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Abstract Mental models provide a cognitive framework allowing for spatially organizing information while reasoning about the world. However, transitive reasoning studies often rely on perception of stimuli that contain visible spatial features, allowing the possibility that associated neural representations are specific to inherently spatial content. Here, we test the hypothesis that neural representations of mental models generated through transitive reasoning rely on a frontoparietal network irrespective of the spatial nature of the stimulus content. Content within three models ranges from expressly visuospatial to abstract. All mental models participants generated were based on inferred relationships never directly observed. Here, using multivariate representational similarity analysis, we show that patterns representative of mental models were revealed in both superior parietal lobule and anterior prefrontal cortex and converged across stimulus types. These results support the conclusion that, independent of content, transitive reasoning using mental models relies on neural mechanisms associated with spatial cognition.
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Mental models, cognitive maps, and the challenge of quantitative analysis of their network representations
Abstract Cognitive maps, or mental maps, are externalized portrayals of mental models—people's mental representations of reality and their presumptions about how the world works. They are often used as the intermediary step toward uncovering individuals' presumptions of the outside world. Yet, the next step is often vague: once one's understanding of the real world is mapped, how can we systematically evaluate the maps and compare and contrast them? In this note, we review several common approaches to analyzing cognitive maps, some rooted in network theories, and apply them to a dataset of 30 graduate students who analyzed a complex socioenvironmental problem. Our analysis shows that these methods provide inconsistent results and often fall short of capturing variations in mental models. The analysis points to a lack of effective methods for examining such maps and helps articulate a major research problem for systems‐thinking scholars. © 2023 System Dynamics Society.
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- Award ID(s):
- 1824594
- PAR ID:
- 10412877
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- System Dynamics Review
- Volume:
- 39
- Issue:
- 2
- ISSN:
- 0883-7066
- Page Range / eLocation ID:
- p. 152-170
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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