Pub Date : 1900-01-01DOI: 10.32470/ccn.2019.1309-0
Benjamin Cuthbert, M. Paré, D. Standage, Gunnar Blohm
Visual working memory experiments typically involve asking a subject to memorize several visual stimuli such as coloured shapes, oriented lines, faces, or objects. Computational accounts of recall performance often assume that each stimulus presented in a trial is encoded independently, ignoring higher-level ensemble statistics that have been shown to bias recall and impact task performance. Here, we analyzed data from a delayed estimation task that required the report of all stimuli (6 coloured squares). We found evidence for serial dependencies in within-trial reports, suggesting that participants clustered similarly coloured stimuli together. These dependencies were supported by estimates of the mutual information of within-trial report distributions. We present a non-parametric clustering model to quantify the clustering properties of randomly-generated stimulus arrays. We believe this is a promising data-driven approach to characterizing the statistical properties of experimental stimuli. Together, these results provide further evidence that humans encode ensemble statistics of visual scenes in working memory.
{"title":"Colour clustering in visual working memory","authors":"Benjamin Cuthbert, M. Paré, D. Standage, Gunnar Blohm","doi":"10.32470/ccn.2019.1309-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1309-0","url":null,"abstract":"Visual working memory experiments typically involve asking a subject to memorize several visual stimuli such as coloured shapes, oriented lines, faces, or objects. Computational accounts of recall performance often assume that each stimulus presented in a trial is encoded independently, ignoring higher-level ensemble statistics that have been shown to bias recall and impact task performance. Here, we analyzed data from a delayed estimation task that required the report of all stimuli (6 coloured squares). We found evidence for serial dependencies in within-trial reports, suggesting that participants clustered similarly coloured stimuli together. These dependencies were supported by estimates of the mutual information of within-trial report distributions. We present a non-parametric clustering model to quantify the clustering properties of randomly-generated stimulus arrays. We believe this is a promising data-driven approach to characterizing the statistical properties of experimental stimuli. Together, these results provide further evidence that humans encode ensemble statistics of visual scenes in working memory.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122867121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.32470/ccn.2019.1306-0
Joon-Young Moon, K. Müsch, C. Schroeder, C. Honey
Human brain dynamics combine external drivers (e.g. sensory information) and internal drivers (e.g. expectations and memories). How do the patterns of inter-regional coupling change when the balance of external and internal information is altered? To investigate this question, we analyzed intracranial (ECoG) recordings from human listeners exposed to an auditory narrative. We measured the latencies of coupling across consecutive stages of cortical auditory processing and we investigated if and how the latencies varied as a function of stimulus drive. We found that the latencies along the auditory pathway vary between no delay (“synchronized state”) and a small, nonzero delay (~20 ms, “propagating state”) depending on the external stimulation. The long-latency propagating state was most often observed in the absence of external information, during the silent boundaries between sentences. Moreover, propagating states were associated with transient increases in alpha-band (8-12 Hz) oscillatory processes. Both synchronized and propagating states were reproduced in a coupled oscillator model by altering the strength of the external drive. The data and model suggest that cortical networks transition between i) synchronized dynamics driven by an external stimulus, and ii) long-latency propagating dynamics in the absence of an external stimulus.
{"title":"Synchronized and Propagating States of Human Auditory Processing","authors":"Joon-Young Moon, K. Müsch, C. Schroeder, C. Honey","doi":"10.32470/ccn.2019.1306-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1306-0","url":null,"abstract":"Human brain dynamics combine external drivers (e.g. sensory information) and internal drivers (e.g. expectations and memories). How do the patterns of inter-regional coupling change when the balance of external and internal information is altered? To investigate this question, we analyzed intracranial (ECoG) recordings from human listeners exposed to an auditory narrative. We measured the latencies of coupling across consecutive stages of cortical auditory processing and we investigated if and how the latencies varied as a function of stimulus drive. We found that the latencies along the auditory pathway vary between no delay (“synchronized state”) and a small, nonzero delay (~20 ms, “propagating state”) depending on the external stimulation. The long-latency propagating state was most often observed in the absence of external information, during the silent boundaries between sentences. Moreover, propagating states were associated with transient increases in alpha-band (8-12 Hz) oscillatory processes. Both synchronized and propagating states were reproduced in a coupled oscillator model by altering the strength of the external drive. The data and model suggest that cortical networks transition between i) synchronized dynamics driven by an external stimulus, and ii) long-latency propagating dynamics in the absence of an external stimulus.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124200373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.32470/ccn.2019.1098-0
Lynn K. A. Sörensen, Davide Zambrano, H. Slagter, H. Scholte, S. Bohté
Visuo-spatial attention is a key mechanism for selecting goal-relevant information in natural scenes. We here implement a variant of the normalization model of attention into a spiking convolutional neural network, which approximates attentional gain with a change in firing rates. We apply this type of attention with different spatial extents to various levels in the processing hierarchy of a network performing object recognition in natural scenes. We find that close to the average objectsize attentional kernels yield the best performance, equivalent to a rather focused attentional enhancement. Furthermore, manipulating spatial attention within a single level was ineffective as benefits of spatial attention only arose from the combination of early-to-mid level modulations in the network hierarchy. Our results demonstrate that one can efficiently boost performance on the challenging task of recognizing objects in cluttered environments in a large-scale vision model by understanding attentional gain as a more or less precise representation of sensory information.
{"title":"Spatial Attention introduces Behavioral Trade-off in a Large-Scale Spiking Neural Network","authors":"Lynn K. A. Sörensen, Davide Zambrano, H. Slagter, H. Scholte, S. Bohté","doi":"10.32470/ccn.2019.1098-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1098-0","url":null,"abstract":"Visuo-spatial attention is a key mechanism for selecting goal-relevant information in natural scenes. We here implement a variant of the normalization model of attention into a spiking convolutional neural network, which approximates attentional gain with a change in firing rates. We apply this type of attention with different spatial extents to various levels in the processing hierarchy of a network performing object recognition in natural scenes. We find that close to the average objectsize attentional kernels yield the best performance, equivalent to a rather focused attentional enhancement. Furthermore, manipulating spatial attention within a single level was ineffective as benefits of spatial attention only arose from the combination of early-to-mid level modulations in the network hierarchy. Our results demonstrate that one can efficiently boost performance on the challenging task of recognizing objects in cluttered environments in a large-scale vision model by understanding attentional gain as a more or less precise representation of sensory information.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126155584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.32470/ccn.2019.1384-0
Yu Zhang, Pierre Bellec
A key goal in neuroscience is to understand brain mechanisms of cognitive functions. An emerging approach is the study of brain states dynamics using functional magnetic resonance imaging (fMRI). In this project, we applied graph convolutional networks (GCN) to decode brain activity over short time windows in a task fMRI dataset, i.e. associate a given window of fMRI time series with the task used. We investigated the performance of this GCN ”cognitive state annotation” in the Human Connectome Project (HCP) database, which features 21 different experimental conditions spanning seven major cognitive domains, and high temporal resolution in task fMRI data. Using a 10-second window, the 21 cognitive states were identified with an excellent average test accuracy of 92% (chance level 4.8%). Performance remained good (60%) even at a temporal resolution of one volume (720 ms of duration). As the HCP task battery was designed to selectively activate a wide range of specialized functional networks, we anticipate the GCN annotation to be applicable over a broad range of paradigms, including resting-state.
{"title":"Functional Decoding using Convolutional Networks on Brain Graphs","authors":"Yu Zhang, Pierre Bellec","doi":"10.32470/ccn.2019.1384-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1384-0","url":null,"abstract":"A key goal in neuroscience is to understand brain mechanisms of cognitive functions. An emerging approach is the study of brain states dynamics using functional magnetic resonance imaging (fMRI). In this project, we applied graph convolutional networks (GCN) to decode brain activity over short time windows in a task fMRI dataset, i.e. associate a given window of fMRI time series with the task used. We investigated the performance of this GCN ”cognitive state annotation” in the Human Connectome Project (HCP) database, which features 21 different experimental conditions spanning seven major cognitive domains, and high temporal resolution in task fMRI data. Using a 10-second window, the 21 cognitive states were identified with an excellent average test accuracy of 92% (chance level 4.8%). Performance remained good (60%) even at a temporal resolution of one volume (720 ms of duration). As the HCP task battery was designed to selectively activate a wide range of specialized functional networks, we anticipate the GCN annotation to be applicable over a broad range of paradigms, including resting-state.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126202115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.32470/ccn.2019.1360-0
Mingyu Song, M. Cai, Y. Niv
Living in a world where any object bears features in many dimensions, it is crucial but also challenging for humans to figure out what dimensions are relevant for rewards. How do humans learn from trial and error to obtain rewards when multiple (or an unknown number of) dimensions need to be taken into account, and feedback is probabilistic? In this work, we designed a paradigm tailored to study such complex but naturalistic scenarios. In the experiment, participants configured threedimensional stimuli by selecting features for each dimension and received probabilistic feedbacks. Participants demonstrated learning, selecting more rewarding features over the course of a game. To investigate their learning process, we compared three classes of learning models: a Bayesian model, reinforcement learning models and serial hypothesis testing models, and found evidence supporting the latter. This suggests that when facing complex learning scenarios with a great number of possible rules, people tend to actively test one hypothesis at a time, as opposed to evaluating all the possibilities or learning values of all features incrementally.
{"title":"Learning what is relevant for rewards via serial hypothesis testing","authors":"Mingyu Song, M. Cai, Y. Niv","doi":"10.32470/ccn.2019.1360-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1360-0","url":null,"abstract":"Living in a world where any object bears features in many dimensions, it is crucial but also challenging for humans to figure out what dimensions are relevant for rewards. How do humans learn from trial and error to obtain rewards when multiple (or an unknown number of) dimensions need to be taken into account, and feedback is probabilistic? In this work, we designed a paradigm tailored to study such complex but naturalistic scenarios. In the experiment, participants configured threedimensional stimuli by selecting features for each dimension and received probabilistic feedbacks. Participants demonstrated learning, selecting more rewarding features over the course of a game. To investigate their learning process, we compared three classes of learning models: a Bayesian model, reinforcement learning models and serial hypothesis testing models, and found evidence supporting the latter. This suggests that when facing complex learning scenarios with a great number of possible rules, people tend to actively test one hypothesis at a time, as opposed to evaluating all the possibilities or learning values of all features incrementally.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126317893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.32470/ccn.2019.1335-0
M. Speekenbrink
The Kalman filter, combined with heuristic choice rules such as softmax, UCB, and Thompson sampling, has been a popular model to identify the role of uncertainty in exploration in human reinforcement learning. Here we show that the Kalman filter combined with a softmax or UCB choice rule is not fully identifiable. By this structural identifiability, we mean that with unlimited data, the true parameter values are determinable. Perhaps surprisingly, the Kalman filter with Thompson sampling is fully identifiable.
{"title":"Identifiability of Gaussian Bayesian bandit models","authors":"M. Speekenbrink","doi":"10.32470/ccn.2019.1335-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1335-0","url":null,"abstract":"The Kalman filter, combined with heuristic choice rules such as softmax, UCB, and Thompson sampling, has been a popular model to identify the role of uncertainty in exploration in human reinforcement learning. Here we show that the Kalman filter combined with a softmax or UCB choice rule is not fully identifiable. By this structural identifiability, we mean that with unlimited data, the true parameter values are determinable. Perhaps surprisingly, the Kalman filter with Thompson sampling is fully identifiable.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126343257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.32470/ccn.2019.1416-0
Tyler Brooke-Wilson
A crucial methodological question for cognitive neuroscience is the question of what constitutes evidence of neural representation. A number of critiques over the last decade have challenged the view that correlation alone, as measured by neural decoding, is sufficient to establish representation. In response to such critiques, correlation is often augmented by a behavioral measure, showing that the decoding accuracy of a classifier and some behavioral performance measure are themselves correlated. I argue that correlation and behavioral causation together are nevertheless still insufficient for establishing representation. Inferring the existence of a neural representation on the basis of correlation and behavior alone is liable to both false positives and false negatives. Reflection on one common theory of representation (functional homomorphism theory, proposed by King and Gallistel 2010) elucidates why correlation + behavior is insufficient and suggests more direct sources of evidence. I present this theory and explain its implications for the question of empirical evidence of representation. Along the way I draw out some of the connections between the functional homomorphism theory of representation and predictive theories of perception.
{"title":"Sources of Evidence for Neural Representation","authors":"Tyler Brooke-Wilson","doi":"10.32470/ccn.2019.1416-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1416-0","url":null,"abstract":"A crucial methodological question for cognitive neuroscience is the question of what constitutes evidence of neural representation. A number of critiques over the last decade have challenged the view that correlation alone, as measured by neural decoding, is sufficient to establish representation. In response to such critiques, correlation is often augmented by a behavioral measure, showing that the decoding accuracy of a classifier and some behavioral performance measure are themselves correlated. I argue that correlation and behavioral causation together are nevertheless still insufficient for establishing representation. Inferring the existence of a neural representation on the basis of correlation and behavior alone is liable to both false positives and false negatives. Reflection on one common theory of representation (functional homomorphism theory, proposed by King and Gallistel 2010) elucidates why correlation + behavior is insufficient and suggests more direct sources of evidence. I present this theory and explain its implications for the question of empirical evidence of representation. Along the way I draw out some of the connections between the functional homomorphism theory of representation and predictive theories of perception.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129882902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.32470/ccn.2019.1234-0
Joao Barbosa, Kartik K. Sreenivasan, A. Compte
Swap-errors occur in working memory (WM) tasks when a wrong response is in fact accurate relative to a non-target stimulus. These errors reflect the failure to bind in memory the conjunction of features that define one object, and the mechanisms implicated remain unknown. Here, we tested the mechanism of synchrony across featurespecific neural assemblies. We built a biophysical neural network model for WM composed of two 1D attractor networks for WM, one representing colors and the other one locations. Within each network, gamma-oscillations were induced during bump-attractor activity through the interplay of fast recurrent excitation and slower feedback inhibition. These two networks are then connected via weak excitation, accomplishing color-location binding through the selective synchronization of pairs of bumps across the networks. Association-encoding was accomplished by stimulating simultaneously the corresponding bumps in each network, and feature-decoding by stimulating the cued location with a .5s pulse, which impacted strongly the corresponding phase-locked bump. In some simulations, “color bumps” abruptly changed their phase relationship with “location bumps” from which we derived a neural prediction: swap-errors are associated with a lower phase consistency of oscillatory activity in the delay period. Finally, we tested this prediction in MEG recorded from n=30 humans.
{"title":"Feature-binding in working memory through neuronal synchronization","authors":"Joao Barbosa, Kartik K. Sreenivasan, A. Compte","doi":"10.32470/ccn.2019.1234-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1234-0","url":null,"abstract":"Swap-errors occur in working memory (WM) tasks when a wrong response is in fact accurate relative to a non-target stimulus. These errors reflect the failure to bind in memory the conjunction of features that define one object, and the mechanisms implicated remain unknown. Here, we tested the mechanism of synchrony across featurespecific neural assemblies. We built a biophysical neural network model for WM composed of two 1D attractor networks for WM, one representing colors and the other one locations. Within each network, gamma-oscillations were induced during bump-attractor activity through the interplay of fast recurrent excitation and slower feedback inhibition. These two networks are then connected via weak excitation, accomplishing color-location binding through the selective synchronization of pairs of bumps across the networks. Association-encoding was accomplished by stimulating simultaneously the corresponding bumps in each network, and feature-decoding by stimulating the cued location with a .5s pulse, which impacted strongly the corresponding phase-locked bump. In some simulations, “color bumps” abruptly changed their phase relationship with “location bumps” from which we derived a neural prediction: swap-errors are associated with a lower phase consistency of oscillatory activity in the delay period. Finally, we tested this prediction in MEG recorded from n=30 humans.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130005784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.32470/ccn.2019.1030-0
Alexander Steinke, F. Lange, B. Kopp
The Wisconsin Card Sorting Test (WCST) is considered to be gold standard for the clinical assessment of executive functions. However, little is known about cognitive processes corresponding to WCST performance. Recent research suggests that multiple levels of control contribute to WCST performance. In this study, we introduce a reinforcement-learning (RL) model, which incorporates category and response learning. We test this multi-level RL model against single-level models, i.e., a category RL model and the state-of-the-art attentional updating model, by means of relative and absolute model performance. A sample of 375 participants completed a computerized version of the WCST (cWCST). Behavioral outcome measures were traditional perseveration and set-loss errors that we further stratified by response demands. The multilevel RL model outperformed both single-level models, with the state-of-the-art attentional updating model performing worst. Only the multi-level RL model was able to simulate all behavioral phenomena under consideration. In conclusion, results of model comparisons support the hypothesis that control processes at multiple levels contribute to cWCST performance. The multi-level RL model might offer a suitable framework for discerning latent cognitive processes contributing to WCST performance in general.
{"title":"A Multi-Level Reinforcement-Learning Model of Wisconsin Card Sorting Test Performance","authors":"Alexander Steinke, F. Lange, B. Kopp","doi":"10.32470/ccn.2019.1030-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1030-0","url":null,"abstract":"The Wisconsin Card Sorting Test (WCST) is considered to be gold standard for the clinical assessment of executive functions. However, little is known about cognitive processes corresponding to WCST performance. Recent research suggests that multiple levels of control contribute to WCST performance. In this study, we introduce a reinforcement-learning (RL) model, which incorporates category and response learning. We test this multi-level RL model against single-level models, i.e., a category RL model and the state-of-the-art attentional updating model, by means of relative and absolute model performance. A sample of 375 participants completed a computerized version of the WCST (cWCST). Behavioral outcome measures were traditional perseveration and set-loss errors that we further stratified by response demands. The multilevel RL model outperformed both single-level models, with the state-of-the-art attentional updating model performing worst. Only the multi-level RL model was able to simulate all behavioral phenomena under consideration. In conclusion, results of model comparisons support the hypothesis that control processes at multiple levels contribute to cWCST performance. The multi-level RL model might offer a suitable framework for discerning latent cognitive processes contributing to WCST performance in general.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130075944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.32470/ccn.2019.1326-0
Katrina R. Quinn, B. Cumming, H. Nienborg
Many V2 neurons are selective for binocular disparity. V2 is also the earliest site in the visual processing hierarchy for which systematic correlations across the population between neural responses and an animal’s behavioral choice in disparity based tasks have been observed. However, while these choice correlations suggest a link between the neural activity and perceptual choice, it has long been recognized that they do not establish a causal relationship. Here, we sought to test whether macaque V2 plays a causal role on coarse disparity judgements. We used microstimulation on disparity selective sites in V2 whilst animals performed a coarse disparity discrimination task. We found that microstimulation led to a systematic shift of the psychometric function towards the preferred disparity of the stimulated site, supporting a causal role for V2 neurons in disparity discrimination.
{"title":"A Causal Effect of Macaque V2 in a Coarse Disparity Discrimination Task","authors":"Katrina R. Quinn, B. Cumming, H. Nienborg","doi":"10.32470/ccn.2019.1326-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1326-0","url":null,"abstract":"Many V2 neurons are selective for binocular disparity. V2 is also the earliest site in the visual processing hierarchy for which systematic correlations across the population between neural responses and an animal’s behavioral choice in disparity based tasks have been observed. However, while these choice correlations suggest a link between the neural activity and perceptual choice, it has long been recognized that they do not establish a causal relationship. Here, we sought to test whether macaque V2 plays a causal role on coarse disparity judgements. We used microstimulation on disparity selective sites in V2 whilst animals performed a coarse disparity discrimination task. We found that microstimulation led to a systematic shift of the psychometric function towards the preferred disparity of the stimulated site, supporting a causal role for V2 neurons in disparity discrimination.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130092465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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