The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Neurophysiological evidence for visual perceptual categorization of words and faces within 150 ms
Abstract
The nature and early time course of the initial processing differences between visually matched linguistic and nonlinguistic images were studied with event-related potentials (ERPs). The first effect began at 90 ms when ERPs to written words diverged from other objects, including faces. By 125 ms, ERPs to words and faces were more positive than those to other objects, effects identified with the P150. The amplitude and scalp distribution of P150s to words and faces were similar. The P150 seemed to be elicited selectively by images resembling any well-learned category of visual patterns. We propose that (a) visual perceptual categorization based on long-term experience begins by 125 ms, (b) P150 amplitude varies with the cumulative experience people have discriminating among instances of specific categories of visual objects (e.g., words, faces), and (c) the P150 is a scalp reflection of letterstring and face intracranial ERPs in posterior fusiform gyrus.
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
Snodgrass, J. G., & Vanderwart, M. (1980) · Journal of Experimental Psychology Human Learning & Memory · doi:10.1037//0278-7393.6.2.174 · Cited by 5,615
Reading Senseless Sentences: Brain Potentials Reflect Semantic Incongruity
Kutas, M., & Hillyard, S. A. (1980) · Science · doi:10.1126/science.7350657 · Cited by 4,888
Self-citationA sharper Bonferroni procedure for multiple tests of significance
HOCHBERG, Y. (1988) · Biometrika · doi:10.1093/biomet/75.4.800 · Cited by 4,829
Principles Of Gestalt Psychology
Koffka, K. (2013) · Routledge eBooks · doi:10.4324/9781315009292 · Cited by 4,263
Electrophysiological Studies of Face Perception in Humans
Bentin, S., Allison, T., Puce, A., Perez, E., & McCarthy, G. (1996) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1996.8.6.551 · Cited by 3,076
Electric Fields of the Brain: The Neurophysics of EEG
Nunez, P. L., & Harth, E. (1982) · Physics Today · doi:10.1063/1.2915137 · Cited by 2,232
Functional MRI evidence for adult motor cortex plasticity during motor skill learning
Kami, A., Meyer, G., Jezzard, P., Adams, M. M., Turner, R., & Ungerleider, L. G. (1995) · Nature · doi:10.1038/377155a0 · Cited by 1,883
McCarthy, G., & Wood, C. C. (1985) · Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section · doi:10.1016/0168-5597(85)90015-2 · Cited by 1,756
Haxby, J., Horwitz, B., Ungerleider, L., Maisog, J., Pietrini, P., & Grady, C. (1994) · The Journal of Neuroscience · doi:10.1523/jneurosci.14-11-06336.1994 · Cited by 1,068
Becoming a “Greeble” Expert: Exploring Mechanisms for Face Recognition
GAUTHIER, I., & TARR, M. J. (1997) · Vision Research · doi:10.1016/s0042-6989(96)00286-6 · Cited by 1,017
Cited by
Allison, T. (1999) · Cerebral Cortex · doi:10.1093/cercor/9.5.415 · Cited by 882
Rossion, B., Joyce, C. A., Cottrell, G. W., & Tarr, M. J. (2003) · NeuroImage · doi:10.1016/j.neuroimage.2003.07.010 · Cited by 768
Rossion, B., Gauthier, I., Tarr, M. J., Despland, P., Bruyer, R., Linotte, S., & Crommelinck, M. (2000) · Neuroreport · doi:10.1097/00001756-200001170-00014 · Cited by 738
Stages of processing in face perception: an MEG study
Liu, J., Harris, A., & Kanwisher, N. (2002) · Nature Neuroscience · doi:10.1038/nn909 · Cited by 683
The time course of visual word recognition as revealed by linear regression analysis of ERP data
Hauk, O., Davis, M. H., Ford, M., Pulvermüller, F., & Marslen-Wilson, W. D. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2005.11.048 · Cited by 586
Metadata
- Authors
- SCHENDAN, H. E., GANIS, G., & KUTAS, M.
- Venue
- Psychophysiology
- Publisher
- Wiley
- Published
- 1998-05
- Volume
- 35
- Issue
- 3
- Pages
- 240-251
- ISSN
- 0048-5772 (ISSN-L)
- OpenAlex
- W2117633770
- Cited by
- 310
- References
- 42
- FWCI
- 5.69
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Neurophysiological evidence for visual perceptual categorization of words and faces within 150 ms. https://app.cochat.ai/papers/10.1017/s004857729897010x
- The paper
- SCHENDAN, H. E., GANIS, G., & KUTAS, M. (1998). Neurophysiological evidence for visual perceptual categorization of words and faces within 150 ms. Psychophysiology. https://doi.org/10.1017/s004857729897010x
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.
