Executive functioning in adverse environments: Using cognitive modeling to integrate deficit and adaptation frameworks

Decades of research show that people from adverse environments tend to score lower on cognitive tasks. These findings have been associated with deficits in various cognitive abilities, which has led to the proliferation of deficit models. These models emphasize the negative effects of chronic stress on social and cognitive development. More recently, research has started to focus on the ways in which adversity exposure may lead to adaptations specific cognitive abilities and strategies. Such adaptations could potentially lead to enhancements in abilities that are useful for solving the unique challenges posed by adverse environments, such as threat detection. A better integration of deficit and adaptation perspectives is important both for basic science and the development of interventions. Despite their importance, deficit and adaptation perspectives are not well-integrated. This dissertation argues that one reason for this might be that research from both perspectives tends to focus on raw performance measures, such as response times and accuracy. However, these measures have two important limitations. First, performance on EF tasks is not just influenced by specific EF abilities, but also by other cognitive processes, such as response caution. Second, performance on EF tasks is additionally influenced by general processes that shape performance across multiple cognitive tasks. These limitations hinder our understanding of how adversity shapes cognitive abilities. To address the first limitation, this dissertation uses Drift Diffusion Modeling (DDM). The DDM explains response times and accuracy on tasks that require binary decision-making as a combination of (1) the speed with which someone accumulates information (Drift rate), (2) the amount of information someone needs before making a decision (Boundary separation), and (3) the speed of non-decisional processes, such as stimulus encoding and respons execution (Non-decision time). By applying DDM to empirical task data, we can obtain participant-level estimates of these three cognitive processes. To address the second limitation, this dissertation uses structural equation modeling. This technique allows us to investigate the extent to which performance on EF tasks is shaped by task-specific processes versus task-general processes. This dissertation shows that adversity researchers may overestimate the association between adversity and specific EF abilities. We find that negative associations between adversity and drift rates are mostly driven by general processing speed. After correcting for general factors, we find little to no evidence for associations between adversity and specific EF abilities. Finally, we show that people with more adversity exposure sometimes use different strategies compared to people with less adversity exposure. These findings show that researchers should account for the fact that performance on EF tasks cannot be explained by specific EF abilities alone. Instead, they should tease apart the distinct cognitive processes that affect performance to better understand how adversity shapes cognitive development. Ideally, studies would include two or more tasks per EF ability in order to estimate abilities on a latent level. Cognitive modeling can be a valuable technique for the next generation of adversity researchers.