Control preference persists with age.

The opportunity to exert control in one's environment is desirable, and individuals are willing to seek out control, even at a financial cost. Additionally, control-related activation of reward regions in the brain and the positive affect associated with the opportunity to exert control suggest that control is rewarding. The present study explores whether there are age-related differences in the preference for control. Older and younger adults chose whether to maintain control and play a guessing game themselves or to cede this control to the computer. Maintaining and ceding control were associated with different amounts of monetary reward that could be banked upon a successful guess. This required participants to weigh the value associated with control compared to monetary rewards. We found that older adults preferred control and traded monetary reward for control, similar to younger adults. The results suggest that the preference for exerting control may be preserved across age. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Value-based attention capture: Differential effects of loss and gain contingencies.

There is evidence that attention can be captured by a feature that is associated with reward. However, it is unclear how associating a feature with loss impacts attentional capture. Some have found evidence for attentional capture by loss-associated stimuli, suggesting that attention is biased toward stimuli predictive of consequence, regardless of the valence of that consequence. However, in those studies, efficient attention to the loss-associated stimulus reduced the magnitude of the loss during training, so attention to the loss-associated stimulus was rewarded in relative terms. In Experiment 1 we associated a color with loss, gain, or no consequence during training and then investigated whether attention is captured by each color. Importantly, our training did not reward, even in a relative sense, attention to the loss-associated color. Although we found robust attentional capture by gain-associated colors, we found no evidence for capture by loss-associated colors. A second experiment showed that the observed effects cannot be explained by selection history and, hence, are specific to value learning. These results suggest that the learning mechanisms of value-based attentional capture are driven by reward, but not by loss or the predictability of consequences in general.

Unsupervised EEG Artifact Detection and Correction.

Electroencephalography (EEG) is used in the diagnosis, monitoring, and prognostication of many neurological ailments including seizure, coma, sleep disorders, brain injury, and behavioral abnormalities. One of the primary challenges of EEG data is its sensitivity to a breadth of non-stationary noises caused by physiological-, movement-, and equipment-related artifacts. Existing solutions to artifact detection are deficient because they require experts to manually explore and annotate data for artifact segments. Existing solutions to artifact correction or removal are deficient because they assume that the incidence and specific characteristics of artifacts are similar across both subjects and tasks (i.e., "one-size-fits-all"). In this paper, we describe a novel EEG noise-reduction method that uses representation learning to perform patient- and task-specific artifact detection and correction. More specifically, our method extracts 58 clinically relevant features and applies an ensemble of unsupervised outlier detection algorithms to identify EEG artifacts that are unique to a given task and subject. The artifact segments are then passed to a deep encoder-decoder network for unsupervised artifact correction. We compared the performance of classification models trained with and without our method and observed a 10% relative improvement in performance when using our approach. Our method provides a flexible end-to-end unsupervised framework that can be applied to novel EEG data without the need for expert supervision and can be used for a variety of clinical decision tasks, including coma prognostication and degenerative illness detection. By making our method, code, and data publicly available, our work provides a tool that is of both immediate practical utility and may also serve as an important foundation for future efforts in this domain.