scholarly journals Is predictability salient? A study of attentional capture by auditory patterns

2017 ◽  
Vol 372 (1714) ◽  
pp. 20160105 ◽  
Author(s):  
Rosy Southwell ◽  
Anna Baumann ◽  
Cécile Gal ◽  
Nicolas Barascud ◽  
Karl Friston ◽  
...  
Keyword(s):  
Attentional Capture ◽  
Predictive Coding ◽  
Visual Scene ◽  
Scene Analysis ◽  
Neural Responses ◽  
Eeg Data ◽  
Visual Domain ◽  
The Brain ◽  
Repeating Patterns

In this series of behavioural and electroencephalography (EEG) experiments, we investigate the extent to which repeating patterns of sounds capture attention. Work in the visual domain has revealed attentional capture by statistically predictable stimuli, consistent with predictive coding accounts which suggest that attention is drawn to sensory regularities. Here, stimuli comprised rapid sequences of tone pips, arranged in regular (REG) or random (RAND) patterns. EEG data demonstrate that the brain rapidly recognizes predictable patterns manifested as a rapid increase in responses to REG relative to RAND sequences. This increase is reminiscent of the increase in gain on neural responses to attended stimuli often seen in the neuroimaging literature, and thus consistent with the hypothesis that predictable sequences draw attention. To study potential attentional capture by auditory regularities, we used REG and RAND sequences in two different behavioural tasks designed to reveal effects of attentional capture by regularity. Overall, the pattern of results suggests that regularity does not capture attention. This article is part of the themed issue ‘Auditory and visual scene analysis’.

scholarly journals Auditory and visual scene analysis: an overview

2017 ◽  
Vol 372 (1714) ◽  
pp. 20160099 ◽  
Author(s):  
Hirohito M. Kondo ◽  
Anouk M. van Loon ◽  
Jun-Ichiro Kawahara ◽  
Brian C. J. Moore
Keyword(s):  
Scene Perception ◽  
Computational Modelling ◽  
Visual Scene ◽  
Scene Analysis ◽  
Theoretical Approaches ◽  
Visual Inputs ◽  
Sensory Modalities ◽  
Stimulus Features ◽  
The Brain ◽  
Selection Of

We perceive the world as stable and composed of discrete objects even though auditory and visual inputs are often ambiguous owing to spatial and temporal occluders and changes in the conditions of observation. This raises important questions regarding where and how ‘scene analysis’ is performed in the brain. Recent advances from both auditory and visual research suggest that the brain does not simply process the incoming scene properties. Rather, top-down processes such as attention, expectations and prior knowledge facilitate scene perception. Thus, scene analysis is linked not only with the extraction of stimulus features and formation and selection of perceptual objects, but also with selective attention, perceptual binding and awareness. This special issue covers novel advances in scene-analysis research obtained using a combination of psychophysics, computational modelling, neuroimaging and neurophysiology, and presents new empirical and theoretical approaches. For integrative understanding of scene analysis beyond and across sensory modalities, we provide a collection of 15 articles that enable comparison and integration of recent findings in auditory and visual scene analysis. This article is part of the themed issue ‘Auditory and visual scene analysis’.

scholarly journals Prediction Error and Repetition Suppression Have Distinct Effects on Neural Representations of Visual Information

2017 ◽  
Author(s):  
Matthew F. Tang ◽  
Cooper A. Smout ◽  
Ehsan Arabzadeh ◽  
Jason B. Mattingley
Keyword(s):  
Visual Information ◽  
Predictive Coding ◽  
Orientation Selectivity ◽  
Recent Experience ◽  
Prediction Errors ◽  
Neural Responses ◽  
Repetition Suppression ◽  
Neural Representations ◽  
Feature Information ◽  
The Brain

AbstractPredictive coding theories argue that recent experience establishes expectations in the brain that generate prediction errors when violated. Prediction errors provide a possible explanation for repetition suppression, where evoked neural activity is attenuated across repeated presentations of the same stimulus. The predictive coding account argues repetition suppression arises because repeated stimuli are expected, whereas non-repeated stimuli are unexpected and thus elicit larger neural responses. Here we employed electroencephalography in humans to test the predictive coding account of repetition suppression by presenting sequences of visual gratings with orientations that were expected either to repeat or change in separate blocks of trials. We applied multivariate forward modelling to determine how orientation selectivity was affected by repetition and prediction. Unexpected stimuli were associated with significantly enhanced orientation selectivity, whereas selectivity was unaffected for repeated stimuli. Our results suggest that repetition suppression and expectation have separable effects on neural representations of visual feature information.

scholarly journals Prediction error and repetition suppression have distinct effects on neural representations of visual information

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Matthew F Tang ◽  
Cooper A Smout ◽  
Ehsan Arabzadeh ◽  
Jason B Mattingley
Keyword(s):  
Visual Information ◽  
Predictive Coding ◽  
Orientation Selectivity ◽  
Recent Experience ◽  
Prediction Errors ◽  
Neural Responses ◽  
Repetition Suppression ◽  
Neural Representations ◽  
Feature Information ◽  
The Brain

Predictive coding theories argue that recent experience establishes expectations in the brain that generate prediction errors when violated. Prediction errors provide a possible explanation for repetition suppression, where evoked neural activity is attenuated across repeated presentations of the same stimulus. The predictive coding account argues repetition suppression arises because repeated stimuli are expected, whereas non-repeated stimuli are unexpected and thus elicit larger neural responses. Here, we employed electroencephalography in humans to test the predictive coding account of repetition suppression by presenting sequences of visual gratings with orientations that were expected either to repeat or change in separate blocks of trials. We applied multivariate forward modelling to determine how orientation selectivity was affected by repetition and prediction. Unexpected stimuli were associated with significantly enhanced orientation selectivity, whereas selectivity was unaffected for repeated stimuli. Our results suggest that repetition suppression and expectation have separable effects on neural representations of visual feature information.

A fuzzy sensitivity analysis approach to estimate brain effective connectivity and its application to epileptic seizure detection

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nader Moharamzadeh ◽  
Ali Motie Nasrabadi
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Effective Connectivity ◽  
Multivariate Time Series ◽  
Inference System ◽  
Eeg Data ◽  
Adaptive Neurofuzzy Inference System ◽  
Intracranial Electroencephalography ◽  
Fuzzy Network ◽  
The Brain

Abstract The brain is considered to be the most complicated organ in human body. Inferring and quantification of effective (causal) connectivity among regions of the brain is an important step in characterization of its complicated functions. The proposed method is comprised of modeling multivariate time series with Adaptive Neurofuzzy Inference System (ANFIS) and carrying out a sensitivity analysis using Fuzzy network parameters as a new approach to introduce a connectivity measure for detecting causal interactions between interactive input time series. The results of simulations indicate that this method is successful in detecting causal connectivity. After validating the performance of the proposed method on synthetic linear and nonlinear interconnected time series, it is applied to epileptic intracranial Electroencephalography (EEG) signals. The result of applying the proposed method on Freiburg epileptic intracranial EEG data recorded during seizure shows that the proposed method is capable of discriminating between the seizure and non-seizure states of the brain.

scholarly journals The initial phase of auditory and visual scene analysis

2012 ◽  
Vol 367 (1591) ◽  
pp. 942-953 ◽  
Author(s):  
Jean-Michel Hupé ◽  
Daniel Pressnitzer
Keyword(s):  
Perceptual Organization ◽  
Initial Phase ◽  
Stimulus Presentation ◽  
Visual Scene ◽  
Scene Analysis ◽  
Systematic Bias ◽  
Auditory Streaming ◽  
Neurophysiological Data ◽  
Presentation Organization ◽  
Over Time

Auditory streaming and visual plaids have been used extensively to study perceptual organization in each modality. Both stimuli can produce bistable alternations between grouped (one object) and split (two objects) interpretations. They also share two peculiar features: (i) at the onset of stimulus presentation, organization starts with a systematic bias towards the grouped interpretation; (ii) this first percept has ‘inertia’; it lasts longer than the subsequent ones. As a result, the probability of forming different objects builds up over time, a landmark of both behavioural and neurophysiological data on auditory streaming. Here we show that first percept bias and inertia are independent. In plaid perception, inertia is due to a depth ordering ambiguity in the transparent (split) interpretation that makes plaid perception tristable rather than bistable: experimental manipulations removing the depth ambiguity suppressed inertia. However, the first percept bias persisted. We attempted a similar manipulation for auditory streaming by introducing level differences between streams, to bias which stream would appear in the perceptual foreground. Here both inertia and first percept bias persisted. We thus argue that the critical common feature of the onset of perceptual organization is the grouping bias, which may be related to the transition from temporally/spatially local to temporally/spatially global computation.

scholarly journals Binaural summation of amplitude modulation involves weak interaural suppression

2018 ◽  
Author(s):  
D.H. Baker ◽  
G. Vilidaite ◽  
E. McClarnon ◽  
E. Valkova ◽  
A. Bruno ◽  
...  
Keyword(s):  
Amplitude Modulation ◽  
Modulation Depth ◽  
Visual Signal ◽  
Data Sets ◽  
Single Object ◽  
Ecological Constraints ◽  
Binaural Summation ◽  
Eeg Data ◽  
Signal Combination ◽  
The Brain

AbstractThe brain combines sounds from the two ears, but what is the algorithm used to achieve this summation of signals? Here we combine psychophysical amplitude modulation discrimination and steady-state electroencephalography (EEG) data to investigate the architecture of binaural combination for amplitude-modulated tones. Discrimination thresholds followed a ‘dipper’ shaped function of pedestal modulation depth, and were consistently lower for binaural than monaural presentation of modulated tones. The EEG responses were greater for binaural than monaural presentation of modulated tones, and when a masker was presented to one ear, it produced only weak suppression of the response to a signal presented to the other ear. Both data sets were well-fit by a computational model originally derived for visual signal combination, but with suppression between the two channels (ears) being much weaker than in binocular vision. We suggest that the distinct ecological constraints on vision and hearing can explain this difference, if it is assumed that the brain avoids over-representing sensory signals originating from a single object. These findings position our understanding of binaural summation in a broader context of work on sensory signal combination in the brain, and delineate the similarities and differences between vision and hearing.

scholarly journals Hyperspherical Manifold for EEG Signals of Epileptic Seizures

2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
Tahir Ahmad ◽  
Vinod Ramachandran
Keyword(s):  
Measure Theory ◽  
Epileptic Seizures ◽  
Physical Symptoms ◽  
Measure Data ◽  
Dimensional Manifold ◽  
Eeg Signals ◽  
Discrete Probability ◽  
Eeg Data ◽  
Manifold Theory ◽  
The Brain

The mathematical modelling of EEG signals of epileptic seizures presents a challenge as seizure data is erratic, often with no visible trend. Limitations in existing models indicate a need for a generalized model that can be used to analyze seizures without the need for apriori information, whilst minimizing the loss of signal data due to smoothing. This paper utilizes measure theory to design a discrete probability measure that reformats EEG data without altering its geometric structure. An analysis of EEG data from three patients experiencing epileptic seizures is made using the developed measure, resulting in successful identification of increased potential difference in portions of the brain that correspond to physical symptoms demonstrated by the patients. A mapping then is devised to transport the measure data onto the surface of a high-dimensional manifold, enabling the analysis of seizures using directional statistics and manifold theory. The subset of seizure signals on the manifold is shown to be a topological space, verifying Ahmad's approach to use topological modelling.

Sign in / Sign up

Export Citation Format

Share Document