Motivated for near impossibility: How task type and reward modulate task enjoyment and the striatal activation for extremely difficult task

In praise of folly: flexible goals and human cognition

Humans often pursue idiosyncratic goals that appear remote from functional ends, including information gain. We suggest that this is valuable because goals (even prima facie foolish or unachievable ones) contain structured information that scaffolds thinking and planning. By evaluating hypotheses and plans with respect to their goals, humans can discover new ideas that go beyond prior knowledge and observable evidence. These hypotheses and plans can be transmitted independently of their original motivations, adapted across generations, and serve as an engine of cultural evolution. Here, we review recent empirical and computational research underlying goal generation and planning and discuss the ways that the flexibility of our motivational system supports cognitive gains for both individuals and societies.

Motivated with joy or anxiety: Does approach-avoidance goal framing elicit differential reward-network activation in the brain?

Psychological research on human motivation repeatedly observed that approach goals (i.e., goals to attain success) increase task enjoyment and intrinsic motivation more strongly than avoidance goals (i.e., goals to avoid failure). The present study sought to address how the reward network in the brain-including the striatum and ventromedial prefrontal cortex-is involved when individuals engage in the same task with a focus on approach or avoidance goals. Participants reported stronger positive emotions when they focused on approach goals, but stronger anxiety and disappointment when they focused on avoidance goals. The fMRI analyses revealed that the reward network in the brain showed similar levels of activity to cues predictive of approach and avoidance goals. In contrast, the two goal states were associated with different patterns of activity in the visual cortex, hippocampus, and cerebellum during success and failure outcomes. Representation similarity analysis further revealed shared and different representations within the striatum and vmPFC between the approach and avoidance goal states, suggesting both the similarity and uniqueness of the mechanisms behind the two goal states. In addition, the distinct patterns of activation in the striatum were associated with distinct subjective experiences participants reported between the approach and the avoidance conditions. These results suggest the importance of examining the pattern of striatal activity in understanding the mechanisms behind different motivational states in humans.

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 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.