Age differences in routine formation: the role of automatization, motivation, and executive functions.

Medication adherence can be vital for one's health, especially in older adults. However, previous research has demonstrated that medication adherence is negatively affected by age-related cognitive decline. In the current study we investigated whether older adults are able to compensate for this decline by relying more on the formation of efficient, automatized routines. To this end, we directly compared daily (placebo) medication adherence in a healthy sample of 68 younger (18-29 years) and 63 older adults (65-86 years) over a period of 4 weeks. We show that despite an age-related decline in cognitive functions (i.e., poorer working memory, prospective memory, task switching, and goal-directed control), older adults adhered better to a daily pill intake routine than younger adults did and, in line with our hypothesis about increased routine formation, reported higher subjective automaticity of pill intake. Across age groups, automatization of pill intake was related to intake regularity and conscientiousness, but not to individual differences in habit tendency as measured in the lab nor to explicit strategic planning. Crucially, the age-related increase in pill intake adherence was mediated by experienced automatization as well as motivation. These findings demonstrate that intact habitual processes and high motivation aid older adults in successfully forming daily routines.

Individual Differences in Corticostriatal White-matter Tracts Predict Successful Daily-life Routine Formation.

Despite good intentions, people often fail to cross the "intention-behavior gap," especially when goal achievement requires repeated action. To bridge this gap, the formation of automatized routines may be crucial. However, people may differ in the tendency to switch from goal-directed toward habitual control. To shed light on why some people succeed in forming routines while others struggle, the present study related the automatization of a novel, daily routine to individual differences in white-matter connectivity in corticostriatal networks that have been implicated in goal-directed and habitual control. Seventy-seven participants underwent diffusion-weighted imaging and formed the daily routine of taking a (placebo) pill for 3 weeks. Pill intake was measured by electronic pill boxes, and participants filled out a daily online questionnaire on the subjective automaticity of this behavior. Automatization of pill intake was negatively related to striatal (mainly caudate) connectivity with frontal goal-directed and cognitive control regions, namely, ventromedial pFC and anterior cingulate gyrus. Furthermore, daily pill intake was positively related to individual differences in striatal (mainly caudate) connectivity with cognitive control regions, including dorsolateral and anterior pFC. Therefore, strong control networks may be relevant for implementing a new routine but may not benefit its automatization. We also show that habit tendency (assessed with an outcome-devaluation task), conscientiousness, and daily life regularity were positively related to routine automatization. This translational study moves the field of habit research forward by relating self-reported routine automatization to individual differences in performance on an experimental habit measure and to brain connectivity.

Psychological Coping and Behavioral Adjustment Among Older Adults in Times of COVID-19: Exploring the Protective Role of Working Memory and Habit Propensity.

The impact of the COVID-19 pandemic on mental health, well-being, and behavior is likely influenced by individual characteristics that determine one's capacity for resilience. In this exploratory study, we examined whether individual differences in working memory (WM) capacity and habit propensity (HP), measured before the outbreak, could predict variation in subsequent psychological coping efficacy (as operationalized by measures of depression, mental well-being, perceived stress, and loneliness) and behavioral adjustment (by evaluating compliance and self-reported automaticity of four COVID-19 guidelines) among Dutch older adults (n = 36) during the pandemic (measured April 25 to May 6, 2020). While we found elevated levels of depression and emotional loneliness, overall mental well-being, and perceived stress were not affected by the pandemic. Contrary to our expectations, we found no robust evidence for a protective role of WM in predicting these outcomes, although our findings hint at a positive relationship with perceived change in mental well-being. Interestingly, WM and HP were found to affect the self-reported automaticity levels of adherence to behavioral COVID-19 guidelines (i.e., washing hands, physical distancing), where a strong HP appeared beneficial when deliberate resources were less available (e.g., low WM capacity). These novel and preliminary findings offer new potential avenues for investigating individual differences in resilience in times of major life events or challenges. Supplementary Information: The online version contains supplementary material available at 10.1007/s10804-022-09404-9.

Relevance of working memory for reinforcement learning in older adults varies with timescale of learning.

In young adults, individual differences in working memory (WM) contribute to reinforcement learning (RL). Age-related RL changes, however, are mostly attributed to decreased reward prediction-error (RPE) signaling. Here, we investigated the contribution of WM to RL in young (18-35) and older (≥65) adults. Because WM supports maintenance across a limited timescale, we only expected a relation between RL and WM with short delays between stimulus repetitions. Our results demonstrated better learning with short than long delays. A week later, however, long-delay associations were remembered better. Computational modeling corroborated that during learning, WM was more engaged by young adults in the short-delay condition than in any other age-condition combination. Crucially, both model-derived and neuropsychological assessments of WM predicted short-delay learning in older adults, who further benefitted from using self-conceived learning strategies. Thus, depending on the timescale of learning, age-related RL changes may not only reflect decreased RPE signaling but also WM decline.

Go/no-go training affects frontal midline theta and mu oscillations to passively observed food stimuli.

The mere perception of high-calorie food items can trigger strong action tendencies towards these foods. Go/no-go training has successfully been applied to reduce such action tendencies. This study investigated the electrophysiological mechanisms that may underlie the beneficial effects of go/no-go training on food consumption. EEG was measured while 19 participants passively observed pictures of food and non-food items, both before and after the go/no-go training. During training, 50% of the food and non-food items were consistently paired with a go/no-go response. After training, food items that had been associated with a response induced larger mu desynchronization at electrodes over sensorimotor regions, whereas food items that had been associated with withholding from responding induced larger increases in theta power at frontal midline electrodes. These findings suggest that the exerted cognitive control during go/no-go training with attractive food stimuli may become associated with these stimuli and signal the required level of control during subsequent encounters.

Interactions between frontal and posterior oscillatory dynamics support adjustment of stimulus processing during reinforcement learning.

Reinforcement learning (RL) in humans is subserved by a network of striatal and frontal brain areas. The electrophysiological signatures of feedback evaluation are increasingly well understood, but how those signatures relate to the use of feedback to guide subsequent behavioral adjustment remains unclear. One mechanism for post-feedback behavioral optimization is the modulation of sensory processing. We used source-reconstructed MEG to test whether feedback affects the interactions between sources of oscillatory activity in the learning network and task-relevant stimulus-processing areas. Participants performed a probabilistic RL task in which they learned associations between colored faces and response buttons using trial-and-error feedback. Delta-band (2-4 Hz) and theta-band (4-8 Hz) power in multiple frontal regions were sensitive to feedback valence. Low and high beta-band power (12-20 and 20-30 Hz) in occipital, parietal, and temporal regions differentiated between color and face information. Consistent with our hypothesis, single-trial power-power correlations between frontal and posterior-sensory areas were modulated by the interaction between feedback valence and the relevant stimulus characteristic (color versus identity). These results suggest that long-range oscillatory coupling supports post-feedback updating of stimulus processing.

Frontostriatal anatomical connections predict age- and difficulty-related differences in reinforcement learning.

Reinforcement learning (RL) is supported by a network of striatal and frontal cortical structures that are connected through white-matter fiber bundles. With age, the integrity of these white-matter connections declines. The role of structural frontostriatal connectivity in individual and age-related differences in RL is unclear, although local white-matter density and diffusivity have been linked to individual differences in RL. Here we show that frontostriatal tract counts in young human adults (aged 18-28), as assessed noninvasively with diffusion-weighted magnetic resonance imaging and probabilistic tractography, positively predicted individual differences in RL when learning was difficult (70% valid feedback). In older adults (aged 63-87), in contrast, learning under both easy (90% valid feedback) and difficult conditions was predicted by tract counts in the same frontostriatal network. Furthermore, network-level analyses showed a double dissociation between the task-relevant networks in young and older adults, suggesting that older adults relied on different frontostriatal networks than young adults to obtain the same task performance. These results highlight the importance of successful information integration across striatal and frontal regions during RL, especially with variable outcomes.

Age-related changes in deterministic learning from positive versus negative performance feedback.

Feedback-based learning declines with age. Because older adults are generally biased toward positive information ("positivity effect"), learning from positive feedback may be less impaired than learning from negative outcomes. The literature documents mixed results, due possibly to variability between studies in task design. In the current series of studies, we investigated the influence of feedback valence on reinforcement learning in young and older adults. We used nonprobabilistic learning tasks, to more systematically study the effects of feedback magnitude, learning of stimulus-response (S-R) versus stimulus-outcome (S-O) associations, and working-memory capacity. In most experiments, older adults benefitted more from positive than negative feedback, but only with large feedback magnitudes. Positivity effects were pronounced for S-O learning, whereas S-R learning correlated with working-memory capacity in both age groups. These results underline the context dependence of positivity effects in learning and suggest that older adults focus on high gains when these are informative for behavior.

Aging affects medial but not anterior frontal learning-related theta oscillations.

Aging induces a decline in the ties that bind anatomical networks centered on the prefrontal cortex, which are critical for reinforcement learning and decision making. At the neurophysiological level, the prefrontal cortex may engage electrophysiological oscillatory synchronization to coordinate other brain systems during learning. We recorded scalp EEG from 21 older (mean age 69 years) and 20 young (mean age 22 years) healthy human adults while they learned stimulus-response mappings by trial-and-error using feedback. In young adults, theta-band (4-8 Hz) oscillatory power over medial frontal and anterior frontal cortex predicted learning after errors. Older adults demonstrated a decrease in the theta-band learning-predictive signals over medial frontal but not anterior frontal cortex. This age-related decrease in task-relevant medial frontal theta power may be related to the more general decrease in medial frontal theta power that we observed during rest. These results demonstrate a shift in cortical networks that support reinforcement learning in older adults, and shed new light on the changes in neurophysiological (oscillatory) mechanisms with neurocognitive aging.

Corticostriatal connectivity underlies individual differences in the balance between habitual and goal-directed action control.

Why are some individuals more susceptible to the formation of inflexible habits than others? In the present study, we used diffusion tensor imaging to demonstrate that brain connectivity predicts individual differences in relative goal-directed and habitual behavioral control in humans. Specifically, vulnerability to habitual "slips of action" toward no-longer-rewarding outcomes was predicted by estimated white matter tract strength in the premotor cortex seeded from the posterior putamen (as well as by gray matter density in the posterior putamen as determined with voxel-based morphometry). In contrast, flexible goal-directed action was predicted by estimated tract strength in the ventromedial prefrontal cortex seeded from the caudate. These findings suggest that integrity of dissociable corticostriatal pathways underlies individual differences in action control in the healthy population, which may ultimately mediate vulnerability to impulse control disorders.

Individual differences in risky decision-making among seniors reflect increased reward sensitivity.

Increasing age is associated with subtle but meaningful changes in decision-making. It is unknown, however, to what degree these psychological changes are reflective of age-related changes in decision quality. Here, we investigated the effect of age on latent cognitive processes associated with risky decision-making on the Balloon Analog Risk Task (BART). In the BART, participants repetitively inflate a balloon in order to increase potential reward. At any point, participants can decide to cash-out to harvest the reward, or they can continue, risking a balloon pop that erases all earnings. We found that among seniors, increasing age was associated with greater reward-related risk taking when the balloon has a higher probability of popping (i.e., a "high risk" condition). Cognitive modeling results from hierarchical Bayesian estimation suggested that performance differences were due to increased reward sensitivity in high risk conditions in seniors.

A tribute to charlie chaplin: induced positive affect improves reward-based decision-learning in Parkinson's disease.

Reward-based decision-learning refers to the process of learning to select those actions that lead to rewards while avoiding actions that lead to punishments. This process, known to rely on dopaminergic activity in striatal brain regions, is compromised in Parkinson's disease (PD). We hypothesized that such decision-learning deficits are alleviated by induced positive affect, which is thought to incur transient boosts in midbrain and striatal dopaminergic activity. Computational measures of probabilistic reward-based decision-learning were determined for 51 patients diagnosed with PD. Previous work has shown these measures to rely on the nucleus caudatus (outcome evaluation during the early phases of learning) and the putamen (reward prediction during later phases of learning). We observed that induced positive affect facilitated learning, through its effects on reward prediction rather than outcome evaluation. Viewing a few minutes of comedy clips served to remedy dopamine-related problems associated with frontostriatal circuitry and, consequently, learning to predict which actions will yield reward.

Frontal oscillatory dynamics predict feedback learning and action adjustment.

Frontal oscillatory dynamics in the theta (4-8 Hz) and beta (20-30 Hz) frequency bands have been implicated in cognitive control processes. Here we investigated the changes in coordinated activity within and between frontal brain areas during feedback-based response learning. In a time estimation task, participants learned to press a button after specific, randomly selected time intervals (300-2000 msec) using the feedback after each button press (correct, too fast, too slow). Consistent with previous findings, theta-band activity over medial frontal scalp sites (presumably reflecting medial frontal cortex activity) was stronger after negative feedback, whereas beta-band activity was stronger after positive feedback. Theta-band power predicted learning only after negative feedback, and beta-band power predicted learning after positive and negative feedback. Furthermore, negative feedback increased theta-band intersite phase synchrony (a millisecond resolution measure of functional connectivity) among right lateral prefrontal, medial frontal, and sensorimotor sites. These results demonstrate the importance of frontal theta- and beta-band oscillations and intersite communication in the realization of reinforcement learning.