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Yoo SA, Martinez-Trujillo JC, Treue S, Tsotsos JK, Fallah M. Attention to visual motion suppresses neuronal and behavioral sensitivity in nearby feature space. BMC Biol 2022; 20:220. [PMID: 36199136 PMCID: PMC9535987 DOI: 10.1186/s12915-022-01428-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 09/30/2022] [Indexed: 11/17/2022] Open
Abstract
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.
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Affiliation(s)
- Sang-Ah Yoo
- Department of Psychology, York University, Toronto, ON, M3J 1P3, Canada. .,Department of Electrical Engineering and Computer Science, York University, Toronto, ON, M3J 1P3, Canada. .,Centre for Vision Research, York University, Toronto, ON, M3J 1P3, Canada.
| | - Julio C Martinez-Trujillo
- Department of Physiology and Pharmacology, and Psychiatry, Western University, London, ON, N6A 5B7, Canada. .,Cognitive Neurophysiology Laboratory, Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, ON, N6A 5B7, Canada.
| | - Stefan Treue
- Cognitive Neuroscience Laboratory, German Primate Centre - Leibniz Institute for Primate Research, 37077, Goettingen, Germany.,Faculty for Biology and Psychology, University of Goettingen, 37073, Goettingen, Germany.,Leibniz ScienceCampus Primate Cognition, 37077, Goettingen, Germany.,Bernstein Center for Computational Neuroscience, 37077, Goettingen, Germany
| | - John K Tsotsos
- Department of Electrical Engineering and Computer Science, York University, Toronto, ON, M3J 1P3, Canada.,Centre for Vision Research, York University, Toronto, ON, M3J 1P3, Canada.,Vision: Science to Application, York University, Toronto, ON, M3J 1P3, Canada.,Center for Innovation and Computing at Lassonde, York University, Toronto, ON, M3J 1P3, Canada
| | - Mazyar Fallah
- Department of Psychology, York University, Toronto, ON, M3J 1P3, Canada.,Centre for Vision Research, York University, Toronto, ON, M3J 1P3, Canada.,Vision: Science to Application, York University, Toronto, ON, M3J 1P3, Canada.,School of Kinesiology and Health Science, York University, Toronto, ON, M3J 1P3, Canada.,Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, ON, N1G 2W1, Canada
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Sachs AJ, Khayat PS, Niebergall R, Martinez-Trujillo JC. A metric-based analysis of the contribution of spike timing to contrast and motion direction coding by single neurons in macaque area MT. Brain Res 2011; 1368:163-84. [PMID: 20831862 DOI: 10.1016/j.brainres.2010.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2010] [Revised: 08/31/2010] [Accepted: 09/01/2010] [Indexed: 11/27/2022]
Abstract
Spike timing is thought to contribute to the coding of motion direction information by neurons in macaque area MT. Here, we examined whether spike timing also contributes to the coding of stimulus contrast. We applied a metric-based approach to spike trains fired by MT neurons in response to stimuli that varied in contrast, or direction. We assessed the performance of three metrics, D(spike) and D(product) (containing spike count and timing information), and the spike count metric D(count). We analyzed responses elicited during the first 200 msec of stimulus presentation from 205 neurons. For both contrast and direction, the large majority of neurons showed the highest mutual information using D(spike), followed by D(product), and D(count). This was corroborated by the performance of a theoretical observer model at discriminating contrast and direction using the three metrics. Our results demonstrate that spike timing can contribute to contrast coding in MT neurons, and support previous reports of its potential contribution to direction coding. Furthermore, they suggest that a combination of spike count with periodic and non-periodic spike timing information (contained in D(spike), but not in D(product) and D(count) which are insensitive to spike counts and timing respectively) provides the largest coding advantage in spike trains fired by MT neurons during contrast and direction discrimination.
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Affiliation(s)
- Adam J Sachs
- Cognitive Neurophysiology Laboratory, Department of Physiology, McGill University, Canada
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