Neural systems for recognizing emotion
Abstract
Recognition of emotion draws on a distributed set of structures that include the occipitotemporal neocortex, amygdala, orbitofrontal cortex and right frontoparietal cortices. Recognition of fear may draw especially on the amygdala and the detection of disgust may rely on the insula and basal ganglia. Two important mechanisms for recognition of emotions are the construction of a simulation of the observed emotion in the perceiver, and the modulation of sensory cortices via top-down influences.
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In conjunction with the SII cortex, these regions have been argued to serve as somatic markers, enabling a cortical representation of body state.
“In conjunction with the SII cortex, these regions have been argued to serve as somatic markers (Damasio, 1999;Adolphs, 2002), enabling a cortical representation of body state (Damasio, 1999;Craig, 2004).”
From Attending to the present: mindfulness meditation reveals distinct neural modes of self-reference (2007) · cites this paper as Adolphs, 2002
The right insula is the best documented region in supporting viscerosomatic awareness.
“Out of the potential posterolateral seed regions, the right insula was chosen as being the best documented in supporting viscerosomatic awareness (Damasio, 1999;Adolphs, 2002;Craig, 2004;Critchley et al., 2004).”
From Attending to the present: mindfulness meditation reveals distinct neural modes of self-reference (2007) · cites this paper as Adolphs, 2002
The proposed model extends the hierarchical visual representation of the CNN and applies it for multimodal emotional state recognition using face expression and body motion.
“Based on the multimodal representation of visual stimuli described by Gu et al. (2013), and in the hierarchical representation present in the human brain as discussed by Adolphs (2002), we propose a novel CNN-based model for automatic emotion recognition.Our model extends the hierarchical visual representation of the CNN, and applies it for multimodal emotional state recognition using face express”
From Multimodal emotional state recognition using sequence-dependent deep hierarchical features (2015) · cites this paper as Adolphs (2002)
The brain is capable of integrating multimodal information and generating a representation for visual stimuli based on all of them together.
“The brain is also capable to integrate this multimodal information and generate a representation for the visual stimuli based on all of them together.”
From Multimodal emotional state recognition using sequence-dependent deep hierarchical features (2015) · cites this paper as Adolphs (2002)
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- Authors
- Adolphs, R.
- Affiliations
- University of Iowa (US)
- Corresponding author
- Adolphs, Ralph
- ORCID
- Adolphs, Ralph
- Venue
- Current Opinion in Neurobiology
- Publisher
- Elsevier BV
- Published
- 2002-04
- Volume
- 12
- Issue
- 2
- Pages
- 169-177
- ISSN
- 0959-4388 (ISSN-L)
- OpenAlex
- W2012289576
- Cited by
- 1,933
- References
- 103
- Citations by year
- 2026: 45 · 2025: 48 · 2024: 52 · 2023: 66 · 2022: 75 · 2021: 112 · 2020: 96 · 2019: 87 · 2018: 76 · 2017: 84 · 2016: 116 · 2015: 92 · 2014: 86 · 2013: 104 · 2012: 106
- FWCI
- 3.73
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Current Opinion in Neurobiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Psychology › Social Psychology › Action Observation and Synchronization
- Keywords
- emotion recognition, neural systems, amygdala, occipitotemporal cortex, orbitofrontal cortex, insula, basal ganglia, fear recognition, disgust, top-down modulation, sensory cortex
- MeSH terms
- Amygdala, Basal Ganglia, Brain, Emotions, Facial Expression, Fear, Frontal Lobe, Humans, Parietal Lobe, Temporal Lobe, Visual Cortex, Recognition, Psychology
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 ›
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