Dissociation of Choice Formation and Choice-Correlated Activity in Macaque Visual Cortex

Goris, Robbe L. T.; Ziemba, Corey M.; Stine, Gabriel M; Simoncelli, Eero P.; Movshon, J. Anthony

doi:10.1523/jneurosci.3331-16.2017

Cited by 53 publications

(74 citation statements)

References 43 publications

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“…In V1, individual neuron CP was negligible and population choice information was only slightly stronger. Some previous studies have also reported that choice signals in V1 are weak or absent (Nienborg and Cumming, 2006;Hass and Horwitz, 2013;Goris et al, 2017), though others have detected measurable choice signals there (Palmer et al, 2007;Nienborg and Cumming, 2014;Goris et al, 2017;Bondy et al, 2018). In addition, fluctuations in V1 population responses, as measured by voltagesensitive dye optical imaging, can be strongly predictive of stimulus detection (Michelson et al, 2017;Seidemann and Geisler, 2018).…”

Section: Discussionmentioning

confidence: 96%

“…3), with statistically significant choice signals, a proportion not notably larger than expected by chance given our statistical criterion (␣ level ϭ 0.05). Another possible explanation for our weak CP values is that the representation of choice in V1 and V4 is dynamic Nienborg and Cumming, 2009;Shiozaki et al, 2012;Wimmer et al, 2015;Goris et al, 2017), and perhaps more prevalent outside our chosen analysis window (0 -250 ms after stimulus onset). We thus performed CP analysis in sliding temporal windows of 100 ms with 50% overlap.…”

Section: Single Neuron Choice Signalsmentioning

confidence: 99%

“…Comparisons of choice signals in different brain areas are even more problematic, because these are often measured in different sessions, animals, or even tasks. Behavioral strategies can vary strongly across similar tasks and even across animals performing identical tasks, and these variations in strategy likely influence choice information (Goris et al, 2017;Bondy et al, 2018). To allow a direct comparison of choice signals in early and midlevel primate visual cortex, we therefore simultaneously recorded neurons with overlapping spatial receptive fields (RFs), in primary visual cortex (V1) and area V4 while monkeys performed a fine orientation discrimination task.…”

Section: Significance Statementmentioning

confidence: 99%

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Predicting Perceptual Decisions Using Visual Cortical Population Responses and Choice History

Jasper¹,

Tanabe²,

Kohn

2019

J. Neurosci.

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Our understanding of the neural basis of perceptual decision making has been built in part on relating co-fluctuations of single neuron responses to perceptual decisions on a trial-by-trial basis. The strength of this relationship is often compared across neurons or brain areas, recorded in different sessions, animals, or variants of a task. We sought to extend our understanding of perceptual decision making in three ways. First, we measured neuronal activity simultaneously in early [primary visual cortex (V1)] and midlevel (V4) visual cortex while macaque monkeys performed a fine orientation discrimination perceptual task. This allowed a direct comparison of choice signals in these two areas, including their dynamics. Second, we asked how our ability to predict animals' decisions would be improved by considering small simultaneously-recorded neuronal populations rather than individual units. Finally, we asked whether predictions would be improved by taking into account the animals' choice and reward histories, which can strongly influence decision making. We found that responses of individual V4 neurons were weakly predictive of decisions, but only in a brief epoch between stimulus offset and the indication of choice. In V1, few neurons showed significant decision-related activity. Analysis of neuronal population responses revealed robust choice-related information in V4 and substantially weaker signals in V1. Including choice-and reward-history information improved performance further, particularly when the recorded populations contained little decision-related information. Our work shows the power of using neuronal populations and decision history when relating neuronal responses to the perceptual decisions they are thought to underlie.

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Section: Discussionmentioning

confidence: 96%

Section: Single Neuron Choice Signalsmentioning

confidence: 99%

Section: Significance Statementmentioning

confidence: 99%

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Predicting Perceptual Decisions Using Visual Cortical Population Responses and Choice History

Jasper¹,

Tanabe²,

Kohn

2019

J. Neurosci.

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“…Thus the decision-making process may be corrupting the sensory representation. However, we find that the sensory stimuli and choice variables are separate at the population level, Introduction 1 Sensory cortical neurons exhibit substantial variability to repeated presentations of the 2 same stimulus [1,2]. This variability depends on the specifics of the sensory stimulus 3 and task being performed [3][4][5][6][7], and is often correlated with the trial-by-trial perceptual 4 report of the animal [8][9][10][11].…”

mentioning

confidence: 88%

“…To accelerate the inference, we initialized algorithm at the result of 114 Gaussian Process Factor Analysis (GPFA). The dimensionality of the latent process was 115 determined to be 4 by leave-one-neuron-out cross-validation on the session with the 116 largest population (2). All the sessions with more than 10 simultaneously recorded units 117 were included in the this study.…”

mentioning

confidence: 99%

Stimulus-choice (mis)alignment in primate MT cortex

Zhao

Yates

Levi

et al. 2019

Preprint

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For stimuli near perceptual threshold, the trial-by-trial activity of single neurons in many sensory areas is correlated with the animal's perceptual report. This phenomenon has often been attributed to feedforward readout of the neural activity by the downstream decision-making circuits. The interpretation of choice-correlated activity is quite ambiguous, but its meaning can be better understood in the light of population-wide correlations among sensory neurons. Using a statistical nonlinear dimensionality reduction technique on single-trial ensemble recordings from the middle temporal area during perceptual-decision-making, we extracted low-dimensional neural trajectories that captured the population-wide fluctuations. We dissected the particular contributions of sensory-driven versus choice-correlated activity in the low-dimensional population code. We found that the neural trajectories strongly encoded the direction of the stimulus in single dimension with a temporal signature similar to that of single MT neurons. If the downstream circuit were optimally utilizing this information, choice-correlated signals should be aligned with this stimulus encoding dimension. Surprisingly, we found that a large component of the choice information resides in the subspace orthogonal to the stimulus representation inconsistent with the optimal readout view. This misaligned choice information allows the feedforward sensory information to coexist with the decision-making process. The time course of these signals suggest that this misaligned contribution likely is feedback from the downstream areas. We hypothesize that this non-corrupting choice-correlated feedback might be related to learning or reinforcing sensory-motor relations in the sensory population. Author summaryIn sensorimotor decision-making, internal representation of sensory stimuli is utilized for the generation of appropriate behavior for the context. Therefore, the correlation between variability in sensory neurons and perceptual decisions is sometimes explained by a causal, feedforward role of sensory noise in behavior. However, this correlation could also originate via feedback from decision-making mechanisms downstream of the sensory representation. This cannot be resolved by analyzing single unit responses, but requires a population level analysis. Area MT contains both sensory and choice information and is known to be the key sensory area for visual motion perception. Thus the decision-making process may be corrupting the sensory representation. However, we find that the sensory stimuli and choice variables are separate at the population level, Introduction 1 Sensory cortical neurons exhibit substantial variability to repeated presentations of the 2 same stimulus [1,2]. This variability depends on the specifics of the sensory stimulus 3 and task being performed [3][4][5][6][7], and is often correlated with the trial-by-trial perceptual 4 report of the animal [8][9][10][11]. This trial-by-trial correlation between neural responses and 5 perceptual reports, ...

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Multisensory Integration for Self-Motion Perception

Hou

2020

The Senses: A Comprehensive Reference

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Dissociation of Choice Formation and Choice-Correlated Activity in Macaque Visual Cortex

Cited by 53 publications

References 43 publications

Predicting Perceptual Decisions Using Visual Cortical Population Responses and Choice History

Predicting Perceptual Decisions Using Visual Cortical Population Responses and Choice History

Stimulus-choice (mis)alignment in primate MT cortex

Multisensory Integration for Self-Motion Perception

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