Diminished Social Value Integration in Older Adults
Poster No:
1136
Submission Type:
Abstract Submission
Authors:
Chris M. Stolle1, Rongjun Yu2, Yi Huang1
Institutions:
1Lingnan University, Hong Kong, China, 2Hong Kong Baptist University, Hong Kong, China
First Author:
Co-Author(s):
Introduction:
Older adults often make vital economic decisions, like managing retirement savings, under social influence. While research shows that social influence can alter decision-making, the impact of ageing on this process is understudied. Economic decision-making involves a mix of objective information and subjective utility (Rangel et al., 2008), with social influences integrated as 'other conferred utility' (OCU) (Chung et al., 2015). An experiment by Chung et al. showed that the OCU is encoded in the ventromedial prefrontal cortex (vmPFC).
Changes in the prefrontal cortex may lead to cognitive decline (Dempster, 1992). Thus, older adults might struggle to incorporate OCU into their value estimation. They may use simpler strategies, meaning that while the OCU model may predict their behaviour, there may be no encoding in the vmPFC.
We propose a "stubborn" model that assumes participants have hard-wired preference scores. This model involves a preference score for choosing risky options alone or under neutral influence (p1), and an adaptation parameter (p2) that adjusts the score based on the social influence. We hypothesise that this simpler model could predict older adults' behavior as well as the OCU model.
Changes in the prefrontal cortex may lead to cognitive decline (Dempster, 1992). Thus, older adults might struggle to incorporate OCU into their value estimation. They may use simpler strategies, meaning that while the OCU model may predict their behaviour, there may be no encoding in the vmPFC.
We propose a "stubborn" model that assumes participants have hard-wired preference scores. This model involves a preference score for choosing risky options alone or under neutral influence (p1), and an adaptation parameter (p2) that adjusts the score based on the social influence. We hypothesise that this simpler model could predict older adults' behavior as well as the OCU model.
Methods:
We conducted an fMRI study with twenty older and twenty-two young adults. Participants who always chose the same option (two older and one younger) were excluded.
The experimental task was adapted from a previous experiment (Chung et al., 2015). The experiment is depicted in Figure 1. Participants had to choose between a risky and a safe gamble that would determine how much participants win: In the safe gamble, the higher and lower amounts were close together. In the risky gamble, the higher amount to win was much higher than in the safe gamble, but the lower amount was also much lower. We simulated social influence by showing participants the choice of two other (simulated) players so that there were four conditions: 1) Safe: both chose safe; 2) risky: both chose risky; 3) indifferent: one chose safe, and one chose risky; 4) non-social: We did not display any others choice.
The experimental task was adapted from a previous experiment (Chung et al., 2015). The experiment is depicted in Figure 1. Participants had to choose between a risky and a safe gamble that would determine how much participants win: In the safe gamble, the higher and lower amounts were close together. In the risky gamble, the higher amount to win was much higher than in the safe gamble, but the lower amount was also much lower. We simulated social influence by showing participants the choice of two other (simulated) players so that there were four conditions: 1) Safe: both chose safe; 2) risky: both chose risky; 3) indifferent: one chose safe, and one chose risky; 4) non-social: We did not display any others choice.
Results:
First, we tested whether participants understood the task or made their choice randomly. Therefore, we compared the performance of the previously described OCU model with a random choice model, in which the probability of choosing risky or safe is always .5. The OCU model outperformed the random choice model for both younger (χ2(3) = 35.88, p < 0.001; pseudo-R² = .55) and older (χ2(3) = 9.89, p < 0.001; pseudo-R² = .18) adults. Thus, both age groups understood the task and behaved non-randomly.
We compared the OCU model to the stubborn model. For young adults, the OCU model performed significantly better than the stubborn model (χ2(1) = 21.92, p < 0.001; pseudo-R² = .16). For older adults, the OCU model performed worse than the stubborn model (pseudo-R² = .37).
The neural results (see Figure 2) showed significant activation for young adults in trials where they were faced with social influence in the vmPFC [peak voxel at x = -5, y = 42, z = -16; peak Z = 3.52; cluster size kE = 13; small volume correction (svc) at p(FEW) = 0.02]. This increased activation was absent in solo trials. For older adults, no such activation were found.
We compared the OCU model to the stubborn model. For young adults, the OCU model performed significantly better than the stubborn model (χ2(1) = 21.92, p < 0.001; pseudo-R² = .16). For older adults, the OCU model performed worse than the stubborn model (pseudo-R² = .37).
The neural results (see Figure 2) showed significant activation for young adults in trials where they were faced with social influence in the vmPFC [peak voxel at x = -5, y = 42, z = -16; peak Z = 3.52; cluster size kE = 13; small volume correction (svc) at p(FEW) = 0.02]. This increased activation was absent in solo trials. For older adults, no such activation were found.
Conclusions:
Our results showed that older adults do not incorporate other conferred utility. Instead, they use simplified compensatory strategies in which they stubbornly make risky choices at a rate that depends on the type of influence (none/indifferent, risky, safe). Correspondingly, unlike young adults, no processing of others conferred utility appears to occur in the vmPFC for older adults.
Higher Cognitive Functions:
Decision Making 2
Lifespan Development:
Aging 1
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Keywords:
Aging
Cognition
Computational Neuroscience
Other - social influence
1|2Indicates the priority used for review
Provide references using author date format
Dempster, F. N. (1992), 'The rise and fall of the inhibitory mechanism: Toward a unified theory of cognitive development and aging', Developmental review, no. 12, pp. 45-75
Rangel, A. (2008), 'A framework for studying the neurobiology of value-based decision making', Nature Reviews: Neuroscience, no. 9(7), pp. 545-556