Cargando…

Attention to visual motion suppresses neuronal and behavioral sensitivity in nearby feature space

BACKGROUND: Feature-based attention prioritizes the processing of the attended feature while strongly suppressing the processing of nearby ones. This creates a non-linearity or “attentional suppressive surround” predicted by the Selective Tuning model of visual attention. However, previously reporte...

Descripción completa

Detalles Bibliográficos
Autores principales: Yoo, Sang-Ah, Martinez-Trujillo, Julio C., Treue, Stefan, Tsotsos, John K., Fallah, Mazyar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9535987/
https://www.ncbi.nlm.nih.gov/pubmed/36199136
http://dx.doi.org/10.1186/s12915-022-01428-7
Descripción
Sumario:BACKGROUND: Feature-based attention prioritizes the processing of the attended feature while strongly suppressing the processing of nearby ones. This creates a non-linearity or “attentional suppressive surround” predicted by the Selective Tuning model of visual attention. However, previously reported effects of feature-based attention on neuronal responses are linear, e.g., feature-similarity gain. Here, we investigated this apparent contradiction by neurophysiological and psychophysical approaches. RESULTS: Responses of motion direction-selective neurons in area MT/MST of monkeys were recorded during a motion task. When attention was allocated to a stimulus moving in the neurons’ preferred direction, response tuning curves showed its minimum for directions 60–90° away from the preferred direction, an attentional suppressive surround. This effect was modeled via the interaction of two Gaussian fields representing excitatory narrowly tuned and inhibitory widely tuned inputs into a neuron, with feature-based attention predominantly increasing the gain of inhibitory inputs. We further showed using a motion repulsion paradigm in humans that feature-based attention produces a similar non-linearity on motion discrimination performance. CONCLUSIONS: Our results link the gain modulation of neuronal inputs and tuning curves examined through the feature-similarity gain lens to the attentional impact on neural population responses predicted by the Selective Tuning model, providing a unified framework for the documented effects of feature-based attention on neuronal responses and behavior. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12915-022-01428-7.