Priority coding in the visual system

Rust, Nicole C.; Cohen, Marlene R.

doi:10.1038/s41583-022-00582-9

Cited by 41 publications

(29 citation statements)

References 223 publications

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“…In contrast to these decision representations, we found that coherence representations disappeared in the easier Attend-Motion task. This observation is consistent with previous experiments finding that feature decoding is stronger for more difficult tasks (Rust and Cohen, 2022; Woolgar et al, 2011, 2015b, 2015a) or when people are incentivized to use cognitive control (Etzel et al, 2016; Hall-McMaster et al, 2019). Moreover, stimuli and responses were matched across tasks, helping to rule out alternative accounts of coherence encoding based on ‘bottom-up’ stimulus salience, decision-making, or eye movements.…”

Section: Discussionsupporting

confidence: 92%

“…When tasks are in conflict, the brain uses orthogonal task representations to minimize cross-talk (Flesch et al, 2022; Kaufman et al, 2014; Mante et al, 2013; Minxha et al, 2020; Pagan et al, 2022; Panichello and Buschman, 2021; Salinas, 2004), consistent with the optimal strategy in artificial neural networks (Flesch et al, 2022; Mante et al, 2013; Musslick et al, 2020). A compelling possibility is that the cognitive control system uses a similar representational format to coordinate multiple control signals within a task as well (Ebitz et al, 2020; Libby and Buschman, 2021; Rust and Cohen, 2022). A large body of work has shown that cognitive control networks encode multiple task parameters (Flesch et al, 2022; Freund et al, 2021; Jackson et al, 2017, 2021; Kayser et al, 2010b; Vermeylen et al, 2020; Woolgar et al, 2011, 2015b, 2015a), and ‘global’ measures of cognitive control like overall difficulty or effort (Freund et al, 2021; Kragel et al, 2018; Smith et al, 2019; Vermeylen et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Orthogonal neural encoding of targets and distractors supports multivariate cognitive control

Ritz

Shenhav

2022

Preprint

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People can overcome a wide array of mental challenges by coordinating their neural information processing to align with their goals. Recent behavioral work has shown that people can independently control their attention across multiple features during perceptual decision-making, but the structure of the neural representations that enables this multivariate control remains mysterious. We hypothesized that the brain solves this complex coordination problem by orthogonalizing feature-specific representations of task demands and attentional priority, allowing the brain to independently monitor and adjust multiple streams of stimulus information. To test this hypothesis, we measured fMRI activity while participants performed a task designed to tag processing and control over feature-specific information that is task-relevant (targets) versus task-irrelevant (distractors). We then characterized the geometry of these neural representations using a novel multivariate analysis (Encoding Geometry Analysis), estimating where the encoding of different task features is correlated versus orthogonal. We identified feature-specific representations of task demands and attentional priority in the dorsal anterior cingulate cortex (dACC) and intraparietal sulcus (IPS), respectively, consistent with differential roles for these regions in monitoring versus directing information processing. Representations of attentional priority in IPS were fully mediated by the control requirements of the task, associated with behavioral performance, and depended on connectivity with nodes in the frontoparietal control network, suggesting that these representations serve a fundamental role in supporting attentional control. Together, these findings provide evidence for a neural geometry that can enable coordinated control over multiple sources of information.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Orthogonal neural encoding of targets and distractors supports multivariate cognitive control

Ritz

Shenhav

2022

Preprint

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“…However, the brain performs computations not merely at the single-neuron level but also in populations of neurons (or neural ensembles) [reviewed in [23][24][25]. The internal representations encoded by neural ensembles often carry a distinct topology [26][27][28][29] which can be studied without reference to external covariates [30,31].…”

Section: Mainmentioning

confidence: 99%

Uncovering 2-D toroidal representations in grid cell ensemble activity during 1-D behavior

Hermansen¹,

Klindt²,

Dunn³

2022

Preprint

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Neuroscience is pushing toward studying the brain during naturalistic behaviors with open-ended tasks. Grid cells are a classic example, where free behavior was key to observing their characteristic spatial representations in two-dimensional environments. In contrast, it has been difficult to identify grid cells and study their computations in more restrictive experiments, such as head-fixed wheel running. Here, we challenge this view by showing that shifting the focus from single neurons to the population level changes the minimal experimental complexity required to study grid cell representations. Specifically, we combine the manifold approximation in UMAP with persistent homology to study the topology of the population activity. With these methods, we show that the population activity of grid cells covers a similar two-dimensional toroidal state space during wheel running as in open field foraging, with and without a virtual reality setup. Trajectories on the torus correspond to single trial runs in virtual reality and changes in experimental conditions are reflected in the internal representation, while the toroidal representation undergoes occasional shifts in its alignment to the environment. These findings show that our method can uncover latent topologies that go beyond the complexity of the task, allowing us to investigate internal dynamics in simple experimental settings in which the analysis of grid cells has so far remained elusive.

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“…The term “priority” has been used to describe different phenomena in the WM literature (e.g., Riddle et al, 2020; Yu et al, 2020; Wan et al, 2022) and in visual neuroscience writ large (Rust & Cohen, 2022). Here, we use the term in the broadest sense to refer to cue-determined changes in the behavioral relevance of stimuli.…”

Section: Discussionmentioning

confidence: 99%

Changes in behavioral priority influence the accessibility but not the quality of working memory content

Ester

Pytel

2022

Preprint

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Efficient behavioral selection requires rapid comparisons of sensory inputs with internal representations of motor affordances and goal states, and many of these comparisons take place in working memory (WM). Selecting an item stored in WM is known to blunt and/or reverse information loss in stimulus-specific representations of that item reconstructed from human brain activity, but extant studies have focused on all-or-none circumstances that allow or prevent an agent from selecting one item stored in WM. Behavioral studies suggest that humans can flexibly assign different levels of priority to different items stored in WM, but how doing so influences neural representations of WM content is unclear. One possibility is that assigning different levels of priority to items in memory influences the quality of those representations, resulting in more robust neural representations of high- vs. low-priority WM content. A second (non-exclusive) possibility is that asymmetries in behavioral priority influence how rapidly neural representations of high- vs. low-priority WM content can be selected and reported. To test these alternatives, we decoded high- and low-priority WM content from EEG recordings obtained while human volunteers performed a retrospectively cued WM task. Probabilistic changes in the behavioral relevance of a remembered item had no effect on asymptotic decoding performance; instead, these changes influenced the latency at which peak decoding performance was reached. Thus, our results indicate that probabilistic changes in the behavioral relevance of WM content influence the ease with which memories can be accessed and retrieved independently of their strength.

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Priority coding in the visual system

Cited by 41 publications

References 223 publications

Orthogonal neural encoding of targets and distractors supports multivariate cognitive control

Orthogonal neural encoding of targets and distractors supports multivariate cognitive control

Uncovering 2-D toroidal representations in grid cell ensemble activity during 1-D behavior

Changes in behavioral priority influence the accessibility but not the quality of working memory content

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