PosterPDF Available

Object-based Attention Modulates EEG Alpha Activity

March 2019

Conference: Cognitive Neuroscience Society 2019

Authors:

Sean Noah

University of California, Berkeley

Travis Powell

University of California, Davis

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Object-based attention is the selection of visual information from an object or object category. It may be deployed in anticipation of a task-relevant object or as a template during visual search. In this study, we investigated whether object-based attention modulates electroencephalographic (EEG) alpha-band activity across the scalp, analogously to observed alpha modulations in spatial and feature-based attention experiments. If endogenous visual attention operates by a common mechanism throughout visual cortex, such alpha modulation should be observable whenever top-down attention exerts selective control over visual-cortical activity, regardless of what kind of visual information is selected. To test this hypothesis, we collected EEG data from 20 human participants, performing an anticipatory object-based attention task with three categories of objects: faces, places, and tools. These object categories were chosen on the basis of their differentiated specialized cortical regions. Although it is not possible using this method to unambiguously localize an alpha topography to a specific brain region, large changes in the underlying loci of alpha activity corresponding to effects in face, place, and tool regions would be expected to yield different patterns of alpha over the scalp. We observed reduced reaction time for valid compared to invalidly cued trials across all three object conditions, suggesting that our task produced the intended attention effect. Using SVM decoding and a random cluster analysis over alpha power, we identified time points that differed significantly between object conditions, suggesting that alpha topography is modulated by attention to specific categories of objects.

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Sean Noaha, Travis Powella, Natalia Khodayaria, Diana Olivana, Mingzhou Dingb, George R. Manguna

Object-based Attention Modulates EEG Alpha Activity

a Center for Mind and Brain, University of California, Davis, Davis, CA b J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL

Reaction time comparisons confirm presence of attention effect

Alpha band topographic difference plots reveal contrasts in

scalp-distributed alpha power between attention conditions

SVM decoding of attention condition reveals time course of

significant differences in alpha topography between conditions

Decoding accuracy during anticipatory period correlates with

magnitude of attention effect in reaction time

Poster D10

CNS 2019

Contact:

seannoah@gmail.com

seannoah.github.io

Trials

Cue

0.2 sec

Anticipation

1 – 2.5 sec

Target

0.1 sec

ITI

1.5 – 2.5 sec

• Random effect of Subject

• Gamma-distributed RT model

• Significant effect of Validity (p <

0.001), estimated difference

between valid and invalid trials =

0.068 sec

• Significant effect of Object (p <

0.001)

Generalized linear mixed model

results (see Lo and Andrews, 2015)

Jensen, O. and Mazaheri, A. (2010). Shaping functional architecture by oscillatory alpha activity: gating by inhibition. Front. Hum. Neurosci., 04 November 2010

Worden, M. S., Foxe, J. J., Wang, N., and Simpson, G. V. (2000). Anticipatory biasing of visuospatial attention indexed by retinotopically specific alpha-band electroencephalography increases over occipital

cortex. J. Neurosci. 20, RC63.

Snyder, A. C. and Foxe, J. J. (2010). Anticipatory attentional suppression of visual features indexed by oscillatory alpha-band power increases: a high-density electrical mapping study. J Neurosci. 2010 Mar

17;30(11):4024-32.

Lo S. and Andrews, S. (2015). To transform or not to transform: using generalized linear mixed models to analyse reaction time data. Front. Psychol., 07 August 2015

Decoding procedure adapted from: Bae, G. Y., & Luck, S. J. (2018). Dissociable Decoding of Working Memory and Spatial Attention from EEG Oscillations and Sustained Potentials. The Journal of

Neuroscience, 38, 409-422.

Supported by MH117991 to GRM and MD

Introduction Results

Conclusions

References & Acknowledgements

Methods

Object-based attention paradigm

Participants were instructed:

• Triangle cue indicates upcoming face

• Square cue indicates upcoming place

• Circle cue indicates upcoming tool

• Use cue to prepare for upcoming target

• Press Button 1 (button box, index finger) for

male face, nature scene, or powered tool

• Press Button 2 (button box, middle finger) for

female face, urban scene, or unpowered tool

• All stimuli presented at center fixation

• 80% cue validity, to measure behavioral

difference between validly cued and

invalidly cued trials

• 20 undergraduate volunteer participants

• 420 trials for each participant

• 64 active scalp electrodes (no electrooculogram)

• Average referenced offline

• Less than 5% of trials rejected by manual

inspection for noise and muscle artifacts

• Eye blink artifacts removed by independent

component analysis (ICA)

Electroencephalogram (EEG) recording

and pre-processing details

Example faces Example places Example tools

actiCAP (Brain Products GmbH, Gilching, Germany)

Face minus Scene Face minus Tool Tool minus Scene

• To assess whether alpha topography

contained information about the

attended object category, we utilized a

support vector machine (SVM) learner

to decode the attention condition at

every sample point across the epoch.

• For a given time point, decoding

accuracy significantly above chance

indicates that alpha topography is

significantly different between attention

conditions.

• Magnitude of a behavioral attention

effect (invalid trial RT minus valid trial

RT) correlates with average decoding

accuracy during the anticipatory period.

• Subsetting participants by increasing RT

effect clarifies the timing of significant

differences in alpha between attention

conditions, suggests greatest difference

occurs 500 – 800 msec post-cue.

Details of decoding procedure Details of statistical significance assessment

• One vs. one error-correcting output codes (ECOC)

classification with SVM learner

• 10 iterations of 6-fold cross validation

• Performed within-subject, at every sample point (250 Hz)

• Decoding software from Matlab Deep Learning Toolbox

• At each time point, we performed a one-tailed t test of decoding

accuracy across all participants against chance (one third).

• We constructed a null distribution of the summed t values (t

masses) of h=1 clusters across 1000 iterations of simulated

decoding results using random sampling with replacement.

• We determined the α=0.05 critical t mass from this null distribution.

• Cue-directed anticipation of different object categories produces behavioral attention

effects as conventionally operationalized.

• Anticipatory attention to different categories of objects produces different patterns of

alpha band EEG power at the scalp, suggesting different alpha generators in the brain.

• Scalp-distributed alpha band power is significantly different between attention to faces,

places, and tools in the range 500 – 800 msec.

• Decoding accuracy during the anticipatory epoch is positively correlated with the

magnitude of the attention effect in reaction time, suggesting that more effective

object-based attention produces more clearly distinguishable alpha topographies.

• Alpha band (8-12 Hz) oscillatory neural activity may reflect functional inhibition (Jensen and

Mazaheri, 2010), and may be part of neural mechanisms of attention.

• Anticipatory visual attention to different spatial hemifields is associated with contralateral

decreases in alpha band EEG power (Worden et al., 2000).

• An analogous modulation of alpha-band EEG activity has been observed in brain areas

associated with color and motion in a visual feature-based attention experiment (Snyder

and Foxe, 2010), suggesting a common visual attentional mechanism.

• If top-down visual attention operates on the cortical areas associated with target visual

information by the same mechanism throughout the visual system, we would expect to

observe alpha-band modulation in forms of attention targeting higher-level visual features,

such as objects (including faces, places, tools, etc.)

• Hypothesis: Different patterns of alpha-band EEG scalp topography accompany

anticipatory attention to different categories of objects.

ResearchGate has not been able to resolve any citations for this publication.

Dissociable Decoding of Spatial Attention and Working Memory from EEG Oscillations and Sustained Potentials

Article

Full-text available

Nov 2017

In human scalp EEG recordings, both sustained potentials and alpha-band oscillations are present during the delay period of working memory tasks and may therefore reflect the representation of information in working memory. However, these signals may instead reflect support mechanisms rather than the actual contents of memory. In particular, alpha-band oscillations have been tightly tied to spatial attention and may not reflect location-independent memory representations per se. To determine how sustained and oscillating EEG signals are related to attention and working memory, we attempted to decode which of 16 orientations was being held in working memory by human observers (both women and men). We found that sustained EEG activity could be used to decode the remembered orientation of a stimulus, even when the orientation of the stimulus varied independently of its location. Alpha-band oscillations also carried clear information about the location of the stimulus, but they provided little or no information about orientation independently of location. Thus, sustained potentials contain information about the object properties being maintained in working memory, consistent with previous evidence of a tight link between these potentials and working memory capacity. In contrast, alpha-band oscillations primarily carry location information, consistent with their link to spatial attention.

To transform or not to transform: using generalized linear mixed models to analyse reaction time data

Article

Full-text available

Aug 2015

Linear mixed-effect models (LMMs) are being increasingly widely used in psychology to analyse multi-level research designs. This feature allows LMMs to address some of the problems identified by Speelman and McGann (2013) about the use of mean data, because they do not average across individual responses. However, recent guidelines for using LMM to analyse skewed reaction time (RT) data collected in many cognitive psychological studies recommend the application of non-linear transformations to satisfy assumptions of normality. Uncritical adoption of this recommendation has important theoretical implications which can yield misleading conclusions. For example, Balota et al. (2013) showed that analyses of raw RT produced additive effects of word frequency and stimulus quality on word identification, which conflicted with the interactive effects observed in analyses of transformed RT. Generalized linear mixed-effect models (GLMM) provide a solution to this problem by satisfying normality assumptions without the need for transformation. This allows differences between individuals to be properly assessed, using the metric most appropriate to the researcher's theoretical context. We outline the major theoretical decisions involved in specifying a GLMM, and illustrate them by reanalysing Balota et al.'s datasets. We then consider the broader benefits of using GLMM to investigate individual differences.

Shaping Functional Architecture by Oscillatory Alpha Activity: Gating by Inhibition

Article

Full-text available

Nov 2010
FRONT HUM NEUROSCI

In order to understand the working brain as a network, it is essential to identify the mechanisms by which information is gated between regions. We here propose that information is gated by inhibiting task-irrelevant regions, thus routing information to task-relevant regions. The functional inhibition is reflected in oscillatory activity in the alpha band (8–13 Hz). From a physiological perspective the alpha activity provides pulsed inhibition reducing the processing capabilities of a given area. Active processing in the engaged areas is reflected by neuronal synchronization in the gamma band (30–100 Hz) accompanied by an alpha band decrease. According to this framework the brain could be studied as a network by investigating cross-frequency interactions between gamma and alpha activity. Specifically the framework predicts that optimal task performance will correlate with alpha activity in task-irrelevant areas. In this review we will discuss the empirical support for this framework. Given that alpha activity is by far the strongest signal recorded by EEG and MEG, we propose that a major part of the electrophysiological activity detected from the working brain reflects gating by inhibition.

Anticipatory Attentional Suppression of Visual Features Indexed by Oscillatory Alpha-Band Power Increases: A High-Density Electrical Mapping Study

Article

Full-text available

Mar 2010
J NEUROSCI

Retinotopically specific increases in alpha-band ( approximately 10 Hz) oscillatory power have been strongly implicated in the suppression of processing for irrelevant parts of the visual field during the deployment of visuospatial attention. Here, we asked whether this alpha suppression mechanism also plays a role in the nonspatial anticipatory biasing of feature-based attention. Visual word cues informed subjects what the task-relevant feature of an upcoming visual stimulus (S2) was, while high-density electroencephalographic recordings were acquired. We examined anticipatory oscillatory activity in the Cue-to-S2 interval ( approximately 2 s). Subjects were cued on a trial-by-trial basis to attend to either the color or direction of motion of an upcoming dot field array, and to respond when they detected that a subset of the dots differed from the majority along the target feature dimension. We used the features of color and motion, expressly because they have well known, spatially separated cortical processing areas, to distinguish shifts in alpha power over areas processing each feature. Alpha power from dorsal regions increased when motion was the irrelevant feature (i.e., color was cued), and alpha power from ventral regions increased when color was irrelevant. Thus, alpha-suppression mechanisms appear to operate during feature-based selection in much the same manner as has been shown for space-based attention.

Anticipatory biasing of visuospatial attention indexed by retinotopically specific alpha-band electroencephalography increases over occipital cortex

Article