Dataset Information

Retrospective Attention Interacts with Stimulus Strength to Shape Working Memory Performance.

ABSTRACT: Orienting attention retrospectively to selective contents in working memory (WM) influences performance. A separate line of research has shown that stimulus strength shapes perceptual representations. There is little research on how stimulus strength during encoding shapes WM performance, and how effects of retrospective orienting might vary with changes in stimulus strength. We explore these questions in three experiments using a continuous-recall WM task. In Experiment 1 we show that benefits of cueing spatial attention retrospectively during WM maintenance (retrocueing) varies according to stimulus contrast during encoding. Retrocueing effects emerge for supraliminal but not sub-threshold stimuli. However, once stimuli are supraliminal, performance is no longer influenced by stimulus contrast. In Experiments 2 and 3 we used a mixture-model approach to examine how different sources of error in WM are affected by contrast and retrocueing. For high-contrast stimuli (Experiment 2), retrocues increased the precision of successfully remembered items. For low-contrast stimuli (Experiment 3), retrocues decreased the probability of mistaking a target with distracters. These results suggest that the processes by which retrospective attentional orienting shape WM performance are dependent on the quality of WM representations, which in turn depends on stimulus strength during encoding.

SUBMITTER: Wildegger T

PROVIDER: S-EPMC5051714 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Retrospective Attention Interacts with Stimulus Strength to Shape Working Memory Performance.

Wildegger Theresa T Humphreys Glyn G Nobre Anna C AC

PloS one 20161005 10

Orienting attention retrospectively to selective contents in working memory (WM) influences performance. A separate line of research has shown that stimulus strength shapes perceptual representations. There is little research on how stimulus strength during encoding shapes WM performance, and how effects of retrospective orienting might vary with changes in stimulus strength. We explore these questions in three experiments using a continuous-recall WM task. In Experiment 1 we show that benefits ...[more]

PMID: 27706240

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Project description:IntroductionPatients with schizophrenia typically exhibit deficits in working memory (WM) associated with abnormalities in brain activity. Alterations in the encoding, maintenance and retrieval phases of sequential WM tasks are well established. However, due to the heterogeneity of symptoms and complexity of its neurophysiological underpinnings, differential diagnosis remains a challenge. We conducted an electroencephalographic (EEG) study during a visual WM task in fifteen schizophrenia patients and fifteen healthy controls. We hypothesized that EEG abnormalities during the task could be identified, and patients successfully classified by an interpretable machine learning algorithm.MethodsWe tested a custom dense attention network (DAN) machine learning model to discriminate patients from control subjects and compared its performance with simpler and more commonly used machine learning models. Additionally, we analyzed behavioral performance, event-related EEG potentials, and time-frequency representations of the evoked responses to further characterize abnormalities in patients during WM.ResultsThe DAN model was significantly accurate in discriminating patients from healthy controls, ACC = 0.69, SD = 0.05. There were no significant differences between groups, conditions, or their interaction in behavioral performance or event-related potentials. However, patients showed significantly lower alpha suppression in the task preparation, memory encoding, maintenance, and retrieval phases F(1,28) = 5.93, p = 0.022, η2 = 0.149. Further analysis revealed that the two highest peaks in the attention value vector of the DAN model overlapped in time with the preparation and memory retrieval phases, as well as with two of the four significant time-frequency ROIs.DiscussionThese results highlight the potential utility of interpretable machine learning algorithms as an aid in diagnosis of schizophrenia and other psychiatric disorders presenting oscillatory abnormalities.

Dataset Information

Retrospective Attention Interacts with Stimulus Strength to Shape Working Memory Performance.

Publications

Retrospective Attention Interacts with Stimulus Strength to Shape Working Memory Performance.

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