational Model

Overview of attention for article published in Cognitive Science, December 2017
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

twitter
7 tweeters

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
17 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
Representing, Running, and Revising Mental Models: A Computational Model
Published in
Cognitive Science, December 2017
DOI 10.1111/cogs.12574
Pubmed ID
Authors

Scott Friedman, Kenneth Forbus, Bruce Sherin

Abstract

People use commonsense science knowledge to flexibly explain, predict, and manipulate the world around them, yet we lack computational models of how this commonsense science knowledge is represented, acquired, utilized, and revised. This is an important challenge for cognitive science: Building higher order computational models in this area will help characterize one of the hallmarks of human reasoning, and it will allow us to build more robust reasoning systems. This paper presents a novel assembled coherence (AC) theory of human conceptual change, whereby people revise beliefs and mental models by constructing and evaluating explanations using fragmentary, globally inconsistent knowledge. We implement AC theory with Timber, a computational model of conceptual change that revises its beliefs and generates human-like explanations in commonsense science. Timber represents domain knowledge using predicate calculus and qualitative model fragments, and uses an abductive model formulation algorithm to construct competing explanations for phenomena. Timber then (a) scores competing explanations with respect to previously accepted beliefs, using a cost function based on simplicity and credibility, (b) identifies a low-cost, preferred explanation and accepts its constituent beliefs, and then (c) greedily alters previous explanation preferences to reduce global cost and thereby revise beliefs. Consistency is a soft constraint in Timber; it is biased to select explanations that share consistent beliefs, assumptions, and causal structure with its other, preferred explanations. In this paper, we use Timber to simulate the belief changes of students during clinical interviews about how the seasons change. We show that Timber produces and revises a sequence of explanations similar to those of the students, which supports the psychological plausibility of AC theory.

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 41%
Student > Master 4 24%
Unspecified 3 18%
Student > Postgraduate 1 6%
Researcher 1 6%
Other 1 6%
Readers by discipline Count As %
Computer Science 6 35%
Psychology 3 18%
Unspecified 3 18%
Nursing and Health Professions 1 6%
Pharmacology, Toxicology and Pharmaceutical Science 1 6%
Other 3 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 10 February 2018.
All research outputs
#4,077,135
of 13,698,878 outputs
Outputs from Cognitive Science
#444
of 1,055 outputs
Outputs of similar age
#136,098
of 392,403 outputs
Outputs of similar age from Cognitive Science
#10
of 26 outputs
Altmetric has tracked 13,698,878 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 1,055 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.6. This one has gotten more attention than average, scoring higher than 57% 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 392,403 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.
We're also able to compare this research output to 26 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 57% of its contemporaries.