Future steps in visual working memory research

Olivers, Christian N. L.; Stigchel, Stefan Van der

doi:10.1080/13506285.2020.1833478

Cited by 4 publications

(2 citation statements)

References 30 publications

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“…VWM allows for the use of past information to guide future behavior. A crucial aspect of this utility concerns the prioritization of the relevant parts of stored content to prepare for action (Heuer et al, 2020; Olivers & Van der Stigchel, 2020; Souza & Oberauer, 2016; van Ede, 2020; van Ede & Nobre, 2023). Prioritizing relevant material in VWM facilitates upcoming task performance, leading to faster and more accurate responses (Oberauer, 2019; Olivers et al, 2011; Olivers & Roelfsema, 2020; Souza & Oberauer, 2016; van Ede, 2020).…”

Section: Prioritizationmentioning

confidence: 99%

Revealing visual working memory operations with pupillometry: Encoding, maintenance, and prioritization

Koevoet,

Strauch,

Van der Stigchel

et al. 2023

WIRES Cognitive Science

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Pupillary dynamics reflect effects of distinct and important operations of visual working memory: encoding, maintenance, and prioritization. Here, we review how pupil size predicts memory performance and how it provides novel insights into the mechanisms of each operation. Visual information must first be encoded into working memory with sufficient precision. The depth of this encoding process couples to arousal‐linked baseline pupil size as well as a pupil constriction response before and after stimulus onset, respectively. Subsequently, the encoded information is maintained over time to ensure it is not lost. Pupil dilation reflects the effortful maintenance of information, wherein storing more items is accompanied by larger dilations. Lastly, the most task‐relevant information is prioritized to guide upcoming behavior, which is reflected in yet another dilatory component. Moreover, activated content in memory can be pupillometrically probed directly by tagging visual information with distinct luminance levels. Through this luminance‐tagging mechanism, pupil light responses reveal whether dark or bright items receive more attention during encoding and prioritization. Together, conceptualizing pupil responses as a sum of distinct components over time reveals insights into operations of visual working memory. From this viewpoint, pupillometry is a promising avenue to study the most vital operations through which visual working memory works.This article is categorized under: Psychology > Attention Psychology > Memory Psychology > Theory and Methods

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Section: Prioritizationmentioning

confidence: 99%

Revealing visual working memory operations with pupillometry: Encoding, maintenance, and prioritization

Koevoet,

Strauch,

Van der Stigchel

et al. 2023

WIRES Cognitive Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…Visual working memory (VWM) has been well established in the field of neuroscience as a cognitive function essential to our daily lives, allowing us to process and temporarily maintain visual information in our working memory in order to focus on and complete tasks (Olivers et al, 2020). Decades of research have allowed us to try and identify the various functions of the brain, as well as the structures that contribute to certain processes, such as VWM.…”

Section: Introductionmentioning

confidence: 99%

Distractor-Specific Single Neuron Activity Predicts Visual Working Memory Task Outcomes

Lakhamraju,

Stewart

2023

J Stud Res

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This paper explores the relationship between neural activity and behavioral performance in the form of visual working memory (VWM) task outcomes, by answering the question: Are there any significant differences in the firing rates of individual neurons during the distractor presentation period of a VWM task between success and error trials that can predict the outcome of a trial? Distractor-specific single neuron firing rates during a VWM task were analyzed to answer this question. A logistic regression was used to identify the predictive capability of neural firing rate on trial outcome with the neural activity of 51 cells from the lateral prefrontal cortex (LPFC) of a primate. This study found that a best-fit logistic model could predict the behavioral performance of the primate (success or error of the VWM task) with 63.01% accuracy, with additional machine learning techniques producing scores upwards of 68% accuracy. Moreover, greater firing rates in response to the distractor, indicating less efficient distractor suppression, accompanied the error trials of the VWM task. This suggests that stronger neural responses to task-specific distractors can hinder the attentional filtering required for efficient working memory, supporting previous research that found that distractor suppression is a mechanism that heavily influences WM efficiency. These findings indicate that people, particularly children, with disorders that affect WM capacity such as ADHD may experience stronger neural responses to distractors, and therefore inefficient distractor suppression, at the single neuron level when engaging in goal-oriented behaviors, which can significantly impact learning and other developmental processes.

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Visual working memory representations bias attention more when they are the target of an action plan

2023

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Future steps in visual working memory research

Cited by 4 publications

References 30 publications

Revealing visual working memory operations with pupillometry: Encoding, maintenance, and prioritization

Revealing visual working memory operations with pupillometry: Encoding, maintenance, and prioritization

Distractor-Specific Single Neuron Activity Predicts Visual Working Memory Task Outcomes

Visual working memory representations bias attention more when they are the target of an action plan

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