Near-Real-Time Feature-Selective Modulations in Human Cortex

Garcia, Javier O.; Srinivasan, Ramesh; Serences, John T.

doi:10.1016/j.cub.2013.02.013

Cited by 123 publications

(153 citation statements)

References 42 publications

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“…To probe sensory representations in the visual cortex, we used a forward modeling approach to reconstruct the orientation of the grating stimuli from the MEG signal (17)(18)(19)57). This method has been shown to be highly successful at reconstructing circular stimulus features, such as color (18), orientation (17,19,57), and motion direction (22), from neural signals. Neural representations in MEG signals have also been successfully investigated using binomial classifiers (58); however, when it comes to a continuous stimulus feature, such as orientation, forward model reconstructions provide a richer decoding signal than binomial classifier accuracy (59).…”

Section: Methodsmentioning

confidence: 99%

Prior expectations induce prestimulus sensory templates

Kok

Mostert

Lange

2017

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

269

317

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Perception can be described as a process of inference, integrating bottom-up sensory inputs and top-down expectations. However, it is unclear how this process is neurally implemented. It has been proposed that expectations lead to prestimulus baseline increases in sensory neurons tuned to the expected stimulus, which in turn, affect the processing of subsequent stimuli. Recent fMRI studies have revealed stimulus-specific patterns of activation in sensory cortex as a result of expectation, but this method lacks the temporal resolution necessary to distinguish pre-from poststimulus processes. Here, we combined human magnetoencephalography (MEG) with multivariate decoding techniques to probe the representational content of neural signals in a time-resolved manner. We observed a representation of expected stimuli in the neural signal shortly before they were presented, showing that expectations indeed induce a preactivation of stimulus templates. The strength of these prestimulus expectation templates correlated with participants' behavioral improvement when the expected feature was task-relevant. These results suggest a mechanism for how predictive perception can be neurally implemented.prediction | perceptual inference | predictive coding | feature-based expectation | feature-based attention P erception is heavily influenced by prior knowledge (1-3). Accordingly, many theories cast perception as a process of inference, integrating bottom-up sensory inputs and top-down expectations (4-6). However, it is unclear how this integration is neurally implemented. It has been proposed that prior expectations lead to baseline increases in sensory neurons tuned to the expected stimulus (7-9), which in turn, leads to improved neural processing of matching stimuli (10, 11). In other words, expectations may induce stimulus templates in sensory cortex before the actual presentation of the stimulus. Alternatively, topdown influences in sensory cortex may exert their influence only after the bottom-up stimulus has been initially processed, and the integration of the two sources of information may become apparent only during later stages of sensory processing (12).The evidence necessary to distinguish between these hypotheses has been lacking. fMRI studies have revealed stimulusspecific patterns of activation in sensory cortex as a result of expectation (9, 13), but this method lacks the temporal resolution necessary to distinguish pre-from poststimulus periods. Here, we combined magnetoencephalography (MEG) with multivariate decoding techniques to probe the representational content of neural signals in a time-resolved manner (14-17). The experimental paradigm was virtually identical to the ones used in our previous fMRI studies that studied how expectations modulate stimulus-specific patterns of activity in the primary visual cortex (9, 11). We trained a forward model to decode the orientation of task-irrelevant gratings from the MEG signal (18,19) and applied this decoder to trials in which participants expected a grating of a pa...

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Section: Methodsmentioning

confidence: 99%

Prior expectations induce prestimulus sensory templates

Kok

Mostert

Lange

2017

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

269

317

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“…The dots were small enough and moving quickly enough that observers could not perform the task by simply focusing spatial attention on any single dot, forcing them into a feature-based attentional strategy. The magnitude of the SSVEP response was significantly larger for dots with the attended-relative to ignored-feature value (see also Andersen, Fuchs, & Müller, 2011;Andersen, Hillyard, & Müller, 2008;Andersen & Müller, 2010;Bridwell & Srinivasan, 2012;Garcia, Srinivasan, & Serences, 2013). These findings further support the notion that attending to objects with a specific feature can modulate early stages of sensory processing.…”

Section: Electrophysiological Studies Of Attentional Capture and Featsupporting

confidence: 74%

Top-down and bottom-up processes in vision

Jacoby¹

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To help us efficiently navigate our information-rich environment, the human brain has developed a set of cognitive mechanisms aimed at quickly identifying information that is likely to be behaviourally relevant, collectively referred to as selective attention. Attentional resources can be directed voluntarily, as occurs when we shift attention from one word to the next while reading; or involuntarily, such as when attention is captured by someone unexpectedly entering the room (Yantis, 2000). These two modes of attention are not as independent and clearly distinguishable as once thought (Gilbert & Li, 2013). For example, the extent to which a stimulus involuntarily captures attention is influenced by whether it possesses features relevant to an observer's current task (the contingent capture hypothesis; Folk, . Attentional capture is also affected by the level of working memory (WM) load an observer is under (Lavie, Hirst, Viding, & De Fockert, 2004), and by an observer's WM capacity (Vogel, McCollough, & Machizawa, 2005).It has been suggested that maintaining top-down biases on attentional capture may require -and compete for -similar neural resources as WM (e.g., Lavie et al., 2004). If so, top-down influences on attentional capture should be reduced (a) when an observer is under high WM load, and (b) in individuals with a low WM capacity. In this thesis these possibilities were tested in a series of seven experiments in which human observers monitored visual stimulus streams for targets defined by a particular feature value (e.g., red). While observers performed these tasks, I took behavioural and electrophysiological measures of attentional capture by distracting visual stimuli that either possessed or did not possess the target feature value. I then investigated whether taxing WM reduces contingent capture. An n-back task that required information-updating eliminated the influence of task set on the distractor-evoked N2pc, suggesting that taxing WM compromised top-down biases on early visual processing. There was no such effect on a behavioural measure of attentional capture, however, possibly because taxing WM also compromised the transfer of information from early visual cortex to later stages of processing. A iii digit rehearsal task that required only information maintenance did not change the effect of task set on either the N2pc or behaviour. This pattern of results suggests that contingent capture is only reduced by WM load manipulations that place demands on the central executive component of WM (Baddeley & Hitch, 1974).Next I tested whether high WM load can reduce the effect of task set on even earlier stages of processing than the N2pc, specifically by measuring the P1 component of the visual-evoked potential. Unexpectedly, I failed to replicate a previous finding of an effect of feature-based task set on the P1. Because I found no effect of task set on the P1, I was unable to test whether such an effect was reduced under high WM load.In a final experiment I examined whether individual dif...

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“…We (and others) have previously shown that the forward model is a useful tool to extract and isolate responses corresponding to subpopulations of neurons with different selectivities from an ensemble measure of neural activity (Anderson et al , 2014Brouwer and Heeger 2009Ester et al 2013;Garcia et al 2013 For the present experiment, we did not assume that neurons selective for the different digits were spatially segregated at the resolution of our measurements. Representations of the thumb and middle finger occupy different, but partially overlapping, subregions of the somatosensory cortex (see introduction for references).…”

Section: Discussionmentioning

confidence: 99%

Normalization in human somatosensory cortex

Brouwer

Arnedo

Offen

et al. 2015

Journal of Neurophysiology

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Brouwer GJ, Arnedo V, Offen S, Heeger DJ, Grant AC. Normalization in human somatosensory cortex. J Neurophysiol 114: 2588 -2599, 2015. First published August 26, 2015 doi:10.1152/jn.00939.2014.-Functional magnetic resonance imaging (fMRI) was used to measure activity in human somatosensory cortex and to test for cross-digit suppression. Subjects received stimulation (vibration of varying amplitudes) to the right thumb (target) with or without concurrent stimulation of the right middle finger (mask). Subjects were less sensitive to target stimulation (psychophysical detection thresholds were higher) when target and mask digits were stimulated concurrently compared with when the target was stimulated in isolation. fMRI voxels in a region of the left postcentral gyrus each responded when either digit was stimulated. A regression model (called a forward model) was used to separate the fMRI measurements from these voxels into two hypothetical channels, each of which responded selectively to only one of the two digits. For the channel tuned to the target digit, responses in the left postcentral gyrus increased with target stimulus amplitude but were suppressed by concurrent stimulation to the mask digit, evident as a shift in the gain of the response functions. For the channel tuned to the mask digit, a constant baseline response was evoked for all target amplitudes when the mask was absent and responses decreased with increasing target amplitude when the mask was concurrently presented. A computational model based on divisive normalization provided a good fit to the measurements for both mask-absent and target ϩ mask stimulation. We conclude that the normalization model can explain cross-digit suppression in human somatosensory cortex, supporting the hypothesis that normalization is a canonical neural computation.

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Near-Real-Time Feature-Selective Modulations in Human Cortex

Cited by 123 publications

References 42 publications

Prior expectations induce prestimulus sensory templates

Prior expectations induce prestimulus sensory templates

Top-down and bottom-up processes in vision

Normalization in human somatosensory cortex

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