The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
The Lower Visual Search Efficiency for Conjunctions Is Due to Noise and not Serial Attentional Processing
Abstract
Models of human visual processing start with an initial stage with parallel independent processing of different physical attributes or features (e.g., color, orientation, motion). A second stage in these models is a temporally serial mechanism (visual attention) that combines or binds information across feature dimensions. Evidence for this serial mechanism is based on experimental results for visual search. I conducted a study of visual search accuracy that carefully controlled for low-level effects: physical similarity of target and distractor, element eccentricity, and eye movements. The larger set-size effects in visual search accuracy for briefly flashed conjunction displays, compared with feature displays, are quantitatively predicted by a simple model in which each feature dimension is processed independently with inherent neural noise and information is combined linearly across feature dimensions. The data are not predicted by a temporally serial mechanism or by a hybrid model with temporally serial and noisy processing. The results do not support the idea that a temporally serial mechanism, visual attention, binds information across feature dimensions and show that the conjunction-feature dichotomy is due to the noisy independent processing of features in the human visual system.
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
Cites
Guided search: An alternative to the feature integration model for visual search.
Wolfe, J. M., Cave, K. R., & Franzel, S. L. (1989) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037//0096-1523.15.3.419 · Cited by 2,147
Cited by
What attributes guide the deployment of visual attention and how do they do it?
Wolfe, J. M., & Horowitz, T. S. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1411 · Cited by 1,766
Lieder, F., & Griffiths, T. L. (2020) · Behavioral and Brain Sciences · doi:10.1017/s0140525x1900061x · Cited by 892
Eckstein, M. P., Thomas, J. P., Palmer, J., & Shimozaki, S. S. (2000) · Perception & Psychophysics · doi:10.3758/bf03212096 · Cited by 274
Self-citationSignal detection theory, uncertainty, and Poisson-like population codes
Ma, W. J. (2010) · Vision Research · doi:10.1016/j.visres.2010.08.035 · Cited by 60
A detailed comparison of optimality and simplicity in perceptual decision making.
Shen, S., & Ma, W. J. (2016) · Psychological Review · doi:10.1037/rev0000028 · Cited by 46
Metadata
- Authors
- Eckstein, M. P.
- Venue
- Psychological Science
- Publisher
- SAGE Publications
- Published
- 1998-03
- Volume
- 9
- Issue
- 2
- Pages
- 111-118
- ISSN
- 0956-7976 (ISSN-L)
- 1467-9280
- OpenAlex
- W1976395482
- Cited by
- 221
- References
- 1
- FWCI
- 4.85
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The Lower Visual Search Efficiency for Conjunctions Is Due to Noise and not Serial Attentional Processing. https://app.cochat.ai/papers/10.1111/1467-9280.00020
- The paper
- Eckstein, M. P. (1998). The Lower Visual Search Efficiency for Conjunctions Is Due to Noise and not Serial Attentional Processing. Psychological Science. https://doi.org/10.1111/1467-9280.00020
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.