Is there feature-based attentional selection in visual search?

Author(s):  
Shui-I Shih ◽  
George Sperling
2021 ◽  
Author(s):  
Angus F. Chapman ◽  
Viola S. Störmer

While many theories of attention highlight the importance of similarity between target and distractor items for selection, few studies have directly quantified the function underlying this relationship. Across two commonly used tasks—visual search and sustained attention—we investigated how target-distractor similarity impacts feature-based attentional selection, in particular asking whether stimulus-based or psychological similarity better explains performance. We found that both similarity measures were non-linearly related to task performance, although psychological similarity explained a big portion of the non-linearities observed in the data, suggesting that measures of psychological similarity are more appropriate when studying effects of target-distractor similarities. Importantly, we found comparable patterns of performance in both visual search and sustained feature-based attention tasks, with performance (RTs and d’, respectively) plateauing at medium target-distractor distances and exponential functions capturing the relationship between stimulus-based and psychological similarity and performance well. In contrast, visual search efficiency, as measured by search slopes, was affected by only a narrow range of similarity levels (10-20°). These findings place novel constraints on models of selective attention and emphasize the importance of considering the similarity structure of the feature space. Broadly, the non-linear effects of similarity on attention are consistent with accounts that propose attention exaggerates the distance between competing representations, possibly through enhancement of off-tuned neurons.


2017 ◽  
Vol 40 ◽  
Author(s):  
Martin Eimer

AbstractHulleman & Olivers (H&O) reject item-based serial models of visual search, and they suggest that items are processed equally and globally during each fixation period. However, neuroscientific studies have shown that attentional biases can emerge in parallel but in a spatially selective item-based fashion. Even within a parallel architecture for visual search, the item remains the critical unit of selection.


2016 ◽  
Author(s):  
Johannes Jacobus Fahrenfort ◽  
Anna Grubert ◽  
Christian N. L. Olivers ◽  
Martin Eimer

AbstractThe primary electrophysiological marker of feature-based selection is the N2pc, a lateralized posterior negativity emerging around 180-200 ms. As it relies on hemispheric differences, its ability to discriminate the locus of focal attention is severely limited. Here we demonstrate that multivariate analyses of raw EEG data provide a much more fine-grained spatial profile of feature-based target selection. When training a pattern classifier to determine target position from EEG, we were able to decode target positions on the vertical midline, which cannot be achieved using standard N2pc methodology. Next, we used a forward encoding model to construct a channel tuning function that describes the continuous relationship between target position and multivariate EEG in an eight-position display. This model can spatially discriminate individual target positions in these displays and is fully invertible, enabling us to construct hypothetical topographic activation maps for target positions that were never used. When tested against the real pattern of neural activity obtained from a different group of subjects, the constructed maps from the forward model turned out statistically indistinguishable, thus providing independent validation of our model. Our findings demonstrate the power of multivariate EEG analysis to track feature-based target selection with high spatial and temporal precision.Significance StatementFeature-based attentional selection enables observers to find objects in their visual field. The spatiotemporal profile of this process is difficult to assess with standard electrophysiological methods, which rely on activity differences between cerebral hemispheres. We demonstrate that multivariate analyses of EEG data can track target selection across the visual field with high temporal and spatial resolution. Using a forward model, we were able to capture the continuous relationship between target position and EEG measurements, allowing us to reconstruct the distribution of cortical activity for target locations that were never shown during the experiment. Our findings demonstrate the existence of a temporally and spatially precise EEG signal that can be used to study the neural basis of feature-based attentional selection.


2021 ◽  
Author(s):  
Einat Rashal ◽  
Mehdi Senoussi ◽  
Elisa Santandrea ◽  
Suliann Ben Hamed ◽  
Emiliano Macaluso ◽  
...  

This work reports an investigation of the effect of combined top-down and bottom-up attentional control sources, using known attention-related EEG components that are thought to reflect target selection (N2pc) and distractor suppression (PD), in easy and difficult visual search tasks.


2014 ◽  
Vol 34 (26) ◽  
pp. 8662-8664 ◽  
Author(s):  
J. J. Foster ◽  
K. C. S. Adam
Keyword(s):  

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