The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Short article: Rapid extraction of mean identity from sets of faces
Abstract
Recent evidence suggests that the notion that the visual system can rapidly extract summary statistics from complex scenes extends to representing sets of faces in terms of mean emotion or gender. Here we show that observers can also extract a mean identity from a set of faces with different identities. Observers first saw a set of four faces with different identities and were subsequently asked whether or not a single test face had been present in the preceding set. They were significantly more likely to respond that the test face had been present in the set if it was the morphed mean of all four set faces than when it was an actual set member. This finding suggests that representations based on summary statistics are available for face identity.
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
Intolerance of uncertainty correlates with insula activation during affective ambiguity
Simmons, A., Matthews, S. C., Paulus, M. P., & Stein, M. B. (2008) · Neuroscience Letters · doi:10.1016/j.neulet.2007.10.030 · Cited by 150 · cited 1×
“Summary processing for sets of faces is also implied by evidence that observers can determine within seconds whether or not a large set of faces is ambiguous in terms of the emotion expressed by the faces, or their gender (Simmons, Matthews, Paulus, & Stein, 2008 ; Simmons, Stein, Matthews, Feinstein, & Paulus, 2006 ).”
Cited by
Representing multiple objects as an ensemble enhances visual cognition
Alvarez, G. A. (2011) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.01.003 · Cited by 586
Metadata
- Authors
- de Fockert, J., & Wolfenstein, C.
- Venue
- Quarterly Journal of Experimental Psychology
- Publisher
- SAGE Publications
- Published
- 2009-09
- Volume
- 62
- Issue
- 9
- Pages
- 1716-1722
- ISSN
- 0033-555X (ISSN-L)
- 1747-0218
- 1747-0226
- OpenAlex
- W2010431563
- Cited by
- 179
- References
- 1
- FWCI
- 2.89
- 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: Short article: Rapid extraction of mean identity from sets of faces. https://app.cochat.ai/papers/10.1080/17470210902811249
- The paper
- de Fockert, J., & Wolfenstein, C. (2009). Short article: Rapid extraction of mean identity from sets of faces. Quarterly Journal of Experimental Psychology. https://doi.org/10.1080/17470210902811249
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.