Most behavior stems from motivation. As we maneuver through the environment we choose actions from a large repertoire of behaviors. These behaviors are strongly affected by our learning history, but also by our current motivational state to approach positive outcomes or avoid negative outcomes. For example, one could be

The common belief that motivation involves simply “trying harder ” is at best simplistic and at worst is inaccurate. In this Chapter we highlight the importance of studying motivation at multiple levels to better understand the conditions that support effort-based learning strategies relative to automatic learning strategies (global motivation, local motivation) and demonstrate how effective these approaches are when seeking immediate or long-run rewards (task-directed). Global motivations represent the overall goal of approaching positive outcomes (e.g., a raise or bonus) or avoiding negative outcomes (e.g., a demotion or pay cut). Local motivations represent the immediately relevant goal of approaching positive feedback (e.g., maximizing rewards or making someone happy) or avoiding negative feedback (e.g., minimizing punishments or avoiding making someone angry). Global and local motivational states interact to influence the competition between executive and automatic strategies. An approach-approach or avoid-avoid match shifts the bias toward cognitive control whereas a mismatch shifts the bias toward habitual procedural processing. The effects of each of these strategies during learning depend on task-demands. Task-directed motivation reflects whether the task is goal-directed, relying heavily on cognitive control processes, or is reward-based, relying on habitual procedural processes. Thus, performance in a task critically depends on a complex three way interaction between local, global, and task-directed motivation. We extend this framework to normal aging and provide evidence from two studies that normal aging is associated with a bias toward reward-based processing. In addition, we argue that computational modeling techniques are underutilized.

k d regarded neurobiological model of learning in the striatum. In Experiment 1 information–integration sented with as well as the properties of the environment in which feedback is given. In this work we examine how feedback timing affects rule-based and procedural forms of perceptual category learning by deriving predictions from a prominent neurobiological theory of learning in the striatum. We first review work on the neurobiology associated with specific responses (e.g. Alexander, DeLong, &

We examined how feedback delay and stimulus offset timing affected declarative, rule-based and procedural, information-integration category-learning. We predicted that small feedback delays of several hundred milliseconds would lead to the best information-integration learning based on a highly regarded neurobiological model of learning in the striatum. In Experiment 1 information-integration learning was best with feedback delays of 500ms compared to delays of 0 and 1,000ms. This effect was only obtained if the stimulus offset following the response. Rule-based learning was unaffected by the length of feedback delay, but was better when the stimulus was present throughout feedback than when it offset following the response. In Experiment 2 we found that a large variance (SD=150ms) in feedback delay times around a mean delay of 500ms attenuated information-integration learning, but a small variance (SD=75ms) did not. In Experiment 3 we found that the delay between stimulus offset and feedback is more critical to information-integration learning than the delay between the response and feedback. These results demonstrate the importance of feedback timing in category-learning situations where a declarative,

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