Abstract
Humans can rapidly categorise visual objects when presented in
isolation. However, in everyday life we encounter multiple objects at
the same time. Far less is known about how simultaneously active object
representations interact. We examined such interactions by asking
participants to categorise a target object at the basic (Experiment 1)
or the superordinate (Experiment 2) level while the representation of
another object was still active. We found that the “prime” object
strongly modulated the response to the target implying that the prime's
category was rapidly and automatically accessed, influencing subsequent
categorical processing. Using drift diffusion modelling, we show that a
prime, whose category is different from that of the target, interferes
with target processing primarily during the evidence accumulation stage.
This suggests that the state of category‐processing neurons is altered
by an active representation and this modifies the processing of other
categories. Interestingly, the strength of interference increases with
the similarity between the distractor and the target category.
Considering these results and previous studies, we propose a general
principle that category interactions are determined by the distance from
a distractor's representation to the target's task‐relevant categorical
boundary. We argue that this principle arises from the specific
architectural organisation of categories in the brain.
Original language | English |
---|---|
Pages (from-to) | 4639-4666 |
Number of pages | 28 |
Journal | European Journal of Neuroscience |
Volume | 52 |
Issue number | 12 |
Early online date | 20 Jul 2020 |
DOIs | |
Publication status | Published - 31 Dec 2020 |
Keywords
- Category interactions
- Drift-diffusion model
- Priming
- Representation similarity
- Visual categorisation
Fingerprint
Dive into the research topics of 'A simple rule to describe interactions between visual categories'. Together they form a unique fingerprint.Datasets
-
A simple rule to describe interactions between visual categories (dataset)
Poncet, M. F. (Creator), OSF, 2020
Dataset