Estimating the influence of attention on population codes in human visual cortex using voxel-based tuning functions

Serences, John T.; Saproo, Sameer; Scolari, Miranda; Ho, Tiffany C.; Muftuler, L. Tugan

doi:10.1016/j.neuroimage.2008.07.043

Cited by 118 publications

(140 citation statements)

References 56 publications

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“…This analysis rests on the assumption that the distribution of feature-selective neurons-in this case the distribution of orientation-selective columns-is not uniform across a given visual area (Boynton 2005a;Kamitani and Tong 2005;Swisher et al 2010). Due to this nonuniform distribution of neural selectivity, a given voxel may contain more neurons tuned to one particular orientation, giving rise to a Gaussian-shaped response profile across orientations, which we refer to as the VTF (Serences et al 2009; see also Kamitani and Tong 2005;Kay et al 2008;Miyawaki et al 2008).…”

Section: Analysis Of Feature-selective Vtfs In Each Roimentioning

confidence: 99%

“…All VTF analyses focused exclusively on trials in which no reward was earned, to rule out reward-related feedback signals as a cause of any observed modulations; trials on which a reward was earned were modeled separately as regressors of no interest. Finally, after estimating the magnitude of the response within each voxel on every trial, the mean response associated with each orientation across all voxels was removed to correct for main effects of stimulus orientation that had a common influence on the response of every voxel (Haxby et al 2001;Serences et al 2009). …”

Section: Analysis Of Feature-selective Vtfs In Each Roimentioning

confidence: 99%

“…Since pattern classifiers pool information from all voxels within a given cortical area, they can only confirm the existence of some type of information about stimulus features and are not designed to differentiate these possibilities. In contrast, assuming that changes in the BOLD response are monotonically related to changes in underlying neural activity (Logothetis 2003;Logothetis et al 2001), VTFs can provide information about how a particular experimental manipulation selectively influences population response profiles in early visual cortex (Serences et al 2009). …”

Section: Analysis Of Feature-selective Vtfs In Each Roimentioning

confidence: 99%

“…A quantitative model was then used to estimate the value of each alternative on a trial-by-trial basis, and fMRI-based voxel tuning functions (or VTFs; Kay et al 2008;Serences et al 2009) were used to estimate the influence of value on the shape of population response profiles in early areas of visual cortex. The data suggest that response profiles in early visual cortex are selectively biased in favor of highvalue stimulus features, particularly in V1, which contains a high proportion of orientation-tuned cells.…”

Section: Introductionmentioning

confidence: 99%

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Population Response Profiles in Early Visual Cortex Are Biased in Favor of More Valuable Stimuli

Serences

Saproo

2010

Journal of Neurophysiology

102

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Serences JT, Saproo S. Population response profiles in early visual cortex are biased in favor of more valuable stimuli. J Neurophysiol 104: 76 -87, 2010. First published April 21, 2010 doi:10.1152/jn.01090.2009. Voluntary and stimulus-driven shifts of attention can modulate the representation of behaviorally relevant stimuli in early areas of visual cortex. In turn, attended items are processed faster and more accurately, facilitating the selection of appropriate behavioral responses. Information processing is also strongly influenced by past experience and recent studies indicate that the learned value of a stimulus can influence relatively late stages of decision making such as the process of selecting a motor response. However, the learned value of a stimulus can also influence the magnitude of cortical responses in early sensory areas such as V1 and S1. These early effects of stimulus value are presumed to improve the quality of sensory representations; however, the nature of these modulations is not clear. They could reflect nonspecific changes in response amplitude associated with changes in general arousal or they could reflect a bias in population responses so that high-value features are represented more robustly. To examine this issue, subjects performed a two-alternative forced choice paradigm with a variableinterval payoff schedule to dynamically manipulate the relative value of two stimuli defined by their orientation (one was rotated clockwise from vertical, the other counterclockwise). Activation levels in visual cortex were monitored using functional MRI and feature-selective voxel tuning functions while subjects performed the behavioral task. The results suggest that value not only modulates the relative amplitude of responses in early areas of human visual cortex, but also sharpens the response profile across the populations of feature-selective neurons that encode the critical stimulus feature (orientation). Moreover, changes in space-or feature-based attention cannot easily explain the results because representations of both the selected and the unselected stimuli underwent a similar feature-selective modulation. This sharpening in the population response profile could theoretically improve the probability of correctly discriminating high-value stimuli from low-value alternatives.

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Section: Analysis Of Feature-selective Vtfs In Each Roimentioning

confidence: 99%

Section: Analysis Of Feature-selective Vtfs In Each Roimentioning

confidence: 99%

Section: Analysis Of Feature-selective Vtfs In Each Roimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Population Response Profiles in Early Visual Cortex Are Biased in Favor of More Valuable Stimuli

Serences

Saproo

2010

Journal of Neurophysiology

102

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“…MI has been used to compare different neural response codes [Ince et al, 2013; Kayser et al, 2009; Reich et al, 2001], characterize different neurons [Sharpee, 2014] as well as quantify the effect of correlations between neurons [Ince et al, 2009, 2010; Moreno‐Bote et al, 2014] and the importance of spike timing [Kayser et al, 2010; Nemenman et al, 2008; Panzeri et al, 2001]. Recent studies have begun to explore its application to neuroimaging [Afshin‐Pour et al, 2011; Caballero‐Gaudes et al, 2013; Gross et al, 2013; Guggenmos et al, 2015; Ostwald and Bagshaw, 2011; Panzeri et al, 2008; Salvador et al, 2007; Saproo and Serences, 2010; Schyns et al, 2011; Serences et al, 2009]. …”

Section: Introductionmentioning

confidence: 99%

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

Ince

Giordano

Kayser

et al. 2016

Human Brain Mapping

247

286

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We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open‐source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541–1573, 2017. © 2016 Wiley Periodicals, Inc.

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Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience

2018

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Estimating the influence of attention on population codes in human visual cortex using voxel-based tuning functions

Cited by 118 publications

References 56 publications

Population Response Profiles in Early Visual Cortex Are Biased in Favor of More Valuable Stimuli

Population Response Profiles in Early Visual Cortex Are Biased in Favor of More Valuable Stimuli

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience

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