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Structure learning and the posterior parietal cortex
Abstract
We propose a theory of structure learning in the primate brain. We argue that the parietal cortex is critical for learning about relations among the objects and categories that populate a visual scene. We suggest that current deep learning models exhibit poor global scene understanding because they fail to perform the relational inferences that occur in the primate dorsal stream. We review studies of neural coding in primate posterior parietal cortex (PPC), drawing the conclusion that neurons in this brain area represent potentially high-dimensional inputs on a low-dimensional manifold that encodes the relative position of objects or features in physical space, and relations among entities in abstract conceptual space. We argue that this low-dimensional code supports generalisation of relational information, even in nonspatial domains. Finally, we propose that structure learning is grounded in the actions that primates take when they reach for objects or fixate them with their eyes. We sketch a model of how this might occur in neural circuits.
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“A posterior region, such as AG, may bind the contents and the structure of experience at a higher level of abstraction than does the HPC 161 and, thus, is probably an important area for relating spatial gists to spatial schema (summarised in Fig. 1).”
From From cognitive maps to spatial schemas (2023) · cites this paper as [161]
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Metadata
- Authors
- Summerfield, C., Luyckx, F., & Sheahan, H.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Summerfield, Christopher
- Venue
- Progress in Neurobiology
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 184
- Pages
- 101717
- ISSN
- 0301-0082 (ISSN-L)
- OpenAlex
- W2981514504
- Cited by
- 118
- References
- 103
- Citations by year
- 2026: 12 · 2025: 19 · 2024: 23 · 2023: 15 · 2022: 25 · 2021: 12 · 2020: 10
- FWCI
- 7.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Progress in Neurobiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Horizon 2020
- European Research Council
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- structure learning, posterior parietal cortex, relational inference, dorsal stream, scene understanding, neural coding, conceptual space, action-based learning, deep learning limitations
- MeSH terms
- Deep Learning, Animals, Gestalt Theory, Humans, Learning, Models, Biological, Parietal Lobe, Primates, Space Perception, Visual Perception, Mathematical Concepts
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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