Late-training amnesic deficits in probabilistic category learning: a neurocomputational analysis

Quantitative modeling of category learning deficits in various patient populations

OBJECTIVE

To provide a select review of our applications of quantitative modeling to highlight the utility of such approaches to better understand the neuropsychological deficits associated with various neurologic and psychiatric diseases.

METHOD

We review our work examining category learning in various patient populations, including individuals with basal ganglia disorders (Huntington's Disease and Parkinson's disease), amnesia and Eating Disorders.

RESULTS

Our review suggests that the use of quantitative models has enabled a better understanding of the learning deficits often observed in these conditions and has allowed us to form novel hypotheses about the neurobiological bases of their deficits.

CONCLUSIONS

We feel that the use of neurobiologically inspired quantitative modeling holds great promise in neuropsychological assessment and that future clinical measures should incorporate the use of such models as part of their standard scoring. Appropriate studies need to be completed, however, to determine whether such modeling techniques adhere to the rigorous psychometric properties necessary for a valid and reliable application in a clinical setting. (PsycINFO Database Record

Tracking the emergence of memories: A category-learning paradigm to explore schema-driven recognition

Previous research has shown that prior knowledge structures or schemas affect recognition memory. However, since the acquisition of schemas occurs over prolonged periods of time, few paradigms allow the direct manipulation of schema acquisition to study their effect on memory performance. Recently, a number of parallelisms in recognition memory between studies involving schemas and studies involving category learning have been identified. The current paper capitalizes on these findings and offers a novel experimental paradigm that allows manipulation of category learning between individuals to study the effects of schema acquisition on recognition. First, participants learn to categorize computer-generated items whose category-inclusion criteria differ between participants. Next, participants study items that belong to either the learned category, the non-learned category, both, or neither. Finally, participants receive a recognition test that includes old and new items, either from the learned, the non-learned, or neither category. Using variations on this paradigm, four experiments were conducted. The results from the first three studies suggest that learning a category increases hit rates for old category-consistent items and false alarm rates for new category-consistent lures. Absent the category learning, no such effects are evident, even when participants are exposed to the same learning trials as those who learned the categories. The results from the fourth experiment suggest that, at least for false alarm rates, the effects of category learning are not solely attributable to frequency of occurrence of category-consistent items during learning. Implications for recognition memory as well as advantages of the proposed paradigm are discussed.

Repetitive transcranial magnetic stimulation of the left dorsolateral prefrontal cortex improves probabilistic category learning

Traditionally, it has been thought that probabilistic category learning, one of the implicit memory functions, is dependent on the basal ganglia. However, there is growing evidence indicating the involvement of the dorsolateral prefrontal cortex (DLPFC) in probabilistic category learning. In order to identify the causal role of DLPFC in probabilistic category learning, we investigated whether repetitive transcranial magnetic stimulation (rTMS) over the left DLPFC influences the learning ability in healthy subjects using the weather prediction task (WPT). Application of 10 Hz rTMS over the left DLPFC induced significant improvement in the total hit rate during the total trials of the WPT, compared with sham stimulation. Specifically, the improvement was significant in the early and late learning blocks of the WPT, but not in the intermediate block of learning. These results indicate that the left DLPFC may play an important role in probabilistic category learning, possibly by influencing not only on embodied information application in late stage of the learning but also on memory encoding for working memory demands in early stage of the learning via frontostriatal and frontohippocampal circuits, respectively. They also may lend support to the possibility that rTMS can improve implicit learning ability in patients with basal ganglia disorders.

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.

Computational models of the hippocampal region: implications for prediction of risk for Alzheimer's disease in non-demented elderly

We have pursued an interdisciplinary research program to develop novel behavioral assessment tools for evaluating specific memory impairments following damage to the medial temporal lobe, including the hippocampus and associated structures that show pathology early in the course of Alzheimer's disease (AD). Our approach uses computational models to identify the functional consequences of hippocampal-region damage, leading to testable predictions in both rodents and humans. Our modeling argues that hippocampal-region dysfunction may selectively impair the ability to generalize when familiar information is presented in novel recombinations. Previous research has shown that specific reductions in hippocampal volume in non-demented elderly individuals correlate with future development of AD. In two previous studies, we tested non-demented elderly with and without mild hippocampal atrophy (HA) on stimulus-response learning tasks. Individuals with and without HA could learn the initial information, but the HA group was selectively impaired on transfer tests where familiar features and objects were recombined. This suggests that such generalization deficits may be behavioral markers of HA, and an early indicator of risk for subsequent cognitive decline. Converging support for the relevance of these tasks to aging and Alzheimer's disease comes from our recent fMRI studies of individuals with mild cognitive impairment (MCI). Activity in the hippocampus declines with progressive training on these tasks, suggesting that the hippocampus is important for learning new stimulus representations that support subsequent transfer. Individuals with HA may be able to learn, but in a more hippocampal-independent fashion that does not support later transfer. Ultimately, this line of research could lead to a novel battery of behavioral tests sensitive to very mild hippocampal atrophy and risk for decline to AD, allowing early diagnosis and also allowing researchers to test new Alzheimer's drugs that target individuals in the earliest stages of the disease - before significant cognitive decline. A new mouse version of one of our tasks shows promise for translating these paradigms into rodents, allowing for future studies of therapeutic interventions in transgenic mouse models of AD.

Cortico-hippocampal interaction and adaptive stimulus representation: a neurocomputational theory of associative learning and memory

Computational models of the hippocampal region link psychological theories of associative learning with their underlying physiological and anatomical substrates. Our approach to theory development began with a broad description of the computations that depend on the hippocampal region in classical conditioning (Gluck and Myers, 1993 and Gluck and Myers, 2001). In this initial model, the hippocampal region was treated as an Information-processing system that transformed stimulus representations, compressing (making more similar) representations of inputs that co-occur or are otherwise redundant, while differentiating (or making less similar) representations of inputs that predict different future events. This model led to novel predictions for the behavioral consequences of hippocampal-region lesions in rodents and of brain damage in humans who have amnesia or are in the earliest stages of Alzheimer's disease. Many of these predictions have, since been confirmed by our lab and others. Functional brain imaging studies have provided further supporting evidence. In more recent computational modeling, we have shown how some aspects of this proposed information-processing function could emerge from known anatomical and physiological characteristics of the hippocampal region, including the entorhinal cortex and the septo-hippocampal cholinergic system. The modeling to date lays the groundwork for future directions that increase the depth of detail of the biological modeling, as well as the breadth of behavioral phenomena addressed. In particular, we are working now to reconcile these kinds of incremental associative learning models with other models of the hippocampal region that account for the rapid formation of declarative memories.

Human Category Learning

Much recent evidence suggests some dramatic differences in the way people learn perceptual categories, depending on exactly how the categories were constructed. Four different kinds of category-learning tasks are currently popular-rule-based tasks, information-integration tasks, prototype distortion tasks, and the weather prediction task. The cognitive, neuropsychological, and neuroimaging results obtained using these four tasks are qualitatively different. Success in rule-based (explicit reasoning) tasks depends on frontal-striatal circuits and requires working memory and executive attention. Success in information-integration tasks requires a form of procedural learning and is sensitive to the nature and timing of feedback. Prototype distortion tasks induce perceptual (visual cortical) learning. A variety of different strategies can lead to success in the weather prediction task. Collectively, results from these four tasks provide strong evidence that human category learning is mediated by multiple, qualitatively distinct systems.

Habit Learning and the Genetics of the Dopamine D₃ Receptor: Evidence From Patients With Schizophrenia and Healthy Controls

In this study, the authors investigated the relationship between the Ser9Gly (SG) polymorphism of the dopamine D3 receptor (DRD3) and striatal habit learning in healthy controls and patients with schizophrenia. Participants were given the weather prediction task, during which probabilistic cue-response associations were learned for tarot cards and weather outcomes (rain or sunshine). In both healthy controls and patients with schizophrenia, participants with Ser9Ser (SS) genotype did not learn during the early phase of the task (1-50 trials), whereas participants with SG genotype did so. During the late phase of the task (51-100 trials), both participants with SS and SG genotype exhibited significant learning. Learning rate was normal in patients with schizophrenia. These results suggest that the DRD3 variant containing glycine is associated with more efficient striatal habit learning in healthy controls and patients with schizophrenia.

The cognitive neuroscience of category learning

Recently, a multidisciplinary approach has provided new insights into the mechanisms of category learning. In this article, results from theoretical modeling, experimental psychology, clinical neuropsychology, functional neuroimaging, and single-cell studies are reviewed. Although the results are not conclusive, some general principles have emerged. Areas localized in the sensory neocortex are responsible for the perceptual representation of category exemplars, whereas lateral and anterior prefrontal structures are necessary for the encoding of category boundaries and abstract rules. The prefrontal cortex may influence categorical representation in the sensory neocortex via top-down control. The neostriatum is important in stimulus-response mapping, and the orbitofrontal cortex/ventral striatum are related to stimulus-reward associations accompanying category learning. Many category learning tasks can be performed implicitly. In conclusion, category learning paradigms provide a unique opportunity to investigate cognitive processes such as perception, memory, and attention in a systematic and interactive manner. Category learning tasks are suitable for mapping damaged brain systems in clinical populations.

Computational models of the hippocampal region: linking incremental learning and episodic memory

The hippocampal region, a group of brain structures important for learning and memory, has been the focus of a large number of computational models. These tend to fall into two groups: (1) models of the role of the hippocampal region in incremental learning, which focus on the development of new representations that are sensitive to stimulus regularities and environmental context; (2) models that focus on the role of the hippocampal region in the rapid storage and retrieval of episodic memories. Rather than being in conflict, it is becoming apparent that both approaches are partially correct and might reflect the different functions of substructures of the hippocampal region. Future computational models will help to elaborate how these different substructures interact.

How do people solve the "weather prediction" task?: individual variability in strategies for probabilistic category learning

Probabilistic category learning is often assumed to be an incrementally learned cognitive skill, dependent on nondeclarative memory systems. One paradigm in particular, the weather prediction task, has been used in over half a dozen neuropsychological and neuroimaging studies to date. Because of the growing interest in using this task and others like it as behavioral tools for studying the cognitive neuroscience of cognitive skill learning, it becomes especially important to understand how subjects solve this kind of task and whether all subjects learn it in the same way. We present here new experimental and theoretical analyses of the weather prediction task that indicate that there are at least three different strategies that describe how subjects learn this task. (1) An optimal multi-cue strategy, in which they respond to each pattern on the basis of associations of all four cues with each outcome; (2) a one-cue strategy, in which they respond on the basis of presence or absence of a single cue, disregarding all other cues; or (3) a singleton strategy, in which they learn only about the four patterns that have only one cue present and all others absent. This variability in how subjects approach this task may have important implications for interpreting how different brain regions are involved in probabilistic category learning.

Probabilistic classification learning in Tourette syndrome

Tourette syndrome (TS) is characterised by stereotyped involuntary movements, called tics. Some evidence suggests that structural and functional abnormalities of the basal ganglia may explain these motor symptoms. In this study, the probabilistic classification learning (PCL) test was used to evaluate basal ganglia functions in 10 children with less severe tics (Yale Global Tic Severity Scale (YGTSS) scores<30) and in 10 children with more severe symptoms (YGTSS score>30). In the PCL task, participants are asked to decide whether different combinations of four geometric forms (cues) predict rainy or sunny weather. Each cue is probabilistically related to a weather outcome, and feedback is provided after each decision. After completion of the probabilistic stimulus-response learning procedure, subjects received a transfer test to assess explicit knowledge about the cues. The children with TS exhibited impaired learning in the PCL task in comparison with the 20 healthy control subjects. This impairment was more pronounced in the TS patients with severe symptoms, and there was a significant negative relationship between the final classification performance and the YGTSS scores. The patients showed normal learning in the transfer test. These results suggest that the neostriatal habit learning system, which may play a central role in the acquisition of probabilistic associations, is dysfunctional in TS, especially in the case of more severe motor symptoms. The classification performance and the severity of tics were independent of the explicit knowledge obtained during the test.

Dissociating basal forebrain and medial temporal amnesic syndromes: insights from classical conditioning

In humans, anterograde amnesia can result from damage to the medial temporal (MT) lobes (including hippocampus), as well as to other brain areas such as basal forebrain. Results from animal classical conditioning studies suggest that there may be qualitative differences in the memory impairment following MT vs. basal forebrain damage. Specifically, delay eyeblink conditioning is spared after MT damage in animals and humans, but impaired in animals with basal forebrain damage. Recently, we have likewise shown delay eyeblink conditioning impairment in humans with amnesia following anterior communicating artery (ACoA) aneurysm rupture, which damages the basal forebrain. Another associative learning task, a computer-based concurrent visual discrimination, also appears to be spared in MT amnesia while ACoA amnesics are slower to learn the discriminations. Conversely, animal and computational models suggest that, even though MT amnesics may learn quickly, they may learn qualitatively differently from controls, and these differences may result in impaired transfer when familiar information is presented in novel combinations. Our initial data suggests such a two-phase learning and transfer task may provide a double dissociation between MT amnesics (spared initial learning but impaired transfer) and ACoA amnesics (slow initial learning but spared transfer). Together, these emerging data suggest that there are subtle but dissociable differences in the amnesic syndrome following damage to the MT lobes vs. basal forebrain, and that these differences may be most visible in non-declarative tasks such as eyeblink classical conditioning and simple associative learning.

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.

Latent learning in medial temporal amnesia: evidence for disrupted representational but preserved attentional processes

Damage to the hippocampus and medial temporal (MT) structures can lead to anterograde amnesia and may also impair latent learning, in which prior exposure to cues affects their subsequent associability. Normally, latent learning may reflect both representational and attentional mechanisms. Prior work has suggested that individuals with MT amnesia have specific deficits in representational processing; thus, latent learning that invokes primarily representational mechanisms might be especially impaired in MT amnesia. The current results provide preliminary confirmation of this prediction. In Experiment 1, a latent learning paradigm expected to invoke representational mechanisms was impaired in individuals with MT amnesia, whereas in Experiment 2, a paradigm expected to invoke other attentional mechanisms was spared in individuals with MT amnesia. This suggests the representational and attentional components of latent learning are dissociable and differentially affected in anterograde amnesia.