ArticleNature human behaviour2024
Using deep neural networks to disentangle visual and semantic information in human perception and memory.
Article in Nature human behaviour, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Deep learning reveals a neurocomputational mechanism predicting depression risk in adolescents.Science advances · 2026Article
- Deliberate Facial Mimicry As a Skill That Predicts Emotion Recognition Performance.Affective science · 2026Article
- The state of modelling face processing in humans with deep learning.British journal of psychology (London, England : 1953) · 2026Review
- How AI can advance psychological science.British journal of psychology (London, England : 1953) · 2026Article
- Unpacking similarity effects in visual memory search: categorical, semantic, and visual contributions.Journal of memory and language · 2026Article
- What makes memories vivid?Journal of experimental psychology. General · 2026Article
- Characterization of the spatiotemporal representations of visual, semantic, and memorability features in the human brain.PLoS biology · 2026Article
- Predicting individual food valuation via vision-language embedding model.PLOS digital health · 2025Article
- Text-related functionality and dynamics of visual human pre-frontal activations revealed through neural network convergence.Communications biology · 2025Article
- Subjective valuation as a domain-general process in creative thinking.Communications psychology · 2025Article
- Concurrent emergence of view invariance, sensitivity to critical features, and identity face classification through visual experience: Insights from deep learning algorithms.Journal of vision · 2025Article
- Deep linear matrix approximate reconstruction with integrated BOLD signal denoising reveals reproducible hierarchical brain connectivity networks from multiband multi-echo fMRI.Frontiers in neuroscience · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
Mental representations of familiar categories are composed of visual and semantic information. Disentangling the contributions of visual and semantic information in humans is challenging because they are intermixed in mental representations. Deep neural networks that are trained either on images or on text or by pairing images and text enable us now to disentangle human mental representations into their visual, visual-semantic and semantic components. Here we used these deep neural networks to uncover the content of human mental representations of familiar faces and objects when they are viewed or recalled from memory. The results show a larger visual than semantic contribution when images are viewed and a reversed pattern when they are recalled. We further reveal a previously unknown unique contribution of an integrated visual-semantic representation in both perception and memory. We propose a new framework in which visual and semantic information contribute independently and interactively to mental representations in perception and memory.
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38332339What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.