Decoding EEG for optimizing naturalistic memory.

BACKGROUND: Spectral features of human electroencephalographic (EEG) recordings during learning predict subsequent recall variability. METHODS: Capitalizing on these fluctuating neural features, we develop a non-invasive closed-loop (NICL) system for real-time optimization of human learning. Participants play a virtual navigation and memory game; recording multi-session data across days allowed us to build participant-specific classification models of recall success. In subsequent closed-loop sessions, our platform manipulated the timing of memory encoding, selectively presenting items during periods of predicted good or poor memory function based on EEG features decoded in real time. RESULTS: We observed greater memory modulation (difference between recall rates when presenting items during predicted good vs. poor learning periods) for participants with higher out-of-sample classification accuracy. COMPARISON WITH EXISTING METHODS: This study demonstrates greater-than-chance memory decoding from EEG recordings in a naturalistic virtual navigation task with greater real-world validity than basic word-list recall paradigms. Here we modulate memory by timing stimulus presentation based on noninvasive scalp EEG recordings, whereas prior closed-loop studies for memory improvement involved intracranial recordings and direct electrical stimulation. Other noninvasive studies have investigated the use of neurofeedback or remedial study for memory improvement. CONCLUSION: These findings present a proof-of-concept for using non-invasive closed-loop technology to optimize human learning and memory through principled stimulus timing, but only in those participants for whom classifiers reliably predict out-of-sample memory function.

Integration of the ecological and error models of overconfidence using a multiple-trace memory model.

A memory processes account of the calibration of probability judgments was examined. A multiple-trace memory model, Minerva-Decision Making (MDM; M. R. P. Dougherty, C. F. Gettys, & E. E. Ogden, 1999), used to integrate the ecological (Brunswikian) and the error (Thurstonian) models of overconfidence, is described. The model predicts that overconfidence should decrease both as a function of experience and as a function of encoding quality. Both increased experience and improved encoding quality result in lower variance in the output of the model, which in turn leads to improved calibration. Three experiments confirmed these predictions. Implications of MDM's account of overconfidence are discussed.

Averaging expectancies and perceptual experiences in the assessment of quality.

This study examines whether people integrate expectancy information with perceptual experiences when evaluating the quality of consumer products. In particular, we investigate the following three questions: (1) Are expectancy effects observed in the evaluation of consumer products? (2) Can these effects be viewed in cognitive processing terms? (3) Can a mathematical model based on the averaging of attribute information describe the effects? Participants in two experiments blindly evaluated (with the product names removed) consumer products from six sensory modalities: vision (computer printer output), tactile (paper towels), olfaction (men's cologne), taste (corn chips), auditory (audio cassette tapes), and tactile/medicinal (hand lotion). Participants in both experiments were asked to: (1) rate the overall quality of the product given arbitrary quality labels (High Quality, Medium Quality, or Low Quality); (2) rate the overall quality of the product without the labels, and (3) estimate the scale values for the quality labels alone. Group results revealed main effects of the quality labels in all product categories. The pattern of results could be described by an averaging model based on Information Integration Theory. These results have implications for placebo effects in consumer behavior and decision making.