Browsing by Subject "Biased-competition"

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Open Access
Biased competition in semantic representation during natural visual search
(Elsevier, 2020) Shahdloo, Mohammad; Çelik, Emin; Çukur, Tolga
Humans divide their attention among multiple visual targets in daily life, and visual search can get more difficult as the number of targets increases. The biased competition hypothesis (BC) has been put forth as an explanation for this phenomenon. BC suggests that brain responses during divided attention are a weighted linear combination of the responses during search for each target individually. This combination is assumed to be biased by the intrinsic selectivity of cortical regions. Yet, it is unknown whether attentional modulation of semantic representations are consistent with this hypothesis when viewing cluttered, dynamic natural scenes. Here, we investigated whether BC accounts for semantic representation during natural category-based visual search. Subjects viewed natural movies, and their whole-brain BOLD responses were recorded while they attended to “humans”, “vehicles” (i.e. single-target attention tasks), or “both humans and vehicles” (i.e. divided attention) in separate runs. We computed a voxelwise linearity index to assess whether semantic representation during divided attention can be modeled as a weighted combination of representations during the two single-target attention tasks. We then examined the bias in weights of this linear combination across cortical ROIs. We find that semantic representations of both target and nontarget categories during divided attention are linear to a substantial degree, and that they are biased toward the preferred target in category-selective areas across ventral temporal cortex. Taken together, these results suggest that the biased competition hypothesis is a compelling account for attentional modulation of semantic representations.
Open Access
Biased competition in semantic representations across the human brain during category-based visual search
(2017-01) Shahdloo, Mohammad
Humans can perceive thousands of distinct object and action categories in the visual scene and successfully divide their attention among multiple target categories. It has been shown that object and action categories are represented in a continuous semantic map across the cortical surface and attending to a specific category causes broad shifts in voxel-wise semantic tuning profiles to enhance the representation of the target category. However, the effects of divided attention to multiple categories on semantic representation remain unclear. In line with predictions of the biased-competition model, recent evidence suggests that brain response to two objects presented simultaneously can be described as a weighted average of the responses to individual objects presented in isolation, and that attention biases these weights in favor of the target object. We question whether this biased-competition hypothesis can also account for attentional modulation of semantic representations. To address this question, we recorded participants’ BOLD responses while they performed category-based search in natural movies that contained 831 distinct objects and actions. Three different tasks were used: search for “humans”, search for “vehicles”, and search for “both humans and vehicles” (i.e. divided attention). Voxel-wise category models were fit separately under each task, and voxel-wise semantic tuning profiles were then obtained using a principal components analysis on the model weights. Semantic tuning profiles were compared across the single-target tasks and the divided-attention task. We find that in higher visual cortex a substantial portion of semantic tuning during divided attention can be expressed as a weighted average of the tuning profiles during attention to single targets. We also find that semantic tuning in categoryselective regions is biased towards the preferred object category. Overall, these results suggest that the biased-competition theory accounts for attentional modulation of semantic representations during natural visual search.