scholarly journals Neural Correlates of Multisensory Reliability and Perceptual Weights Emerge at Early Latencies during Audio-visual Integration

2017 ◽  
Author(s):  
Stephanie C Boyle ◽  
Stephanie J Kayser ◽  
Christoph Kayser
Keyword(s):  
Temporal Dynamics ◽  
Sensory Information ◽  
Neural Correlates ◽  
Stimulus Onset ◽  
Neuronal Responses ◽  
Visual Integration ◽  
Neural Processes ◽  
Process Effects ◽  
The Brain ◽  
Perceptual Weights

To make accurate perceptual estimates observers must take the reliability of sensory information into account. Despite many behavioural studies showing that subjects weight individual sensory cues in proportion to their reliabilities, it is still unclear when during a trial neuronal responses are modulated by the reliability of sensory information, or when they reflect the perceptual weights attributed to each sensory input during decision making. We investigated these questions using a combination of psychophysics, EEG based neuroimaging and single-trial decoding. Our results show that the weighted integration of sensory information in the brain is a dynamic process; effects of sensory reliability on task-relevant EEG components were evident around 84ms after stimulus onset, while neural correlates of perceptual weights emerged around 120ms after stimulus onset. These neural processes also had different underlying topographies, arising from areas consistent with sensory and parietal regions. Together these results reveal the temporal dynamics of perceptual and neural audio-visual integration and support the notion of temporally early and functionally specific multisensory processes in the brain.

scholarly journals Neural Correlates of Evidence and Urgency During Human Perceptual Decision-Making in Dynamically Changing Conditions

2020 ◽  
Vol 30 (10) ◽  
pp. 5471-5483
Author(s):  
Y Yau ◽  
M Dadar ◽  
M Taylor ◽  
Y Zeighami ◽  
L K Fellows ◽  
...  
Keyword(s):  
Decision Making ◽  
Computational Models ◽  
Sensory Information ◽  
Neural Correlates ◽  
Primate Research ◽  
Perceptual Decision Making ◽  
Emotional Information ◽  
Perceptual Decision ◽  
The Face ◽  
The Brain

Abstract Current models of decision-making assume that the brain gradually accumulates evidence and drifts toward a threshold that, once crossed, results in a choice selection. These models have been especially successful in primate research; however, transposing them to human fMRI paradigms has proved it to be challenging. Here, we exploit the face-selective visual system and test whether decoded emotional facial features from multivariate fMRI signals during a dynamic perceptual decision-making task are related to the parameters of computational models of decision-making. We show that trial-by-trial variations in the pattern of neural activity in the fusiform gyrus reflect facial emotional information and modulate drift rates during deliberation. We also observed an inverse-urgency signal based in the caudate nucleus that was independent of sensory information but appeared to slow decisions, particularly when information in the task was ambiguous. Taken together, our results characterize how decision parameters from a computational model (i.e., drift rate and urgency signal) are involved in perceptual decision-making and reflected in the activity of the human brain.

Impulsivity and neural correlates of response inhibition in schizophrenia

2010 ◽  
Vol 41 (2) ◽  
pp. 291-299 ◽  
Author(s):  
A. Kaladjian ◽  
R. Jeanningros ◽  
J.-M. Azorin ◽  
J.-L. Anton ◽  
P. Mazzola-Pomietto
Keyword(s):  
Response Inhibition ◽  
Healthy Subjects ◽  
Neural Correlates ◽  
Brain Network ◽  
Cerebral Activation ◽  
Neural Processes ◽  
Psychometric Measures ◽  
The Right ◽  
The Relationship ◽  
The Brain

BackgroundThe clinical picture of schizophrenia is frequently worsened by manifestations of impulsivity. However, the neural correlates of impulsivity in this disorder are poorly known. Although impulsivity has been related to disturbances of the neural processes underlying response inhibition, no studies have yet examined the relationship between these processes and psychometric measures of impulsivity in schizophrenia. This was the aim of the current investigation.MethodEvent-related functional magnetic resonance imaging in conjunction with a Go/NoGo task was employed to probe the neural activity associated with response inhibition in 26 patients with schizophrenia and 30 healthy comparison subjects. All participants also completed the Barratt Impulsiveness Scale – version 11 (BIS-11). Voxel-wise regression analyses were used to examine the relationship between the BIS-11 score and brain activation during response inhibition in each group.ResultsPatients with schizophrenia were more impulsive than healthy subjects, as indicated by higher BIS-11 scores. Patients, but not healthy subjects, were found to display a positive correlation between these scores and cerebral activation associated with response inhibition. This correlation involves a unique cluster localized within the right ventrolateral prefrontal cortex (VLPFC), a key node of the brain network subserving response inhibition.ConclusionsWe evidenced in patients with schizophrenia that greater BIS-11 scores are associated with greater activation within the right VLPFC during response inhibition. This finding suggests that the efficiency of this brain region to process inhibitory control is reduced in the more impulsive patients.

scholarly journals Autistic traits, but not schizotypy, predict overweighting of sensory information in Bayesian visual integration

2017 ◽  
Author(s):  
Povilas Karvelis ◽  
Aaron R. Seitz ◽  
Stephen M. Lawrie ◽  
Peggy Seriès
Keyword(s):  
Bayesian Inference ◽  
Statistical Learning ◽  
Bayesian Modeling ◽  
Autistic Spectrum Disorder ◽  
Sensory Information ◽  
Autistic Traits ◽  
Veridical Perception ◽  
Visual Integration ◽  
The Brain ◽  
Healthy Participants

Recent theories propose that schizophrenia/schizotypy and autistic spectrum disorder are related to impairments in Bayesian inference i.e. how the brain integrates sensory information (likelihoods) with prior knowledge. However existing accounts fail to clarify: i) how proposed theories differ in accounts of ASD vs. schizophrenia and ii) whether the impairments result from weaker priors or enhanced likelihoods. Here, we directly address these issues by characterizing how 91 healthy participants, scored for autistic and schizotypal traits, implicitly learned and combined priors with sensory information. This was accomplished through a visual statistical learning paradigm designed to quantitatively assess variations in individuals’ likelihoods and priors. The acquisition of the priors was found to be intact along both traits spectra. However, autistic traits were associated with more veridical perception and weaker influence of expectations. Bayesian modeling revealed that this was due not to weaker prior expectations but to more precise sensory representations.

scholarly journals Neural correlates of evidence and urgency during human perceptual decision-making in dynamically changing conditions

2019 ◽  
Author(s):  
Y. Yau ◽  
M. Dadar ◽  
M. Taylor ◽  
Y. Zeighami ◽  
L.K. Fellows ◽  
...  
Keyword(s):  
Decision Making ◽  
Computational Models ◽  
Sensory Information ◽  
Neural Correlates ◽  
Primate Research ◽  
Perceptual Decision Making ◽  
Emotional Information ◽  
Perceptual Decision ◽  
The Face ◽  
The Brain

AbstractCurrent models of decision-making assume that the brain gradually accumulates evidence and drifts towards a threshold which, once crossed, results in a choice selection. These models have been especially successful in primate research, however transposing them to human fMRI paradigms has proved challenging. Here, we exploit the face-selective visual system and test whether decoded emotional facial features from multivariate fMRI signals during a dynamic perceptual decision-making task are related to the parameters of computational models of decision-making. We show that trial-by-trial variations in the pattern of neural activity in the fusiform gyrus reflect facial emotional information and modulate drift rates during deliberation. We also observed an inverse-urgency signal based in the caudate nucleus that was independent of sensory information but appeared to slow decisions, particularly when information in the task was ambiguous. Taken together, our results characterize how decision parameters from a computational model (i.e., drift rate and urgency signal) are involved in perceptual decision-making and reflected in the activity of the human brain.

scholarly journals Temporal Dynamics of Neural Activity at the Moment of Emergence of Conscious Percept

2012 ◽  
Vol 24 (10) ◽  
pp. 1983-1997 ◽  
Author(s):  
Yasuki Noguchi ◽  
Takemasa Yokoyama ◽  
Megumi Suzuki ◽  
Shinichi Kita ◽  
Ryusuke Kakigi
Keyword(s):  
Neural Activity ◽  
Temporal Dynamics ◽  
Rapid Change ◽  
Neural Correlates ◽  
Conscious Perception ◽  
Bottom Up ◽  
Neural Correlates Of Consciousness ◽  
Ventral Pathway ◽  
The Moment ◽  
The Brain

From which regions of the brain do conscious representations of visual stimuli emerge? This is an important but controversial issue in neuroscience because some studies have reported a major role of the higher visual regions of the ventral pathway in conscious perception, whereas others have found neural correlates of consciousness as early as in the primary visual areas and in the thalamus. One reason for this controversy has been the difficulty in focusing on neural activity at the moment when conscious percepts are generated in the brain, excluding any bottom–up responses (not directly related to consciousness) that are induced by stimuli. In this study, we address this issue with a new approach that can induce a rapid change in conscious perception with little influence from bottom–up responses. Our results reveal that the first consciousness-related activity emerges from the higher visual region of the ventral pathway. However, this activity is rapidly diffused to the entire brain, including the early visual cortex. These results thus integrate previous “higher” and “lower” views on the emergence of neural correlates of consciousness, providing a new perspective for the temporal dynamics of consciousness.

scholarly journals Autistic traits, but not schizotypy, predict increased weighting of sensory information in Bayesian visual integration

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Povilas Karvelis ◽  
Aaron R Seitz ◽  
Stephen M Lawrie ◽  
Peggy Seriès
Keyword(s):  
Bayesian Inference ◽  
Statistical Learning ◽  
Bayesian Modeling ◽  
Autistic Spectrum Disorder ◽  
Sensory Information ◽  
Autistic Traits ◽  
Veridical Perception ◽  
Visual Integration ◽  
The Brain ◽  
Healthy Participants

Recent theories propose that schizophrenia/schizotypy and autistic spectrum disorder are related to impairments in Bayesian inference that is, how the brain integrates sensory information (likelihoods) with prior knowledge. However existing accounts fail to clarify: (i) how proposed theories differ in accounts of ASD vs. schizophrenia and (ii) whether the impairments result from weaker priors or enhanced likelihoods. Here, we directly address these issues by characterizing how 91 healthy participants, scored for autistic and schizotypal traits, implicitly learned and combined priors with sensory information. This was accomplished through a visual statistical learning paradigm designed to quantitatively assess variations in individuals’ likelihoods and priors. The acquisition of the priors was found to be intact along both traits spectra. However, autistic traits were associated with more veridical perception and weaker influence of expectations. Bayesian modeling revealed that this was due, not to weaker prior expectations, but to more precise sensory representations.

Relation Between Level of Prefrontal Activity and Subject's Performance

1999 ◽  
Vol 13 (2) ◽  
pp. 117-125 ◽  
Author(s):  
Laurence Casini ◽  
Françoise Macar ◽  
Marie-Hélène Giard
Keyword(s):  
Brain Activity ◽  
Sensory Information ◽  
Practice Phase ◽  
Trained Subjects ◽  
Anagram Task ◽  
Economic Information ◽  
Long Duration ◽  
Level Of Activity ◽  
The Brain ◽  
Processing Mechanism

Abstract The experiment reported here was aimed at determining whether the level of brain activity can be related to performance in trained subjects. Two tasks were compared: a temporal and a linguistic task. An array of four letters appeared on a screen. In the temporal task, subjects had to decide whether the letters remained on the screen for a short or a long duration as learned in a practice phase. In the linguistic task, they had to determine whether the four letters could form a word or not (anagram task). These tasks allowed us to compare the level of brain activity obtained in correct and incorrect responses. The current density measures recorded over prefrontal areas showed a relationship between the performance and the level of activity in the temporal task only. The level of activity obtained with correct responses was lower than that obtained with incorrect responses. This suggests that a good temporal performance could be the result of an efficacious, but economic, information-processing mechanism in the brain. In addition, the absence of this relation in the anagram task results in the question of whether this relation is specific to the processing of sensory information only.

Measuring the World

2018 ◽  
Author(s):  
Ann-Sophie Barwich
Keyword(s):  
Sensory Information ◽  
Network Models ◽  
Model System ◽  
Stimulus Input ◽  
Expectancy Effects ◽  
Neural Network Models ◽  
Perceptual Object ◽  
External Objects ◽  
The Common ◽  
The Brain

How much does stimulus input shape perception? The common-sense view is that our perceptions are representations of objects and their features and that the stimulus structures the perceptual object. The problem for this view concerns perceptual biases as responsible for distortions and the subjectivity of perceptual experience. These biases are increasingly studied as constitutive factors of brain processes in recent neuroscience. In neural network models the brain is said to cope with the plethora of sensory information by predicting stimulus regularities on the basis of previous experiences. Drawing on this development, this chapter analyses perceptions as processes. Looking at olfaction as a model system, it argues for the need to abandon a stimulus-centred perspective, where smells are thought of as stable percepts, computationally linked to external objects such as odorous molecules. Perception here is presented as a measure of changing signal ratios in an environment informed by expectancy effects from top-down processes.

scholarly journals Stability and flexibility in multisensory sampling: insights from perceptual illusions

2019 ◽  
Vol 121 (5) ◽  
pp. 1588-1590 ◽  
Author(s):  
Luca Casartelli
Keyword(s):  
Temporal Dynamics ◽  
Multisensory Processing ◽  
General Idea ◽  
Perceptual Illusions ◽  
The Brain ◽  
Multisensory Processes

Neural, oscillatory, and computational counterparts of multisensory processing remain a crucial challenge for neuroscientists. Converging evidence underlines a certain efficiency in balancing stability and flexibility of sensory sampling, supporting the general idea that multiple parallel and hierarchically organized processing stages in the brain contribute to our understanding of the (sensory/perceptual) world. Intriguingly, how temporal dynamics impact and modulate multisensory processes in our brain can be investigated benefiting from studies on perceptual illusions.

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