Learning to avoid in older age.

Frank, Michael J.; Kong, Lauren L.

doi:10.1037/0882-7974.23.2.392

Cited by 119 publications

(181 citation statements)

References 67 publications

(121 reference statements)

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“…This interaction was also reflected in the significant, albeit smaller, curvilinear contrast effect for the differences between the age groups for switching after losses, t = −3.42, p < .01, d = .57, than for switching after gains, t = −6.91, p < .01, d = 1.16 ( Figure 1B). Hence, the switching behavior of children and older adults differed more from adolescents and younger adults after gains than after losses (cf., Frank & Kong, 2008;Frank et al, 2004), suggesting that gain outcomes affected future choices less than loss outcomes in children and older adults as compared with adolescents and younger adults. Figure 2 displays the grand averages of stimulus-locked ERPs to gain and loss feedbacks as well as the difference wave between the reaction to losses and gains.…”

Section: Behavioral Datamentioning

confidence: 99%

“…Specifically, as DA dips are thought to contribute to learning from negative outcomes through the striatal DA D2 receptors, an amplification of these dips in individuals with lower baseline levels of dopaminergic modulation is assumed to result in a greater reliance on negative feedbacks (Frank & Kong, 2008;Frank, 2005;Frank et al, 2004). In line with this view, older adults and Parkinson patients, whose tonic DA levels are lower, show greater sensitivity to negative outcomes (Frank & Kong, 2008;Frank et al, 2004). There is also more direct evidence on DAʼs effect on the FRN: DA agonists that presumably reduce phasic DA hamper reinforcement learning from positive outcomes and affect the monitoring of positive outcomes, as indicated by the FRN (Santesso et al, 2009;Pizzagalli et al, 2008).…”

Section: Age Differences In the Frnmentioning

confidence: 99%

“…The effects of DA dips in the midbrain during negative feedback (Schultz, 2002) are assumed to be amplified when tonic levels of DA are low (Frank & Kong, 2008;Frank, Seeberger, & OʼReilly, 2004). Specifically, as DA dips are thought to contribute to learning from negative outcomes through the striatal DA D2 receptors, an amplification of these dips in individuals with lower baseline levels of dopaminergic modulation is assumed to result in a greater reliance on negative feedbacks (Frank & Kong, 2008;Frank, 2005;Frank et al, 2004). In line with this view, older adults and Parkinson patients, whose tonic DA levels are lower, show greater sensitivity to negative outcomes (Frank & Kong, 2008;Frank et al, 2004).…”

Section: Age Differences In the Frnmentioning

confidence: 99%

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Life Span Differences in Electrophysiological Correlates of Monitoring Gains and Losses during Probabilistic Reinforcement Learning

Hämmerer¹,

Li²,

Müller³

et al. 2011

Journal of Cognitive Neuroscience

164

208

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Abstract■ By recording the feedback-related negativity (FRN) in response to gains and losses, we investigated the contribution of outcome monitoring mechanisms to age-associated differences in probabilistic reinforcement learning. Specifically, we assessed the difference of the monitoring reactions to gains and losses to investigate the monitoring of outcomes according to task-specific goals across the life span. The FRN and the behavioral indicators of learning were measured in a sample of 44 children, 45 adolescents, 46 younger adults, and 44 older adults. The amplitude of the FRN after gains and losses was found to decrease monotonically from childhood to old age. Furthermore, relative to adolescents and younger adults, both children and older adults (a) showed smaller differences between the FRN after losses and the FRN after gains, indicating a less differentiated classification of outcomes on the basis of task-specific goals; (b) needed more trials to learn from choice outcomes, particularly when differences in reward likelihood between the choices were small; and (c) learned less from gains than from losses. We suggest that the relatively greater loss sensitivity among children and older adults may reflect ontogenetic changes in dopaminergic neuromodulation. ■

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Section: Behavioral Datamentioning

confidence: 99%

Section: Age Differences In the Frnmentioning

confidence: 99%

Section: Age Differences In the Frnmentioning

confidence: 99%

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Life Span Differences in Electrophysiological Correlates of Monitoring Gains and Losses during Probabilistic Reinforcement Learning

Hämmerer¹,

Li²,

Müller³

et al. 2011

Journal of Cognitive Neuroscience

164

208

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“…The PST has provided insight into individual differences related to reinforcement learning (Cohen & Frank, 2008), genetics (Frank, D'Lauro, & Curran, 2007;Frank & Hutchison, 2009;Frank, Moustafa, Haughey, Curran, & Hutchison, 2007), normal aging (Frank & Kong, 2008), "top-down" modulation by orbital frontal cortex and anterior cingulate cortex (Frank & Claus, 2006;Paulus & Frank, 2006), pharmaceutical manipulations (Frank & O'Reilly, 2006), and psychiatric conditions (especially Parkinson's disease, attention-deficit hyperactivity disorder, and schizophrenia; for a review, see Maia & Frank, 2011). Consistent with the predictions of the go/no-go model, this empirical work indicates that reinforcement learning signals carried by the midbrain dopamine system are instrumental to the decision making function implemented by the basal ganglia.…”

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confidence: 99%

Constraints on decision making: Implications from genetics, personality, and addiction

Baker

Stockwell

Holroyd

2013

Cogn Affect Behav Neurosci

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An influential neurocomputational theory of the biological mechanisms of decision making, the "basal ganglia go/no-go model," holds that individual variability in decision making is determined by differences in the makeup of a striatal system for approach and avoidance learning. The model has been tested empirically with the probabilistic selection task (PST), which determines whether individuals learn better from positive or negative feedback. In accordance with the model, in the present study we examined whether an individual's ability to learn from positive and negative reinforcement can be predicted by genetic factors related to the midbrain dopamine system. We also asked whether psychiatric and personality factors related to substance dependence and dopamine affect PST performance. Although we found characteristics that predicted individual differences in approach versus avoidance learning, these observations were qualified by additional findings that appear inconsistent with the predictions of the go/no-go model. These results highlight a need for future research to validate the PST as a measure of basal ganglia reward learning. Keywords Individual differences . Addiction . Personality . Midbrain dopamine system . Basal ganglia . Reinforcement learning . Decision making . Probabilistic selection task Neuroimaging studies reveal that normal performance on decision making tasks is associated with widespread activations of the basal ganglia, midbrain dopamine system, and connected structures. These neural pathways are integral components of complex functional neuroanatomical loops underlying reinforcement learning and decision-making that appear critical for several cognitive, motor, and emotional Seeberger, & O'Reilly, 2004;Maia & Frank, 2011; Moustafa, Cohen, Sherman, & Frank, 2008; Moustafa, Sherman, & Frank, 2008;Solomon, Smith, Frank, Ly, & Carter, 2011;Steele, Kumar, & Ebmeier, 2007;Waltz, Frank, Robinson, & Gold, 2007;Waltz, Frank, Wiecki, & Gold, 2011;Wiecki & Frank, 2010) are associated with biases in basal ganglia function in such tasks, reflecting idiosyncratic differences in our abilities to learn and make choices. Yet the neural mechanisms that underlie individual differences in decision making remain poorly understood.According to an influential neurocomputational model of decision making, "the basal ganglia go/no-go model," dopaminergic signaling in the basal ganglia facilitates or suppresses action representations during reinforcement learning tasks: Phasic bursts of dopamine activity facilitate reward learning by reinforcing striatal connections that express D1 receptors (the "go/approach" pathway), whereas phasic dips in dopamine activity facilitate avoidance learning by reinforcing striatal connections that express D2 receptors (the "no-go/avoidance" pathway; Frank et al., 2004). Empirically, the model predictions are typically tested with the probabilistic selection task (PST), a trial-and-error learning task in which participants are required to learn three concurrent discrimi...

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“…In order to evaluate the effects of the type of stimulation on feedback processing and behavioral adaptation, we analyzed the occurrence of (1) Bwin-stay^responses, defined as the percentage of trials in which participants chose the same stimulus after having received a positive feedback the last time that the chosen stimulus was presented, and (2) Blose-shift^re-sponses, defined as the percentage of trials in which participants changed their choice of stimulus after having received a negative feedback the last time that the chosen stimulus was presented (Evenden and Robbins 1983;Frank and Kong 2008). Such analysis was performed with a 3 × 2 × 4 repeated measures ANOVA with type of stimulation (anodal, cathodal, sham), trial-by-trial behavioral adaptation (win-stay, loseshift), and block (1st, 2nd, 3rd, 4th) as within-subject factors.…”

Section: Discussionmentioning

confidence: 99%

Transcranial Direct Current Stimulation (tDCS) of the Anterior Prefrontal Cortex (aPFC) Modulates Reinforcement Learning and Decision-Making Under Uncertainty: a Double-Blind Crossover Study

et al. 2017

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Reinforcement learning refers to the ability to acquire information from the outcomes of prior choices (i.e. positive and negative) in order to make predictions on the effect of future decision and adapt the behaviour basing on past experiences. The anterior prefrontal cortex (aPFC) is considered to play a key role in the representation of event value, reinforcement learning and decision-making. However, a causal evidence of the involvement of this area in these processes has not been provided yet. The aim of the study was to test the role of the orbitofrontal cortex in feedback processing, reinforcement learning and decision-making under uncertainly. Eighteen healthy individuals underwent three sessions of tDCS over the prefrontal pole (anodal, cathodal, sham) during a probabilistic learning (PL) task. In the PL task, participants were invited to learn the covert probabilistic stimulusoutcome association from positive and negative feedbacks in order to choose the best option. Afterwards, a probabilistic selection (PS) task was delivered to assess decisions based on the stimulus-reward associations acquired in the PL task. During cathodal tDCS, accuracy in the PL task was reduced and participants were less prone to maintain their choice after positive feedback or to change it after a negative one (i.e., winstay and lose-shift behavior). In addition, anodal tDCS affected the subsequent PS task by reducing the ability to choose the best alternative during hard probabilistic decisions. In conclusion, the present study suggests a causal role of aPFC in feedback trial-by-trial behavioral adaptation and decision-making under uncertainty.

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Learning to avoid in older age.

Cited by 119 publications

References 67 publications

Life Span Differences in Electrophysiological Correlates of Monitoring Gains and Losses during Probabilistic Reinforcement Learning

Life Span Differences in Electrophysiological Correlates of Monitoring Gains and Losses during Probabilistic Reinforcement Learning

Constraints on decision making: Implications from genetics, personality, and addiction

Transcranial Direct Current Stimulation (tDCS) of the Anterior Prefrontal Cortex (aPFC) Modulates Reinforcement Learning and Decision-Making Under Uncertainty: a Double-Blind Crossover Study

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