The DOI matches its Crossref record.
How Diagrams Can Improve Reasoning
Abstract
We report an experimental study on the effects of diagrams on deductive reasoning with double disjunctions, for example: Raphael is in Tacoma or Julia is in Atlanta, or both. Julia is in Atlanta or Paul is in Philadelphia, or both. What follows? We confirmed that subjects find it difficult to deduce a valid conclusion, such as Julia is in Atlanta, or both Raphael is in Tacoma and Paul is in Philadelphia. In a preliminary study, the format of the premises was either verbal or diagrammatic, and the diagrams used icons to distinguish between inclusive and exclusive disjunctions. The diagrams had no effect on performance. In the main experiment, the diagrams made the alternative possibilities more explicit. The subjects responded faster (about 35 s) and drew many more valid conclusions (nearly 30%) from the diagrams than from the verbal premises. These results corroborate the theory of mental models and have implications for the role of diagrams in reasoning.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
External cognition: how do graphical representations work?
Scaife, M., & Rogers, Y. (1996) · International Journal of Human-Computer Studies · doi:10.1006/ijhc.1996.0048 · Cited by 1,066
A theory of medical decision making and health: fuzzy trace theory
Reyna, V. F. (2008) · Medical Decision Making · doi:10.1177/0272989x08327066 · Cited by 602
How do gestures influence thinking and speaking? The gesture-for-conceptualization hypothesis.
Kita, S., Alibali, M. W., & Chu, M. (2017) · Psychological Review · doi:10.1037/rev0000059 · Cited by 342
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models
Shi, Y., Siddharth, N., Paige, B., & Torr, P. H. S. (2019) · arXiv · doi:10.48550/arxiv.1911.03393 · Cited by 186
Verbal and visual causal arguments
Oestermeier, U., & Hesse, F. W. (2000) · Cognition · doi:10.1016/s0010-0277(00)00060-3 · Cited by 88
Metadata
- Authors
- Bauer, M. I., & Johnson-Laird, P. N.
- Venue
- Psychological Science
- Publisher
- SAGE Publications
- Published
- 1993-11
- Volume
- 4
- Issue
- 6
- Pages
- 372-378
- ISSN
- 0956-7976 (ISSN-L)
- 1467-9280
- OpenAlex
- W1970844071
- Cited by
- 334
- References
- 0
- FWCI
- 24.69
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
