scholarly journals Biased competition in semantic representation during natural visual search

2019 ◽  
Author(s):  
Mohammad Shahdloo ◽  
Emin Çelik ◽  
Tolga Çukur

AbstractHumans divide their attention among multiple visual targets in daily life, and visual search gets 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. Furthermore, this combination is biased by the intrinsic selectivity of cortical regions. Yet, it is unknown whether attentional modulations of semantic representations of cluttered and dynamic natural scenes are consistent with this hypothesis. Here, we investigated whether BC accounts for semantic representation during natural category-based visual search. Human 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 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 modulations of semantic representation across cortex.Significance StatementNatural vision is a complex task that involves splitting attention between multiple search targets. According to the biased competition hypothesis (BC), limited representational capacity of the cortex inevitably leads to a competition among representation of these targets and the competition is biased by intrinsic selectivity of cortical areas. Here we examined BC for semantic representation of hundreds of object and action categories in natural movies. We observed that: 1) semantic representation during simultaneous attention to two object categories is a weighted linear combination of representations during attention to each of them alone, and 2) the linear combination is biased toward semantic representation of the preferred object category in strongly category-selective areas. These findings suggest BC as a compelling account for attentional modulations of semantic representation across cortex in natural vision.

NeuroImage ◽  
2020 ◽  
Vol 216 ◽  
pp. 116383 ◽  
Author(s):  
Mohammad Shahdloo ◽  
Emin Çelik ◽  
Tolga Çukur

2013 ◽  
Vol 23 (4) ◽  
pp. 300-313 ◽  
Author(s):  
Johna K. Register-Mihalik ◽  
Ashley C. Littleton ◽  
Kevin M. Guskiewicz

2021 ◽  
Author(s):  
Mo Shahdloo ◽  
Emin Çelik ◽  
Burcu A Urgen ◽  
Jack L. Gallant ◽  
Tolga Çukur

Object and action perception in cluttered dynamic natural scenes relies on efficient allocation of limited brain resources to prioritize the attended targets over distractors. It has been suggested that during visual search for objects, distributed semantic representation of hundreds of object categories is warped to expand the representation of targets. Yet, little is known about whether and where in the brain visual search for action categories modulates semantic representations. To address this fundamental question, we studied human brain activity recorded via functional magnetic resonance imaging while subjects viewed natural movies and searched for either communication or locomotion actions. We find that attention directed to action categories elicits tuning shifts that warp semantic representations broadly across neocortex, and that these shifts interact with intrinsic selectivity of cortical voxels for target actions. These results suggest that attention serves to facilitate task performance during social interactions by dynamically shifting semantic selectivity towards target actions, and that tuning shifts are a general feature of conceptual representations in the brain.


Author(s):  
A. Bouzekri ◽  
H. Benmessaoud

The objective of this work is to study and analyze the human impact on agro-forestry-pastoral ecosystem of Khenchela region through the application of multi-criteria analysis methods to integrate geographic information systems, our methodology is based on a weighted linear combination of information on four criteria chosen in our analysis representative in the vicinity of variables in relation to roads, urban areas, water resources and agricultural space, the results shows the effect of urbanization and socio-economic activity on the degradation of the physical environment and found that 32% of the total area are very sensitive to human impact.


2020 ◽  
pp. 1-7
Author(s):  
Ryan E. Miller ◽  
Timothy L. Brown ◽  
Stella Lee ◽  
Ishaan Tibrewal ◽  
Gary G. Gaffney ◽  
...  

2021 ◽  
Vol 14 ◽  
Author(s):  
Riccardo Pernice ◽  
Yuri Antonacci ◽  
Matteo Zanetti ◽  
Alessandro Busacca ◽  
Daniele Marinazzo ◽  
...  

In this work, we extend to the multivariate case the classical correlation analysis used in the field of network physiology to probe dynamic interactions between organ systems in the human body. To this end, we define different correlation-based measures of the multivariate interaction (MI) within and between the brain and body subnetworks of the human physiological network, represented, respectively, by the time series of δ, θ, α, and β electroencephalographic (EEG) wave amplitudes, and of heart rate, respiration amplitude, and pulse arrival time (PAT) variability (η, ρ, π). MI is computed: (i) considering all variables in the two subnetworks to evaluate overall brain–body interactions; (ii) focusing on a single target variable and dissecting its global interaction with all other variables into contributions arising from the same subnetwork and from the other subnetwork; and (iii) considering two variables conditioned to all the others to infer the network topology. The framework is applied to the time series measured from the EEG, electrocardiographic (ECG), respiration, and blood volume pulse (BVP) signals recorded synchronously via wearable sensors in a group of healthy subjects monitored at rest and during mental arithmetic and sustained attention tasks. We find that the human physiological network is highly connected, with predominance of the links internal of each subnetwork (mainly η−ρ and δ−θ, θ−α, α−β), but also statistically significant interactions between the two subnetworks (mainly η−β and η−δ). MI values are often spatially heterogeneous across the scalp and are modulated by the physiological state, as indicated by the decrease of cardiorespiratory interactions during sustained attention and by the increase of brain–heart interactions and of brain–brain interactions at the frontal scalp regions during mental arithmetic. These findings illustrate the complex and multi-faceted structure of interactions manifested within and between different physiological systems and subsystems across different levels of mental stress.


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