Learning progress mediates the link between cognitive effort and task engagement

Short-Term Memory Capacity Predicts Willingness to Expend Cognitive Effort for Reward

We must often decide whether the effort required for a task is worth the reward. Past rodent work suggests that willingness to deploy cognitive effort can be driven by individual differences in perceived reward value, depression, or chronic stress. However, many factors driving cognitive effort deployment-such as short-term memory ability-cannot easily be captured in rodents. Furthermore, we do not fully understand how individual differences in short-term memory ability, depression, chronic stress, and reward anticipation impact cognitive effort deployment for reward. Here, we examined whether these factors predict cognitive effort deployment for higher reward in an online visual short-term memory task. Undergraduate participants were grouped into high and low effort groups (n HighEffort = 348, n LowEffort = 81; n Female = 332, n Male = 92, M Age = 20.37, Range Age = 16-42) based on decisions in this task. After completing a monetary incentive task to measure reward anticipation, participants completed short-term memory task trials where they could choose to encode either fewer (low effort/reward) or more (high effort/reward) squares before reporting whether or not the color of a target square matched the square previously in that location. We found that only greater short-term memory ability predicted whether participants chose a much higher proportion of high versus low effort trials. Drift diffusion modeling showed that high effort group participants were more biased than low effort group participants toward selecting high effort trials. Our findings highlight the role of individual differences in cognitive effort ability in explaining cognitive effort deployment choices.

Curiosity and the dynamics of optimal exploration

What drives our curiosity remains an elusive and hotly debated issue, with multiple hypotheses proposed but a cohesive account yet to be established. This review discusses traditional and emergent theories that frame curiosity as a desire to know and a drive to learn, respectively. We adopt a model-based approach that maps the temporal dynamics of various factors underlying curiosity-based exploration, such as uncertainty, information gain, and learning progress. In so doing, we identify the limitations of past theories and posit an integrated account that harnesses their strengths in describing curiosity as a tool for optimal environmental exploration. In our unified account, curiosity serves as a 'common currency' for exploration, which must be balanced with other drives such as safety and hunger to achieve efficient action.

A dual process model of spontaneous conscious thought

In the present article, I review theory and evidence on the psychological mechanisms of mind wandering, paying special attention to its relation with executive control. I then suggest applying a dual-process framework (i.e., automatic vs. controlled processing) to mind wandering and goal-directed thought. I present theoretical arguments and empirical evidence in favor of the view that mind wandering is based on automatic processing, also considering its relation to the concept of working memory. After that, I outline three scenarios for an interplay between mind wandering and goal-directed thought during task performance (parallel automatic processing, off-task thought substituting on-task thought, and non-disruptive mind wandering during controlled processing) and address the ways in which the mind-wandering and focused-attention spells can terminate. Throughout the article, I formulate empirical predictions. In conclusion, I discuss how automatic and controlled processing may be balanced in human conscious cognition.

Effort Foraging Task reveals positive correlation between individual differences in the cost of cognitive and physical effort in humans

Effort-based decisions, in which people weigh potential future rewards against effort costs required to achieve those rewards involve both cognitive and physical effort, though the mechanistic relationship between them is not yet understood. Here, we use an individual differences approach to isolate and measure the computational processes underlying effort-based decisions and test the association between cognitive and physical domains. Patch foraging is an ecologically valid reward rate maximization problem with well-developed theoretical tools. We developed the Effort Foraging Task, which embedded cognitive or physical effort into patch foraging, to quantify the cost of both cognitive and physical effort indirectly, by their effects on foraging choices. Participants chose between harvesting a depleting patch, or traveling to a new patch that was costly in time and effort. Participants' exit thresholds (reflecting the reward they expected to receive by harvesting when they chose to travel to a new patch) were sensitive to cognitive and physical effort demands, allowing us to quantify the perceived effort cost in monetary terms. The indirect sequential choice style revealed effort-seeking behavior in a minority of participants (preferring high over low effort) that has apparently been missed by many previous approaches. Individual differences in cognitive and physical effort costs were positively correlated, suggesting that these are perceived and processed in common. We used canonical correlation analysis to probe the relationship of task measures to self-reported affect and motivation, and found correlations of cognitive effort with anxiety, cognitive function, behavioral activation, and self-efficacy, but no similar correlations with physical effort.

A goal-centric outlook on learning

Goals play a central role in human cognition. However, computational theories of learning and decision-making often take goals as given. Here, we review key empirical findings showing that goals shape the representations of inputs, responses, and outcomes, such that setting a goal crucially influences the central aspects of any learning process: states, actions, and rewards. We thus argue that studying goal selection is essential to advance our understanding of learning. By following existing literature in framing goal selection within a hierarchy of decision-making problems, we synthesize important findings on the principles underlying goal value attribution and exploration strategies. Ultimately, we propose that a goal-centric perspective will help develop more complete accounts of learning in both biological and artificial agents.