Catherine E. Myers

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.

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.

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.

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.

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.

Context, conditioning, and hippocampal rerepresentation in animal learning.

The researchers argue that a previous computational account of hippocampal region function in associative learning (M. Gluck & C. Myers, 1993) has emergent implications that accurately describe the role of the hippocampal region in contextual processing. This article unifies 2 seemingly conflicting views of contextual processing: It accords contextual cues no special representational status (e.g., R. Rescorla & A. Wagner, 1972), yet it also allows context to stand in a superordinate relationship to the cues it contains (e.g., L. Nadel & J. Willner, 1980). As a result, the account correctly expects that context can develop occasion-setting properties and that context shifts can weaken learned responses or attenuate latent inhibition. The article also explains data suggesting that hippocampal lesions reduce contextual sensitivity. It may help unify several previous theoretical accounts of the hippocampal regions role in contextual processing.

A Role for Hilar Cells in Pattern Separation in the Dentate Gyrus: A Computational Approach

We present a simple computational model of the dentate gyrus to evaluate the hypothesis that pattern separation, defined as the ability to transform a set of similar input patterns into a less‐similar set of output patterns, is dynamically regulated by hilar neurons. Prior models of the dentate gyrus have generally fallen into two categories: simplified models that have focused on a single granule cell layer and its ability to perform pattern separation, and large‐scale and biophysically realistic models of dentate gyrus, which include hilar cells, but which have not specifically addressed pattern separation. The present model begins to bridge this gap. The model includes two of the major subtypes of hilar cells: excitatory hilar mossy cells and inhibitory hilar interneurons that receive input from and project to the perforant path terminal zone (HIPP cells). In the model, mossy cells and HIPP cells provide a mechanism for dynamic regulation of pattern separation, allowing the system to upregulate and downregulate pattern separation in response to environmental and task demands. Specifically, pattern separation in the model can be strongly decreased by decreasing mossy cell function and/or by increasing HIPP cell function; pattern separation can be increased by the opposite manipulations. We propose that hilar cells may similarly mediate dynamic regulation of pattern separation in the dentate gyrus in vivo, not only because of their connectivity within the dentate gyrus, but also because of their modulation by brainstem inputs and by the axons that “backproject” from area CA3 pyramidal cells.

Neural Mechanisms Underlying Probabilistic Category Learning in Normal Aging

Probabilistic category learning engages neural circuitry that includes the prefrontal cortex and caudate nucleus, two regions that show prominent changes with normal aging. However, the specific contributions of these brain regions are uncertain, and the effects of normal aging have not been examined previously in probabilistic category learning. In the present study, using a blood oxygenation level-dependent functional magnetic resonance imaging block design, 18 healthy young adults (mean age, 25.5 ± 2.6 years) and 15 older adults (mean age, 67.1 ± 5.3 years) were assessed on the probabilistic category learning “weather prediction” test. Whole-brain functional images acquired using a 1.5T scanner (General Electric, Milwaukee, WI) with gradient echo, echo planar imaging (3/1 mm; repetition time, 3000 ms; echo time, 50 ms) were analyzed using second-level random-effects procedures [SPM99 (Statistical Parametric Mapping)]. Young and older adults displayed equivalent probabilistic category learning curves, used similar strategies, and activated analogous neural networks, including the prefrontal and parietal cortices and the caudate nucleus. However, the extent of caudate and prefrontal activation was less and parietal activation was greater in older participants. The percentage correct and reaction time were mainly positively correlated with caudate and prefrontal activation in young individuals but positively correlated with prefrontal and parietal cortices in older individuals. Differential activation within a circumscribed neural network in the context of equivalent learning suggests that some brain regions, such as the parietal cortices, may provide a compensatory mechanism for healthy older adults in the context of deficient prefrontal cortex and caudate nuclei responses.

Dissociation of hippocampal and entorhinal function in associative learning: A computational approach

Unsupervised stimulus-stimulus redundancy compression, one component of Gluck and Myers’s (1993) representational theory of the hippocampal-region function, could emerge from the anatomy and physiology of the entorhinal cortex. This hypothesis is suggested by a physiologically and anatomically realistic model of the entorhinal cortex derived from a similar model of the olfactory cortex previously proposed by Ambros-Ingerson, Granger, and Lynch (1990). To the extent that entorhinal function can survive damage strictly limited to the hippocampal formation (the H lesion), this has implications for interpreting the behavioral consequences of lesions which either do or do not spare overlying cortical areas. In particular, we expect that the H lesion should not interrupt stimulus-stimulus redundancy compression, thereby sparing conditioning behaviors, such as latent inhibition, which are eliminated by broader (H++) lesions to the hippocampal region. However, such other behaviors as the context sensitivity of latent inhibition and of learned responses are expected to be affected by the H lesion. These predictions are consistent with empirical data. The theory also leads to several novel predictions for behavioral comparisons of intact, H-lesioned, and H++-lesioned animals on tasks such as sensory preconditioning, compound preconditioning, and easy-hard transfer. A major theme of this paper is to illustrate how a bottom-up model of cortical processing can be integrated with a top-down model of hippocampal-region function to yield a more complete mapping from physiology to behavior.

Pattern separation in the dentate gyrus: A role for the CA3 backprojection

Many theories of hippocampal function assume that area CA3 of hippocampus is capable of performing rapid pattern storage, as well as pattern completion when a partial version of a familiar pattern is presented, and that the dentate gyrus (DG) is a preprocessor that performs pattern separation, facilitating storage and recall in CA3. The latter assumption derives partly from the anatomical and physiological properties of DG. However, the major output of DG is from a large number of DG granule cells to a smaller number of CA3 pyramidal cells, which potentially negates the pattern separation performed in the DG. Here, we consider a simple CA3 network model, and consider how it might interact with a previously developed computational model of the DG. The resulting “standard” DG‐CA3 model performs pattern storage and completion well, given a small set of sparse, randomly derived patterns representing entorhinal input to the DG and CA3. However, under many circumstances, the pattern separation achieved in the DG is not as robust in CA3, resulting in a low storage capacity for CA3, compared to previous mathematical estimates of the storage capacity for an autoassociative network of this size. We also examine an often‐overlooked aspect of hippocampal anatomy that might increase functionality in the combined DG‐CA3 model. Specifically, axon collaterals of CA3 pyramidal cells project “back” to the DG (“backprojections”), exerting inhibitory effects on granule cells that could potentially ensure that different subpopulations of granule cells are recruited to respond to similar patterns. In the model, addition of such backprojections improves both pattern separation and storage capacity. We also show that the DG‐CA3 model with backprojections provides a better fit to empirical data than a model without backprojections. Therefore, we hypothesize that CA3 backprojections might play an important role in hippocampal function.