A new perspective on the perceptual selectivity of attention under load

Abstract

The human attention system helps us cope with a complex environment by supporting the selective processing of information relevant to our current goals. Understanding the perceptual, cognitive, and neural mechanisms that mediate selective attention is a core issue in cognitive neuroscience. One prominent model of selective attention, known as load theory, offers an account of how task demands determine when information is selected and an account of the efficiency of the selection process. However, load theory has several critical weaknesses that suggest that it is time for a new perspective. Here we review the strengths and weaknesses of load theory and offer an alternative biologically plausible computational account that is based on the neural theory of visual attention. We argue that this new perspective provides a detailed computational account of how bottom-up and top-down information is integrated to provide efficient attentional selection and allocation of perceptual processing resources.

Irrelevant reward and selection histories have different influences on task-relevant attentional selection

“…More specifically, in the present task, optimal performance required efficient selectivity, so that multiple targets could be encoded into working memory and multiple distractors would not be. The need for a higher level of efficiency in selection due to the increased task demands relative to typical single-target designs could have reduced the influence of task-irrelevant information (e.g., Giesbrecht, Sy, Bundesen, & Kyllingsbaek, 2014;Kyllingsbaek, Sy, & Giesbrecht, 2011;Lavie, 2005;Lavie et al, 2014), yet the irrelevant reward-associated features continued to capture attention and impact performance. Thus, the influence of irrelevant features on visual selection-specifically, of irrelevant reward-associated features in the present study-appears even under conditions of high task difficulty.…”

Identifying visual targets amongst interfering distractors: Sorting out the roles of perceptual load, dilution, and attentional zoom

“…An infrequently presented onset distractor leads to significant distraction processing while a frequently presented one does not (Cosman & Vecera, 2010), and both perceptual grouping and the salience of a target relative to the distractor and/or the other stimuli in a display affect the degree of distractor processing (Baylis & Driver, 1992;Biggs & Gibson, 2013;Biggs, Kreager, & Davoli, 2015;Eltiti, Wallas, & Fox, 2005;Yeh & Lin, 2013;Yeshurun & Marciano, 2013). These and other empirical findings Biggs & Gibson, 2010;Chen & Chan, 2007;Wilson et al, 2011;Yeh, Lee, Chen, & Chen, 2014 ), together with the conceptual and methodological issues raised by a number of researchers (see Benoni & Tsal, 2013;Giesbrecht, Sy, Bundesen, & Kyllingsbaek, 2014;and Murphy, Groeger, & Greene, 2016, for reviews), indicate that the simple principles of PLT do not fully capture the complex interactions among targets and nontargets in complex stimulus arrays. In this review we will focus primarily on behavioral studies that do not involve a secondary task.…”

“…Their model includes mechanisms that have been part of many attentional theories over the years, including bottom-up attentional activations based on featural differences (salience) and competition among stimulus objects for representation. Like the earlier theory of visual attention (TVA; Giesbrecht et al, 2014;Kyllingsbaek et al, 2011), Neokleous et al's model is designed to explain the results of these experiments without any mechanism that explicitly implements the concept of perceptual load. The model has simulated the results from a small number of different experiments to show that this mechanism can provide accounts of results cited to support PLT as well as those used to support the dilution account.…”

Contrasting the Effects of Task Difficulty and Perceptual Load on Auditory Detection Sensitivity in Individuals with Autism

“…By contrast, a task with low perceptual load does not exhaust full capacity, resulting in automatic spill-over of remaining attentional resources to the processing of task-irrelevant information until overall capacity is reached. While there have been conceptual and methodological challenges to load theory (see Benoni and Tsal 2012;Giesbrecht et al 2014;Murphy et al 2016 for reviews), the large amount of research supporting the theory converging on similar evidence across different experimental paradigms and data modalities suggests that load theory constitutes a useful paradigm to test the implications for selective attention in clinical populations.…”