The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Two Distinct Neural Mechanisms for Category-selective Responses
Abstract
The cognitive and neural mechanisms mediating category-selective responses in the human brain remain controversial. Using functional magnetic resonance imaging and effective connectivity analyses (Dynamic Causal Modelling), we investigated animal- and tool-selective responses by manipulating stimulus modality (pictures versus words) and task (implicit versus explicit semantic). We dissociated two distinct mechanisms that engender category selectivity: in the ventral occipito-temporal cortex, tool-selective responses were observed irrespective of task, greater for pictures and mediated by bottom-up effects. In a left temporo-parietal action system, tool-selective responses were observed irrespective of modality, greater for explicit semantic tasks and mediated by top-down modulation from the left prefrontal cortex. These distinct activation and connectivity patterns suggest that the two systems support different cognitive operations, with the ventral occipito-temporal regions engaged in structural processing and the dorsal visuo-motor system in strategic semantic processing. Consistent with current semantic theories, explicit semantic processing of tools might thus rely on reactivating their associated action representations via top-down modulation. In terms of neuronal mechanisms, the category selectivity may be mediated by distinct top-down (task-dependent) and bottom-up (stimulus-dependent) mechanisms.
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
Large-scale neurocognitive networks and distributed processing for attention, language, and memory
Mesulam, M. (1990) · Annals of Neurology · doi:10.1002/ana.410280502 · Cited by 2,684
Damasio, A. R. (1989) · Cognition · doi:10.1016/0010-0277(89)90005-x · Cited by 1,538
Semantic memory and the brain: structure and processes
Martin, A., & Chao, L. L. (2001) · Current Opinion in Neurobiology · doi:10.1016/s0959-4388(00)00196-3 · Cited by 1,339
Grafton, S., Arbib, M., Fadiga, L., & Rizzolatti, G. (1996) · Experimental Brain Research · doi:10.1007/bf00227183 · Cited by 1,045
Demonstrating the implicit processing of visually presented words and pseudowords
Price, C. J., Wise, R. J. S., & Frackowiak, R. S. J. (1996) · Cerebral Cortex · doi:10.1093/cercor/6.1.62 · Cited by 544
Self-citationThe Predictive Value of Changes in Effective Connectivity for Human Learning
Büchel, C., Coull, J. T., & Friston, K. J. (1999) · Science · doi:10.1126/science.283.5407.1538 · Cited by 447 · cited 2×
“To further characterize the connectivity results, we investigated the effect of subject’s performance on the modulatory effects (see Buchel et al., 1999; Goncalves and Hall, 2003; Glabus et al., 2003) using subject-specific reaction times as predictors for the connection strengths.”
BackgroundMethodSelf-citation
Cited by
Kiefer, M., & Pulvermüller, F. (2012) · Cortex · doi:10.1016/j.cortex.2011.04.006 · Cited by 869
Effective Connectivity within the Distributed Cortical Network for Face Perception
Fairhall, S. L., & Ishai, A. (2007) · Cerebral Cortex · doi:10.1093/cercor/bhl148 · Cited by 471
Yee, E., & Thompson-Schill, S. L. (2016) · Psychonomic Bulletin & Review · doi:10.3758/s13423-015-0948-7 · Cited by 382
What drives the organization of object knowledge in the brain?
Mahon, B. Z., & Caramazza, A. (2011) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.01.004 · Cited by 366
Negation in the brain: Modulating action representations
Tettamanti, M., Manenti, R., Della Rosa, P. A., Falini, A., Perani, D., Cappa, S. F., & Moro, A. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.08.004 · Cited by 288
Metadata
- Authors
- Noppeney, U., Price, C. J., Penny, W. D., & Friston, K. J.
- Venue
- Cerebral Cortex
- Publisher
- Oxford University Press (OUP)
- Published
- 2005-06-08
- Volume
- 16
- Issue
- 3
- Pages
- 437-445
- ISSN
- 1047-3211 (ISSN-L)
- 1460-2199
- OpenAlex
- W2137994045
- Cited by
- 197
- References
- 6
- FWCI
- 6.38
- 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: Two Distinct Neural Mechanisms for Category-selective Responses. https://app.cochat.ai/papers/10.1093/cercor/bhi123
- The paper
- Noppeney, U., Price, C. J., Penny, W. D., & Friston, K. J. (2006). Two Distinct Neural Mechanisms for Category-selective Responses. Cerebral Cortex. https://doi.org/10.1093/cercor/bhi123
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.