Intrinsic connectivity between the hippocampus, nucleus accumbens, and ventral tegmental area in humans.

Recent studies suggest that memory formation in the hippocampus is modulated by the motivational significance of events, allowing past experience to adaptively guide behavior. The effects of motivation on memory are thought to depend on interactions between the hippocampus, the ventral tegmental area (VTA), and the nucleus accumbens (NAcc). Indeed, animal studies reveal anatomical pathways for circuit-level interaction between these regions. However, a homologue circuit connectivity in humans remains to be shown. We characterized this circuitry in humans by exploiting spontaneous low-frequency modulations in the fMRI signal (termed resting-state functional connectivity), which are thought to reflect functionally related regions and their organization into functional networks in the brain. We examined connectivity in this network across two datasets (hi-resolution, n = 100; standard resolution, n = 894). Results reveal convergent connectivity between the hippocampus, and both the NAcc and the VTA centered on ventral regions in the body of the hippocampus. Additionally, we found individual differences in the strength of connectivity within this network. Together, these results provide a novel task-independent characterization of circuitry underlying interactions between the hippocampus, NAcc, and VTA and provide a framework with which to understand how connectivity might reflect and constrain the effects of motivation on memory.

Acquired equivalence changes stimulus representations.

Acquired equivalence is a paradigm in which generalization is increased between two superficially dissimilar stimuli (or antecedents) that have previously been associated with similar outcomes (or consequents). Several possible mechanisms have been proposed, including changes in stimulus representations, either in the form of added associations or a change of feature salience. A different way of conceptualizing acquired equivalence is in terms of strategic inference: Confronted with a choice on which it has no evidence, the organism may infer from its history of reinforcement what the best option is, and that inference is observed as acquired equivalence. To test this account, we combined an incremental learning task with an episodic memory test. Drawings of faces were made equivalent through acquired equivalence training, and then paired with words in a list learning paradigm. When participants were asked to recognize specific face-word pairings, they confused faces more often when they had been made equivalent. This suggests that prior acquired equivalence training does influence how memories are coded. We also tested whether this change in coding reflected acquisition of new associations, as suggested by the associative mediation account, or whether stimuli become more similar through a reweighting of stimulus features, as assumed by some categorization theories. Results supported the associative mediation view. We discuss similarities between this view and exemplar theories of categorization performance.

Basal ganglia and dopamine contributions to probabilistic category learning.

Studies of the medial temporal lobe and basal ganglia memory systems have recently been extended towards understanding the neural systems contributing to category learning. The basal ganglia, in particular, have been linked to probabilistic category learning in humans. A separate parallel literature in systems neuroscience has emerged, indicating a role for the basal ganglia and related dopamine inputs in reward prediction and feedback processing. Here, we review behavioral, neuropsychological, functional neuroimaging, and computational studies of basal ganglia and dopamine contributions to learning in humans. Collectively, these studies implicate the basal ganglia in incremental, feedback-based learning that involves integrating information across multiple experiences. The medial temporal lobes, by contrast, contribute to rapid encoding of relations between stimuli and support flexible generalization of learning to novel contexts and stimuli. By breaking down our understanding of the cognitive and neural mechanisms contributing to different aspects of learning, recent studies are providing insight into how, and when, these different processes support learning, how they may interact with each other, and the consequence of different forms of learning for the representation of knowledge.

Dissociating entorhinal and hippocampal involvement in latent inhibition.

This study used anatomical cues to suggest a functional dissociation between the roles of the entorhinal cortex and the hippocampus in learning. The authors proposed that the highly convergent inputs to the entorhinal cortex indicate this region may be particularly important for selecting or compressing information. This hypothesis was tested in rabbits (Oryctolagus cunniculus) trained on an associative learning task that is a common index of stimulus selection. In this task, known as latent inhibition, preexposure to a stimulus (such as a tone) leads to slowed learning when the same tone is subsequently paired with an outcome (such as an airpuff to the eye). As hypothesized, rabbits with neurotoxic lesions of the entorhinal cortex failed to show slowed learning following preexposure (no latent inhibition) and learned the association faster than control rabbits. In contrast, hippocampal-lesioned animals showed normal (slowed) learning.

Role of the basal ganglia in category learning: how do patients with Parkinson's disease learn?

The purpose of the present study was to gain a deeper understanding of the role of the basal ganglia in learning and memory by examining learning strategies among patients with basal ganglia dysfunction. Using a probabilistic category learning task (the "weather prediction" task) previously shown to be sensitive to basal ganglia function, the authors examined patterns of performance during learning and used mathematical models to capture different learning strategies. Results showed that patients with Parkinson's disease exhibit different patterns of strategy use. Specifically, most controls initially used a simple, but suboptimal, strategy that focused on single-cue-outcome associations; eventually, however, most controls adopted a more complex, optimal learning strategy, integrating single-cue associations to predict outcomes for multiple-cue stimuli. In contrast, the majority of individuals with Parkinson's disease continued to rely on simple single-cue learning strategies throughout the experiment.

Risk and protective haplotypes of the alpha-synuclein gene associated with Parkinson's disease differentially affect cognitive sequence learning.

Alpha-synuclein (SNCA) is a key factor in the regulation of dopaminergic transmission and is related to Parkinson's disease. In this study, we investigated the effects of risk and protective SNCA haplotypes associated with Parkinson's disease on cognitive sequence learning in 204 healthy volunteers. We found that the 3'-block risk SNCA haplotypes are associated with less effective stimulus-reward learning of sequences and with superior context representation of sequences. In contrast, participants with protective haplotypes exhibit better stimulus-reward learning and worse context representation, which suggest that these functions are inversely affected by risk and protective haplotypes. The Rep1 promoter polymorphism does not influence cognitive sequence learning. Because stimulus-reward learning may be mediated by the basal ganglia and context learning may be related to the medial temporal lobe, our data raise the possibility that dopaminergic signals regulated by SNCA inversely affect these memory systems.

Dopaminergic contribution to cognitive sequence learning.

Evidence suggests that dopaminergic mechanisms in the basal ganglia are important in feedback-guided habit learning. To test hypothesis, we assessed cognitive sequence learning in 120 healthy volunteers and measured plasma levels of homovanillic acid [HVA] (a metabolite of dopamine), 5-hydroxyindoleacetic acid [5-HIAA] (a metabolite of serotonin), and 3-methoxy-4-hydroxypheylglycol [MHPG] (a metabolite of norepinephrine). Results revealed a significant negative relationship between errors in the feedback-guided training phase of the sequence learning task and the plasma HVA level. The HVA level accounted for 10.5% of variance of performance. Participant who had lower HVA level than the median value of the whole sample committed more errors during the training phase compared with participants who had higher HVA plasma level than the median value. A similar phenomenon was not observed for the context-dependent phase of the task and for 5-HIAA and MHPG. These results suggest that dopamine plays a special role in feedback-guided cognitive sequence learning.

Cortico-striatal contributions to feedback-based learning: converging data from neuroimaging and neuropsychology.

The striatum has been widely implicated in cognition, but a precise understanding of its role remains elusive. Here we present converging evidence for the role of the striatum in feedback-based learning. In a prior functional imaging study, healthy controls showed striatal activity during a feedback-based learning task, which was decreased when the same task was learned without feedback. In the present study, we show that individuals with striatal dysfunction due to Parkinson's disease are impaired on the feedback-based task, but not on a non-feedback version of the same task. Parkinson's patients and controls also used different learning strategies depending on feedback structure. This study provides direct behavioural evidence from humans that cortico-striatal systems are necessary for feedback-based learning on a cognitive task. These findings also link between learning impairments in Parkinson's disease and the physiological and computational evidence for the role of midbrain dopaminergic systems in feedback processing.

Human midbrain sensitivity to cognitive feedback and uncertainty during classification learning.

Mesencephalic dopaminergic system (MDS) neurons may participate in learning by providing a prediction error signal to their targets, which include ventral striatal, orbital, and medial frontal regions, as well as by showing sensitivity to the degree of uncertainty associated with individual stimuli. We investigated the mechanisms of probabilistic classification learning in humans using functional magnetic resonance imaging to examine the effects of feedback and uncertainty. The design was optimized for separating neural responses to stimulus, delay, and negative and positive feedback components. Compared with fixation, stimulus and feedback activated brain regions consistent with the MDS, whereas the delay period did not. Midbrain activity was significantly different for negative versus positive feedback (consistent with coding of the "prediction error") and was reliably correlated with the degree of uncertainty as well as with activity in MDS target regions. Purely cognitive feedback apparently engages the same regions as rewarding stimuli, consistent with a broader characterization of this network.

Interactive memory systems in the human brain.

Learning and memory in humans rely upon several memory systems, which appear to have dissociable brain substrates. A fundamental question concerns whether, and how, these memory systems interact. Here we show using functional magnetic resonance imaging (FMRI) that these memory systems may compete with each other during classification learning in humans. The medial temporal lobe and basal ganglia were differently engaged across subjects during classification learning depending upon whether the task emphasized declarative or nondeclarative memory, even when the to-be-learned material and the level of performance did not differ. Consistent with competition between memory systems suggested by animal studies and neuroimaging, activity in these regions was negatively correlated across individuals. Further examination of classification learning using event-related FMRI showed rapid modulation of activity in these regions at the beginning of learning, suggesting that subjects relied upon the medial temporal lobe early in learning. However, this dependence rapidly declined with training, as predicted by previous computational models of associative learning.