Feedforward and feedback sources of choice probability in neural population responses

Cumming, Bruce G.; Nienborg, Hendrikje

doi:10.1016/j.conb.2016.01.009

Cited by 90 publications

(87 citation statements)

References 68 publications

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“…The seminal work of showed that responses from single cells in area MT of monkeys during the performance of a random dot discrimination task covaried with behavioral choices. Similar activity-choice covariations have been found in multiple sensory areas and for a variety of two-choice tasks, including both discrimination and detection tasks (see Nienborg et al, 2012;Cumming and Nienborg, 2016, for a review). Identifying the location of cells whose activity encodes choice and how and when choice information is encoded in neural activity is essential to understand how the brain generates appropriate behaviors based on sensory information.…”

Section: Introductionsupporting

confidence: 55%

Stimulus dependent relationships between behavioral choice and sensory neural responses

Chicharro

Panzeri

Haefner

2019

Preprint

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Understanding the relationship between trial-to-trial variability in neural responses of sensory areas and behavioral choices is fundamental to elucidate the mechanisms of perceptual decision-making. In two-choice tasks, activity-choice covariations have traditionally been quantified with choice probabilities (CP). It has been so far commonly assumed that choice-related neural signals are separable from stimulus-driven responses, which has led to characterizing activity-choice covariations only with a single CP value estimated combining trials from all stimulus levels. In this work we provide theoretical and experimental evidence for the stimulus dependence of the relationship between neural responses and behavioral choices. We derived a general analytical CP expression for this dependency under the general assumption that a decision threshold converts an internal stimulus estimate into a binary choice. This expression predicts a stereotyped thresholdinduced CP modulation by the stimulus information content. We reanalyzed data from and found evidence of this modulation in the responses of macaque MT cells during a random dot discrimination task. Moreover, we developed new methods of analysis that allowed us to further identify a richer structure of cell-specific CP stimulus dependencies. Finally, we capitalised on this progress to develop new generalized linear models (GLMs) with stimulus-choice interaction terms, which show a higher predictive power and lead to a more precise assessment of how much each neuron is stimulus-or choice-driven, hence allowing a more accurate comparison across areas or cell types. Our work suggests that characterizing the patterns of stimulus dependence of choice-related signals is essential to properly determine how neurons in different areas contribute to linking sensory representations to perceptual decisions.

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Section: Introductionsupporting

confidence: 55%

Stimulus dependent relationships between behavioral choice and sensory neural responses

Chicharro

Panzeri

Haefner

2019

Preprint

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“…These perturbation studies suggest that multiple redundant pathways, which involve other areas besides the parietal lobe, are likely involved in multisensory integration (and decision-making). The perturbation results also imply that choice-related activity in an area may not always imply a causal role in discrimination behavior and highlight the need for a better understanding of the factors that lead to neural responses in an area to covary with choice [56–58]. Together, the recent results suggest that the feed forward view is insufficient to explain multisensory integration [13].…”

Section: A Distributed Network With Multiple Redundant Pathways Mediamentioning

confidence: 98%

Computational principles and models of multisensory integration

Chandrasekaran

2017

Current Opinion in Neurobiology

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Combining information from multiple senses creates robust percepts, speeds up responses, enhances learning, and improves detection, discrimination, and recognition. In this review, I discuss computational models and principles that provide insight into how this process of multisensory integration occurs at the behavioral and neural level. My initial focus is on drift-diffusion and Bayesian models that can predict behavior in multisensory contexts. I then highlight how recent neurophysiological and perturbation experiments provide evidence for a distributed redundant network for multisensory integration. I also emphasize studies which show that task-relevant variables in multisensory contexts are distributed in heterogeneous neural populations. Finally, I describe dimensionality reduction methods and recurrent neural network models that may help decipher heterogeneous neural populations involved in multisensory integration.

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“…Freiwald and Tsao, 2010). Third, there are clear feedback effects that have important computational roles (Cumming and Nienborg, 2016; Lee and Mumford, 2003). Finally, by focusing on the cerebral cortex, we ignore the important roles of subcortical structures, including the striatum, the colliculi, and the midbrain, all of which play important economic roles.…”

Section: The Reward System and The Motor Systemmentioning

confidence: 99%

Economic Choice as an Untangling of Options into Actions

Yoo

Hayden

2018

Neuron

115

123

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SUMMARY We propose that economic choice can be understood as a gradual transformation from domain of options to one of the actions. We draw an analogy with the idea of untangling information in the form vision system and propose that form vision and economic choice may be two aspects of a larger process that sculpts actions based on sensory inputs. From this viewpoint, choice results from the accumulated effect of repetitions of simple computations. These may consist primarily of relative valuations (evaluations relative to the value of rejection, perhaps in a manner akin to divisive normalization) applied to individual offers. With regard to economic choice, cortical brain regions differ primarily in their position and in what information they prioritize, and do not—with a few exceptions—have categorically distinct roles. Each region’s specific contribution is determined largely by its inputs; thus, understanding connectivity is crucial for understanding choice. This view suggests that there is no single site of choice, that there is no meaningful distinction between pre- and post-decisionality, and that there is no explicit representation of value in the brain.

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Feedforward and feedback sources of choice probability in neural population responses

Cited by 90 publications

References 68 publications

Stimulus dependent relationships between behavioral choice and sensory neural responses

Stimulus dependent relationships between behavioral choice and sensory neural responses

Computational principles and models of multisensory integration

Economic Choice as an Untangling of Options into Actions

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