Wake Forest University, Winston-Salem, NC.
Duke University, Durham, NC.
J Cogn Neurosci. 2024 Feb 1;36(2):377-393. doi: 10.1162/jocn_a_02091.
An individual's readiness to switch tasks (cognitive flexibility) varies over time, in part, as the result of reinforcement learning based on the statistical structure of the world around them. Consequently, the behavioral cost associated with task-switching is smaller in contexts where switching is frequent than where it is rare, but the underlying brain mechanisms of this adaptation in cognitive flexibility are not well understood. Here, we manipulated the likelihood of switches across blocks of trials in a classic cued task-switching paradigm while participants underwent fMRI. As anticipated, behavioral switch costs decreased as the probability of switching increased, and neural switch costs were observed in lateral and medial frontoparietal cortex. To study moment-by-moment adjustments in cognitive flexibility at the neural level, we first fitted the behavioral RT data with reinforcement learning algorithms and then used the resulting trial-wise prediction error estimate as a regressor in a model-based fMRI analysis. The results revealed that lateral frontal and parietal cortex activity scaled positively with unsigned switch prediction error and that there were no brain regions encoding signed (i.e., switch- or repeat-specific) prediction error. Taken together, this study documents that adjustments in cognitive flexibility to time-varying switch demands are mediated by frontoparietal cortex tracking the likelihood of forthcoming task switches.
个体切换任务的准备程度(认知灵活性)会随时间变化,部分原因是基于他们周围世界的统计结构的强化学习。因此,在频繁切换的情况下,与任务切换相关的行为成本比在很少切换的情况下要小,但这种认知灵活性适应性的潜在大脑机制尚不清楚。在这里,我们在经典的提示任务切换范式中,在不同的试次块中操纵切换的可能性,同时让参与者接受 fMRI 扫描。正如预期的那样,随着切换概率的增加,行为切换成本降低,并且在外侧和内侧额顶叶皮层中观察到了神经切换成本。为了在神经水平上研究认知灵活性的瞬间调整,我们首先使用强化学习算法拟合行为 RT 数据,然后将得到的逐试预测误差估计用作基于模型的 fMRI 分析中的回归量。结果表明,外侧额顶叶皮层的活动与未符号化的切换预测误差呈正相关,并且没有大脑区域编码符号化的(即切换或重复特定的)预测误差。总的来说,这项研究表明,额叶顶叶皮层对即将到来的任务切换可能性的跟踪,介导了对时变切换需求的认知灵活性的调整。