Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data.

AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated depression symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals: sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups.

A model-based analysis of decision making under risk in obsessive-compulsive and hoarding disorders.

Attitudes towards risk are highly consequential in clinical disorders thought to be prone to "risky behavior", such as substance dependence, as well as those commonly associated with excessive risk aversion, such as obsessive-compulsive disorder (OCD) and hoarding disorder (HD). Moreover, it has recently been suggested that attitudes towards risk may serve as a behavioral biomarker for OCD. We investigated the risk preferences of participants with OCD and HD using a novel adaptive task and a quantitative model from behavioral economics that decomposes risk preferences into outcome sensitivity and probability sensitivity.

On the Functional Form of Temporal Discounting: An Optimized Adaptive Test.

The tendency to discount the value of future rewards has become one of the best-studied constructs in the behavioral sciences. Although hyperbolic discounting remains the dominant quantitative characterization of this phenomenon, a variety of models have been proposed and consensus around the one that most accurately describes behavior has been elusive. To help bring some clarity to this issue, we propose an Adaptive Design Optimization (ADO) method for fitting and comparing models of temporal discounting.

The effect of cognitive challenge on delay discounting.

Recent findings suggest that the dorsolateral prefrontal cortex (DLPFC), a region consistently associated with impulse control, is vulnerable to transient suppression of its activity and attendant functions by excessive stress and/or cognitive demand. Using functional magnetic resonance imaging, we show that a capacity-exceeding cognitive challenge induced decreased DLPFC activity and correlated increases in the preference for immediately available rewards. Consistent with growing evidence of a link between working memory capacity and delay discounting, the effect was inversely proportional to baseline performance on a working memory task.