Pub Date : 2019-09-01DOI: 10.32470/ccn.2019.1095-0
Josephine Cruzat, J. Cabral, G. Knudsen, R. Carhart-Harris, P. Whybrow, N. Logothetis, M. Kringelbach, G. Deco
Although a variety of studies have shown the role of neurotransmitters at the neuronal level, their impact on the dynamics of the system at a macroscopic scale is poorly understood. Here, we provide a causal explanation using the first whole-brain model integrating multimodal imaging in healthy human participants undergoing manipulation of the serotonin system. Specifically, we combined anatomical and functional data with a detailed map of the serotonin 2A receptor (5-HT2AR) densities obtained with positron emission tomography (PET). This allowed us to model the resting state and mechanistically explain the functional effects of 5-HT2AR stimulation with lysergic acid diethylamide (LSD). The whole-brain model used a dynamical mean-field quantitative description of populations of excitatory and inhibitory neurons as well as the associated synaptic dynamics, where the neuronal gain function of the model is modulated by the 5-HT2AR density. The results show that the precise distribution of 5-HT2AR is crucial to predict the neuromodulatory effects of LSD. The model identified the causative mechanisms for the non-linear interactions between the neuronal and neurotransmitter system, which are uniquely linked to the underlying neuroanatomical network, the modulation by the specific brain-wide distribution of neurotransmitter receptors, and the non-linear interactions between the two. Keywords: Whole-Brain Model; Mean Field Model; Neurotransmitters; Serotonin; Psychedelics.
{"title":"Mechanisms of the non-linear interactions between the neuronal and neurotransmitter systems explained by causal whole-brain modeling","authors":"Josephine Cruzat, J. Cabral, G. Knudsen, R. Carhart-Harris, P. Whybrow, N. Logothetis, M. Kringelbach, G. Deco","doi":"10.32470/ccn.2019.1095-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1095-0","url":null,"abstract":"Although a variety of studies have shown the role of neurotransmitters at the neuronal level, their impact on the dynamics of the system at a macroscopic scale is poorly understood. Here, we provide a causal explanation using the first whole-brain model integrating multimodal imaging in healthy human participants undergoing manipulation of the serotonin system. Specifically, we combined anatomical and functional data with a detailed map of the serotonin 2A receptor (5-HT2AR) densities obtained with positron emission tomography (PET). This allowed us to model the resting state and mechanistically explain the functional effects of 5-HT2AR stimulation with lysergic acid diethylamide (LSD). The whole-brain model used a dynamical mean-field quantitative description of populations of excitatory and inhibitory neurons as well as the associated synaptic dynamics, where the neuronal gain function of the model is modulated by the 5-HT2AR density. The results show that the precise distribution of 5-HT2AR is crucial to predict the neuromodulatory effects of LSD. The model identified the causative mechanisms for the non-linear interactions between the neuronal and neurotransmitter system, which are uniquely linked to the underlying neuroanatomical network, the modulation by the specific brain-wide distribution of neurotransmitter receptors, and the non-linear interactions between the two. Keywords: Whole-Brain Model; Mean Field Model; Neurotransmitters; Serotonin; Psychedelics.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132379594","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 : 2019-09-01DOI: 10.32470/ccn.2019.1249-0
Tuisku Tammi, Noora Lehtonen, B. Cowley
Physiological arousal can be a signal of attention, reflecting predictability and significance of stimuli or events. We explored temporal patterns in task-related physiological arousal and their connection to performance in repeated trials of a visuomotor steering task. Participants (N = 9) played a total of forty trials of a high-speed steering task in eight sessions over a period of 2-3 weeks. Temporal changes in electrodermal activity during task performance were modelled as habituation, and connections between performance, perceived importance and individual differences in habituation rate were examined. Additionally, withinsubject changes in habituation were compared to deviations from predicted performance. We found that sustained task-related arousal (slow habituation) was connected to better performance both between groups and within participants. Slow habituation was also related to higher subjective reports of perceived importance. Taken together, these results suggest that temporal changes in task-related arousal during learning are related to the processing of task-relevant cues and may reflect motivational states that direct selective attention.
{"title":"Temporal Pattern Models for Physiological Arousal During a Steering Task","authors":"Tuisku Tammi, Noora Lehtonen, B. Cowley","doi":"10.32470/ccn.2019.1249-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1249-0","url":null,"abstract":"Physiological arousal can be a signal of attention, reflecting predictability and significance of stimuli or events. We explored temporal patterns in task-related physiological arousal and their connection to performance in repeated trials of a visuomotor steering task. Participants (N = 9) played a total of forty trials of a high-speed steering task in eight sessions over a period of 2-3 weeks. Temporal changes in electrodermal activity during task performance were modelled as habituation, and connections between performance, perceived importance and individual differences in habituation rate were examined. Additionally, withinsubject changes in habituation were compared to deviations from predicted performance. We found that sustained task-related arousal (slow habituation) was connected to better performance both between groups and within participants. Slow habituation was also related to higher subjective reports of perceived importance. Taken together, these results suggest that temporal changes in task-related arousal during learning are related to the processing of task-relevant cues and may reflect motivational states that direct selective attention.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124741001","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 : 2019-09-01DOI: 10.32470/ccn.2019.1193-0
A. Baram, Timothy H. Muller, H. Nili, M. Garvert, Timothy Edward John Behrens
The ability to appropriately generalise previously acquired knowledge to novel situations is a hallmark of human intelligence. A possible neural solution to this problem is to devote pools of neurons to represent the relations between entities in the environment explicitly, in a manner that is divorced from the entities themselves. Such an explicit representation can generalise to novel situations with the same relational structure. Grid cells, originally found in the entorhinal cortex, have been proposed as such an explicit representation of the relations between different locations in physical space. However, the neural representations underlying the generalisation of relational structures in abstract tasks remain poorly understood. Here we use fMRI in humans to show that the entorhinal cortex explicitly represents the relations between reward-predicting stimuli in a reinforcement learning task with different underlying correlation structures between the reward probabilities associated with different stimuli. Our results demonstrate that the same brain regions, perhaps with the same mechanisms, represent the relational structure of the task in both spatial and abstract decision-making tasks. This suggests that the brain uses a common coding framework for the structure of tasks across a wide range of domains.
{"title":"The relational structure of a reinforcement learning task is represented and generalised in the entorhinal cortex","authors":"A. Baram, Timothy H. Muller, H. Nili, M. Garvert, Timothy Edward John Behrens","doi":"10.32470/ccn.2019.1193-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1193-0","url":null,"abstract":"The ability to appropriately generalise previously acquired knowledge to novel situations is a hallmark of human intelligence. A possible neural solution to this problem is to devote pools of neurons to represent the relations between entities in the environment explicitly, in a manner that is divorced from the entities themselves. Such an explicit representation can generalise to novel situations with the same relational structure. Grid cells, originally found in the entorhinal cortex, have been proposed as such an explicit representation of the relations between different locations in physical space. However, the neural representations underlying the generalisation of relational structures in abstract tasks remain poorly understood. Here we use fMRI in humans to show that the entorhinal cortex explicitly represents the relations between reward-predicting stimuli in a reinforcement learning task with different underlying correlation structures between the reward probabilities associated with different stimuli. Our results demonstrate that the same brain regions, perhaps with the same mechanisms, represent the relational structure of the task in both spatial and abstract decision-making tasks. This suggests that the brain uses a common coding framework for the structure of tasks across a wide range of domains.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"2021 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130846685","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 : 2019-09-01DOI: 10.32470/ccn.2019.1212-0
Gaurav Malhotra, B. D. Evans, J. Bowers
In this study, we show that when standard convolutional neural networks (CNNs) are trained end-to-end on datasets containing low-level and spatially high-frequency features, they are susceptible to learning these potentially idiosyncratic features if they are predictive of the output class. Such features are extremely unlikely to play a major role in human object recognition, where instead a strong preference for shape is observed. Through a series of empirical studies, we show that standard CNNs cannot overcome this reliance on non-shape features merely by making training more ecologically plausible or using standard regularisation methods. However, we show that these problems can be ameliorated by forgoing end-to-end learning and processing images initially with Gabor filters, in a manner that more closely resembles biological vision.
{"title":"Adding biological constraints to CNNs makes image classification more human-like and robust","authors":"Gaurav Malhotra, B. D. Evans, J. Bowers","doi":"10.32470/ccn.2019.1212-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1212-0","url":null,"abstract":"In this study, we show that when standard convolutional neural networks (CNNs) are trained end-to-end on datasets containing low-level and spatially high-frequency features, they are susceptible to learning these potentially idiosyncratic features if they are predictive of the output class. Such features are extremely unlikely to play a major role in human object recognition, where instead a strong preference for shape is observed. Through a series of empirical studies, we show that standard CNNs cannot overcome this reliance on non-shape features merely by making training more ecologically plausible or using standard regularisation methods. However, we show that these problems can be ameliorated by forgoing end-to-end learning and processing images initially with Gabor filters, in a manner that more closely resembles biological vision.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126387997","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}
How do people generalize and explore structured spaces? We study human behavior on a multi-armed bandit task, where rewards are influenced by the connectivity structure of a graph. A detailed predictive model comparison shows that a Gaussian Process regression model using a diffusion kernel is able to best describe participant choices, and also predict judgments about expected reward and confidence. This model unifies psychological models of function learning with the Successor Representation used in reinforcement learning, thereby building a bridge between different models of generalization.
{"title":"Searching for rewards in graph-structured spaces","authors":"Charley M. Wu, Eric Schulz, S. Gershman","doi":"10.31234/osf.io/vey38","DOIUrl":"https://doi.org/10.31234/osf.io/vey38","url":null,"abstract":"How do people generalize and explore structured spaces? We study human behavior on a multi-armed bandit task, where rewards are influenced by the connectivity structure of a graph. A detailed predictive model comparison shows that a Gaussian Process regression model using a diffusion kernel is able to best describe participant choices, and also predict judgments about expected reward and confidence. This model unifies psychological models of function learning with the Successor Representation used in reinforcement learning, thereby building a bridge between different models of generalization.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133151321","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}
We update our beliefs based on evidence. Aberrant belief updating has been linked to schizophrenia and autism. It is not clear whether the faulty updating is due to reducedgeneral cognitive abilities, overweighting of recent information, or lower thresholds for switching from one belief to another. A common task to assess belief updating isthe beads task. Patients with schizophrenia show hasty decision-making.We here present a model describing the deviations from an ideal Bayesian observer and apply the model to three independent datasets, totalling n=176 healthy controlsand n=128 patients with schizophrenia. The parameters describe a) the number of beads considered (memory), b) systematic deviation and c) unsystematic deviations (volatility) from probability estimates.We find that, on average, patients use fewer beads and or more volatile responding. However, patients have, on average, probability estimates that are closer to the true probabilities. Closer investigations yielded relevant differences among the datasets and sequences used. Morechallenging sequences improve the performance of patients.Our model captures well the cognitive mechanisms proposed to contribute to the performance differences in the beads task.
{"title":"Probabilistic reasoning in schizophrenia is volatile but not biased","authors":"G. Pfuhl, H. Tjelmeland","doi":"10.31219/osf.io/r69km","DOIUrl":"https://doi.org/10.31219/osf.io/r69km","url":null,"abstract":"We update our beliefs based on evidence. Aberrant belief updating has been linked to schizophrenia and autism. It is not clear whether the faulty updating is due to reducedgeneral cognitive abilities, overweighting of recent information, or lower thresholds for switching from one belief to another. A common task to assess belief updating isthe beads task. Patients with schizophrenia show hasty decision-making.We here present a model describing the deviations from an ideal Bayesian observer and apply the model to three independent datasets, totalling n=176 healthy controlsand n=128 patients with schizophrenia. The parameters describe a) the number of beads considered (memory), b) systematic deviation and c) unsystematic deviations (volatility) from probability estimates.We find that, on average, patients use fewer beads and or more volatile responding. However, patients have, on average, probability estimates that are closer to the true probabilities. Closer investigations yielded relevant differences among the datasets and sequences used. Morechallenging sequences improve the performance of patients.Our model captures well the cognitive mechanisms proposed to contribute to the performance differences in the beads task.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"55 s191","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113953443","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 : 2019-06-01DOI: 10.32470/ccn.2019.1089-0
David Meder, Finn Rabe, Tobias Morville, Kristoffer Hougaard Madsen, Magnus T. Koudahl, R. Dolan, H. Siebner, O. Hulme
Ergodicity describes an equivalence between the expectation value and the time average of observables. Applied to human behaviour, ergodic theory reveals how individuals should tolerate risk in different environments. To optimise wealth over time, agents should adapt their utility function according to the dynamical setting they face. Linear utility is optimal for additive dynamics, whereas logarithmic utility is optimal for multiplicative dynamics. Whether humans approximate time optimal behavior across different dynamics is unknown. Here we compare the effects of additive versus multiplicative gamble dynamics on risky choice. We show that utility functions are modulated by gamble dynamics in ways not explained by prevailing economic theory. Instead, as predicted by time optimality, risk aversion increases under multiplicative dynamics, distributing close to the values that maximise the time average growth of wealth. We suggest that our findings motivate a need for explicitly grounding theories of decision-making on ergodic considerations.
{"title":"Ergodicity-Breaking Reveals Time Optimal Economic Behavior in Humans","authors":"David Meder, Finn Rabe, Tobias Morville, Kristoffer Hougaard Madsen, Magnus T. Koudahl, R. Dolan, H. Siebner, O. Hulme","doi":"10.32470/ccn.2019.1089-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1089-0","url":null,"abstract":"Ergodicity describes an equivalence between the expectation value and the time average of observables. Applied to human behaviour, ergodic theory reveals how individuals should tolerate risk in different environments. To optimise wealth over time, agents should adapt their utility function according to the dynamical setting they face. Linear utility is optimal for additive dynamics, whereas logarithmic utility is optimal for multiplicative dynamics. Whether humans approximate time optimal behavior across different dynamics is unknown. Here we compare the effects of additive versus multiplicative gamble dynamics on risky choice. We show that utility functions are modulated by gamble dynamics in ways not explained by prevailing economic theory. Instead, as predicted by time optimality, risk aversion increases under multiplicative dynamics, distributing close to the values that maximise the time average growth of wealth. We suggest that our findings motivate a need for explicitly grounding theories of decision-making on ergodic considerations.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114855332","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.2019.1215-0
K. Standvoss, Lukas Großberger
Any organism that senses its environment only has an incomplete and noisy perspective on the world, which creates a necessity for nervous systems to represent uncertainty. While the principles of encoding uncertainty in biological neural ensembles are still under investigation, deep learning became a popular and effective machine learning method. In these models, sampling through dropout has been proposed as a mechanism to encode uncertainty. Moreover, dropout has previously been linked to variability in spiking networks under specific assumptions. We compare the relationship between dropout and spiking neuron models by means of the variation ratio over their output. We demonstrate that in cases of incomplete world knowledge (epistemic uncertainty) as well as for noisy observations (aleatoric uncertainty) both neuron models show similar uncertainty representations. These findings provide evidence that sampling could play a fundamental role in representing uncertainties in neural systems.
{"title":"Uncertainty through Sampling: The Correspondence of Monte Carlo Dropout and Spiking in Artificial Neural Networks","authors":"K. Standvoss, Lukas Großberger","doi":"10.32470/ccn.2019.1215-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1215-0","url":null,"abstract":"Any organism that senses its environment only has an incomplete and noisy perspective on the world, which creates a necessity for nervous systems to represent uncertainty. While the principles of encoding uncertainty in biological neural ensembles are still under investigation, deep learning became a popular and effective machine learning method. In these models, sampling through dropout has been proposed as a mechanism to encode uncertainty. Moreover, dropout has previously been linked to variability in spiking networks under specific assumptions. We compare the relationship between dropout and spiking neuron models by means of the variation ratio over their output. We demonstrate that in cases of incomplete world knowledge (epistemic uncertainty) as well as for noisy observations (aleatoric uncertainty) both neuron models show similar uncertainty representations. These findings provide evidence that sampling could play a fundamental role in representing uncertainties in neural systems.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"66 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":"115671048","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.2019.1434-0
Denis Alevi, R. Kempter, Henning Sprekeler
Systems memory consolidation describes the process of transferring and transforming initially hippocampusdependent declarative memories into stable representations in the neocortex. Experimental evidence indicates that neural replay during sleep is linked to this process. While multiple phenomenological theories of systems consolidation have been proposed, a mechanistic theory on the level of neurons and synapses is missing. Here, we study how episodic memories change over time in a recently suggested computational model for the neuronal basis of systems memory consolidation. We implement the proposed mechanism in artificial neural networks and show that memory transfer in the model facilitates the forgetting of episodic detail in memories and enhances the extraction of semantic generalizations. Moreover, we show that neural replay enhances the speed of consolidation and can in certain situations be necessary for the extraction of semantic memories. The latter appears to be the case specifically for the extraction of semantic content from a rapidly learning hippocampal system.
{"title":"From episodic to semantic memory: A computational model","authors":"Denis Alevi, R. Kempter, Henning Sprekeler","doi":"10.32470/ccn.2019.1434-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1434-0","url":null,"abstract":"Systems memory consolidation describes the process of transferring and transforming initially hippocampusdependent declarative memories into stable representations in the neocortex. Experimental evidence indicates that neural replay during sleep is linked to this process. While multiple phenomenological theories of systems consolidation have been proposed, a mechanistic theory on the level of neurons and synapses is missing. Here, we study how episodic memories change over time in a recently suggested computational model for the neuronal basis of systems memory consolidation. We implement the proposed mechanism in artificial neural networks and show that memory transfer in the model facilitates the forgetting of episodic detail in memories and enhances the extraction of semantic generalizations. Moreover, we show that neural replay enhances the speed of consolidation and can in certain situations be necessary for the extraction of semantic memories. The latter appears to be the case specifically for the extraction of semantic content from a rapidly learning hippocampal system.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"13 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":"123109319","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.2019.1237-0
A. Braun, Anne E. Urai, T. Donner
Perceptual decisions under uncertainty are often biased by the history of preceding events. For example, observers tend to repeat (or alternate) their judgments of the sensory environment more often than expected by chance (Braun, Urai, & Donner, 2018; Frund, Wichmann, & Macke, 2014). We test the idea that such choice history biases arise from the context-dependent accumulation of internal decision signals across trials (Glaze, Kable, & Gold, 2015). Observers performed a standard visual random dot motion discrimination task near psychophysical threshold in several different environments. Those were made up of different levels of auto-correlation between the stimulus categories in successive trials (Repetitive, Random, or Alternating), and the absence or presence of single-trial outcome feedback. Participants adjusted both the strength and the sign of their history biases to the environment. When no feedback was available this adjustment was driven by previous choices modulated by confidence. When feedback was provided the adjustment was predominantly based on previous stimuli.
{"title":"Choice History Biases Depend on Environmental Stability and State Uncertainty","authors":"A. Braun, Anne E. Urai, T. Donner","doi":"10.32470/ccn.2019.1237-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1237-0","url":null,"abstract":"Perceptual decisions under uncertainty are often biased by the history of preceding events. For example, observers tend to repeat (or alternate) their judgments of the sensory environment more often than expected by chance (Braun, Urai, & Donner, 2018; Frund, Wichmann, & Macke, 2014). We test the idea that such choice history biases arise from the context-dependent accumulation of internal decision signals across trials (Glaze, Kable, & Gold, 2015). Observers performed a standard visual random dot motion discrimination task near psychophysical threshold in several different environments. Those were made up of different levels of auto-correlation between the stimulus categories in successive trials (Repetitive, Random, or Alternating), and the absence or presence of single-trial outcome feedback. Participants adjusted both the strength and the sign of their history biases to the environment. When no feedback was available this adjustment was driven by previous choices modulated by confidence. When feedback was provided the adjustment was predominantly based on previous stimuli.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"314 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":"124459575","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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