The topography of alpha-band activity tracks the content of spatial working memory

Foster, Joshua J.; Sutterer, David; Serences, John T.; Vogel, Edward K.; Awh, Edward

doi:10.1152/jn.00860.2015

Cited by 224 publications

(376 citation statements)

References 60 publications

Supporting

Mentioning

344

Contrasting

Order By: Relevance

“…This location specificity of alpha-band dynamics is not limited to the hemifield level but also tracks the specific location that is attended (Foster, Sutterer, Serences, Vogel, & Awh, 2017; Rihs, Michel, & Thut, 2007). Indeed, recent studies have used inverted encoding models (IEMs) to reconstruct population-level channel tuning functions (CTFs) from the topographic distribution of alpha-band activity that reveal location-specific information during both covert attention tasks (Foster et al, 2017; Samaha, Sprague, & Postle, 2016) and the maintenance of spatial working memories (Foster, Bsales, Jaffe, & Awh, 2017; Foster, Sutterer, Serences, Vogel, & Awh, 2016). Together, these findings suggest a tight link between alpha-band dynamics and the focus of spatial attention, regardless of whether attention is directed toward external stimuli or toward remembered locations.…”

Section: Introductionmentioning

confidence: 99%

Spatially Selective Alpha Oscillations Reveal Moment-by-Moment Trade-offs between Working Memory and Attention

Moorselaar

Foster

Sutterer

et al. 2018

Journal of Cognitive Neuroscience

Self Cite

View full text Add to dashboard Cite

Current theories assume a functional role for covert attention in the maintenance of spatial information in working memory. Consistent with this view, both the locus of attention and positions stored in working memory can be decoded based on the topography of oscillatory alpha-band (8–12 Hz) activity on the scalp. Thus far, however, alpha modulation has been studied in isolation for covert attention and working memory tasks. Here, we applied an inverted spatial encoding model in combination with EEG to study the temporal dynamics of spatially specific alpha activity during a task that required observers to visually select a target location while maintaining another independently varying location in working memory. During the memory delay period, alpha-based spatial tuning functions shifted from the position stored in working memory to the covertly attended position and back again after the attention task was completed. The findings provide further evidence for a common oscillatory mechanism in both the selection and the maintenance of relevant spatial visual information and demonstrate the dynamic trade-off in prioritization between two spatial tasks.

show abstract

Section: Introductionmentioning

confidence: 99%

Spatially Selective Alpha Oscillations Reveal Moment-by-Moment Trade-offs between Working Memory and Attention

Moorselaar

Foster

Sutterer

et al. 2018

Journal of Cognitive Neuroscience

Self Cite

View full text Add to dashboard Cite

show abstract

“…Neuroscientific studies have established that the encoding of a subset of the available visual information into working memory can be measured by frequency-specific oscillations of subjects' electroencephalograms (EEGs) (17)(18)(19)(20). Specifically, the magnitude of suppression of alpha-band oscillations (8-13 Hz) measured across parieto-occipital channels as new information is held in working memory changes as additional information is loaded into working memory and hits an asymptote at a subject's working-memory capacity estimated from behavioral performance.…”

mentioning

confidence: 99%

Visual working memory buffers information retrieved from visual long-term memory

Fukuda

Woodman

2017

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Human memory is thought to consist of long-term storage and shortterm storage mechanisms, the latter known as working memory. Although it has long been assumed that information retrieved from long-term memory is represented in working memory, we lack neural evidence for this and need neural measures that allow us to watch this retrieval into working memory unfold with high temporal resolution. Here, we show that human electrophysiology can be used to track information as it is brought back into working memory during retrieval from long-term memory. Specifically, we found that the retrieval of information from long-term memory was limited to just a few simple objects' worth of information at once, and elicited a pattern of neurophysiological activity similar to that observed when people encode new information into working memory. Our findings suggest that working memory is where information is buffered when being retrieved from long-term memory and reconcile current theories of memory retrieval with classic notions about the memory mechanisms involved.visual working memory | visual long-term memory retrieval | EEG H umans are capable of storing an essentially limitless amount of information in long-term memory and can remember specific pieces of information when needed to guide behavior. For over a century, researchers have proposed that, when humans retrieve information from long-term memory, we do this by bringing the information back into working memory (1-3). This has become so engrained in the dogma regarding how human memory works that retrieving information from long-term memory into working memory is described in introductory textbooks (e.g., refs. 4 and 5). However, although there is behavioral evidence in line with the notion that information retrieved from long-term memory is brought back into working memory (ref. 6; see ref. 7 for a review of additional classic evidence; refs. 8-10), a direct demonstration that this is occurring in brain is needed. If we were able to track this cognitive process, we could use it to understand the fine-grained dynamics of memory retrieval.In the present study, we took advantage of the fact that encoding new sensory information into working memory is capacity limited (11-16). Neuroscientific studies have established that the encoding of a subset of the available visual information into working memory can be measured by frequency-specific oscillations of subjects' electroencephalograms (EEGs) (17)(18)(19)(20). Specifically, the magnitude of suppression of alpha-band oscillations (8-13 Hz) measured across parieto-occipital channels as new information is held in working memory changes as additional information is loaded into working memory and hits an asymptote at a subject's working-memory capacity estimated from behavioral performance. We chose this neural correlate of working memory over other correlates (e.g., the contralateral delay activity in ref. 12) because it allowed the estimation of working-memory load without necessitating the presentation of to-be-ignored dis...

show abstract

“…Fernandino et al, 2016). Rather, it is important to examine the actual correspondence of the representations with additional RSA methods or dimensionality reduction and visualization techniques (e.g., Brouwer and Heeger, 2009;Foster et al, 2015). For example, in studying color representation in the early visual cortex, Brouwer and Heeger (2009) used principal components analysis (PCA) on the predicted voxel activation patterns and showed that the two main components present in the signal corresponded well to the actual organization of the colors in the color space they were using.…”

Section: Absolute Model Performancementioning

confidence: 99%

“…For example, in studying color representation in the early visual cortex, Brouwer and Heeger (2009) used principal components analysis (PCA) on the predicted voxel activation patterns and showed that the two main components present in the signal corresponded well to the actual organization of the colors in the color space they were using. Similarly, Foster et al (2015), who attempted to decode the orientation of lines that were held in visual working memory, showed that activity in the hypothesized angular channels, on which their encoding model was based, follows a graded pattern that peaks at their preferred orientation. In summary, we believe it is crucial to carefully examine the model predictions.…”

Section: Absolute Model Performancementioning

confidence: 99%

“…In order to simulate attentional modulation, this time we simulated neural data from the RGB space in a different way. We assumed that each dimension is represented by a population code of five units, where each unit had a different preferred value on that dimension and its activity followed a Gaussian distribution around that preferred value (similarly to Foster et al, 2015). The preferred values were equally spaced from 0 to 1 (i.e., 0.1, 0.3, 0.5, 0.7, 0.9), and the default standard deviation of the response was 1 in the absence of attentional modulation.…”

Section: Simulation 3: Attentional Modulationmentioning

confidence: 99%

See 1 more Smart Citation

Practices and pitfalls in inferring neural representations

2018

View full text Add to dashboard Cite

A B S T R A C TA key challenge for cognitive neuroscience is deciphering the representational schemes of the brain. Stimulusfeature-based encoding models are becoming increasingly popular for inferring the dimensions of neural representational spaces from stimulus-feature spaces. We argue that such inferences are not always valid because successful prediction can occur even if the two representational spaces use different, but correlated, representational schemes. We support this claim with three simulations in which we achieved high prediction accuracy despite systematic differences in the geometries and dimensions of the underlying representations. Detailed analysis of the encoding models' predictions showed systematic deviations from ground-truth, indicating that high prediction accuracy is insufficient for making representational inferences. This fallacy applies to the prediction of actual neural patterns from stimulus-feature spaces and we urge caution in inferring the nature of the neural code from such methods. We discuss ways to overcome these inferential limitations, including model comparison, absolute model performance, visualization techniques and attentional modulation. IntroductionA key challenge for cognitive neuroscience is to understand the neural code that underlies the encoding and representation of sensory, motor, spatial, emotional, semantic and other types of information. To decipher the representational schemes of the brain, researchers often employ neuroimaging techniques such as functional magnetic resonance imaging (fMRI). fMRI measures the blood oxygenation level-dependent (BOLD) activation in the brain that is elicited when participants engage with different stimuli. The neural representation underlying each stimulus is assumed to have measurable but complex effects on the BOLD activation patterns. In order to understand what those patterns of activity can tell us about how the brain processes and represents information, researchers have used various analytical tools such as univariate subtraction methods, multivariate pattern (MVP) classification, representational similarity analysis (RSA) and, recently, explicit stimulus-feature-based encoding and decoding models (for reviews, see Davis and Poldrack, 2013, Haxby et al., 2014, or Naselaris et al., 2011. Despite their differences, all of these methods have the same goal -to quantify how changes in task conditions and the properties of the stimuli relate to changes in BOLD activation and vice versa. One way in which these methods differ is in how they achieve that mapping and in what inferences they allow us to draw.In this article, we review some of the known inferential limitations of existing fMRI analysis methods and we highlight an often-overlooked issue in interpreting results from stimulus-feature-based encoding and decoding models. The latter are steadily becoming the de facto gold standard for investigating neural representational spaces (Haxby et al., 2014;Naselaris and Kay, 2015). Using simulated data with known representati...

show abstract

The topography of alpha-band activity tracks the content of spatial working memory

Cited by 224 publications

References 60 publications

Spatially Selective Alpha Oscillations Reveal Moment-by-Moment Trade-offs between Working Memory and Attention

Spatially Selective Alpha Oscillations Reveal Moment-by-Moment Trade-offs between Working Memory and Attention

Visual working memory buffers information retrieved from visual long-term memory

Practices and pitfalls in inferring neural representations

Contact Info

Product

Resources

About