Revealing the multidimensional mental representations of natural objects underlying human similarity judgements

2020/10/12 by Martin N. Hebart, Charles Zheng, Charles Y. Zheng +2 · 1 voice · 3 citations
Neuroscience · Psychology · #Categorization, perception, and language #Child and Animal Learning Development #Face Recognition and Perception

paper · doi:10.1038/s41562-020-00951-3

crossref issued 2020/10/12 · crossref published 2020/10/12 · crossref published-online 2020/10/12 · openalex publication_date 2020/10/12 · crossref created 2020/10/12 · crossref deposited 2022/12/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01 · crossref indexed 2026/08/04

Citations

• Multidimensional Scaling: I. Theory and Method
• Basic objects in natural categories
• Features of similarity.
• Representation is representation of similarities
• TensorFlow: A system for large-scale machine learning
• Attention, similarity, and the identification–categorization relationship.
• Toward a brain-based componential semantic representation

Cited by

• Neural correlates reveal separate stages of spontaneous face perception
• Human shape perception spontaneously discovers the biological origin of novel, but natural, stimuli
• Exploring internal representation of self-supervised networks: few-shot learning abilities and comparison with human semantics and recognition of objects

Discussions

• We build on previous work from Hebart & colleagues (doi.org/10.1038/s415...), which used the THINGS database + data-driven methods to obtain a series of key behaviourally-relevant stimulus dimensions. [bsky, 0 points, 1 comments]