Shaped by accumulating experience, shifting goals, and neurobiological development, decision-making changes across the adult human lifespan. The Affect-Integration-Motivation (AIM) framework has served as a key model for understanding how affective and motivational processes, along with biological and neural processes, influence decision behavior. This chapter revisits and updates the AIM framework in light of recent neuroeconomic research focused on age-related change. Specifically, we summarize the current evidence on age-related differences in four critical aspects of decision-making under uncertainty, including (1) value learning, (2) risky choice, (3) temporal discounting, and (4) effort-based decision-making, addressing both behavioral patterns and their neural substrates. In addition, we highlight key gaps in the extant literature and argue that advancing the AIM framework requires integrating it with broader ecological models of decision-making and accounting for individual differences within age groups. This updated perspective aims to sharpen our understanding of how aging affects the motivational and affective foundations of decision behavior.

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Decision-Making in the Aging Brain: An Update of the Evidence Base for the “Affect-Integration-Motivation” Framework

  • Loreen Tisdall,
  • Kendra L. Seaman

摘要

Shaped by accumulating experience, shifting goals, and neurobiological development, decision-making changes across the adult human lifespan. The Affect-Integration-Motivation (AIM) framework has served as a key model for understanding how affective and motivational processes, along with biological and neural processes, influence decision behavior. This chapter revisits and updates the AIM framework in light of recent neuroeconomic research focused on age-related change. Specifically, we summarize the current evidence on age-related differences in four critical aspects of decision-making under uncertainty, including (1) value learning, (2) risky choice, (3) temporal discounting, and (4) effort-based decision-making, addressing both behavioral patterns and their neural substrates. In addition, we highlight key gaps in the extant literature and argue that advancing the AIM framework requires integrating it with broader ecological models of decision-making and accounting for individual differences within age groups. This updated perspective aims to sharpen our understanding of how aging affects the motivational and affective foundations of decision behavior.