Shengjie Xu

People adjust how fast they update task rules, depending on the volatility of their environment, by regulating cognitive control accordingly. We investigated whether this adaptation is primarily driven by recently experienced volatility in task demands, or can also be shaped by learned environmental knowledge indicating expected levels of volatility. To this end, we trained participants on a Wisconsin Card Sorting Test task where different environments required different speeds of task rule updating. We demonstrate that, initially, participants adopted task rule updating strategies depending on the most recently experienced levels of volatility and feedback (Experiment 1). However, after extensive (4 days) training (Experiment 2), participants updated task rules using strategies based on developed environmental knowledge. We also show how participants fine‐tuned their task rule updating strategies over learning. In sum, our findings provide important insights into how humans update task rules using different strategies and highlight the importance of multiday training in developing environmental knowledge for the regulation of cognitive control.

Humans are remarkably efficient at adapting to different contextual demands by exerting optimal levels of cognitive flexibility versus stability for switching between different tasks. However, empirical findings document that these control adaptation processes can occur across multiple timescales, suggesting that different computational mechanisms may be involved. Here, we developed a recurrent neural network model to simulate behavioral indices of cognitive flexibility versus stability and investigated its dynamics. Throughout four simulation studies, we revealed that control adaptations that benefit from recent control states (activation-based) can support fast adjustments in cognitive flexibility, whereas control adaptations that depend on changes in control settings (weight-based) evolve more slowly over learning. Interestingly, like humans, the model further learned to associate contextual features to different control settings and use this knowledge to shift along a flexibility-stability continuum when encountering these same contexts. We further verified model-specific predictions through an analysis of an existing human dataset (N = 102). In sum, our work integrates fast and slow control adaptations within a unified framework and advances our understanding on human adaptative behavior and its regulatory mechanisms through the lens of activation-based versus weight-based adaptations.

People adjust how fast they update task rules, depending on the volatility of their environment, by regulating cognitive control accordingly. We investigated whether this adaptation is primarily driven by recently experienced volatility in task demands, or can also be shaped by learned, environmental knowledge indicating expected levels of volatility. To this end, we trained participants on a Wisconsin Card Sorting Test task where different environments required different speeds of task rule updating. We demonstrate that, initially, participants adopted task rule updating strategies depending on the most recent experienced levels of volatility and feedback (Experiment 1). However, after extensive (four days) training (Experiment 2), participants updated task rules using strategies based on developed environmental knowledge. We also show how participants fine-tuned their task rule updating strategies over learning. In sum, our findings provide important insights into how humans update task rules using different strategies and highlight the importance of multi-day training in developing environmental knowledge for the regulation of cognitive control.

Humans are efficient at learning new behaviors through verbal instructions, bypassing the need for trial-and-error learning, but requiring a certain degree of cognitive flexibility. Recent theories suggest that abstract functions of cognition, like cognitive flexibility, can be (up)regulated by contextual features in our environment with which they are associated. Here, we tested whether people show a more flexible control mode in contexts where one frequently learns new task instructions. Specifically, participants had to encode and execute a series of new task instructions, and the likelihood of encountering a new (relative to old) task instruction varied in function of whether the instruction was presented above versus below the center of the screen (Experiment 1, n = 40), or in a blue versus green font (Experiment 2, n = 40). We measured the level of interference from retained new task instructions on secondary task performance to index cognitive flexibility, and show enhanced flexibility (i.e., enhanced receptibility for new task instructions) for contexts in which the likelihood of encountering new task instructions was relatively high. Similarly, participants were also faster to encode new instructions in these contexts. A third, online study (n = 77) replicated the main finding of Experiment 2, whose context manipulation (i.e., font color) was most effective. Together, these findings suggest that cognitive flexibility can bind to, and be signaled by, contextual features in our environment. Such ‘learning when to learn’ provides a (self-)regulating mechanism for the cognitive processes that support the encoding (and execution) of new task instructions.

Humans are remarkably efficient at adapting to different contextual demands by exerting optimal levels of cognitive flexibility versus stability for switching between different tasks. Here, we show how a recurrent neural network can be used to simulate behavioral indices of cognitive flexibility versus stability, and investigate how people learn and apply optimal control settings across different contexts. Our model showed both fast but transient adaptation (in activation space) to high- versus low-switch frequency conditions, and slow but more enduring shallow versus deep task attractor settings (in weight space) in these same contexts. Interestingly, it further learned to associate and use information from its context to different task attractor settings and shift along a flexibility-stability continuum. In sum, we provide a novel framework that sheds new light on classic measures of cognitive flexibility versus stability when people must switch between several tasks, through the lens of activation-based versus weight-based adaptations.

Cognitive flexibility refers to a mental state that allows efficient switching between tasks. While deciding to be flexible is often ascribed to a strategic resource-intensive executive process, people may also simply use their environment to trigger different states of cognitive flexibility. We developed a paradigm where participants were exposed to two environments with different task-switching probabilities, followed by a probe phase to test the impact of environmental cues. Our results show that people were more efficient at switching in a high-switch environment. Critically, we observe environment-specific triggering of cognitive flexibility after a 4-day training period (Experiment 2, N = 51), but not after a 1-day training period (Experiment 1, N = 52). Together, these findings suggest that people can associate the need for cognitive flexibility with their environment, providing an environmental triggering mechanism for cognitive control. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Cognitive control refers to the ability to override prepotent response tendencies to achieve goal-directed behavior. Instead, reinforcement learning refers to the learning of actions through feedback and reward. Although cognitive control and reinforcement learning are often viewed as opposing forces in driving behavior, recent theories have emphasized possible similarities in their underling processes. With this study, we aimed to investigate whether a similar time window of integration could be observed during the learning of control on the one hand, and the learning rate in reinforcement learning paradigms on the other. To this end, we performed a correlational analysis on a large public dataset (n = 522), including data from two reinforcement learning tasks, that is, a probabilistic selection task and a probabilistic Wisconsin Card Sorting Task (WCST), and data from a classic conflict task (i.e., the Stroop task). Results showed expected correlations between time scale of control indices and learning rate in the probabilistic WCST. Moreover, the learning rate parameters of the two reinforcement learning tasks did not correlate with each other. Together, these findings suggest a reliance on a shared learning mechanism between these two traditionally distinct domains, while, at the same time, emphasizing that value updating processes can still be very task-specific. We speculate that updating processes in the Stroop and WCST may be more related because both tasks require task-specific updating of stimulus features (e.g., color, word meaning, pattern, shape), as opposed to stimulus identity.

Cognitive flexibility refers to a mental state that allows efficient switching between tasks. While deciding to be flexible is often ascribed to a strategic resource-intensive executive process, people may also simply use their environment to trigger different states of mental flexibility. We developed a paradigm where participants were exposed to two environments with different task switching probabilities, followed by a probe phase to test the impact of environmental cues. Our results show that people were more efficient at switching in a high-switch environment. Critically, we observe environment-specific triggering of cognitive flexibility after a four-day training period (Experiment 2, N = 51), but not after a one-day training period (Experiment 1, N = 52). Together, these findings suggest that people can associate the need for cognitive flexibility to their environment, providing an environmental triggering mechanism for cognitive control.