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Stimulus structures and mental representations in expert comprehension of computer programs

Pennington, Nancy·1987·Cognitive Psychology

DOI 10.1016/0010-0285(87)90007-7

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Human Problem Solving.
Axten, N., Newell, A., & Simon, H. A. (1973) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2063712 · Cited by 6,886
Proceedings of the 19th International Joint Conference on Artificial Intelligence
Pujol, J. M., Delgado, J., Ramon Sangüesa, A., & Flache, A. (2005) · International Joint Conference on Artificial Intelligence · Cited by 5,771
Toward a model of text comprehension and production.
Kintsch, W., & van Dijk, T. A. (1978) · Psychological Review · doi:10.1037/0033-295x.85.5.363 · Cited by 5,298
Mental Models
Johnson-Laird, P. N. (1983) · Cited by 5,190
SCRIPTS, PLANS, GOALS, AND UNDERSTANDING
Schank, R. C., & Abelson, R. P. (1988) · Elsevier eBooks · doi:10.1016/b978-1-4832-1446-7.50019-4 · Cited by 4,544
Perception in chess
Chase, W. G., & Simon, H. A. (1973) · Cognitive Psychology · doi:10.1016/0010-0285(73)90004-2 · Cited by 4,479 · cited 1×
“First, well-known empirical results across a wide range of problem-solving domains, such as chess (e.g., Chase & Simon, 1973a, 1973b), GO (Reitman, 1976), bridge (Engle & Bukstel, 1978), music composition (Halpern & Bower, 1982; Sloboda, 1976), and computer programming (McKeithen et al., 1981;…”
Visual Information Processing
Solso, R. L., & Chase, W. G. (1974) · The American Journal of Psychology · doi:10.2307/1421395 · Cited by 2,134
Remembrance of things parsed: Story structure and recall
Mandler, J. M., & Johnson, N. S. (1977) · Cognitive Psychology · doi:10.1016/0010-0285(77)90006-8 · Cited by 2,085
NOTES ON A SCHEMA FOR STORIES
Rumelhart, D. E. (1975) · Representation and Understanding · doi:10.1016/b978-0-12-108550-6.50013-6 · Cited by 1,671
Expertise in Problem Solving.
Michelene, T. H., Glaser, R. A., & Rees, E. T. (1981) · Defense Technical Information Center (DTIC) · Cited by 1,529

Show all 82 references

Research works printed but not linked: 4

Hayes, 1977, Psychological differences among problem isomorphs, Vol. 2
Pennington, 1985, Cognitive components of expertise in computer programming: A review of the literature, Psychological Documents, 15
Rumelhardt, 1980, A reply to Black and Wilensky, Cognitive Science, 4, 313
Stein, 1979, An analysis of story comprehension in elementary school children, Vol. 2

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Authors: Pennington, N.
Affiliations: University of Chicago (US)
Corresponding author: Pennington, Nancy
Venue: Cognitive Psychology
Publisher: Elsevier BV
Published: 1987-07
Volume: 19
Issue: 3
Pages: 295-341
ISSN: 0010-0285 (ISSN-L)
DOI: 10.1016/0010-0285(87)90007-7
OpenAlex: W2154671002
Cited by: 526
References: 82
Citations by year: 2026: 14 · 2025: 16 · 2024: 3 · 2023: 10 · 2022: 8 · 2021: 12 · 2020: 18 · 2019: 9 · 2018: 12 · 2017: 11 · 2016: 10 · 2015: 11 · 2014: 17 · 2013: 10 · 2012: 17
FWCI: 32.83
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Cognitive Psychology (publishedVersion)
Defense Technical Information Center (DTIC) (submittedVersion)
Topics: Physical Sciences › Computer Science › Information Systems › Software Engineering Research
Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
Keywords: program comprehension, mental representation, programming knowledge, control flow, functional relationships, goal hierarchy, expert programmers, task goals

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Stimulus structures and mental representations in expert comprehension of computer programs

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