Are older adults less generous? Age differences in emotion-related social decision making.

In social interaction, age-related differences in emotional processing may lead to varied social decision making between young and older adults. However, previous studies of social decision making have paid less attention to the interactants' emotions, leaving age differences and underlying neural mechanisms unexplored. To address this gap, the present study combined functional and structural magnetic resonance imaging, employing a modified dictator game task with recipients displaying either neutral or sad facial expressions. Behavioral results indicated that although older adults' overall allocations did not differ significantly from those of young adults, older adults' allocations showing a decrease in emotion-related generosity compared to young adults. Using representational similarity analysis, we found that older adults showed reduced neural representations of recipients' emotions and gray matter volume in the right anterior cingulate gyrus (ACC), right insula, and left dorsomedial prefrontal cortex (DMPFC) compared to young adults. More importantly, mediation analyses indicated that age influenced allocations not only through serial mediation of neural representations of the right insula and left DMPFC, but also through serial mediation of the mean gray matter volume of the right ACC and left DMPFC. This study identifies the potential neural pathways through which age affects emotion-related social decision making, advancing our understanding of older adults' social interaction behavior that they may not be less generous unless confronted with individuals with specific emotions.

Neural correlates of valence-dependent belief and value updating during uncertainty reduction: An fNIRS study.

Selective use of new information is crucial for adaptive decision-making. Combining a gamble bidding task with assessing cortical responses using functional near-infrared spectroscopy (fNIRS), we investigated potential effects of information valence on behavioral and neural processes of belief and value updating during uncertainty reduction in young adults. By modeling changes in the participants' expressed subjective values using a Bayesian model, we dissociated processes of (i) updating beliefs about statistical properties of the gamble, (ii) updating values of a gamble based on new information about its winning probabilities, as well as (iii) expectancy violation. The results showed that participants used new information to update their beliefs and values about the gambles in a quasi-optimal manner, as reflected in the selective updating only in situations with reducible uncertainty. Furthermore, their updating was valence-dependent: information indicating an increase in winning probability was underweighted, whereas information about a decrease in winning probability was updated in good agreement with predictions of the Bayesian decision theory. Results of model-based and moderation analyses showed that this valence-dependent asymmetry was associated with a distinct contribution of expectancy violation, besides belief updating, to value updating after experiencing new positive information regarding winning probabilities. In line with the behavioral results, we replicated previous findings showing involvements of frontoparietal brain regions in the different components of updating. Furthermore, this study provided novel results suggesting a valence-dependent recruitment of brain regions. Individuals with stronger oxyhemoglobin responses during value updating was more in line with predictions of the Bayesian model while integrating new information that indicates an increase in winning probability. Taken together, this study provides first results showing expectancy violation as a contributing factor to sub-optimal valence-dependent updating during uncertainty reduction and suggests limitations of normative Bayesian decision theory.

Subjective sleep more predictive of global cognitive function than objective sleep in older adults: A specification curve analysis.

OBJECTIVES: Sleep is associated with cognitive function in older adults. In the current study, we examined this relationship from subjective and objective perspectives, and determined the robustness and dimensional specificity of the associations using a comprehensive modelling approach. METHODS: Multiple dimensions of subjective (sleep quality and daytime sleepiness) and objective sleep (sleep stages, sleep parameters, sleep spindles, and slow oscillations), as well as subjectively reported and objectively measured cognitive function were collected from 55 older adults. Specification curve analysis was used to examine the robustness of correlations for the effects of sleep on cognitive function. RESULTS: Robust associations were found between sleep and objectively measured cognitive function, but not with subjective cognitive complaints. In addition, subjective sleep showed robust and consistent associations with global cognitive function, whereas objective sleep showed a more domain-specific association with episodic memory. Specifically, subjective sleep quality and daytime sleepiness correlated with global cognitive function, and objective sleep parameters correlated with episodic memory. CONCLUSIONS: Overall, associations between sleep and cognitive function in older adults depend on how they are measured and which specific dimensions of sleep and domains of cognitive function are considered. It highlights the importance of focusing on specific associations to ameliorate the detrimental effects of sleep disturbance on cognitive function in later life.

Intergenerational or intragenerational learning? The relationship between interpersonal neural synchrony and older adult's learning acquisition.

OBJECTIVES: Lifelong learning facilitates active ageing, and intragenerational learning-the process by which older adults learn from their peers-is an effective means of achieving this goal. The present research aims to elucidate the mechanisms and differences between intergenerational and intragenerational learning models for older adults as evidenced by brain-to-brain synchrony. METHODS: Fifty-six instructor-learner dyads completed a study comparing intergenerational and intragenerational learning models, as well as task difficulty. The study utilized a block puzzle task and functional near-infrared spectroscopy (fNIRS) for hyperscanning. RESULTS: The instructor-learner dyads showed greater interpersonal neural synchrony (INS) and learning acquisition in the intragenerational learning model in the difficult task condition (t (54) = 3.49, p < 0.01), whereas the two learning models yielded similar results in the easy condition (t (54) = 1.96, p = 0.06). In addition, INS and self-efficacy mediated the association between learning models and learning acquisition in older adults (b = 0.14, SEM = 0.04, 95 % CI [0.01 0.16]). DISCUSSION: This study is the first to provide evidence of interbrain synchrony in an investigation of the intragenerational learning model in older adults. Our findings suggest that intra-learning is as effective as traditional inter-learning and may be more effective in certain contexts, such as difficult tasks. Encouraging intra-learning in community service or educational activities can effectively mitigate the challenge of limited volunteers and enhance learning acquisition among older adults.

Sleep Quality Modulates the Association between Dynamic Functional Network Connectivity and Cognitive Function in Healthy Older Adults.

Aging is associated with changes in sleep, brain activity, and cognitive function, as well as the association among these factors; however, the precise nature of these changes has not been elucidated. This study systematically investigated the modulatory effect of sleep on the relationship between brain functional network connectivity (FNC) and cognitive function in older adults. In total, 107 community-dwelling healthy older adults were recruited and assigned into poor sleep and good sleep groups based on the Pittsburgh Sleep Quality Index. The static functional network connectivity (sFNC), the temporal variability of dynamic FNC (dFNC) from variance (dFNC-var), and the dFNC from clustering state (dFNC-state) were calculated. Corresponding cognition-predictive models were constructed for each sleep group. dFNC but not sFNC, was able to significantly predict the cognitive function in older adults. Specifically, sleep played a modulatory role in the association between dFNC and cognitive function, with sleep-specific variations at both microscopic (i.e., specific edges) and macroscopic levels (i.e., specific states) of dFNC.

The Age-related Neural Strategy Alterations in Decision Making Under Risk.

Previous studies have shown that aging is associated with changes in decision behavior. However, the neural mechanisms that underpin such age differences are inadequately understood. In this study, we aim to characterize the optimal neural model underlying a dynamic decision making task in both young and older adults, and further examine the age differences from the perspective of effective connectivity. Twenty-five young and 23 older adults performed a dynamic risk taking task, i.e., the balloon analogue risk task, in the functional magnetic resonance imaging scanner. The dynamic causal modeling analysis, with the coupling between the ventromedial prefrontal cortex (VMPFC), dorsolateral prefrontal cortex (DLPFC) and anterior insula (AI) that were identified in our task-related activation and psychophysiological interaction analysis, was performed to address the best fitting neural model and characterize age differences. Although both age groups adopted the same optimal model with bidirectional connection between the VMPFC and DLPFC, older adults exhibited up-regulation in several connections and among which the increased modulatory effect of AI-to-VMPFC subserving their decision quality. Our finding suggests that older adults might utilize different neural strategy via compensation to counteract the impact of advanced age in risk taking process.

Deciphering Age Differences in Experience-Based Decision-Making: The Role of Sleep.

OBJECTIVE: Recent studies have demonstrated that sleep not only facilitates memory consolidation but also benefits more complex cognitive skills such as decision-making in young adults. Older adults use different decision strategies compared with young adults, which leaves the role of sleep in older adults' decision-making unclear. We investigated the age-by-sleep effect on decision-making. METHODS: We recruited 67 young adults (ages 18 to 29 years) and 66 older adults (ages 60 to 79 years) and randomly assigned them into the "sleep" or "wake" study condition. They were given a modified Iowa gambling task to perform before and after a 12-hour interval with sleep or wakefulness. RESULTS: Using the typical model-free analysis, we found that young adults' between-session performance improved greater than that of older adults regardless of the sleep/wake condition. Furthermore, older adults with longer total sleep time showed a greater improvement in the selection of one "good" deck. To further examine the sleep effect on age-related differences in cognitive processes underlying decision-making, we conducted computational modelling. This more fine-grained analysis revealed that sleep improved feedback sensitivity for both young and older adults while it increased loss aversion for older adults but not for young adults. CONCLUSION: These findings indicate that sleep promotes learning-based decision-making performance via facilitating value representation, and such modulation is distinct in young compared to older adults.