Attention During Natural Vision Warps Semantic Representation Across the Human Brain

By Tolga Cukur, Shinji Nishimoto, Alexander G. Huth, Jack L. Gallant. 2013

377 citations recorded by the source corpus.

Little is known about how attention changes the cortical representation of sensory information in humans. On the basis of neurophysiological evidence, we hypothesized that attention causes tuning changes to expand the representation of attended stimuli at the cost of unattended stimuli. To investigate this issue, we used functional magnetic resonance imaging to measure how semantic representation changed during visual search for different object categories in natural movies. We found that many voxels across occipito-temporal and fronto-parietal cortex shifted their tuning toward the attended category. These tuning shifts expanded the representation of the attended category and of semantically related, but unattended, categories, and compressed the representation of categories that were semantically dissimilar to the target. Attentional warping of semantic representation occurred even when the attended category was not present in the movie; thus, the effect was not a target-detection artifact. These results suggest that attention dynamically alters visual representation to optimize processing of behaviorally relevant objects during natural vision.

Subfields: Cognitive Neuroscience

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Citations in the corpus

Orthogonal representations for robust context-dependent task performance in brains and neural networks (2022, neutral): “, 1990; Miller and Cohen, 2001), consistent with accounts emphasizing that neural codes are sculpted by task demands (Duncan, 2001) or through attention to scenes and objects (Çukur et al., 2013).”
Learning Visual Question Answering by Bootstrapping Hard Attention (2018, background): “Visual attention is instrumental to many aspects of complex visual reasoning in humans [1,2].”
Neural mechanisms of object-based attention. (2015, methodology): “…that featurebased attention leads to reliable modulations in early visual areas (Kamitani and Tong 2005; Jehee et al. 2011), and such methods have been successfully applied to investigate attentional modulation of high-level object areas (Peelen et al. 2009; Reddy et al. 2009; Cukur et al. 2013).”
Neuroscience-Inspired Artificial Intelligence. (2017, methodology): “…the analysis of neuroimaging datasets—for example, in the multivariate analysis of fMRI and magnetoence-phalographic (MEG) data (Cichy et al., 2014; C¸ukur et al., 2013; Kriegeskorte and Kievit, 2013)—with promise for expediting con-nectomic analysis (Glasser et al., 2016), among other techniques.”
How concepts are encoded in the human brain: A modality independent, category-based cortical organization of semantic knowledge (2016, methodology): “…does knowledge organization rely on a small-scale localized representation based on an anatomically (and thus functionally) well-defined set of regions (Binder et al., 2009), or rather does it count on large-scale representational patterns of neural activity (Cukur et al., 2013; Huth et al., 2012)?”
Intelligent Information Loss: The Coding of Facial Identity, Head Pose, and Non-Face Information in the Macaque Face Patch System (2015, background): “Consistent with this, multiple fMRI studies have demonstrated that distributed response patterns within a brain region contain a rich amount of information above and beyond what is present in univariate modulation (Kamitani and Tong, 2005; Kriegeskorte and Bandettini, 2007; Çukur et al., 2013).”
Analyzing Linguistic Knowledge in Sequential Model of Sentence (2016, methodology): “certain words, inspired by the work in Cukur et al. (2013) and Huth et al.”
Cognitive Penetrability of Perception in the Age of Prediction: Predictive Systems are Penetrable Systems (2015, background): “Viewed this way, attention functions through continued modulation of perceptual states (Çukur et al. 2013; Ghose and Maunsell 2002; Jack et al. 2006) and calling an effect Bmerely^ attentional as distinct from Btruly^ percep-”
Semantic reconstruction of continuous language from non-invasive brain recordings (2022, background): “Because semantic representations are modulated by attention 37,38 , our semantic decoder should selectively reconstruct attended stimuli 39,40 .”
Words and the World (2015, background): “On this view, words and larger linguistic constructions latch onto preexisting concepts, enabling highly flexible communication, but do not alter the workings of “nonverbal” systems involved in, for instance, categorization, memory, and perception (Pinker, 1994; Snedeker & Gleitman, 2004).”