Age differences in risky choice: A meta-analysis

Does risk taking change as a function of age? We conducted a systematic literature search and found 29 comparisons between younger and older adults on behavioral tasks thought to measure risk taking (N= 4,093). The reports relied on various tasks differing in several respects, such as the amount of learning required or the choice framing (gains vs. losses). The results suggest that age-related differences vary considerably as a function of task characteristics, in particular the learning requirements of the task. In decisions from experience, age-related differences in risk taking were a function of decreased learning performance: older adults were more risk seeking compared to younger adults when learning led to risk-avoidant behavior, but were more risk averse when learning led to risk-seeking behavior. In decisions from description, younger adults and older adults showed similar risk-taking behavior for the majority of the tasks, and there were no clear age-related differences as a function of gain/loss framing. We discuss limitations and strengths of past research and provide suggestions for future work on age-related differences in risk taking

Cognitive function is associated with risk aversion in community-based older persons

<p>Abstract</p> <p>Background</p> <p>Emerging data from younger and middle-aged persons suggest that cognitive ability is negatively associated with risk aversion, but this association has not been studied among older persons who are at high risk of experiencing loss of cognitive function.</p> <p>Methods</p> <p>Using data from 369 community-dwelling older persons without dementia from the Rush Memory and Aging Project, an ongoing longitudinal epidemiologic study of aging, we examined the correlates of risk aversion and tested the hypothesis that cognition is negatively associated with risk aversion. Global cognition and five specific cognitive abilities were measured via detailed cognitive testing, and risk aversion was measured using standard behavioral economics questions in which participants were asked to choose between a certain monetary payment ($15) versus a gamble in which they could gain more than$15 or gain nothing; potential gamble gains ranged from $21.79 to$151.19 with the gain amounts varied randomly over questions. We first examined the bivariate associations of age, education, sex, income and cognition with risk aversion. Next, we examined the associations between cognition and risk aversion via mixed models adjusted for age, sex, education, and income. Finally, we conducted sensitivity analyses to ensure that our results were not driven by persons with preclinical cognitive impairment.</p> <p>Results</p> <p>In bivariate analyses, sex, education, income and global cognition were associated with risk aversion. However, in a mixed effect model, only sex (estimate = -1.49, standard error (SE) = 0.39, p < 0.001) and global cognitive function (estimate = -1.05, standard error (SE) = 0.34, p < 0.003) were significantly inversely associated with risk aversion. Thus, a lower level of global cognitive function and female sex were associated with greater risk aversion. Moreover, performance on four out of the five cognitive domains was negatively related to risk aversion (<it>i.e</it>., semantic memory, episodic memory, working memory, and perceptual speed); performance on visuospatial abilities was not.</p> <p>Conclusion</p> <p>A lower level of cognitive ability and female sex are associated with greater risk aversion in advanced age.</p

Motor Preparatory Activity in Posterior Parietal Cortex is Modulated by Subjective Absolute Value

For optimal response selection, the consequences associated with behavioral success or failure must be appraised. To determine how monetary consequences influence the neural representations of motor preparation, human brain activity was scanned with fMRI while subjects performed a complex spatial visuomotor task. At the beginning of each trial, reward context cues indicated the potential gain and loss imposed for correct or incorrect trial completion. FMRI-activity in canonical reward structures reflected the expected value related to the context. In contrast, motor preparatory activity in posterior parietal and premotor cortex peaked in high “absolute value” (high gain or loss) conditions: being highest for large gains in subjects who believed they performed well while being highest for large losses in those who believed they performed poorly. These results suggest that the neural activity preceding goal-directed actions incorporates the absolute value of that action, predicated upon subjective, rather than objective, estimates of one's performance

Variance and Autocorrelation of the Spontaneous Slow Brain Activity

Slow (<0.1 Hz) oscillatory activity in the human brain, as measured by functional magnetic imaging, has been used to identify neural networks and their dysfunction in specific brain diseases. Its intrinsic properties may also be useful to investigate brain functions. We investigated the two functional maps: variance and first order autocorrelation coefficient (r1). These two maps had distinct spatial distributions and the values were significantly different among the subdivisions of the precuneus and posterior cingulate cortex that were identified in functional connectivity (FC) studies. The results reinforce the functional segregation of these subdivisions and indicate that the intrinsic properties of the slow brain activity have physiological relevance. Further, we propose a sample size (degree of freedom) correction when assessing the statistical significance of FC strength with r1 values, which enables a better understanding of the network changes related to various brain diseases

Your Resting Brain CAREs about Your Risky Behavior

Research on the neural correlates of risk-related behaviors and personality traits has provided insight into mechanisms underlying both normal and pathological decision-making. Task-based neuroimaging studies implicate a distributed network of brain regions in risky decision-making. What remains to be understood are the interactions between these regions and their relation to individual differences in personality variables associated with real-world risk-taking.We employed resting state functional magnetic resonance imaging (R-fMRI) and resting state functional connectivity (RSFC) methods to investigate differences in the brain's intrinsic functional architecture associated with beliefs about the consequences of risky behavior. We obtained an individual measure of expected benefit from engaging in risky behavior, indicating a risk seeking or risk-averse personality, for each of 21 participants from whom we also collected a series of R-fMRI scans. The expected benefit scores were entered in statistical models assessing the RSFC of brain regions consistently implicated in both the evaluation of risk and reward, and cognitive control (i.e., orbitofrontal cortex, nucleus accumbens, lateral prefrontal cortex, dorsal anterior cingulate). We specifically focused on significant brain-behavior relationships that were stable across R-fMRI scans collected one year apart. Two stable expected benefit-RSFC relationships were observed: decreased expected benefit (increased risk-aversion) was associated with 1) stronger positive functional connectivity between right inferior frontal gyrus (IFG) and right insula, and 2) weaker negative functional connectivity between left nucleus accumbens and right parieto-occipital cortex.Task-based activation in the IFG and insula has been associated with risk-aversion, while activation in the nucleus accumbens and parietal cortex has been associated with both risk seeking and risk-averse tendencies. Our results suggest that individual differences in attitudes toward risk-taking are reflected in the brain's functional architecture and may have implications for engaging in real-world risky behaviors

Transient and sustained incentive effects on electrophysiological indices of cognitive control in younger and older adults

Preparing for upcoming events, separating task-relevant from task-irrelevant information and efficiently responding to stimuli all require cognitive control. The adaptive recruitment of cognitive control depends on activity in the dopaminergic reward system as well as the frontoparietal control network. In healthy aging, dopaminergic neuromodulation is reduced, resulting in altered incentive-based recruitment of control mechanisms. In the present study, younger adults (18–28 years) and healthy older adults (66–89 years) completed an incentivized flanker task that included gain, loss, and neutral trials. Event-related potentials (ERPs) were recorded at the time of incentive cue and target presentation. We examined the contingent negative variation (CNV), implicated in stimulus anticipation and response preparation, as well as the P3, which is involved in the evaluation of visual stimuli. Both younger and older adults showed transient incentive-based modulation of CNV. Critically, cue-locked and target-locked P3s were influenced by transient and sustained effects of incentives in younger adults, while such modulation was limited to a sustained effect of gain incentives on cue-P3 in older adults. Overall, these findings are in line with an age-related reduction in the flexible recruitment of preparatory and target-related cognitive control processes in the presence of motivational incentives

Translating upwards: linking the neural and social sciences via neuroeconomics

The social and neural sciences share a common interest in understanding the mechanisms that underlie human behaviour. However, interactions between neuroscience and social science disciplines remain strikingly narrow and tenuous. We illustrate the scope and challenges for such interactions using the paradigmatic example of neuroeconomics. Using quantitative analyses of both its scientific literature and the social networks in its intellectual community, we show that neuroeconomics now reflects a true disciplinary integration, such that research topics and scientific communities with interdisciplinary span exert greater influence on the field. However, our analyses also reveal key structural and intellectual challenges in balancing the goals of neuroscience with those of the social sciences. To address these challenges, we offer a set of prescriptive recommendations for directing future research in neuroeconomics

Interoception in anxiety and depression

We review the literature on interoception as it relates to depression and anxiety, with a focus on belief, and alliesthesia. The connection between increased but noisy afferent interoceptive input, self-referential and belief-based states, and top-down modulation of poorly predictive signals is integrated into a neuroanatomical and processing model for depression and anxiety. The advantage of this conceptualization is the ability to specifically examine the interface between basic interoception, self-referential belief-based states, and enhanced top-down modulation to attenuate poor predictability. We conclude that depression and anxiety are not simply interoceptive disorders but are altered interoceptive states as a consequence of noisily amplified self-referential interoceptive predictive belief states

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed