Repetitive transcranial magnetic stimulation over dorsolateral prefrontal cortex modulates value-based learning during sequential decision-making

Adaptive behavior in daily life often requires the ability to acquire and represent sequential contingencies between actions and the associated outcomes. Although accumulating evidence implicates the role of dorsolateral prefrontal cortex (dlPFC) in complex value-based learning and decision-making, direct evidence for involvements of this region in integrating information across sequential decision states is still scarce. Using a 3-stage deterministic Markov decision task, here we applied offline, inhibitory low-frequency 1-Hz repetitive transcranial magnetic stimulation (rTMS) over the left dlPFC in young male adults (n = 31, mean age = 23.8 years, SD = 2.5 years) in a within-subject cross-over design to study the roles of this region in influencing value-based sequential decision-making. In two separate sessions, each participant received 1-Hz rTMS stimulation either over the left dlPFC or over the vertex. The results showed that transiently inhibiting the left dlPFC impaired choice accuracy, particularly in situations in which the acquisition of sequential transitions between decision states and temporally lagged action-outcome contingencies played a greater role. Estimating parameters of a diffusion model from behavioral choices, we found that the diffusion drift rate, which reflects the efficiency of information integration, was attenuated by the stimulation. Moreover, the effects of rTMS interacted with session: individuals who could not efficiently integrate information across sequential states in the first session due to disrupted dlPFC function also could not catch up in performance during the second session with those individuals who could learn sequential transitions with intact dlPFC function in the first session. Taken together, our findings suggest that the left dlPFC is crucially involved in the acquisition of complex sequential relations and in the potential of such learning

Repetitive transcranial magnetic stimulation over dorsolateral prefrontal cortex modulates value-based learning during sequential decision-making

Adaptive behavior in daily life often requires the ability to acquire and represent sequential contingencies between actions and the associated outcomes. Although accumulating evidence implicates the role of dorsolateral prefrontal cortex (dlPFC) in complex value-based learning and decision-making, direct evidence for involvements of this region in integrating information across sequential decision states is still scarce. Using a 3-stage deterministic Markov decision task, here we applied offline, inhibitory low-frequency 1-Hz repetitive transcranial magnetic stimulation (rTMS) over the left dlPFC in young male adults (n = 31, mean age = 23.8 years, SD = 2.5 years) in a within-subject cross-over design to study the roles of this region in influencing value-based sequential decision-making. In two separate sessions, each participant received 1-Hz rTMS stimulation either over the left dlPFC or over the vertex. The results showed that transiently inhibiting the left dlPFC impaired choice accuracy, particularly in situations in which the acquisition of sequential transitions between decision states and temporally lagged action-outcome contingencies played a greater role. Estimating parameters of a diffusion model from behavioral choices, we found that the diffusion drift rate, which reflects the efficiency of information integration, was attenuated by the stimulation. Moreover, the effects of rTMS interacted with session: individuals who could not efficiently integrate information across sequential states in the first session due to disrupted dlPFC function also could not catch up in performance during the second session with those individuals who could learn sequential transitions with intact dlPFC function in the first session. Taken together, our findings suggest that the left dlPFC is crucially involved in the acquisition of complex sequential relations and in the potential of such learning

Exploring the neuro-computational mechanisms underlying age-related changes in complex decision-making

Over the last decade, research in decision-making has made remarkable advancements in understanding how the relative engagement in model-based and model-free decision-making changes with healthy aging. While we are beginning to understand the factors that affect older adults’ shift away from model-based decision-making, the exact mechanisms at play are still poorly understood. This dissertation presents findings as well as a novel theory which aims to advance our understanding of these neuro-computational mechanisms. Chapter 2 demonstrates that, in contrast to younger adults, older adults do not benefit from more distinct probabilistic transitions between stages in a two-step decision-making task. By examining trial-by-trial neurocomputational dynamics, this first empirical paper provides evidence for age-related deficits in the ability to represent probabilistic transitions, and predict the value of upcoming choice options. Chapter 3 presents a novel theory: the diminished state space theory of human aging. This theoretical contribution proposes that older adults’ deficits in model-based learning are due to their underlying difficulties in representing state spaces. Chapter 4 examines one of the computational explanations brought forward in this theoretical paper. Namely, that older adults’ diminished state spaces may be explained (at least in part) by their difficulties updating their internal task representation. In line with this hypothesis, results demonstrate that in contrast to younger adults, older adults show difficulties identifying outcomes that signal the need to update their internal model. Together, these findings suggest that older adults’ deficits in model-based decision-making can be explained by their diminished state space representations, which in turn may in part result from their difficulty updating their internal model during cognitive tasks. Ultimately, this dissertation provides important insights regarding older adults’ deficits, and opens future directions for the study of age-related changes in representational abilities