Differential aspects of attention predict the depth of visual working memory encoding: Evidence from pupillometry

Persistent resampling of external information despite 25 repetitions of the same visual search templates

We commonly load visual working memory minimally when to-be-remembered information remains available in the external world. In visual search, this is characterised by participants frequently resampling previously encoded templates, which helps minimize cognitive effort and improves task performance. If all search templates have been rehearsed many times, they should become strongly represented in memory, possibly eliminating the benefit of reinspections. To test whether repetition indeed leads to less resampling, participants searched for sets of 1, 2, and 4 continuously available search templates. Critically, each unique set of templates was repeated 25 trials consecutively. Although the number of inspections and inspection durations initially decreased strongly when a template set was repeated, behaviour largely stabilised between the tenth and last repetition: Participants kept resampling templates frequently. In Experiment 2, participants performed the same task, but templates became unavailable after 15 repetitions. Strikingly, accuracy remained high even when templates could not be inspected, suggesting that resampling was not strictly necessary in later repetitions. We further show that seemingly 'excessive' resampling behaviour had no direct within-trial benefit to speed nor accuracy, and did not improve performance on long-term memory tests. Rather, we argue that resampling was partially used to boost metacognitive confidence regarding memory representations. As such, eliminating the benefit of minimizing working memory load does not eliminate the persistence with which we sample information from the external world - although the underlying reason for resampling behaviour may be different.

Open-DPSM: An open-source toolkit for modeling pupil size changes to dynamic visual inputs

Pupil size change is a widely adopted, sensitive indicator for sensory and cognitive processes. However, the interpretation of these changes is complicated by the influence of multiple low-level effects, such as brightness or contrast changes, posing challenges to applying pupillometry outside of extremely controlled settings. Building on and extending previous models, we here introduce Open Dynamic Pupil Size Modeling (Open-DPSM), an open-source toolkit to model pupil size changes to dynamically changing visual inputs using a convolution approach. Open-DPSM incorporates three key steps: (1) Modeling pupillary responses to both luminance and contrast changes; (2) Weighing of the distinct contributions of visual events across the visual field on pupil size change; and (3) Incorporating gaze-contingent visual event extraction and modeling. These steps improve the prediction of pupil size changes beyond the here-evaluated benchmarks. Open-DPSM provides Python functions, as well as a graphical user interface (GUI), enabling the extension of its applications to versatile scenarios and adaptations to individualized needs. By obtaining a predicted pupil trace using video and eye-tracking data, users can mitigate the effects of low-level features by subtracting the predicted trace or assess the efficacy of the low-level feature manipulations a priori by comparing estimated traces across conditions.

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

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.

The Intensity of Internal and External Attention Assessed with Pupillometry

Not only is visual attention shifted to objects in the external world, attention can also be directed to objects in memory. We have recently shown that pupil size indexes how strongly items are attended externally, which was reflected in more precise encoding into visual working memory. Using a retro-cuing paradigm, we here replicated this finding by showing that stronger pupil constrictions during encoding were reflective of the depth of encoding. Importantly, we extend this previous work by showing that pupil size also revealed the intensity of internal attention toward content stored in visual working memory. Specifically, pupil dilation during the prioritization of one among multiple internally stored representations predicted the precision of the prioritized item. Furthermore, the dynamics of the pupillary responses revealed that the intensity of internal and external attention independently determined the precision of internalized visual representations. Our results show that both internal and external attention are not all-or-none processes, but should rather be thought of as continuous resources that can be deployed at varying intensities. The employed pupillometric approach allows to unravel the intricate interplay between internal and external attention and their effects on visual working memory.

Don't hide the instruction manual: A dynamic trade-off between using internal and external templates during visual search

Visual search is typically studied by requiring participants to memorize a template initially, for which they subsequently search in a crowded display. Search in daily life, however, often involves templates that remain accessible externally, and may therefore be (re)attended for just-in-time encoding or to refresh internal template representations. Here, we show that participants indeed use external templates during search when given the chance. This behavior was observed during both simple and complex search, scaled with task difficulty, and was associated with improved performance. Furthermore, we show that participants used external sampling not only to offload memory, but also as a means of verifying whether the template was remembered correctly at the end of trials. We conclude that the external world may not only provide the challenge (e.g., distractors), but may dynamically ease search. These results argue for extensions of state-of-the-art models of search, because external sampling seems to be used frequently, in at least two ways and is actually beneficial for task performance. Our findings support a model of visual working memory that emphasizes a resource-efficient trade-off between storing and (re)attending external information.