Enhanced memory for emotional pictures: A product of increased attention to affective stimuli?

The current experiment addressed the question, is enhanced memory for emotional pictures due to increased attention to affective stimuli? Participants viewed pairs of pictures (emotional-neutral or neutral-neutral) whilst their eye movements were recorded; participants had to decide which picture out of each pair they preferred. There was increased attention to positive pictures and decreased attention to negative images during picture viewing. Despite this, when a recognition test was given 1 week later, memory enhancements were found for negative pictures only. Moreover, although there was a general correlation between total inspection time and memory performance, this reliability was clear only for neutral pictures, and not for emotional images. The results suggest that memory advantages for emotional pictures can occur without increased attention to such images.     

Working memory and target-related distractor effects on visual search

We examined two forms of top-down effects on visual selection: (1) information held in working memory (WM) and (2) the semantic relations between targets and distractors. We found that items held in WM affected search for a different target. This WM-based interference effect generalized across different exemplars, even though participants could remember the specific exemplar on the trial. This argues against a memory top-up account of performance. In addition, there was interference from distractors that were not held in WM but were semantically related to the target. The effects of WM capture and the effects of capture by a distractor related to the target combined additively. The data suggest that task-irrelevant information in WM and task-relevant templates for targets compete separately for selection. The implications for understanding top-down processes in search are discussed.     

Bridging the gap between physiology and behavior: evidence from the sSoTS model of human visual attention

We present the case for a role of biologically plausible neural network modeling in bridging the gap between physiology and behavior. We argue that spiking-level networks can allow "vertical" translation between physiological properties of neural systems and emergent "whole-system" performance-enabling psychological results to be simulated from implemented networks and also inferences to be made from simulations concerning processing at a neural level. These models also emphasize particular factors (e.g., the dynamics of performance in relation to real-time neuronal processing) that are not highlighted in other approaches and that can be tested empirically. We illustrate our argument from neural-level models that select stimuli by biased competition. We show that a model with biased competition dynamics can simulate data ranging from physiological studies of single-cell activity (Study 1) to whole-system behavior in human visual search (Study 2), while also capturing effects at an intermediate level, including performance breakdown after neural lesion (Study 3) and data from brain imaging (Study 4). We also show that, at each level of analysis, novel predictions can be derived from the biologically plausible parameters adopted, which we proceed to test (Study 5). We argue that, at least for studying the dynamics of visual attention, the approach productively links single-cell to psychological data.