Adult age differences in learning and generalization of feedback-based associations.

Feedback-based associative learning (e.g., acquiring new associations from positive or negative outcomes) and generalization (e.g., applying past learning to new settings) are important cognitive skills that enable people to make economic decisions or social judgments. This ability to acquire new skills based on feedback and transfer those experiences to predict positive outcomes in novel situations is essential at all ages, but especially among older adults who must continually adapt to new people, environments, and technologies. Ample evidence from animal work, clinical research, and computational modeling has demonstrated that feedback-based associative learning is sensitive to basal ganglia dysfunction and generalization to medial temporal lobe dysfunction. This dissociation is relevant because of recent evidence that has suggested healthy aging compromises the basal ganglia system earlier than the medial temporal lobes. However, few studies have investigated how healthy aging influences these cognitive processes. Here, we examined both feedback-based associative learning and generalization in younger, middle-aged, and older adults using a computerized acquired equivalence task. Results revealed a significant effect of age group on feedback-based associative learning, consistent with evidence of persistent age-related declines in the basal ganglia. In contrast, generalization was spared in all but the oldest adult group, likely reflecting preserved medial temporal lobe function until advanced old age. Our findings add behavioral evidence to the emerging view that healthy aging affects the striatal system before the medial temporal lobes. Although further evidence is needed, this finding may shed light on the possible time course of neural system dysfunction in healthy aging.

Caudate resting connectivity predicts implicit probabilistic sequence learning.

Abstract Implicit probabilistic sequence learning (IPSL) involves extracting statistical regularities from sequences of events without awareness, and is thought to underlie learning of language and behavioral repertoires of everyday life. We examined whether resting-state functional connectivity networks of the caudate predicted individual differences in IPSL performance measured on a separate day. Whole-brain connectivity maps of a bilateral dorsal caudate (DC) seed were created for each subject and examined for voxelwise correlations with sequence learning performance, as well as with overall response speed. Higher learning scores (but not overall response speed) were associated with stronger resting-state connectivity between the DC and right medial temporal lobe, as well as with lower resting-state connectivity between the DC and premotor regions involved in motor planning. Thus, how well one learns probabilistic regularities without awareness is predicted by the strength of a striato-cortical network in the resting brain.

Depression impairs learning, whereas the selective serotonin reuptake inhibitor, paroxetine, impairs generalization in patients with major depressive disorder.

To better understand how medication status and task demands affect cognition in major depressive disorder (MDD), we evaluated medication-naïve patients with MDD, medicated patients with MDD receiving the selective serotonin reuptake inhibitors (SSRI) paroxetine, and healthy controls. All three groups were administered a computer-based cognitive task with two phases, an initial phase in which a sequence is learned through reward-based feedback (which our prior studies suggest is striatal-dependent), followed by a generalization phase that involves a change in the context where learned rules are to be applied (which our prior studies suggest is hippocampal-region dependent). Medication-naïve MDD patients were slow to learn the initial sequence but were normal on subsequent generalization of that learning. In contrast, medicated patients learned the initial sequence normally, but were impaired at the generalization phase. We argue that these data suggest (i) an MDD-related impairment in striatal-dependent sequence learning which can be remediated by SSRIs and (ii) an SSRI-induced impairment in hippocampal-dependent generalization of past learning to novel contexts, not otherwise seen in the medication-naïve MDD group. Thus, SSRIs might have a beneficial effect on striatal function required for sequence learning, but a detrimental effect on the hippocampus and other medial temporal lobe structures is critical for generalization.

The effects of aging on the neural basis of implicit associative learning in a probabilistic triplets learning task.

Few studies have investigated how aging influences the neural basis of implicit associative learning, and available evidence is inconclusive. One emerging behavioral pattern is that age differences increase with practice, perhaps reflecting the involvement of different brain regions with training. Many studies report hippocampal involvement early on with learning becoming increasingly dependent on the caudate with practice. We tested the hypothesis that the contribution of these regions to learning changes with age because of differential age-related declines in the striatum and hippocampi. We assessed age-related differences in brain activation during implicit associative learning using the Triplets Learning Task. Over three event-related fMRI runs, 11 younger and 12 healthy older adults responded to only the third (target) stimulus in sequences of three stimuli ("triplets") by corresponding key press. Unbeknown to participants, the first stimulus' location predicted one target location for 80% of trials and another target location for 20% of trials. Both age groups learned associative regularities but differences in favor of the younger adults emerged with practice. The neural basis of learning (response to predictability) was examined by identifying regions that showed a greater response to triplets that occurred more frequently. Both age groups recruited the hippocampus early, but with training, the younger adults recruited their caudate whereas the older adults continued to rely on their hippocampus. This pattern enables older adults to maintain near-young levels of performance early in training, but not later, and adds to evidence that implicit associative learning is supported by different brain networks in younger and older adults.

Dopamine transporter genotype predicts implicit sequence learning.

Implicit learning, the non-conscious acquisition of sequential and spatial environmental regularities, underlies skills such as language, social intuition, or detecting a target in a complex scene. We examined relationships between a variation of the dopamine transporter (DAT1) gene (SLC6A3), which influences dopamine transporter expression in the striatum, and two forms of implicit learning that differ in the regularity to be learned and in striatal involvement. Participants, grouped as 9-repeat carriers or 10/10 homozygotes, completed the triplets learning task (TLT) and the spatial contextual cueing task (SCCT). The TLT assesses sequence learning, recruiting the striatal system, particularly as training continues. In contrast, the SCCT assesses spatial context learning, recruiting medial temporal brain networks. For both tasks, participants demonstrated learning in faster and/or more accurate responses to repeating patterns or spatial arrays. As predicted, TLT learning was greater for the 9-repeat carriers than the 10/10 group (despite equal overall accuracy and response speed) whereas there were no significant group differences in SCCT. Thus, presence of the DAT1 9-repeat allele was beneficial only for implicit sequence learning, indicating the influence of DAT1 genotype on one form of implicit learning and supporting evidence that implicit learning of sequential dependencies and spatial layouts recruit different neural systems.

Age differences in implicit learning of probabilistic unstructured sequences.

OBJECTIVE: It is unclear whether implicit probabilistic learning, the acquisition of regularities without intent or explicit knowledge, declines with healthy aging. METHODS: Because age differences in previous work might reflect motor or rule learning deficits, we used the implicit Triplets Learning Task with reduced motor sequencing and non-rule-based associations. Fifteen young and 15 old adults responded only to the last event in a series of discrete 3-event sequences or triplets. A randomly chosen set of triplets occurred with high frequency, so there was no underlying rule to be learned. RESULTS: Both age groups learned associative regularities, but age differences in favor of the young emerged with practice. Discussion. Age differences may reflect the different neural regions that are involved as training progresses, which differ in the extent to which they are compromised by aging.

Adult age differences in learning from positive and negative probabilistic feedback.

OBJECTIVE: Past research has investigated age differences in frontal-based decision making, but few studies have focused on the behavioral effects of striatal-based changes in healthy aging. Feedback learning has been found to vary with dopamine levels; increases in dopamine facilitate learning from positive feedback, whereas decreases facilitate learning from negative feedback. Given previous evidence of striatal dopamine depletion in healthy aging, we investigated behavioral differences between college-aged and healthy older adults using a feedback learning task that is sensitive to both frontal and striatal processes. METHOD: Seventeen college-aged (M = 18.9 years) and 24 healthy, older adults (M = 70.3 years) completed the Probabilistic Selection task, in which participants are trained on probabilistic stimulus-outcome information and then tested to determine whether they learned more from positive or negative feedback. RESULTS: As a group, the older adults learned equally well from positive and negative feedback, whereas the college-aged group learned more from positive than negative feedback, F(1, 39) = 4.10, p < .05, r(effect) = .3. However, these group differences were not due to older individuals being more balanced learners. Most individuals of both ages were balanced learners, but while all of the remaining young learners had a positive bias, the remaining older learners were split between those with positive and negative learning biases (chi(2)(2) = 6.12, p < .047). CONCLUSIONS: These behavioral results are consistent with the dopamine theory of striatal aging, and suggest there might be adult age differences in the kinds of information people use when faced with a current choice.