Visual working memory performance with contrast and external noise: A load-dependent perceptual template model account

Hierarchical Bayesian perceptual template modeling of mechanisms of spatial attention in central and peripheral cuing

The external noise paradigm and perceptual template model (PTM) have successfully been applied to characterize observer properties and mechanisms of observer state changes (e.g. attention and perceptual learning) in several research domains, focusing on individual level analysis. In this study, we developed a new hierarchical Bayesian perceptual template model (HBPTM) to model the trial-by-trial data from all individuals and conditions in a published spatial cuing study within a single structure and compared its performance to that of a Bayesian Inference Procedure (BIP), which separately infers the posterior distributions of the model parameters for each individual subject without the hierarchical structure. The HBPTM allowed us to compute the joint posterior distribution of the hyperparameters and parameters at the population, observer, and experiment levels and make statistical inferences at all these levels. In addition, we ran a large simulation study that varied the number of observers and number of trials in each condition and demonstrated the advantage of the HBPTM over the BIP across all the simulated datasets. Although it is developed in the context of spatial attention, the HBPTM and its extensions can be used to model data from the external noise paradigm in other domains and enable predictions of human performance at both the population and individual levels.

Luminance Contrast Drives Interactions between Perception and Working Memory

Visual working memory (WM) enables the use of past sensory experience in guiding behavior. Yet, laboratory tasks commonly evaluate WM in a way that separates it from its sensory bottleneck. To understand how perception interacts with visual memory, we used a delayed shape recognition task to probe how WM may differ for stimuli that bias processing toward different visual pathways. Luminance compared with chromatic signals are more efficient in driving the processing of shapes and may thus also lead to better WM encoding, maintenance, and memory recognition. To evaluate this prediction, we conducted two experiments. In the first psychophysical experiment, we measured contrast thresholds for different WM loads. Luminance contrast was encoded into WM more efficiently than chromatic contrast, even when both sets of stimuli were equated for discriminability. In the second experiment, which also equated stimuli for discriminability, early sensory responses in the EEG that are specific to luminance pathways were modulated by WM load and thus likely reflect the neural substrate of the increased efficiency. Our results cannot be accounted for by simple saliency differences between luminance and color. Rather, they provide evidence for a direct connection between low-level perceptual mechanisms and WM by showing a crucial role of luminance for forming WM representations of shape.