Age affects reinforcement learning through dopamine-based learning imbalance and high decision noise—not through Parkinsonian mechanisms

Abstract

Probabilistic reinforcement learning declines in healthy cognitive aging. While some findings suggest impairments are especially conspicuous in learning from rewards, resembling deficits in Parkinson’s disease, others also show impairments in learning from punishments. To reconcile these findings, we tested 252 adults from three age groups on a probabilistic reinforcement learning task, analyzed trial-by-trial performance with a Q-reinforcement learning model, and correlated both fitted model parameters and behavior to polymorphisms in dopamine-related genes. Analyses revealed that learning from both positive and negative feedback declines with age, but through different mechanisms: When learning from negative feedback, older adults were slower due to noisy decision-making; when learning from positive feedback, they tended to settle for a non-optimal solution due to an imbalance in learning from positive and negative prediction errors. The imbalance was associated with polymorphisms in the DARPP-32 gene and appeared to arise from mechanisms different from those previously attributed to Parkinson’s disease. Moreover, this imbalance predicted previous findings on aging using the Probabilistic Selection Task, which were misattributed to Parkinsonian mechanisms.

<p>Deciphering Age Differences in Experience-Based Decision-Making: The Role of Sleep</p>

“… 48 Similarly, findings were found with a reinforcement learning paradigm that older adults showed more decision-making noises compared to younger counterparts when learning from negative feedbacks. 49 , 50 The increased noises and decreased anticipation of loss may lead older adults to make more high-risk choices, manifested as choosing more disadvantageous decks in the IGT. Importantly, the current study found that sleep could promote older adults’ feedback sensitivity and loss aversion, both of which are essential components of error prediction.…”

Age-related variability in decision-making: Insights from neurochemistry

“…Some studies report greater individual differences in the balance between positively and negatively motivated choices in aging (see Fig. 2b; Simon, Howard, & Howard, 2010; Sojitra et al, 2018), or have shown selective reduction in positive learning but not negative learning (Eppinger, Schuck, Nystrom, & Cohen, 2013). Other aging studies have found no effects of valence (Lighthall, Gorlick, Schoeke, Frank, & Mather, 2013; Pietschmann, Endrass, Czerwon, & Kathmann, 2011).…”

“…Since the original description of the PST task, there has been significant interest in understanding how age-related changes in dopamine function may affect biases in decision-making. A simple hypodopaminergic account of aging would predict that age effects mimic those observed in Parkinson’s disease, but to a lesser degree given the relative sparing of dopaminergic function (though see Sojitra, Lerner, Petok, & Gluck, 2018, for discussion of dissociations between healthy aging and Parkinson’s disease). Such biases, if they produce inoptimal choice behavior, would be a prime target for intervention in aging.…”

Divergent Effects of Metformin on an Inflammatory Model of Parkinson’s Disease

“…Parkinson’s disease (PD) is a neurodegenerative disorder featuring the loss of the dopaminergic neurons in the SN and Lewy pathology (intracellular accumulation of α-synuclein and other proteins in the so-called Lewy bodies), with a resultant alteration of the motor function that produces the loss of autonomy; cognitive function and mood are also impaired ( Marinus et al, 2018 ; Odin et al, 2018 ; Sojitra et al, 2018 ). The disease occurs in two forms with different causes and prevalence, so that the so-called early onset family form is due to mutations in a few genes and represent no more than 10% of cases, while the most frequent form, associated with aging (the idiopathic or sporadic form, typically occurring around 65 years of age), shows a diffuse etiology.…”