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Shriki, O.

Publications and source records attributed to Shriki, O..

2 recordsLinked to original sources

Surprise response as a probe for compressed memory states

The limited capacity of recent memory inevitably leads to partial memory of past stimuli. There is also evidence that behavioral and neural responses to novel or rare stimuli are dependent on ones memory of past stimuli. Thus, these responses may serve as a probe of different individuals remembering and forgetting characteristics. Here, we utilize two lossy compression models of stimulus sequences that inherently involve forgetting, which in addition to being a necessity under many conditions, also has theoretical and behavioral advantages. One model is based on a simple stimulus counter and the other on the Information Bottleneck (IB) framework. These models are applied to analyze a novelty-detection event-related potential commonly known as the P300. The trial-by-trial variations of the P300 response, recorded in an auditory oddball paradigm, were subjected to each model to extract two stimulus-compression parameters for each subject: memory length and representation accuracy. These parameters were then utilized to estimate the subjects recent memory capacity limit under the task conditions. The results, along with recently published findings on single neurons on the IB model, underscore how a lossy compression framework can be utilized to account for trial-by-trial variability of neural responses at different spatial scales and in different individuals, while at the same time providing estimates of individual memory characteristics at different levels of representation using a theoretically-based parsimonious model.\n\nAuthor summarySurprise responses reflect expectations based on preceding stimuli representations, and hence can be used to infer the characteristics of memory utilized for a task. We suggest a quantitative method for extracting an individual estimate of effective memory capacity dedicated for a task based on the correspondence between a theoretical surprise model and electrophysiological single-trial surprise responses. We demonstrate this method on EEG responses recorded while participants were performing a simple auditory task; we show the correspondence between the theoretical and physiological surprise, and calculate an estimate of the utilized memory. The generality of this framework allows it to be applied to different EEG features that reflect different modes and levels of the processing hierarchy, as well as other physiological measures of surprise responses. Future studies may use this framework to construct a handy diagnostic tool for a quantitative, individualized characterization of memory-related disorders.

neuroscience

EEG-based Prediction of Cognitive Load in Intelligence Tests

Measuring and assessing the cognitive load associated with different tasks is crucial for many applications, from the design of instructional materials to monitoring the mental wellbeing of aircraft pilots. The goal of this paper is to utilize EEG to infer the cognitive workload of subjects during intelligence tests. We chose the well established advanced progressive matrices test, an ideal work-frame because it presents problems at increasing levels of difficulty, and has been rigorously validated in past experiments. We train classic machine learning models using basic EEG measures as well as measures of network connectivity and signal complexity. Our findings demonstrate that cognitive load can be well predicted using these features, even for a low number of channels. We show that by creating an individually tuned neural network for each subject, we can improve prediction compared to a general model and that such models are robust to decreasing the number of available channels as well.

neuroscience