Variability in training unlocks generalization in visual perceptual learning through invariant representations.
Curr Biol
; 33(5): 817-826.e3, 2023 03 13.
Article
em En
| MEDLINE
| ID: mdl-36724782
ABSTRACT
Stimulus and location specificity are long considered hallmarks of visual perceptual learning. This renders visual perceptual learning distinct from other forms of learning, where generalization can be more easily attained, and therefore unsuitable for practical applications, where generalization is key. Based on the hypotheses derived from the structure of the visual system, we test here whether stimulus variability can unlock generalization in perceptual learning. We train subjects in orientation discrimination, while we vary the amount of variability in a task-irrelevant feature, spatial frequency. We find that, independently of task difficulty, this manipulation enables generalization of learning to new stimuli and locations, while not negatively affecting the overall amount of learning on the task. We then use deep neural networks to investigate how variability unlocks generalization. We find that networks develop invariance to the task-irrelevant feature when trained with variable inputs. The degree of learned invariance strongly predicts generalization. A reliance on invariant representations can explain variability-induced generalization in visual perceptual learning. This suggests new targets for understanding the neural basis of perceptual learning in the higher-order visual cortex and presents an easy-to-implement modification of common training paradigms that may benefit practical applications.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Córtex Visual
/
Percepção Visual
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Curr Biol
Assunto da revista:
BIOLOGIA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Alemanha