Advancing Naturalistic Affective Science with Deep Learning.

People express their own emotions and perceive others' emotions via a variety of channels, including facial movements, body gestures, vocal prosody, and language. Studying these channels of affective behavior offers insight into both the experience and perception of emotion. Prior research has predominantly focused on studying individual channels of affective behavior in isolation using tightly controlled, non-naturalistic experiments. This approach limits our understanding of emotion in more naturalistic contexts where different channels of information tend to interact. Traditional methods struggle to address this limitation: manually annotating behavior is time-consuming, making it infeasible to do at large scale; manually selecting and manipulating stimuli based on hypotheses may neglect unanticipated features, potentially generating biased conclusions; and common linear modeling approaches cannot fully capture the complex, nonlinear, and interactive nature of real-life affective processes. In this methodology review, we describe how deep learning can be applied to address these challenges to advance a more naturalistic affective science. First, we describe current practices in affective research and explain why existing methods face challenges in revealing a more naturalistic understanding of emotion. Second, we introduce deep learning approaches and explain how they can be applied to tackle three main challenges: quantifying naturalistic behaviors, selecting and manipulating naturalistic stimuli, and modeling naturalistic affective processes. Finally, we describe the limitations of these deep learning methods, and how these limitations might be avoided or mitigated. By detailing the promise and the peril of deep learning, this review aims to pave the way for a more naturalistic affective science.

Distinct monitoring strategies underlie costs and performance in prospective memory.

Prospective memory (PM) describes the ability to remember to perform goal-relevant actions at an appropriate time in the future amid concurrent demands. A key contributor to PM performance is thought to be the effortful monitoring of the environment for PM-related cues, a process whose existence is typically inferred from a behavioral interference measure of reaction times. This measure, referred to as "PM costs," is an informative but indirect proxy for monitoring, and it may not be sufficient to understand PM behaviors in all situations. In this study, we asked participants to perform a visual search task with arrows that varied in difficulty while concurrently performing a delayed-recognition PM task with pictures of faces and scenes. To gain a precise measurement of monitoring behavior, we used eye-tracking to record fixations to all task-relevant stimuli and related these fixation measures to both PM costs and PM accuracy. We found that PM costs reflected dissociable monitoring strategies: higher costs were associated with early and frequent monitoring while lower costs were associated with delayed and infrequent monitoring. Moreover, the link between fixations and PM costs varied with cognitive load, and the inclusion of fixation data yielded better predictions of PM accuracy than using PM costs alone. This study demonstrates the benefit of eye-tracking to disentangle the nature of PM costs and more precisely describe strategies involved in prospective remembering.