Cognition

Motivated learning and memory processes across development

The ability to learn from and remember salient information is essential for an individual to survive and thrive throughout life. Considerable research has focused on emotional reactivity, regulation, and their neural mechanisms during the transitional stage of adolescence. However, fewer studies have examined how emotion and motivation shape how we learn and what we remember from childhood to adulthood. My work aims to characterize how aversive and appetitive motivational inputs influence learning and lasting memories across adolescence. We find that the context in which learning takes place modulates age-related differences in learning from positive and negative outcomes. We also find memory enhancements by both aversive and appetitive motivation across all ages as well as adolescent-specific differences in reward-motivated memory. Neuroimaging results suggest potential developmental differences in the contributions of subcortical and prefrontal cortical brain mechanisms supporting reward-motivated memory. Ultimately, this research has the potential to uncover cognitive and neural mechanisms through which motivation shapes the memories that drive future behavior from childhood to adulthood.

Physiological and psychosocial correlates of “good sleep”: Implications for cognition, health, and aging

What makes your sleep “good”? Few wake intrusions? Falling asleep once your head hits the pillow? Waking up refreshed and ready for your day? All the above? Science is still grappling with the answers to this question, yet we do know that a period of sleep helps us think, learn, and remember better. Additionally, specific neural changes during sleep support human cognition. To date, my research program has examined how these neural features and specific changes in our body 1) help us define “good” sleep and 2) support cognition. In this talk, I will review this body of work and identify future directions aligned with this research trajectory. Additionally, data suggests that 35% of people do not get the recommended amount of sleep at night. This widespread sleeplessness comes with significant costs to our overall health. Yet, the burden of sleep loss does not fall on everyone equally. I will discuss disparities in sleep health and access, discuss historical links, and on-going and future projects that address these topics.

The Development of Decision-Making Across Diverse Cultural Contexts

The human behavioral repertoire is uniquely diverse, with an unmatched flexibility that has allowed our species to flourish in every ecology on the planet. Despite its importance, the roots of this behavioral diversity — and how it manifests across development and contexts — remain largely unexplored. I argue that a full account of human behavior requires a cross-cultural, developmental approach that systematically examines how environmental variability shapes behavioral processes. In this talk, I use the development of decision-making across diverse contexts as a window into the relationship between the socioecological environment and behavior. First, I present the results of a cross-cultural investigation of risk and time preferences among children in India, Argentina, the United States, and the Ecuadorian Amazon, suggesting that market integration and related socioecological shifts lead to the development of more risk-seeking and future-oriented preferences. Second, I present the early results of a five-culture investigation into the ontogeny of social preferences — namely, trustworthiness, forgiveness, and fairness. Taken together, these studies help elucidate the developmental origins of behavioral diversity across diverse contexts, and underscore the utility of interdisciplinary research for explaining human behavior.

Cognitive state fluctuations impact learning in different contexts

We are constantly learning from the world around us. How do changes in our cognitive and attentional states impact this process? I will describe two projects examining relationships between internal state fluctuations and an automatic, fundamental process of learning—statistical learning, and a noisy, dynamic form of learning—adaptive learning. In the first project, we examined the consequences of sustained attention fluctuations for statistical learning. Participants completed a continuous performance task with shape stimuli online. Unbeknownst to participants, we manipulated what they saw in real time by inserting visual regularities (a sequence of three regular shapes) into the task trial stream when their response times suggested that they were in especially high or low attentional states. Demonstrating that attentional state impacts statistical learning, we observed greater evidence for learning of the regular sequence encountered in the high vs. the low attentional state. In project two, we reanalyzed an openly available fMRI dataset collected as participants performed an adaptive learning task in which they learned to make accurate predictions about the locations of a fallen object in an noisy and dynamically changing environment. Individual differences in a brain network signature of sustained attention predicted individual learning style, with individuals with network signatures of stronger attention showing a learning style more like that of a normative model. In addition, trial-to-trial fluctuations in a distinct network signature of working memory predicted learning performance, such that trials on which participants showed a network signature of stronger working memory were followed by closer alignment between human and model predictions on the next trial. Together, these studies reveal consequences of sustained attention and working memory fluctuations for learning in different contexts.

Stable attentional control demands across individuals despite extensive learning

Classic models of expertise propose that when first learning a task, success is primarily determined by the individual’s attention and working memory ability. However, as skill is developed performance becomes less dependent on attention control and loads more on acquired long term memory structures for the task. Here, we tested whether individual differences in attentional control ability continued to predict long term memory performance for picture sequences even after participants showed massive learning increases for the sequence via multiple repetitions. In Experiment 1-3, subjects performed a location source memory task in which they were presented a sequence of 30 objects shown in one of four quadrants, or 30 centrally positioned objects with an external black square in one of the four quadrants, and then were tested on each item’s position. We then repeated the procedure with the same object sequences, such that each subject was shown and tested on the same sequence 5 times. We replicated the prior findings of a relationship between attentional control and overall memory accuracy. Interestingly, we discovered that individual differences in attentional control continually predicted memory accuracy across all repetitions. In Experiment 4, we sought to replicate our finding with verbal materials, so that the participants were asked to memorize 45 word pairs and perform cued recall tasks as memory measure. We replicated the correlation between attentional control and overall memory accuracy, as well as the stable attentional control demands even with extensive learning of word pairs. Together, these results suggest that developing expertise does not eliminate the contribution of attentional control ability for long term memory, but may instead reflect more optimized attention control during expert task performance.

Towards naturalistic reinforcement learning in health and disease 

Adaptive decision-making relies on our ability to organize experience into useful representations of the environment. This ability is critical in the real world: each person’s experience is dynamic and continuous, and no two situations we encounter are exactly the same. In this talk, I will first show that attention and memory contribute to inferring a set of features of the environment relevant for learning and decision-making (i.e. a “state representation”). I will then present results from ongoing work attempting to understand how such inference can take place in naturalistic environments. One line of work leverages virtual reality in combination with eye-tracking to study what features of naturalistic scenes guide goal-directed search. A second study examines the role of language in providing a prior for which features are relevant for decision-making. And a third thread focuses on how mood biases attention to different features of a decision. I will conclude with a discussion of the potential of naturalistic reinforcement learning as a model of mental health dynamics.