Mark A. Gluck

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.

From conditioning to category learning: An adaptive network model

We used adaptive network theory to extend the Rescorla-Wagner (1972) least mean squares (LMS) model of associative learning to phenomena of human learning and judgment. In three experiments subjects learned to categorize hypothetical patients with particular symptom patterns as having certain diseases. When one disease is far more likely than another, the model predicts that subjects will substantially overestimate the diagnosticity of the more valid symptom for the rare disease. The results of Experiments 1 and 2 provide clear support for this prediction in contradistinction to predictions from probability matching, exemplar retrieval, or simple prototype learning models. Experiment 3 contrasted the adaptive network model with one predicting pattern-probability matching when patients always had four symptoms (chosen from four opponent pairs) rather than the presence or absence of each of four symptoms, as in Experiment 1. The results again support the Rescorla-Wagner LMS learning rule as embedded within an adaptive network model.

Pictures and names: Making the connection ☆

Abstract In order to identify an object sensory input must somehow access stored information. A series of results supports two general assertions about this process: First, objects are identified first at a particular level of abstraction which is neither the most general nor the most specific possible. Time to provide names more general than “entry point” names is predicted by the degree of association between the “entry point” concept and the required name, not by perceptual factors. In contrast, providing more specific names than that corresponding to the “entry point” concept does require more detailed perceptual analysis. Second, the particular entry point for a given object covaries with its typicality, which affects whether or not the object will be identified at the “basic” level. Atypical objects have their entry point at a level subordinate to the basic level. The generality and usefulness of the notion of “basic level” is discussed in the face of these results.

Reward-learning and the novelty-seeking personality: a between- and within-subjects study of the effects of dopamine agonists on young Parkinson's patients

Parkinsons disease is characterized by the degeneration of dopaminergic pathways projecting to the striatum. These pathways are implicated in reward prediction. In this study, we investigated reward and punishment processing in young, never-medicated Parkinsons disease patients, recently medicated patients receiving the dopamine receptor agonists pramipexole and ropinirole and healthy controls. The never-medicated patients were also re-evaluated after 12 weeks of treatment with dopamine agonists. Reward and punishment processing was assessed by a feedback-based probabilistic classification task. Personality characteristics were measured by the temperament and character inventory. Results revealed that never-medicated patients with Parkinsons disease showed selective deficits on reward processing and novelty seeking, which were remediated by dopamine agonists. These medications disrupted punishment processing. In addition, dopamine agonists increased the correlation between reward processing and novelty seeking, whereas these drugs decreased the correlation between punishment processing and harm avoidance. Our finding that dopamine agonist administration in young patients with Parkinsons disease resulted in increased novelty seeking, enhanced reward processing, and decreased punishment processing may shed light on the cognitive and personality bases of the impulse control disorders, which arise as side-effects of dopamine agonist therapy in some Parkinsons disease patients.

Comparing modes of rule-based classification learning: A replication and extension of Shepard, Hovland, and Jenkins (1961)

We partially replicate and extend Shepard, Hovland, and Jenkinss (1961) classic study of task difficulty for learning six fundamental types of rule-based categorization problems. Our main results mirrored those of Shepard et al., with the ordering of task difficulty being the same as in the original study. A much richer data set was collected, however, which enabled the generation of block-by-block learning curves suitable for quantitative fitting. Four current computational models of classification learning were fitted to the learning data: ALCOVE (Kruschke, 1992), the rational model (Anderson, 1991), the configural-cue model (Gluck & Bower, 1988b), and an extended version of the configural-cue model with dimensionalized, adaptive learning rate mechanisms. Although all of the models captured important qualitative aspects of the learning data, ALCOVE provided the best overall quantitative fit. The results suggest the need to incorporate some form of selective attention to dimensions in category-learning models based on stimulus generalization and cue conditioning.

Dissociating Hippocampal versus Basal Ganglia Contributions to Learning and Transfer

Based on prior animal and computational models, we propose a double dissociation between the associative learning deficits observed in patients with medial temporal (hippocampal) damage versus patients with Parkinsons disease (basal ganglia dysfunction). Specifically, we expect that basal ganglia dysfunction may result in slowed learning, while individuals with hippocampal damage may learn at normal speed. However, when challenged with a transfer task where previously learned information is presented in novel recombinations, we expect that hippocampal damage will impair generalization but basal ganglia dysfunction will not. We tested this prediction in a group of healthy elderly with mild-to-moderate hippocampal atrophy, a group of patients with mild Parkinsons disease, and healthy controls, using an acquired equivalence associative learning task. As predicted, Parkinsons patients were slower on the initial learning but then transferred well, while the hippocampal atrophy group showed the opposite pattern: good initial learning with impaired transfer. To our knowledge, this is the first time that a single task has been used to demonstrate a double dissociation between the associative learning impairments caused by hippocampal versus basal ganglia damage/dysfunction. This finding has implications for understanding the distinct contributions of the medial temporal lobe and basal ganglia to learning and memory.

Intact delay-eyeblink classical conditioning in amnesia.

The status of classical conditioning in human amnesia was examined by comparing conditioning of the eyeblink response (the unconditional response) to a tone conditioned stimulus (CS) paired with an airpuff unconditioned stimulus (US) in the delay paradigm between 7 amnesic and 7 age- and education-matched normal control participants. Amnesic patients exhibited normal baseline performance in pseudoconditioning and normal acquisition and extinction of conditioned responses in terms of the number, latency, and magnitude of eyeblinks. These results indicate that in humans, as in rabbits, brain structures critical for declarative memory are not essential for the acquisition of elementary CS-US associations.

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.

Long-term test–retest reliability of functional MRI in a classification learning task

Functional MRI is widely used for imaging the neural correlates of psychological processes and how these brain processes change with learning, development and neuropsychiatric disorder. In order to interpret changes in imaging signals over time, for example, in patient studies, the long-term reliability of fMRI must first be established. Here, eight healthy adult subjects were scanned on two sessions, 1 year apart, while performing a classification learning task known to activate frontostriatal circuitry. We show that behavioral performance and frontostriatal activation were highly concordant at a group level at both time-points. Furthermore, intra-class correlation coefficients (ICCs), which index the degree of correlation between subjects at different time-points, were high for behavior and for functional activation. ICC was significantly higher within the network recruited by learning than outside that network. We conclude that fMRI can have high long-term test-retest reliability, making it suitable as a biomarker for brain development and neurodegeneration.

Dopaminergic Drugs Modulate Learning Rates and Perseveration in Parkinson's Patients in a Dynamic Foraging Task

Making appropriate choices often requires the ability to learn the value of available options from experience. Parkinsons disease is characterized by a loss of dopamine neurons in the substantia nigra, neurons hypothesized to play a role in reinforcement learning. Although previous studies have shown that Parkinsons patients are impaired in tasks involving learning from feedback, they have not directly tested the widely held hypothesis that dopamine neuron activity specifically encodes the reward prediction error signal used in reinforcement learning models. To test a key prediction of this hypothesis, we fit choice behavior from a dynamic foraging task with reinforcement learning models and show that treatment with dopaminergic drugs alters choice behavior in a manner consistent with the theory. More specifically, we found that dopaminergic drugs selectively modulate learning from positive outcomes. We observed no effect of dopaminergic drugs on learning from negative outcomes. We also found a novel dopamine-dependent effect on decision making that is not accounted for by reinforcement learning models: perseveration in choice, independent of reward history, increases with Parkinsons disease and decreases with dopamine therapy.