scholarly journals Fast ventral stream neural activity enables rapid visual categorization

2015 ◽  
Author(s):  
Maxime Cauchoix ◽  
Sébastien M Crouzet ◽  
Denis Fize ◽  
Thomas Serre
Keyword(s):  
Neural Activity ◽  
Stimulus Onset ◽  
Ventral Stream ◽  
Natural Scenes ◽  
Neural Decoding ◽  
Multivariate Pattern Analysis ◽  
Visual Categorization ◽  
Shape Processing ◽  
Perceptual Abilities ◽  
Multivariate Pattern

Primates can recognize objects embedded in complex natural scenes in a glimpse. Rapid categorization paradigms have been extensively used to study our core perceptual abilities when the visual system is forced to operate under strong time constraints. However, the neural underpinning of rapid categorization remains to be understood, and the incredible speed of sight has yet to be reconciled with modern ventral stream cortical theories of shape processing. Here we recorded multichannel subdural electrocorticogram (ECoG) signals from intermediate areas (V4/PIT) of the ventral stream of the visual cortex while monkeys were actively engaged in detecting the presence or absence of animal targets in natural scenes. Using multivariate pattern analysis (MVPA) techniques, we quantified at millisecond precision task-relevant signals conveyed by ECoG data. Reliable neural decoding was possible shortly after stimulus onset from single trials with a degree of generalization to experimental manipulations closely mimicking monkeys’ accuracy and reaction time. Together, the present study suggests that rapid ventral stream neural activity induces a selective task-relevant signal subsequently used to drive visual categorization.

scholarly journals Decoding the Brain: Neural Representation and the Limits of Multivariate Pattern Analysis in Cognitive Neuroscience

2017 ◽  
Author(s):  
J. Brendan Ritchie ◽  
David Michael Kaplan ◽  
Colin Klein
Keyword(s):  
Cognitive Neuroscience ◽  
Strong Evidence ◽  
Neural Activity ◽  
Pattern Analysis ◽  
Neural Representation ◽  
Neural Decoding ◽  
Multivariate Pattern Analysis ◽  
Neural Representations ◽  
Multivariate Pattern ◽  
The Brain

AbstractSince its introduction, multivariate pattern analysis (MVPA), or “neural decoding”, has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the Decoder’s Dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence about what information those patterns represent. Although the Dictum is a widely held and well-motivated principle in decoding research, it has received scant philosophical attention. We critically evaluate the Dictum, arguing that it is false: decodability is a poor guide for revealing the content of neural representations. However, we also suggest how the Dictum can be improved on, in order to better justify inferences about neural representation using MVPA.

Stable Task Representations under Attentional Load Revealed with Multivariate Pattern Analysis of Human Brain Activity

2015 ◽  
Vol 27 (9) ◽  
pp. 1789-1800 ◽  
Author(s):  
Jason L. Chan ◽  
Aaron Kucyi ◽  
Joseph F. X. DeSouza
Keyword(s):  
Neural Activity ◽  
Pattern Analysis ◽  
Univariate Analysis ◽  
Visual Presentation ◽  
Error Rates ◽  
Oculomotor Control ◽  
Multivariate Pattern Analysis ◽  
Attentional Load ◽  
Multivariate Pattern ◽  
Task Representations

Performing multiple tasks concurrently places a load on limited attentional resources and results in disrupted task performance. Although human neuroimaging studies have investigated the neural correlates of attentional load, how attentional load affects task processing is poorly understood. Here, task-related neural activity was investigated using fMRI with conventional univariate analysis and multivariate pattern analysis (MVPA) while participants performed blocks of prosaccades and antisaccades, either with or without a rapid serial visual presentation (RSVP) task. Performing prosaccades and antisaccades with RSVP increased error rates and RTs, decreased mean activation in frontoparietal brain areas associated with oculomotor control, and eliminated differences in activation between prosaccades and antisaccades. However, task identity could be decoded from spatial patterns of activation both in the absence and presence of an attentional load. Furthermore, in the FEFs and intraparietal sulcus, these spatial representations were found to be similar using cross-trial-type MVPA, which suggests stability under attentional load. These results demonstrate that attentional load may disrupt the strength of task-related neural activity, rather than the identity of task representations.

scholarly journals Visual processing and attention rather than face and emotion processing play a distinct role in ASD: an EEG study

2019 ◽  
Author(s):  
Adyasha Tejaswi Khuntia ◽  
Rechu Divakar ◽  
Fabio Apicella ◽  
Filippo Muratori ◽  
Koel Das
Keyword(s):  
Visual Processing ◽  
Emotion Processing ◽  
Autism Spectrum ◽  
Stimulus Onset ◽  
Perceptual Task ◽  
Typically Developing ◽  
Multivariate Pattern Analysis ◽  
Social Reciprocity ◽  
Multivariate Pattern

AbstractAutism Spectrum Disorder results in deficit in social interaction, non-verbal communication and social reciprocity. Cognitive tasks pertaining to emotion processing are often preferred to distinguish the ASD children from the typically developing ones. We analysed the role of face and emotion processing in ASD and explored the feasibility of using EEG as a neural marker for detecting ASD. Subjects performed a visual perceptual task with face and nonface stimuli. Successful ASD detection was possible as early as 50 ms. post stimulus onset. Alpha and Beta oscillations seem to best identify autistic individuals. Multivariate pattern analysis and source localization studies points to the role of early visual processing and attention rather than emotion and face processing in detecting autism.

scholarly journals An empirically-driven guide on using Bayes Factors for M/EEG decoding

2021 ◽  
Author(s):  
Lina Teichmann ◽  
Denise Moerel ◽  
Chris I Baker ◽  
Tijl Grootswagers
Keyword(s):  
Time Series ◽  
Cognitive Science ◽  
Pattern Analysis ◽  
Analysis Data ◽  
Bayes Factors ◽  
Neural Decoding ◽  
Multivariate Pattern Analysis ◽  
Multivariate Pattern

Bayes Factors can be used to provide quantifiable evidence for contrasting hypotheses and have thus become increasingly popular in cognitive science. However, Bayes Factors are rarely used to statistically assess the results of neuroimaging experiments. Here, we provide an empirically-driven guide on implementing Bayes Factors for time-series neural decoding results. Using real and simulated Magnetoencephalography (MEG) data, we examine how parameters such as the shape of the prior and data size affect Bayes Factors. Additionally, we discuss benefits Bayes Factors bring to analysing multivariate pattern analysis data and show how using Bayes Factors can be used instead or in addition to traditional frequentist approaches.

scholarly journals Fast ventral stream neural activity enables rapid visual categorization

NeuroImage ◽  
2016 ◽  
Vol 125 ◽  
pp. 280-290 ◽  
Author(s):  
Maxime Cauchoix ◽  
Sébastien M. Crouzet ◽  
Denis Fize ◽  
Thomas Serre
Keyword(s):  
Neural Activity ◽  
Ventral Stream ◽  
Visual Categorization

scholarly journals STMVPA: Spatiotemporal multivariate pattern analysis permits fine-grained visual categorization

2011 ◽  
Vol 11 (11) ◽  
pp. 814-814 ◽  
Author(s):  
S. V. Fogelson ◽  
P. J. Kohler ◽  
M. Hanke ◽  
Y. O. Halchenko ◽  
J. V. Haxby ◽  
...  
Keyword(s):  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Visual Categorization ◽  
Fine Grained ◽  
Multivariate Pattern

scholarly journals Early Numerosity Encoding in Visual Cortex Is Not Sufficient for the Representation of Numerical Magnitude

2018 ◽  
Vol 30 (12) ◽  
pp. 1788-1802 ◽  
Author(s):  
Michele Fornaciai ◽  
Joonkoo Park
Keyword(s):  
Visual Cortex ◽  
Neural Activity ◽  
Stimulus Onset ◽  
Neural Representation ◽  
Numerical Magnitude ◽  
Sensory Representation ◽  
Area V2 ◽  
Early Visual Cortex ◽  
Multivariate Pattern ◽  
Number Perception

Recent studies have demonstrated that the numerosity of visually presented dot arrays is represented in low-level visual cortex extremely early in latency. However, whether or not such an early neural signature reflects the perceptual representation of numerosity remains unknown. Alternatively, such a signature may indicate the raw sensory representation of the dot-array stimulus before becoming the perceived representation of numerosity. Here, we addressed this question by using the connectedness illusion, whereby arrays with pairwise connected dots are perceived to be less numerous compared with arrays containing isolated dots. Using EEG and fMRI in two independent experiments, we measured neural responses to dot-array stimuli comprising 16 or 32 dots, either isolated or pairwise connected. The effect of connectedness, which reflects the segmentation of the visual stimulus into perceptual units, was observed in the neural activity after 150 msec post stimulus onset in the EEG experiment and in area V3 in the fMRI experiment using a multivariate pattern analysis. In contrast, earlier neural activity before 100 msec and in area V2 was strictly modulated by numerosity regardless of connectedness, suggesting that this early activity reflects the sensory representation of a dot array before perceptual segmentation. Our findings thus demonstrate that the neural representation for numerosity in early visual cortex is not sufficient for visual number perception and suggest that the perceptual encoding of numerosity occurs at or after the segmentation process that takes place later in area V3.

scholarly journals Decoding Attended Information in Short-term Memory: An EEG Study

2013 ◽  
Vol 25 (1) ◽  
pp. 127-142 ◽  
Author(s):  
Joshua J. LaRocque ◽  
Jarrod A. Lewis-Peacock ◽  
Andrew T. Drysdale ◽  
Klaus Oberauer ◽  
Bradley R. Postle
Keyword(s):  
Neural Activity ◽  
Memory Trace ◽  
Pattern Analysis ◽  
Short Term Memory ◽  
Focus Of Attention ◽  
Multivariate Pattern Analysis ◽  
Short Term ◽  
Term Retention ◽  
Fmri Study ◽  
Multivariate Pattern

For decades it has been assumed that sustained, elevated neural activity—the so-called active trace—is the neural correlate of the short-term retention of information. However, a recent fMRI study has suggested that this activity may be more related to attention than to retention. Specifically, a multivariate pattern analysis failed to find evidence that information that was outside the focus of attention, but nonetheless in STM, was retained in an active state. Here, we replicate and extend this finding by querying the neural signatures of attended versus unattended information within STM with electroencephalograpy (EEG), a method sensitive to oscillatory neural activity to which the previous fMRI study was insensitive. We demonstrate that in the delay-period EEG activity, there is information only about memory items that are also in the focus of attention. Information about items outside the focus of attention is not detectable. This result converges with the fMRI findings to suggest that, contrary to conventional wisdom, an active memory trace may be unnecessary for the short-term retention of information.

Decoding natural scenes based on sounds of objects within scenes using multivariate pattern analysis

2019 ◽  
Vol 148 ◽  
pp. 9-18 ◽  
Author(s):  
Xiaojing Wang ◽  
Jin Gu ◽  
Junhai Xu ◽  
Xianglin Li ◽  
Junzu Geng ◽  
...  
Keyword(s):  
Pattern Analysis ◽  
Natural Scenes ◽  
Multivariate Pattern Analysis ◽  
Multivariate Pattern
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