Browsing by Subject "Cognitive Flexibility"

Of the core faculties underlying higher order human cognition, two of the most fundamental are the ability to focus on particular task while avoiding distraction (cognitive stability) and the ability to switch to new tasks in light of changing circumstances (cognitive flexibility). Research into the regulation of stability and flexibility has revealed that they often display an inverse relationship: prioritizing task focus (stability) is associated with an impaired ability to switch to new tasks (flexibility), and vice versa. Such findings have led to the common conception that stability and flexibility are endpoints of a one-dimensional stability-flexibility continuum, a perspective that requires them to reciprocate in all cases. However, many empirical findings seemingly contradict a one-dimensional account, such that stability and flexibility might better be described by two separate dimensions. The relationship between stability and flexibility therefore remains unclear. In the current dissertation, I present three studies that investigated the behavioral and computational mechanisms underlying concurrent stability and flexibility regulation. In the first study, I test the assumption of an obligatory stability-flexibility tradeoff, demonstrating across three behavioral experiments that stability and flexibility can be regulated independently. Next, I explore the putative mechanisms explaining tradeoffs, revealing that tradeoffs can be attributed to cost-of-control calculations seeking to reduce cognitive exertion in contexts where it is not incentivized. Third, I conduct a formal model comparison between a one- and a two-dimensional model of stability and flexibility, demonstrating that only the two-dimensional account can accurately reproduce empirical data patterns. The better-fitting two-dimensional model also reveals the dissociable influences stability and flexibility have on the putative decision process underlying task shielding and switching. Finally, I summarize the results and explore the implications of these studies for translational research.

The stability-flexibility dilemma describes the challenge of balancing the antagonistic goals of focusing on the current task-set (cognitive stability) and updating that task-set in response to changes in the environment (cognitive flexibility). Dynamic adjustments of cognitive flexibility are observed in cued task-switching paradigms, wherein switch costs, or the performance costs associated with switching between tasks, have been shown to decrease as the proportion of switch trials within a block increase. This effect is referred to as the list-wide proportion switch (LWPS) effect, and presents evidence of meta-flexibility, or people’s ability to find an optimal level of flexibility based on contextual demands. While context-sensitive control adaptations have been extensively researched in the conflict literature, fewer studies have been dedicated to investigating such flexibility adaptations. Consequently, the underlying mechanisms of meta-flexibility remain unknown. Across four behavioral experiments, Chapter 2 teases apart the different levels of learning that may contribute to list-wide flexibility adaptations, controlling for stimulus- and task-level associations. Chapter 3 investigates the EEG neural signatures of meta-flexibility. In Chapter 4, we test for whether learned flexibility adjustments benefit from memory consolidation, like other forms of associative learning. Lastly, Chapter 5 utilized three novel behavioral paradigms to investigate different conditions under which flexibility learning transfers or fails to transfer. Collectively, the results in this dissertation suggest that flexibility adjustments to contextual demands occurs rapidly and transfers reliably across novel stimuli, such that, in high switch frequency blocks, participants could perform more rapid task switches even on trials involving items never seen before. However, both behavioral and neural evidence suggest that flexibility learning is also task specific, such that switch performance boosts to tasks that appear more often as switch trials do not generalize to other tasks that occur in the same temporal contexts.