The DOI matches its Crossref record; 74 of the 74 references it cites were found in a registry or an index.
Absolute Identification by Relative Judgment.
Abstract
In unidimensional absolute identification tasks, participants identify stimuli that vary along a single dimension. Performance is surprisingly poor compared with discrimination of the same stimuli. Existing models assume that identification is achieved using long-term representations of absolute magnitudes. The authors propose an alternative relative judgment model (RJM) in which the elemental perceptual units are representations of the differences between current and previous stimuli. These differences are used, together with the previous feedback, to respond. Without using long-term representations of absolute magnitudes, the RJM accounts for (a) information transmission limits, (b) bowed serial position effects, and (c) sequential effects, where responses are biased toward immediately preceding stimuli but away from more distant stimuli (assimilation and contrast).
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Cites
Numerical recipes in C: the art of scientific computing
(1993) · Choice Reviews Online · doi:10.5860/choice.30-5638 · Cited by 17,933
The magical number seven, plus or minus two: Some limits on our capacity for processing information.
Miller, G. A. (1956) · Psychological Review · doi:10.1037/h0043158 · Cited by 17,812
The magical number seven, plus or minus two: Some limits on our capacity for processing information.
Miller, G. A. (1994) · Psychological Review · doi:10.1037/0033-295x.101.2.343 · Cited by 17,161
Numerical recipes in Pascal: the art of scientific computing
(1990) · Choice Reviews Online · doi:10.5860/choice.27-5158 · Cited by 11,811
(1966) · Medical Entomology and Zoology · Cited by 10,293
Signal detection theory and psychophysics
Ingleby, J. D. (1967) · Journal of Sound and Vibration · doi:10.1016/0022-460x(67)90197-6 · Cited by 8,509
Numerical Recipes in C: The Art of Scientific Computing.
Birchenhall, C., Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1994) · The Economic Journal · doi:10.2307/2234664 · Cited by 4,355
Context theory of classification learning
Medin, D. L., & Schaffer, M. M. (1978) · Psychological Review · doi:10.1037/0033-295x.85.3.207 · Cited by 3,061
Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability; Vol. IV
Roy, H. L. L., Lecam, L., & Neyman, J. (1969) · Revue de l'Institut International de Statistique / Review of the International Statistical Institute · doi:10.2307/1402306 · Cited by 2,835
Attention, similarity, and the identification-categorization relationship.
Nosofsky, R. M. (1986) · Journal of Experimental Psychology General · doi:10.1037//0096-3445.115.1.39 · Cited by 2,766
Cited by
A temporal ratio model of memory
Brown, G. D. A., Neath, I., & Chater, N. (2007) · Psychological Review · doi:10.1037/0033-295x.114.3.539 · Cited by 798
Self-citationStewart, N., Chater, N., & Brown, G. D. A. (2006) · Cognitive Psychology · doi:10.1016/j.cogpsych.2005.10.003 · Cited by 737
Self-citationChater, N., & Loewenstein, G. (2023) · Behavioral and Brain Sciences · doi:10.1017/s0140525x22002023 · Cited by 710
Self-citationDeep Learning the City: Quantifying Urban Perception at a Global Scale
Dubey, A., Naik, N., Parikh, D., Raskar, R., & Hidalgo, C. A. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_12 · Cited by 513
Reinforcement learning and episodic memory in humans and animals: an integrative framework
Gershman, S. J., & Daw, N. D. (2017) · Annual Review of Psychology · doi:10.1146/annurev-psych-122414-033625 · Cited by 446
Metadata
- Authors
- Stewart, N., Brown, G. D. A., & Chater, N.
- Venue
- Psychological Review
- Published
- 2005-10
- Volume
- 112
- Issue
- 4
- Pages
- 881-911
- ISSN
- 0033-295X (ISSN-L)
- 1939-1471
- OpenAlex
- W2067906953
- Cited by
- 337
- References
- 74
- FWCI
- 5.76
- Type
- journal article
- Language
- en
- Open access
- closed
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