Pub Date : 1900-01-01DOI: 10.32470/ccn.2019.1120-0
Lei Zhang, F. I. Kandil, C. Hilgetag, J. Gläscher
Decision-making in social contexts is commonly driven by two major sources of social influence: normative influence and informational influence. Our previous work has dissociated these two types of social influence, and have identified that bilateral temporoparietal junction (TPJ) encodes normative influence. However, it remains unclear whether the effect of normative influence causally depends on activity in the TPJ. Here, we present a transcranial magnetic stimulation (TMS) study using a similar paradigm in a within-subject design (i.e., right TPJ, left TPJ, and vertex). Behavioral results indicate that disrupting activity in the left TPJ resulted in reduced choice switch probability (i.e., less influenced by dissenting social information), relative to the right TPJ and vertex conditions. Computational modeling with hierarchical Bayesian parameter estimation suggests that the corresponding parameter quantifying normative influence significantly decreased in the left TPJ condition. However, the extent to which informational influence (i.e., social learning) was integrated into individuals’ valuation processes was comparable in all three conditions. Together, our results provide evidence for the causal role of left TPJ in computing normative social influence in human decision-making, whereas the integration of informative social influence in value computation remains intact.
{"title":"The causal role of temporoparietal junction in computing social influence in human decision-making","authors":"Lei Zhang, F. I. Kandil, C. Hilgetag, J. Gläscher","doi":"10.32470/ccn.2019.1120-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1120-0","url":null,"abstract":"Decision-making in social contexts is commonly driven by two major sources of social influence: normative influence and informational influence. Our previous work has dissociated these two types of social influence, and have identified that bilateral temporoparietal junction (TPJ) encodes normative influence. However, it remains unclear whether the effect of normative influence causally depends on activity in the TPJ. Here, we present a transcranial magnetic stimulation (TMS) study using a similar paradigm in a within-subject design (i.e., right TPJ, left TPJ, and vertex). Behavioral results indicate that disrupting activity in the left TPJ resulted in reduced choice switch probability (i.e., less influenced by dissenting social information), relative to the right TPJ and vertex conditions. Computational modeling with hierarchical Bayesian parameter estimation suggests that the corresponding parameter quantifying normative influence significantly decreased in the left TPJ condition. However, the extent to which informational influence (i.e., social learning) was integrated into individuals’ valuation processes was comparable in all three conditions. Together, our results provide evidence for the causal role of left TPJ in computing normative social influence in human decision-making, whereas the integration of informative social influence in value computation remains intact.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"16 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":"117129018","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.2019.1291-0
Jan Boelts, Jan-Matthis Lueckmann, P. J. Gonçalves, Henning Sprekeler, J. Macke
A common problem in computational neuroscience is the comparison of competing models in the light of observed data. Bayesian model comparison provides a statistically sound framework for this comparison based on the evidence each model provides for the data. In practice, however, models are often defined through complex simulators so that methods relying on likelihood functions are not applicable. Previous approaches in the field of Approximate Bayesian Computation (ABC) rely on rejection sampling to circumvent the likelihood, but are typically computationally inefficient. We propose an efficient method to perform Bayesian model comparison for simulation-based models. Using recent advances in posterior density estimation, we train a mixture-density network to map features of the observed data to the parameters of the posterior over models. We show that the method performs accurately on two tractable example problems, and present an application to a use case scenario from computational neuroscience – the comparison of ion channel models.
{"title":"Comparing neural simulations by neural density estimation","authors":"Jan Boelts, Jan-Matthis Lueckmann, P. J. Gonçalves, Henning Sprekeler, J. Macke","doi":"10.32470/ccn.2019.1291-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1291-0","url":null,"abstract":"A common problem in computational neuroscience is the comparison of competing models in the light of observed data. Bayesian model comparison provides a statistically sound framework for this comparison based on the evidence each model provides for the data. In practice, however, models are often defined through complex simulators so that methods relying on likelihood functions are not applicable. Previous approaches in the field of Approximate Bayesian Computation (ABC) rely on rejection sampling to circumvent the likelihood, but are typically computationally inefficient. We propose an efficient method to perform Bayesian model comparison for simulation-based models. Using recent advances in posterior density estimation, we train a mixture-density network to map features of the observed data to the parameters of the posterior over models. We show that the method performs accurately on two tractable example problems, and present an application to a use case scenario from computational neuroscience – the comparison of ion channel models.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"214 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":"116014878","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.2019.1332-0
Nils Neupärtl, C. Rothkopf
Although humans are prone to perceptual illusions and decision biases, they perform very well in every-day tasks with varying difficulties and complexities. It has been shown that humans learn to adopt to the statistical regularities of the environment. However, whether humans have correct physical intuitions about these ordinary processes and reflect related dynamics in an appropriate internal model has been disputed. Recent studies have shown that human behavior in diverse physical judgment tasks can indeed be explained with probabilistic models based on realistic, Newtonian functions while considering sensory uncertainties. Here, we examined whether humans use physical models of their environment in a control task, which involves non-linearities in the involved dynamics. Participants were asked to shoot a puck into a target area affected by realistic friction. By deploying Bayesian models we can show that humans are capable to adopt to these physical relationships and have appropriate internal beliefs about relevant quantities.
{"title":"Adaptation to environmental statistics in an action control task","authors":"Nils Neupärtl, C. Rothkopf","doi":"10.32470/ccn.2019.1332-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1332-0","url":null,"abstract":"Although humans are prone to perceptual illusions and decision biases, they perform very well in every-day tasks with varying difficulties and complexities. It has been shown that humans learn to adopt to the statistical regularities of the environment. However, whether humans have correct physical intuitions about these ordinary processes and reflect related dynamics in an appropriate internal model has been disputed. Recent studies have shown that human behavior in diverse physical judgment tasks can indeed be explained with probabilistic models based on realistic, Newtonian functions while considering sensory uncertainties. Here, we examined whether humans use physical models of their environment in a control task, which involves non-linearities in the involved dynamics. Participants were asked to shoot a puck into a target area affected by realistic friction. By deploying Bayesian models we can show that humans are capable to adopt to these physical relationships and have appropriate internal beliefs about relevant quantities.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"192 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":"122143765","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.2019.1123-0
Michael Kleinman, Chandramouli Chandrasekaran, J. Kao
We studied how a network of recurrently connected artificial units solve a visual perceptual decision-making task. The goal of this task is to discriminate the dominant color of a central static checkerboard and report the decision with an arm movement. This task has been used to study neural activity in the dorsal premotor (PMd) cortex. When a single recurrent neural network (RNN) was trained to perform the task, the activity of artificial units in the RNN differed from neural recordings in PMd, suggesting that inputs to PMd differed from inputs to the RNN. We expanded our architecture and examined how a multi-stage RNN performed the task. In the multi-stage RNN, the last stage exhibited similarities with PMd by representing direction information but not color information. We then investigated how the representation of color and direction information evolve across RNN stages. Together, our results are a demonstration of the importance of incorporating architectural constraints into RNN models. These constraints can improve the ability of RNNs to model neural activity in association areas.
{"title":"A multi-stage recurrent neural network better describes decision-related activity in dorsal premotor cortex","authors":"Michael Kleinman, Chandramouli Chandrasekaran, J. Kao","doi":"10.32470/ccn.2019.1123-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1123-0","url":null,"abstract":"We studied how a network of recurrently connected artificial units solve a visual perceptual decision-making task. The goal of this task is to discriminate the dominant color of a central static checkerboard and report the decision with an arm movement. This task has been used to study neural activity in the dorsal premotor (PMd) cortex. When a single recurrent neural network (RNN) was trained to perform the task, the activity of artificial units in the RNN differed from neural recordings in PMd, suggesting that inputs to PMd differed from inputs to the RNN. We expanded our architecture and examined how a multi-stage RNN performed the task. In the multi-stage RNN, the last stage exhibited similarities with PMd by representing direction information but not color information. We then investigated how the representation of color and direction information evolve across RNN stages. Together, our results are a demonstration of the importance of incorporating architectural constraints into RNN models. These constraints can improve the ability of RNNs to model neural activity in association areas.","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":"125904507","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.2019.1079-0
Morteza Rezanejad, Gabriel Downs, J. Wilder, Dirk B. Walther, A. Jepson, Sven J. Dickinson, Kaleem Siddiqi
Humans can accurately recognize natural scenes from line drawings, consisting solely of contour-based shape cues. Deep learning strategies for this complex task, however, have thus far been applied directly to photographs, exploiting all the cues available in colour images at the pixel level. Here we report the results of fine tuning off-the-shelf pre-trained Convolutional Neural Networks (CNNs) to perform scene classification given only contour information as input. To do so we exploit the Iverson-Zucker logical/linear framework to obtain line drawings from popular scene categorization databases, including an artist’s scene database and MIT67. We demonstrate a high level of performance despite the absence of colour, texture and shading information. We also show that the inclusion of medial-axis based contour salience weights leads to a further boost, adding useful information that does not appear to be exploited when CNNs are trained to use contours alone.
{"title":"Gestalt-based Contour Weights Improve Scene Categorization by CNNs","authors":"Morteza Rezanejad, Gabriel Downs, J. Wilder, Dirk B. Walther, A. Jepson, Sven J. Dickinson, Kaleem Siddiqi","doi":"10.32470/ccn.2019.1079-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1079-0","url":null,"abstract":"Humans can accurately recognize natural scenes from line drawings, consisting solely of contour-based shape cues. Deep learning strategies for this complex task, however, have thus far been applied directly to photographs, exploiting all the cues available in colour images at the pixel level. Here we report the results of fine tuning off-the-shelf pre-trained Convolutional Neural Networks (CNNs) to perform scene classification given only contour information as input. To do so we exploit the Iverson-Zucker logical/linear framework to obtain line drawings from popular scene categorization databases, including an artist’s scene database and MIT67. We demonstrate a high level of performance despite the absence of colour, texture and shading information. We also show that the inclusion of medial-axis based contour salience weights leads to a further boost, adding useful information that does not appear to be exploited when CNNs are trained to use contours alone.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"20 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":"126149237","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.2019.1387-0
Xiangbin Teng, D. Poeppel
Natural sounds convey perceptually relevant information over multiple timescales, and the necessary extraction of multi-timescale information requires the human auditory system to work over distinct ranges. Here, we show behavioral and neural evidence that acoustic information at two discrete timescales (~ 30 ms and ~ 200 ms) is preferably coded and that the theta and gamma neural bands of the auditory cortical system correlate with temporal coding of acoustic information. We then propose an computational approach to investigate how the cortical auditory system implements canonical computations at the two prominent timescales – the auditory system constructs a multi-timescale feature space to achieve sound recognition.
{"title":"Experimental evidence on computational mechanisms of concurrent temporal channels for auditory processing","authors":"Xiangbin Teng, D. Poeppel","doi":"10.32470/ccn.2019.1387-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1387-0","url":null,"abstract":"Natural sounds convey perceptually relevant information over multiple timescales, and the necessary extraction of multi-timescale information requires the human auditory system to work over distinct ranges. Here, we show behavioral and neural evidence that acoustic information at two discrete timescales (~ 30 ms and ~ 200 ms) is preferably coded and that the theta and gamma neural bands of the auditory cortical system correlate with temporal coding of acoustic information. We then propose an computational approach to investigate how the cortical auditory system implements canonical computations at the two prominent timescales – the auditory system constructs a multi-timescale feature space to achieve sound recognition.","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":"124678896","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.2019.1110-0
Francesco Silvestrin, Thomas H. B. FitzGerald, W. Penny
Learning about the uncertainty of environmental stimuli is a fundamental requirement of adaptive behaviour. In this experiment we probe whether pupil dilation in response to brief auditory stimuli reflects statistical learning about the underlying stimulus distributions. Specifically, we consider whether pupil dilation reflects automatic (task-irrelevant) learning about the precision of Gaussian distributions of tones. By comparing responses to perceptually identical outlier and standard tones in low and high precision blocks, we provide clear evidence that subjects do indeed learn about precision, as reflected by increased responses to surprising (outlier) tones during high precision blocks. This extends previous work looking at electrophysiological effects of precision learning, and provides new evidence that the putatively noradrenergic processes underlying pupil dilation reflect learning about the uncertainty of stimulus distributions. In addition, we use our data to test a new convolution-based approach for analysing pupillometry data, which we believe has considerable promise for this and future studies.
{"title":"Pupil dilation indexes statistical learning about the uncertainty of stimulus distributions","authors":"Francesco Silvestrin, Thomas H. B. FitzGerald, W. Penny","doi":"10.32470/ccn.2019.1110-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1110-0","url":null,"abstract":"Learning about the uncertainty of environmental stimuli is a fundamental requirement of adaptive behaviour. In this experiment we probe whether pupil dilation in response to brief auditory stimuli reflects statistical learning about the underlying stimulus distributions. Specifically, we consider whether pupil dilation reflects automatic (task-irrelevant) learning about the precision of Gaussian distributions of tones. By comparing responses to perceptually identical outlier and standard tones in low and high precision blocks, we provide clear evidence that subjects do indeed learn about precision, as reflected by increased responses to surprising (outlier) tones during high precision blocks. This extends previous work looking at electrophysiological effects of precision learning, and provides new evidence that the putatively noradrenergic processes underlying pupil dilation reflect learning about the uncertainty of stimulus distributions. In addition, we use our data to test a new convolution-based approach for analysing pupillometry data, which we believe has considerable promise for this and future studies.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"42 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":"128380188","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.2019.1287-0
D. Lindh, I. Sligte, K. Shapiro, I. Charest
When two targets (T1 and T2) are presented in a rapidly sequentially-presented stream of distractors, subjects often show a clear deficiency to report T2 when presented 200-500 ms after T1. This effect is known as the Attentional Blink (AB). Using the AB as a method to quantify the probability of conscious access, we investigate why some images seem to rise to consciousness more readily. By defining the representational relationships between images using fMRI and CNNs, we show that images that are distinct in high-level representations are more resilient to the AB effect, while low-level similarity to other images increase the probability of conscious access. These results were replicated using representational geometries derived from both functional Magnetic Resonance Imaging (fMRI) and Convolutional Neural Network (CNN). This provides additional parallels between the hierarchical complexity of CNNs trained on object classification and the human visual ventral stream, with CNN and brain representations predicting behaviour in a similar way.
{"title":"Brain and DCNN representational geometries predict variability in conscious access","authors":"D. Lindh, I. Sligte, K. Shapiro, I. Charest","doi":"10.32470/ccn.2019.1287-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1287-0","url":null,"abstract":"When two targets (T1 and T2) are presented in a rapidly sequentially-presented stream of distractors, subjects often show a clear deficiency to report T2 when presented 200-500 ms after T1. This effect is known as the Attentional Blink (AB). Using the AB as a method to quantify the probability of conscious access, we investigate why some images seem to rise to consciousness more readily. By defining the representational relationships between images using fMRI and CNNs, we show that images that are distinct in high-level representations are more resilient to the AB effect, while low-level similarity to other images increase the probability of conscious access. These results were replicated using representational geometries derived from both functional Magnetic Resonance Imaging (fMRI) and Convolutional Neural Network (CNN). This provides additional parallels between the hierarchical complexity of CNNs trained on object classification and the human visual ventral stream, with CNN and brain representations predicting behaviour in a similar way.","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":"130762104","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.2019.1362-0
Kimia C. Yaghoubi, Mahsa Alizadeh Shalchy, Sana Hussain, Xu Chen, Ilana Benette, M. Mather, Xiaoping Hu, A. Seitz, Megan A. K. Peters
{"title":"Computational fMRI Reveals Separable Representations Of Stimulus and Behavioral Choice In Auditory Cortex: A Tool for Studying the Locus Coeruleus Circuit","authors":"Kimia C. Yaghoubi, Mahsa Alizadeh Shalchy, Sana Hussain, Xu Chen, Ilana Benette, M. Mather, Xiaoping Hu, A. Seitz, Megan A. K. Peters","doi":"10.32470/ccn.2019.1362-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1362-0","url":null,"abstract":"","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"38 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":"132988591","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.2019.1297-0
Aspen H. Yoo, W. Ma
Unlike in perceptual tasks, it is unclear whether humans near-optimally use uncertainty information in their visual working memory (VWM) decisions. Some circumstantial evidence is available: people can explicitly report their uncertainty after a delay and can near-optimally integrate knowledge of uncertainty with working memories. However, it is unclear whether people can do the conjunction: accurately store uncertainty information in VWM and use it in a subsequent decision. To investigate this, we collected data in two orientation change detection tasks. One task did not require the maintenance of uncertainty information and the other did. We factorially evaluate Bayesian observer models with different assumptions about the memory noise generating process, the observer’s assumption of this process, and the observer’s decision rule. For both experiments, the model that best fits human data assumes that memory precision varies as a function of stimulus reliability and other internal fluctuations, observers know their memory uncertainty on an individual-item basis, and observers optimally integrate information across items when making their decision. These results provide evidence that participants are able to maintain uncertainty information across a delay, and use it optimally in subsequent decisions.
{"title":"Optimal maintenance and use of uncertainty in visual working memory","authors":"Aspen H. Yoo, W. Ma","doi":"10.32470/ccn.2019.1297-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1297-0","url":null,"abstract":"Unlike in perceptual tasks, it is unclear whether humans near-optimally use uncertainty information in their visual working memory (VWM) decisions. Some circumstantial evidence is available: people can explicitly report their uncertainty after a delay and can near-optimally integrate knowledge of uncertainty with working memories. However, it is unclear whether people can do the conjunction: accurately store uncertainty information in VWM and use it in a subsequent decision. To investigate this, we collected data in two orientation change detection tasks. One task did not require the maintenance of uncertainty information and the other did. We factorially evaluate Bayesian observer models with different assumptions about the memory noise generating process, the observer’s assumption of this process, and the observer’s decision rule. For both experiments, the model that best fits human data assumes that memory precision varies as a function of stimulus reliability and other internal fluctuations, observers know their memory uncertainty on an individual-item basis, and observers optimally integrate information across items when making their decision. These results provide evidence that participants are able to maintain uncertainty information across a delay, and use it optimally in subsequent decisions.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"4 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":"131980543","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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