Karin Foerde

The Neural Correlates of Motor Skill Automaticity

Acquisition of a new skill is generally associated with a decrease in the need for effortful control over performance, leading to the development of automaticity. Automaticity by definition has been achieved when performance of a primary task is minimally affected by other ongoing tasks. The neural basis of automaticity was examined by testing subjects in a serial reaction time (SRT) task under both single-task and dual-task conditions. The diminishing cost of dual-task performance was used as an index for automaticity. Subjects performed the SRT task during two functional magnetic imaging sessions separated by 3 h of behavioral training over multiple days. Behavioral data showed that, by the end of testing, subjects had automated performance of the SRT task. Before behavioral training, performance of the SRT task concurrently with the secondary task elicited activation in a wide network of frontal and striatal regions, as well as parietal lobe. After extensive behavioral training, dual-task performance showed comparatively less activity in bilateral ventral premotor regions, right middle frontal gyrus, and right caudate body; activity in other prefrontal and striatal regions decreased equally for single-task and dual-task conditions. These data suggest that lateral and dorsolateral prefrontal regions, and their corresponding striatal targets, subserve the executive processes involved in novice dual-task performance. The results also showed that supplementary motor area and putamen/globus pallidus regions showed training-related decreases for sequence conditions but not for random conditions, confirming the role of these regions in the representation of learned motor sequences.

Automatic Independent Component Labeling for Artifact Removal in fMRI

Blood oxygenation level dependent (BOLD) signals in functional magnetic resonance imaging (fMRI) are often small compared to the level of noise in the data. The sources of noise are numerous including different kinds of motion artifacts and physiological noise with complex patterns. This complicates the statistical analysis of the fMRI data. In this study, we propose an automatic method to reduce fMRI artifacts based on independent component analysis (ICA). We trained a supervised classifier to distinguish between independent components relating to a potentially task-related signal and independent components clearly relating to structured noise. After the components had been classified as either signal or noise, a denoised fMR time-series was reconstructed based only on the independent components classified as potentially task-related. The classifier was a novel global (fixed structure) decision tree trained in a Neyman-Pearson (NP) framework, which allowed the shape of the decision regions to be controlled effectively. Additionally, the conservativeness of the classifier could be tuned by modifying the NP threshold. The classifier was tested against the component classifications by an expert with the data from a category learning task. The test set as well as the expert were different from the data used for classifier training and the expert labeling the training set. The misclassification rate was between 0.2 and 0.3 for both the event-related and blocked designs and it was consistent among variety of different NP thresholds. The effects of denoising on the group-level statistical analyses were as expected: The denoising generally decreased Z-scores in the white matter, where extreme Z-values can be expected to reflect artifacts. A similar but weaker decrease in Z-scores was observed in the gray matter on average. These two observations suggest that denoising was likely to reduce artifacts from gray matter and could be useful to improve the detection of activations. We conclude that automatic ICA-based denoising offers a potentially useful approach to improve the quality of fMRI data and consequently increase the accuracy of the statistical analysis of these data.

Category learning and the memory systems debate

A substantial and growing body of evidence from cognitive neuroscience supports the concept of multiple memory systems (MMS). However, the existence of multiple systems has been questioned by theorists who instead propose that dissociations can be accounted for within a single memory system. We present convergent evidence from neuroimaging and neuropsychological studies of category learning in favor of the existence of MMS for category learning and declarative knowledge. Whereas single-system theorists have argued that their approach is more parsimonious because it only postulates a single form of memory representation, we show that the MMS approach is superior in its ability to account for a broad range of data from psychology and neuroscience.

The role of the basal ganglia in learning and memory: Insight from Parkinson's disease

It has long been known that memory is not a single process. Rather, there are different kinds of memory that are supported by distinct neural systems. This idea stemmed from early findings of dissociable patterns of memory impairments in patients with selective damage to different brain regions. These studies highlighted the role of the basal ganglia in non-declarative memory, such as procedural or habit learning, contrasting it with the known role of the medial temporal lobes in declarative memory. In recent years, major advances across multiple areas of neuroscience have revealed an important role for the basal ganglia in motivation and decision making. These findings have led to new discoveries about the role of the basal ganglia in learning and highlighted the essential role of dopamine in specific forms of learning. Here we review these recent advances with an emphasis on novel discoveries from studies of learning in patients with Parkinsons disease. We discuss how these findings promote the development of current theories away from accounts that emphasize the verbalizability of the contents of memory and towards a focus on the specific computations carried out by distinct brain regions. Finally, we discuss new challenges that arise in the face of accumulating evidence for dynamic and interconnected memory systems that jointly contribute to learning.

Feedback Timing Modulates Brain Systems for Learning in Humans

The ability to learn from the consequences of actions—no matter when those consequences take place—is central to adaptive behavior. Despite major advances in understanding how immediate feedback drives learning, it remains unknown precisely how the brain learns from delayed feedback. Here, we present converging evidence from neuropsychology and neuroimaging for distinct roles for the striatum and the hippocampus in learning, depending on whether feedback is immediate or delayed. We show that individuals with striatal dysfunction due to Parkinsons disease are impaired at learning when feedback is immediate, but not when feedback is delayed by a few seconds. Using functional imaging (fMRI) combined with computational model-derived analyses, we further demonstrate that healthy individuals show activation in the striatum during learning from immediate feedback and activation in the hippocampus during learning from delayed feedback. Additionally, later episodic memory for delayed feedback events was enhanced, suggesting that engaging distinct neural systems during learning had consequences for the representation of what was learned. Together, these findings provide direct evidence from humans that striatal systems are necessary for learning from immediate feedback and that delaying feedback leads to a shift in learning from the striatum to the hippocampus. The results provide a link between learning impairments in Parkinsons disease and evidence from single-unit recordings demonstrating that the timing of reinforcement modulates activity of midbrain dopamine neurons. Collectively, these findings indicate that relatively small changes in the circumstances under which information is learned can shift learning from one brain system to another.

Selective Corticostriatal Dysfunction in Schizophrenia: Examination of Motor and Cognitive Skill Learning

It has been suggested that patients with schizophrenia have corticostriatal circuit dysfunction (Carlsson & Carlsson, 1990). Skill learning is thought to rely on corticostriatal circuitry and different types of skill learning may be related to separable corticostriatal loops (Grafton, Hazeltine, & Ivry, 1995; Poldrack, Prabhakaran, Seger, & Gabrieli, 1999). The authors examined motor (Serial Reaction Time task, SRT) and cognitive (Probabilistic Classification task, PCT) skill learning in patients with schizophrenia and normal controls. Development of automaticity was examined, using a dual task paradigm, across three training sessions. Patients with schizophrenia were impaired at learning on the PCT compared to controls. Performance gains of controls occurred within the first session, whereas patients only improved gradually and never reached the performance level of controls. In contrast, patients were not impaired at learning on the SRT relative to controls, suggesting that patients with schizophrenia may have dysfunction in a specific corticostriatal subcircuit.

A Role for the Medial Temporal Lobe in Feedback-Driven Learning: Evidence from Amnesia

The ability to learn from feedback is a key component of adaptive behavior. This type of learning is traditionally thought to depend on neural substrates in the striatum and not on the medial temporal lobe (MTL). Here we show that in humans the MTL becomes necessary for feedback-based learning when feedback is delayed. Specifically, amnesic patients with MTL damage were impaired at probabilistic learning of cue–outcome associations when response-contingent feedback was delayed by a few seconds, but not when feedback was immediate. By contrast, patients with striatal dysfunction due to Parkinsons disease demonstrated the opposite pattern: impaired learning when trial-by-trial feedback was immediate but not when feedback was delayed, indicating that the striatum is necessary for learning only when feedback is immediate. Together, these results reveal that multiple complementary learning processes support what appears to be identical behavior in healthy individuals and point to an important role for the MTL in feedback-driven learning.

Neural mechanisms supporting maladaptive food choices in anorexia nervosa

People routinely make poor choices, despite knowledge of negative consequences. The authors found that individuals with anorexia nervosa, who make maladaptive food choices to the point of starvation, engaged the dorsal striatum more than healthy controls when making choices about what to eat, and that activity in fronto-striatal circuits was correlated with their actual food consumption in a meal the next day.

Secondary-task effects on classification learning

Probabilistic classification learning can be supported by implicit knowledge of cue-response associations. We investigated whether forming these associations depends on attention by assessing the effect of performing a secondary task on learning in the probabilistic classification task (PCT). Experiment 1 showed that concurrent task performance significantly interfered with performance of the PCT. Experiment 2 showed that this interference did not prevent learning from occurring. On the other hand, the secondary task did disrupt acquisition of explicit knowledge about cue-outcome associations. These results show that concurrent task performance can have different effects on implicit and explicit knowledge acquired within the same task and also underscore the importance of considering effects on learning and performance separately.

An Upside to Reward Sensitivity: The Hippocampus Supports Enhanced Reinforcement Learning in Adolescence

Adolescents are notorious for engaging in reward-seeking behaviors, a tendency attributed to heightened activity in the brains reward systems during adolescence. It has been suggested that reward sensitivity in adolescence might be adaptive, but evidence of an adaptive role has been scarce. Using a probabilistic reinforcement learning task combined with reinforcement learning models and fMRI, we found that adolescents showed better reinforcement learning and a stronger link between reinforcement learning and episodic memory for rewarding outcomes. This behavioral benefit was related to heightened prediction error-related BOLD activity in the hippocampus and to stronger functional connectivity between the hippocampus and the striatum at the time of reinforcement. These findings reveal an important role for the hippocampus in reinforcement learning in adolescence and suggest that reward sensitivity in adolescence is related to adaptive differences in how adolescents learn from experience.