Biology subjects

Kiat, J. E.

Publications and source records attributed to Kiat, J. E..

3 recordsLinked to original sources

bioRxiv2026

A Population Vector Model of Visual Working Memory for Real-World Scenes

Visual working memory is essential for navigating through and interacting with complex real-world environments. It is therefore important to understand how natural visual inputs--characterized by complex contours, continuously varying feature gradients, and spatial relationships--are represented in working memory. However, most research in this field has focused on simplified arrays of discrete artificial objects, favoring experimental control and modeling simplicity over ecological validity. This has led to quantitative models of working memory that require inputs consisting of easily parsed objects defined by a single value along one or more simple feature dimensions. It is not clear how these models could be updated to represent complex, photograph-like scenes. To overcome this limitation, we introduce a population vector model of working memory that was designed specifically for real-world scenes. This model represents a scene as a noisy vector of neural firing rates across one or more areas of the ventral pathway, as estimated by a deep neural network model. We show that this model can account for both variations in behavioral performance and patterns of brain activity in tasks that require storing naturalistic scenes in working memory. These results demonstrate the viability of our general modeling approach, setting the stage for more sophisticated models that can fully account for the storage of real-world scenes in working memory. Public Significance StatementPeople unconsciously store visual information briefly in memory thousands of times each day and use this information to help them perform a broad range of natural tasks. Although the visual working memory system used for this purpose has been intensively studied using simple and highly controlled experimental stimuli (e.g., arrays of colored squares), there has been little progress in developing formal quantitative accounts of how real-world scenes are stored in this system. Here, we provide a new model of visual working memory that was designed for real-world scenes and can predict both behavior and brain activity when people store these scenes in memory.

neuroscience↗

bioRxiv2024

Enhanced Working Memory Representations for Rare Events

Rare events (oddballs) produce a variety of enhanced physiological responses relative to frequent events (standards), including the P3b component of the event-related potential (ERP) waveform. Previous research has suggested that the P3b is related to working memory, which implies that working memory representations will be enhanced for rare stimuli. To test this hypothesis, we devised a modified oddball paradigm where a target disk was presented at one of 16 different locations, which were divided into rare and frequent sets. Participants made a binary response on each trial to report whether the target appeared in the rare set or the frequent set. As expected, the P3b was much larger for stimuli appearing at a location within the rare set. We also included occasional probe trials in which the subject reported the exact location of the target. Accuracy was higher for rare than frequent locations. In addition, memory reports on rare trials were more accurate in participants with larger P3b amplitudes on rare trials (although reports were not more accurate for trials with larger P3b amplitudes within participants). We also applied multivariate pattern analysis to the ERP data to "decode" the remembered location of the target. Decoding accuracy was greater for locations within the rare set than for locations within the frequent set. We then replicated and extended our behavioral findings in a follow-up experiment. These behavioral and electrophysiological results demonstrate that although both frequent and rare events are stored in working memory, working memory performance is enhanced for rare oddball events. Impact StatementFor many decades, researchers have observed that rare events elicit a broad range of physiological responses, and there has been much speculation about the functional significance of these responses. One such response is the P3b component, which is a large voltage deflection in scalp EEG recordings. Over 40 years ago, the P3b was hypothesized to reflect "context updating" (now often called "working memory updating"). However, there has been no direct evidence that working memory is actually enhanced for rare, P3b-eliciting events. In the present study, we found that both behavioral and electrophysiological measures of working memory were enhanced for rare events. However, it is not clear that the increased P3b-related brain activity actually caused the enhancement of working memory.

neuroscience↗

bioRxiv2023

Evaluating the Effectiveness of a Common Approach to Artifact Correction and Rejection in Event-related Potential Research

Eyeblinks and other large artifacts can create two major problems in event-related potential (ERP) research, namely confounds and increased noise. Here, we developed a method for assessing the effectiveness of artifact correction and rejection methods at minimizing these two problems. We then used this method to assess a common artifact minimization approach, in which independent component analysis (ICA) is used to correct ocular artifacts, and artifact rejection is used to reject trials with extreme values resulting from other sources (e.g., movement artifacts). This approach was applied to data from five common ERP components (P3b, N400, N170, mismatch negativity, and error-related negativity). Four common scoring methods (mean amplitude, peak amplitude, peak latency, and 50% area latency) were examined for each component. We found that eyeblinks differed systematically across experimental conditions for several of the components. We also found that artifact correction was reasonably effective at minimizing these confounds, although it did not usually eliminate them completely. In addition, we found that the rejection of trials with extreme voltage values was effective at reducing noise, with the benefits of eliminating these trials outweighing the reduced number of trials available for averaging. For researchers who are analyzing similar ERP components and participant populations, this combination of artifact correction and rejection approaches should minimize artifact-related confounds and lead to improved data quality. Researchers who are analyzing other components or participant populations can use the method developed in this study to determine which artifact minimization approaches are effective in their data.

neuroscience↗