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Groh JM, Schmehl MN, Caruso VC, Tokdar ST. Signal switching may enhance processing power of the brain. Trends Cogn Sci 2024:S1364-6613(24)00103-7. [PMID: 38763804 DOI: 10.1016/j.tics.2024.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 04/17/2024] [Accepted: 04/21/2024] [Indexed: 05/21/2024]
Abstract
Our ability to perceive multiple objects is mysterious. Sensory neurons are broadly tuned, producing potential overlap in the populations of neurons activated by each object in a scene. This overlap raises questions about how distinct information is retained about each item. We present a novel signal switching theory of neural representation, which posits that neural signals may interleave representations of individual items across time. Evidence for this theory comes from new statistical tools that overcome the limitations inherent to standard time-and-trial-pooled assessments of neural signals. Our theory has implications for diverse domains of neuroscience, including attention, figure binding/scene segregation, oscillations, and divisive normalization. The general concept of switching between functions could also lend explanatory power to theories of grounded cognition.
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Affiliation(s)
- Jennifer M Groh
- Department of Psychology and Neuroscience, Duke University, Durham, NC, 27705, USA; Department of Neurobiology, Duke University, Durham, NC, 27705, USA; Department of Biomedical Engineering, Duke University, Durham, NC, 27705, USA; Department of Computer Science, Duke University, Durham, NC, 27705, USA; Center for Cognitive Neuroscience, Duke University, Durham, NC, 27705, USA; Duke Institute for Brain Sciences, Duke University, Durham, NC, 27705, USA.
| | - Meredith N Schmehl
- Department of Neurobiology, Duke University, Durham, NC, 27705, USA; Center for Cognitive Neuroscience, Duke University, Durham, NC, 27705, USA; Duke Institute for Brain Sciences, Duke University, Durham, NC, 27705, USA
| | - Valeria C Caruso
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Surya T Tokdar
- Department of Statistical Science, Duke University, Durham, NC, 27705, USA
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Unraveling brain interactions in vision: The example of crowding. Neuroimage 2021; 240:118390. [PMID: 34271157 DOI: 10.1016/j.neuroimage.2021.118390] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 07/09/2021] [Accepted: 07/12/2021] [Indexed: 11/22/2022] Open
Abstract
Crowding, the impairment of target discrimination in clutter, is the standard situation in vision. Traditionally, crowding is explained with (feedforward) models, in which only neighboring elements interact, leading to a "bottleneck" at the earliest stages of vision. It is with this implicit prior that most functional magnetic resonance imaging (fMRI) studies approach the identification of the "neural locus" of crowding, searching for the earliest visual area in which the blood-oxygenation-level-dependent (BOLD) signal is suppressed under crowded conditions. Using this classic approach, we replicated previous findings of crowding-related BOLD suppression starting in V2 and increasing up the visual hierarchy. Surprisingly, under conditions of uncrowding, in which adding flankers improves performance, the BOLD signal was further suppressed. This suggests an important role for top-down connections, which is in line with global models of crowding. To discriminate between various possible models, we used dynamic causal modeling (DCM). We show that recurrent interactions between all visual areas, including higher-level areas like V4 and the lateral occipital complex (LOC), are crucial in crowding and uncrowding. Our results explain the discrepancies in previous findings: in a recurrent visual hierarchy, the crowding effect can theoretically be detected at any stage. Beyond crowding, we demonstrate the need for models like DCM to understand the complex recurrent processing which most likely underlies human perception in general.
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