Decision making in the ageing brain: changes in affective and motivational circuits

Key Points

  • Research has begun to explore how age-related changes in the brain systems that are implicated in affect and motivation influence decision making.

  • Older and younger adults show similar affective and neural sensitivity to anticipated financial gains during value assessment as well as to gain and loss outcomes. However, older adults show reduced affective and neural sensitivity to anticipated financial losses.

  • Older adults make more suboptimal choices during financial risk taking, which seems to be related not to shifts in risk preference but rather to increased variability in nucleus accumbens (NAc) activity.

  • Older adults make more optimal choices during delay discounting by assigning higher values to future gains, which appears to be related to increased NAc activity during consideration of future rewards.

  • Older adults make more suboptimal choices when engaging in probabilistic reward learning. This seems to be related to decreased NAc activity associated with reward prediction errors (but not necessarily reward predictions), which may result from reduced medial prefrontal cortex input into striatal circuits.

  • Understanding how ageing variably influences brain function and structure may better inform targeted interventions designed to improve decision performance in individuals of all ages.


As the global population ages, older decision makers will be required to take greater responsibility for their own physical, psychological and financial well-being. With this in mind, researchers have begun to examine the effects of ageing on decision making and associated neural circuits. A new 'affect–integration–motivation' (AIM) framework may help to clarify how affective and motivational circuits support decision making. Recent research has shed light on whether and how ageing influences these circuits, providing an interdisciplinary account of how ageing can alter decision making.

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Figure 1: Important components of the affect–integration–motivation framework.
Figure 2: Age-related differences in incentive anticipation and risky decision making.
Figure 3: Age-related differences in temporal decision making and value learning.


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Much of the research and ideas mentioned were supported by grants from the US National Institute on Aging (R21AG030778 to B.K. and F31AG032804, F32AG039131 and R00AG042596 to G.R.S-L.) and the FINRA Investor Education Foundation. The authors thank B. Eppinger and three anonymous reviewers for comments on earlier drafts of the manuscript.

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Fluid cognitive abilities

Abilities to flexibly generate, transform and manipulate new information.

Crystallized cognitive abilities

Abilities to invoke previously stored information that is drawn from experience or accumulated knowledge.


Emotional responses that include a combination of subjective valence and arousal. Affect is sometimes depicted as a two-dimensional space, in which the two dimensions correspond to valence and arousal.

Cross-sectional studies

Studies that compare individuals (for example, individuals of different ages) at one simultaneous time point.

Longitudinal studies

Studies that compare the same individuals (for example, individuals of different ages) repeatedly over multiple time points to assess change.

Positron emission tomography

(PET). A nuclear imaging technique that produces three-dimensional images of brain activity by detecting photons that are emitted by a positron-emitting radionuclide tracer.


The subjectively positive or negative feeling evoked by an experience.


The subjective level of alertness, activation or energy elicited by an experience.

Functional MRI

(fMRI). A functional imaging technique that uses a magnetic field and radio waves to measure the blood-oxygenation-level-dependent signal, which indexes regional brain activity.

Probabilistic learning

Learning in which individuals use recent feedback to guide future choices among options of uncertain value.

Reward prediction

A quantity denoting the expected reward.

Reward prediction errors

Quantities denoting the difference between the received versus expected reward.

Diffusion tensor imaging

A neuroimaging technique that uses the restricted diffusion of water around neural membranes and myelinated fibres to map anatomical connectivity between brain areas.

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Samanez-Larkin, G., Knutson, B. Decision making in the ageing brain: changes in affective and motivational circuits. Nat Rev Neurosci 16, 278–289 (2015).

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