Modeling the Effects of Perceptual Load: Saliency, Competitive Interactions, and Top-Down Biases

Abstract

A computational model of visual selective attention has been implemented to account for experimental findings on the Perceptual Load Theory (PLT) of attention. The model was designed based on existing neurophysiological findings on attentional processes with the objective to offer an explicit and biologically plausible formulation of PLT. Simulation results verified that the proposed model is capable of capturing the basic pattern of results that support the PLT as well as findings that are considered contradictory to the theory. Importantly, the model is able to reproduce the behavioral results from a dilution experiment, providing thus a way to reconcile PLT with the competing Dilution account. Overall, the model presents a novel account for explaining PLT effects on the basis of the low-level competitive interactions among neurons that represent visual input and the top-down signals that modulate neural activity. The implications of the model concerning the debate on the locus of selective attention as well as the origins of distractor interference in visual displays of varying load are discussed.

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

“…The Neokleous et al (2016) model, like the Scalf et al (2013) account and the Chen and Cave (2013 attentional zoom account, portrays the different results across these different experiments as reflecting the different levels of interference among distractor items. In the Scalf et al and the attentional zoom accounts, higher interference leads to more attentional control, which favors the target over other items in the display and thus limits the interference from a salient distractor.…”

“…Neokleous et al (2016) built a computer simulation of the interactions and interference among the different stimuli. Rather than attributing the results of PL and dilution experiments to changes in attentional control, they explain the results as differences in competition as interference levels vary.…”

“…To explore the model's response to dilution stimuli, Neokleous et al (2016) simulated processing of the stimuli from Benoni and Tsal (2010), which includes low-load-lowdilution, low-load-high-dilution, and high-load-high-dilution conditions. In both high-dilution conditions, the addition of the extra nontargets diminishes activation of the distractor, leading to lower distractor interference, although the specific interactions among the different stimuli are different in each condition.…”

“…Thus, the model produces results similar to Benoni and Tsal's experiment. Neokleous et al (2016) use these simulations to argue that a single model can account for the results from both perceptual load and dilution experiments, and that it can do so without mechanisms that are specially designed to produce perceptual load effects or dilution effects. Instead, these effects can arise from the bottom-up activation that objects receive when they differ from other objects in basic feature dimensions, because this activation can allow both target and nontargets to inhibit a salient distractor and can allow the target to overcome interference from that salient distractor.…”

“…We will start with Lavie's (1995Lavie's ( , 2005Lavie & Tsal, 1994) perceptual load theory (PLT) and the four different types of evidence offered to support it. Then, we will review experimental evidence that has led to a series of alternative accounts, including the dilution accounts by Tsal and Benoni (2010) and Wilson et al (2011), and a detailed model recently presented by Neokleous, Shimi, and Avraamides (2016). One important factor in these experiments is the broadening of attention that comes from sudden onsets; once these effects are controlled with methods from Jonides (1984, 1990), the resulting data fit suggests that performance on these tasks is shaped by an attentional zoom mechanism that adjusts the size of the attended region according to the level of interference between different stimulus elements.…”