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

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

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 66 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 21%
Student > Master 12 18%
Researcher 6 9%
Student > Bachelor 5 8%
Student > Doctoral Student 2 3%
Other 8 12%
Unknown 19 29%
Readers by discipline Count As %
Computer Science 10 15%
Psychology 7 11%
Medicine and Dentistry 6 9%
Engineering 4 6%
Business, Management and Accounting 3 5%
Other 15 23%
Unknown 21 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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
#15,805,597
of 25,468,789 outputs
Outputs from Cognitive Science
#1,040
of 1,568 outputs
Outputs of similar age
#247,851
of 449,597 outputs
Outputs of similar age from Cognitive Science
#22
of 33 outputs
Altmetric has tracked 25,468,789 research outputs across all sources so far. This one is in the 36th percentile – i.e., 36% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,568 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.6. This one is in the 32nd percentile – i.e., 32% of its peers scored the same or lower than it.
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 449,597 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.