Dissociation between Striatal Regions while Learning to Categorize via Feedback and via Observation

The tail of the caudate is sensitive to both gain and loss feedback during information integration categorization

Although most category learning studies use feedback for training, little attention has been paid to how individuals utilize feedback implemented as gains or losses during categorization. We compared skilled categorization under three different conditions: Gain (earn points for correct answers), Gain and Loss (earn points for correct answers and lose points for wrong answers) and Correct or Wrong (accuracy feedback only). We also manipulated difficulty and point value, with near boundary stimuli having the highest number of points to win or lose, and stimuli far from the boundary having the lowest point value. We found that the tail of the caudate was sensitive to feedback condition, with highest activity when both Gain and Loss feedback were present and least activity when only Gain or accuracy feedback was present. We also found that activity across the caudate was affected by distance from the decision bound, with greatest activity for the near boundary high value stimuli, and lowest for far low value stimuli. Overall these results indicate that the tail of the caudate is sensitive not only to positive rewards but also to loss and punishment, consistent with recent animal research finding tail of the caudate activity in aversive learning.

Visual training after central retinal loss limits structural white matter degradation: an MRI study

BACKGROUND

Macular degeneration of the eye is a common cause of blindness and affects 8% of the worldwide human population. In adult cats with bilateral lesions of the central retina, we explored the possibility that motion perception training can limit the associated degradation of the visual system. We evaluated how visual training affects behavioral performance and white matter structure. Recently, we proposed (Kozak et al. in Transl Vis Sci Technol 10:9, 2021) a new motion-acuity test for low vision patients, enabling full visual field functional assessment through simultaneous perception of shape and motion. Here, we integrated this test as the last step of a 10-week motion-perception training.

RESULTS

Cats were divided into three groups: retinal-lesioned only and two trained groups, retinal-lesioned trained and control trained. The behavioral data revealed that trained cats with retinal lesions were superior in motion tasks, even when the difficulty relied only on acuity. 7 T-MRI scanning was done before and after lesioning at 5 different timepoints, followed by Fixel-Based and Fractional Anisotropy Analysis. In cats with retinal lesions, training resulted in a more localized and reduced percentage decrease in Fixel-Based Analysis metrics in the dLGN, caudate nucleus and hippocampus compared to untrained cats. In motion-sensitive area V5/PMLS, the significant decreases in fiber density were equally strong in retinal-lesioned untrained and trained cats, up to 40% in both groups. The only cortical area with Fractional Anisotropy values not affected by central retinal loss was area V5/PMLS. In other visual ROIs, the Fractional Anisotropy values increased over time in the untrained retinal lesioned group, whereas they decreased in the retinal lesioned trained group and remained at a similar level as in trained controls.

CONCLUSIONS

Overall, our MRI results showed a stabilizing effect of motion training applied soon after central retinal loss induction on white matter structure. We propose that introducing early motion-acuity training for low vision patients, aimed at the intact and active retinal peripheries, may facilitate brain plasticity processes toward better vision.

Declarative Learning Mechanisms Support Declarative but Not Probabilistic Feedback-Based Learning in Children with Developmental Language Disorder (DLD)

Declarative and probabilistic feedback-based learning was evaluated in 8-12-year-old school-age children with developmental language disorder (DLD; n = 14) and age-matched children with typical development (TD; n = 15). Children performed a visual two-choice word-learning task and a visual probabilistic classification task while their electroencephalogram (EEG) was recorded non-invasively from the scalp. Behavioral measures of accuracy and response to feedback, and electrophysiological responses to feedback were collected and compared between the two groups. While behavioral data indicated poorer performance by children with DLD in both learning paradigms, and similar response patterns to positive and negative feedback, electrophysiological data highlighted processing patterns in the DLD group that differed by task. More specifically, in this group, feedback processing in the context of declarative learning, which is known to be dominated by the medial temporal lobe (MTL), was associated with enhanced N170, an event-related brain potential (ERP) associated with MTL activation. The N170 amplitude was found to be correlated with declarative task performance in the DLD group. During probabilistic learning, known to be governed by the striatal-based learning system, the feedback-related negativity (FRN) ERP, which is the product of the cortico-striatal circuit dominated feedback processing. Within the context of probabilistic learning, enhanced N170 was associated with poor learning in the TD group, suggesting that MTL activation during probabilistic learning disrupts learning. These results are interpreted within the context of a proposed feedback parity hypothesis suggesting that in children with DLD, the system that dominates learning (i.e., MTL during declarative learning and the striatum during probabilistic learning) dominates and supports feedback processing.

The contribution of theta and delta to feedback processing in children with developmental language disorder

PURPOSE

The study aimed at evaluating feedback processing at the electrophysiological level and its relation to learning in children with developmental language disorder (DLD) to further advance our understanding of the underlying neural mechanisms of feedback-based learning in children with this disorder.

METHOD

A feedback-based probabilistic learning task required children to classify novel cartoon animals into two categories that differ on five binary features, the probabilistic combination of which determined classification. The learning outcomes' variance in relation to time- and time-frequency measures of feedback processing were examined and compared between 20 children with developmental language disorder and 25 age-matched children with typical language development.

RESULTS

Children with developmental language disorder (DLD) performed poorer on the task when compared with their age-matched peers with typical language development (TD). The electrophysiological data in the time domain indicated no differences in the processing of positive and negative feedback among children with DLD. However, the time-frequency analysis revealed a strong theta activity in response to negative feedback in this group, suggesting an initial distinction between positive and negative feedback that was not captured by the ERP data. In the TD group, delta activity played a major role in shaping the FRN and P3a and was found to predict test performance. Delta did not contribute to the FRN and P3a in the DLD group. Additionally, theta and delta activities were not associated with the learning outcomes of children with DLD.

CONCLUSION

Theta activity, which is associated with the initial processing of feedback at the level of the anterior cingulate cortex, was detected in children with developmental language disorder (DLD) but was not associated with their learning outcomes. Delta activity, which is assumed to be generated by the striatum and to be linked to elaborate processing of outcomes and adjustment of future actions, contributed to processing and learning outcomes of children with typical language development but not of children with DLD. The results provide evidence for atypical striatum-based feedback processing in children with DLD.

Rule and Exemplar-based Transfer in Category Learning

We compared the neural systems involved in transfer to novel stimuli via rule application versus exemplar processing. Participants learned a categorization task involving abstraction of a complex rule and then categorized different types of transfer stimuli without feedback. Rule stimuli used new features and therefore could only be categorized using the rule. Exemplar stimuli included only one of the features necessary to apply the rule and therefore required participants to categorize based on similarity to individual previously learned category members. Consistent and inconsistent stimuli were formed so that both the rule and feature similarity indicated the same category (consistent) or opposite categories (inconsistent). We found that all conditions eliciting rule-based transfer recruited a medial prefrontal-anterior hippocampal network associated with schematic memory. In contrast, exemplar-based transfer recruited areas of the intraparietal sulcus associated with learning and executing stimulus-category mappings along with the posterior hippocampus. These results support theories of categorization that postulate complementary learning and generalization strategies based on schematic and exemplar mechanisms.

The role of basal ganglia network in neural plasticity in neuromyelitis optica spectrum disorder with myelitis

OBJECTIVES

To explore the alternation of the brain baseline activity in neuromyelitis optica spectrum disorder (NMOSD) patients after myelitis, and characterize the representation of the neural plasticity process.

METHODS

Clinical evaluation and resting-state fMRI were obtained from 20 NMOSD patients with myelitis and 20 healthy controls, matched in gender and age. Resting-state networks (RSNs) were identified through independent component analysis (ICA), and functional connectivity (FC) intra-RSNs and between region-of-interest (ROI) seed to whole-brain voxels were analyzed. Between-group comparisons and correlations with motor performance were also assessed.

RESULTS

A total of 14 main functional RSNs were identified. Group comparison of intra-network FCs revealed that FC strengths increased in basal ganglia network (BGN) and left frontoparietal network, decreased in sensorimotor network and default mode network in NMOSD. Better motor performance was found closely correlated with higher FC of BGN. Additionally, remarkably increased FC between caudate in BGN with cerebellum, frontal lobe and parietal lobe was discovered in further ROI-based whole-brain voxels FC analysis.

CONCLUSIONS

NMOSD patients presented wide brain resting-state functional connectivity alterations after myelitis, and BGN might be highly active in the process of neural plasticity in chronic stage of NMOSD. Besides, understanding neural plasticity representation, especially that in NMOSD patients after myelitis, might have important applications in monitoring and designing rehabilitative approaches.

Distributed Neural Systems Support Flexible Attention Updating during Category Learning

To accurately categorize items, humans learn to selectively attend to stimulus dimensions that are most relevant to the task. Models of category learning describe the interconnected cognitive processes that contribute to attentional tuning as labeled stimuli are progressively observed. The Adaptive Attention Representation Model (AARM), for example, provides an account whereby categorization decisions are based on the perceptual similarity of a new stimulus to stored exemplars, and dimension-wise attention is updated on every trial in the direction of a feedback-based error gradient. As such, attention modulation as described by AARM requires interactions among orienting, visual perception, memory retrieval, prediction error, and goal maintenance to facilitate learning across trials. The current study explored the neural bases of attention mechanisms using quantitative predictions from AARM to analyze behavioral and fMRI data collected while participants learned novel categories. Generalized linear model analyses revealed patterns of BOLD activation in the parietal cortex (orienting), visual cortex (perception), medial temporal lobe (memory retrieval), basal ganglia (prediction error), and pFC (goal maintenance) that covaried with the magnitude of model-predicted attentional tuning. Results are consistent with AARM's specification of attention modulation as a dynamic property of distributed cognitive systems.

Learning With and Without Feedback in Children With Developmental Language Disorder

Purpose Intervention provided to school-age children with developmental language disorder often relies on the provision of performance feedback, yet it is unclear whether children with this disorder benefit from feedback-based learning. The study evaluates the effect of performance feedback on learning in children with developmental language disorder. Method Thirteen 8- to 12-year-old children with developmental language disorder and 14 age- and gender-matched children with typical language development completed two learning tasks whose objective was to pair nonword novel names with novel objects. The two tasks differed in the presence of performance feedback to guide learning. Learning outcomes on immediate and follow-up tests were compared between the feedback-based and feedback-free tasks. Additionally, an electrophysiological marker of feedback processing was compared between children with and without developmental language disorder. Results Children with developmental language disorder demonstrated poorer learning outcomes on both tasks when compared with their peers, but both groups achieved better accuracy on the feedback-free task when compared with the feedback-based task. Within the feedback-based task, children were more likely to repeat a correct response than to change it after positive feedback but were as likely to repeat an error as they were to correct it after receiving negative feedback. While children with typical language elicited a feedback-related negativity with greater amplitude to negative feedback, this event-related potential had no amplitude differences between positive and negative feedback in children with developmental language disorder. Conclusions Findings indicate that 8- to 12-year-old children benefit more from a feedback-free learning environment and that negative feedback is not as effective as positive feedback in facilitating learning in children. The behavioral and electrophysiological data provide evidence that feedback processing is impaired in children with developmental language disorders. Future research should evaluate feedback-based learning in children with this disorder using other learning paradigms.

Tracking prototype and exemplar representations in the brain across learning

There is a long-standing debate about whether categories are represented by individual category members (exemplars) or by the central tendency abstracted from individual members (prototypes). Neuroimaging studies have shown neural evidence for either exemplar representations or prototype representations, but not both. Presently, we asked whether it is possible for multiple types of category representations to exist within a single task. We designed a categorization task to promote both exemplar and prototype representations and tracked their formation across learning. We found only prototype correlates during the final test. However, interim tests interspersed throughout learning showed prototype and exemplar representations across distinct brain regions that aligned with previous studies: prototypes in ventromedial prefrontal cortex and anterior hippocampus and exemplars in inferior frontal gyrus and lateral parietal cortex. These findings indicate that, under the right circumstances, individuals may form representations at multiple levels of specificity, potentially facilitating a broad range of future decisions.

Selective and distributed attention in human and pigeon category learning

Attention to relevant stimulus features in a categorization task helps to optimize performance. However, the relationship between attention and categorization is not fully understood. For example, even when human adults and young children exhibit comparable categorization behavior, adults tend to attend selectively during learning, whereas young children tend to attend diffusely (Deng & Sloutsky, 2016). Here, we used a comparative approach to investigate the link between attention and categorization in two different species. Given the noteworthy categorization ability of avian species, we compared the attentional profiles of pigeons and human adults. We gave human adults (Experiment 1) and pigeons (Experiment 2) a categorization task that could be learned on the basis of either one deterministic feature (encouraging selective attention) or multiple probabilistic features (encouraging distributed attention). Both humans and pigeons relied on the deterministic feature to categorize the stimuli, albeit humans did so to a much greater degree. Furthermore, computational modeling revealed that most of the adults exhibited maximal selectivity, whereas pigeons tended to distribute their attention among several features. Our findings indicate that human adults focus their attention on deterministic information and filter less predictive information, but pigeons do not. Implications for the underlying brain mechanisms of attention and categorization are discussed.

Left Inferior Frontal Gyrus Integrates Multisensory Information in Category Learning

Humans are able to categorize things they encounter in the world (e.g., a cat) by integrating multisensory information from the auditory and visual modalities with ease and speed. However, how the brain learns multisensory categories remains elusive. The present study used functional magnetic resonance imaging to investigate, for the first time, the neural mechanisms underpinning multisensory information-integration (II) category learning. A sensory-modality-general network, including the left insula, right inferior frontal gyrus (IFG), supplementary motor area, left precentral gyrus, bilateral parietal cortex, and right caudate and globus pallidus, was recruited for II categorization, regardless of whether the information came from a single modality or from multiple modalities. Putamen activity was higher in correct categorization than incorrect categorization. Critically, the left IFG and left body and tail of the caudate were activated in multisensory II categorization but not in unisensory II categorization, which suggests this network plays a specific role in integrating multisensory information during category learning. The present results extend our understanding of the role of the left IFG in multisensory processing from the linguistic domain to a broader role in audiovisual learning.

Abnormal Baseline Brain Activity in Neuromyelitis Optica Patients Without Brain Lesion Detected by Resting-State Functional Magnetic Resonance Imaging

OBJECTIVE

To investigate the baseline brain activity in neuromyelitis optica patients without brain lesion using the regional amplitude of low-frequency fluctuation (ALFF) and fractional amplitude of low-frequency fluctuation (fALFF) as indexes.

MATERIALS AND METHODS

Forty-two patients of NMO with normal performance in conventional MRI and 42 healthy controls, matched in gender and age, were enrolled in this study. Resting-state functional magnetic resonance imaging (rs-fMRI) data acquired using the rs-fMRI Data Analysis Toolkit. The relationships between expanded disability states scale (EDSS) scores, abnormal baseline brain activity and disease duration were explored.

RESULTS

The left inferior temporal, left cerebellum_4_5, bilateral superior temporal pole, left caudate, right superior temporal, left middle frontal and left superior occipital showed significantly increased ALFF in the NMO. Regions of abnormal fALFF were similar to those of ALFF except that increased fALFF were also indicated in the right cerebellum crus2, right hippocampus, left parahippocampal gyrus and left supplementary motor area. Furthermore, a significant correlation between EDSS scores and ALFF/fALFF was noted in the left inferior temporal gyrus.

CONCLUSION

Results confirmed the disturbances in NMO-related neural networks, which probably be related to spinal cord damage.

Competitive and cooperative interactions between medial temporal and striatal learning systems

The striatum and medial temporal lobes (MTL) exhibit dissociable roles during learning. Whereas the striatum and its network of thalamic relays and cortical nodes are necessary for nondeclarative learning, the MTL and associated network are required for declarative learning. Several studies have suggested that these networks are functionally competitive during learning. Since these discoveries, however, evidence has accumulated that they can operate in a cooperative fashion. In this review, we discuss evidence for both competition and cooperation between these systems during learning, with the aim of reconciling these seemingly contradictory findings. Examples of cooperation between the striatum and MTL have been provided, especially during consolidation and generalization of knowledge, and do not appear to be precluded by differences in functional specialization. However, whether these systems cooperate or compete does seem to depend on the phase of learning and cognitive or motor aspects of the task. The involvement of other regions, such as midbrain dopaminergic nuclei and the prefrontal cortex, may promote and mediate cooperation between the striatum and the MTL during learning. Building on this body of research, we propose a model for striatum-MTL interactions in learning and memory and attempt to predict, in general terms, when cooperation or competition will occur.

Role of the striatum in incidental learning of sound categories

Humans are born as “universal listeners.” However, over the first year, infants’ perception is shaped by native speech categories. How do these categories naturally emerge without explicit training or overt feedback? Using fMRI, we examined the neural basis of incidental sound category learning as participants played a videogame in which sound category exemplars had functional utility in guiding videogame success. Even without explicit categorization of the sounds, participants learned functionally relevant sound categories that generalized to novel exemplars when exemplars had an organized distributional structure. Critically, the striatum was engaged and functionally connected to the auditory cortex during game play, and this activity and connectivity predicted the learning outcome. These findings elucidate the neural mechanism by which humans incidentally learn “real-world” categories.

Humans are born as “universal listeners” without a bias toward any particular language. However, over the first year of life, infants’ perception is shaped by learning native speech categories. Acoustically different sounds—such as the same word produced by different speakers—come to be treated as functionally equivalent. In natural environments, these categories often emerge incidentally without overt categorization or explicit feedback. However, the neural substrates of category learning have been investigated almost exclusively using overt categorization tasks with explicit feedback about categorization decisions. Here, we examined whether the striatum, previously implicated in category learning, contributes to incidental acquisition of sound categories. In the fMRI scanner, participants played a videogame in which sound category exemplars aligned with game actions and events, allowing sound categories to incidentally support successful game play. An experimental group heard nonspeech sound exemplars drawn from coherent category spaces, whereas a control group heard acoustically similar sounds drawn from a less structured space. Although the groups exhibited similar in-game performance, generalization of sound category learning and activation of the posterior striatum were significantly greater in the experimental than control group. Moreover, the experimental group showed brain–behavior relationships related to the generalization of all categories, while in the control group these relationships were restricted to the categories with structured sound distributions. Together, these results demonstrate that the striatum, through its interactions with the left superior temporal sulcus, contributes to incidental acquisition of sound category representations emerging from naturalistic learning environments.

Tasks determine what is learned in visual statistical learning

Visual statistical learning (VSL), the unsupervised learning of statistical contingencies across time and space, may play a key role in efficient and predictive encoding of the perceptual world. How VSL capabilities vary as a function of ongoing task demands is still poorly understood. VSL is modulated by selective attention and faces interference from some secondary tasks, but there is little evidence that the types of contingencies learned in VSL are sensitive to task demands. We found a powerful effect of task on what is learned in VSL. Participants first completed a visual familiarization task requiring judgments of face gender (female/male) or scene location (interior/exterior). Statistical regularities were embedded between stimulus pairs. During a surprise recognition phase, participants showed less recognition for pairs that had required a change in response key (e.g., female followed by male) or task (e.g., female followed by indoor) during familiarization. When familiarization required detection of "flicker" or "jiggle" events unrelated to image content, there was weaker, but uniform, VSL across pair types. These results suggest that simple task manipulations play a strong role in modulating the distribution of learning over different pair combinations. Such variations may arise from task and response conflict or because the manner in which images are processed is altered.

On the Role of Cortex-Basal Ganglia Interactions for Category Learning: A Neurocomputational Approach

In addition to the prefrontal cortex (PFC), the basal ganglia (BG) have been increasingly often reported to play a fundamental role in category learning, but the circuit mechanisms mediating their interaction remain to be explored. We developed a novel neurocomputational model of category learning that particularly addresses the BG-PFC interplay. We propose that the BG bias PFC activity by removing the inhibition of cortico-thalamo-cortical loop and thereby provide a teaching signal to guide the acquisition of category representations in the corticocortical associations to the PFC. Our model replicates key behavioral and physiological data of macaque monkey learning a prototype distortion task from Antzoulatos and Miller (2011) Our simulations allowed us to gain a deeper insight into the observed drop of category selectivity in striatal neurons seen in the experimental data and in the model. The simulation results and a new analysis of the experimental data based on the model's predictions show that the drop in category selectivity of the striatum emerges as the variability of responses in the striatum rises when confronting the BG with an increasingly larger number of stimuli to be classified. The neurocomputational model therefore provides new testable insights of systems-level brain circuits involved in category learning that may also be generalized to better understand other cortico-BG-cortical loops.SIGNIFICANCE STATEMENT Inspired by the idea that basal ganglia (BG) teach the prefrontal cortex (PFC) to acquire category representations, we developed a novel neurocomputational model and tested it on a task that was recently applied in monkey experiments. As an advantage over previous models of category learning, our model allows to compare simulation data with single-cell recordings in PFC and BG. We not only derived model predictions, but already verified a prediction to explain the observed drop in striatal category selectivity. When testing our model with a simple, real-world face categorization task, we observed that the fast striatal learning with a performance of 85% correct responses can teach the slower PFC learning to push the model performance up to almost 100%.

Cognitive changes in conjunctive rule-based category learning: An ERP approach

When learning rule-based categories, sufficient cognitive resources are needed to test hypotheses, maintain the currently active rule in working memory, update rules after feedback, and to select a new rule if necessary. Prior research has demonstrated that conjunctive rules are more complex than unidimensional rules and place greater demands on executive functions like working memory. In our study, event-related potentials (ERPs) were recorded while participants performed a conjunctive rule-based category learning task with trial-by-trial feedback. In line with prior research, correct categorization responses resulted in a larger stimulus-locked late positive complex compared to incorrect responses, possibly indexing the updating of rule information in memory. Incorrect trials elicited a pronounced feedback-locked P300 elicited which suggested a disconnect between perception, and the rule-based strategy. We also examined the differential processing of stimuli that were able to be correctly classified by the suboptimal single-dimensional rule ("easy" stimuli) versus those that could only be correctly classified by the optimal, conjunctive rule ("difficult" stimuli). Among strong learners, a larger, late positive slow wave emerged for difficult compared with easy stimuli, suggesting differential processing of category items even though strong learners performed well on the conjunctive category set. Overall, the findings suggest that ERP combined with computational modelling can be used to better understand the cognitive processes involved in rule-based category learning.

Stress Elevates Frontal Midline Theta in Feedback-based Category Learning of Exceptions

Adapting behavior based on category knowledge is a fundamental cognitive function, which can be achieved via different learning strategies relying on different systems in the brain. Whereas the learning of typical category members has been linked to implicit, prototype abstraction learning, which relies predominantly on prefrontal areas, the learning of exceptions is associated with explicit, exemplar-based learning, which has been linked to the hippocampus. Stress is known to foster implicit learning strategies at the expense of explicit learning. Procedural, prefrontal learning and cognitive control processes are reflected in frontal midline theta (4–8 Hz) oscillations during feedback processing. In the current study, we examined the effect of acute stress on feedback-based category learning of typical category members and exceptions and the oscillatory correlates of feedback processing in the EEG. A computational modeling procedure was applied to estimate the use of abstraction and exemplar strategies during category learning. We tested healthy, male participants who underwent either the socially evaluated cold pressor test or a nonstressful control procedure before they learned to categorize typical members and exceptions based on feedback. The groups did not differ significantly in their categorization accuracy or use of categorization strategies. In the EEG, however, stressed participants revealed elevated theta power specifically during the learning of exceptions, whereas the theta power during the learning of typical members did not differ between the groups. Elevated frontal theta power may reflect an increased involvement of medial prefrontal areas in the learning of exceptions under stress.

The network architecture of value learning

Value guides behavior. With knowledge of stimulus values and action consequences, behaviors that maximize expected reward can be selected. Prior work has identified several brain structures critical for representing both stimuli and their values. Yet, it remains unclear how these structures interact with one another and with other regions of the brain to support the dynamic acquisition of value-related knowledge. Here, we use a network neuroscience approach to examine how BOLD functional networks change as 20 healthy human subjects learn the values of novel visual stimuli over the course of four consecutive days. We show that connections between regions of the visual, frontal, and cingulate cortices become stronger as learning progresses, with some of these changes being specific to the type of feedback received during learning. These results demonstrate that functional networks dynamically track behavioral improvement in value judgments, and that interactions between network communities form predictive biomarkers of learning.

Rational human behavior is the pursuit of actions that maximize expected reward. These rewards can be understood as stimulus-value contingencies, learned by experience throughout our lives. Various structures have been recognized to participate in these learning processes. Yet, an understanding of how these structures interact with one another and with other brain regions remains vastly unexplored. Here, we propose a novel analytical framework utilizing and extending techniques from the dynamic network neuroscience to ask “How do our brains change when we learn values?” We find that interactions between sensory and fronto-cingulate structures grow stronger as learning progresses, bringing together several isolated findings in the cognitive neuroscience of value-based behavior and extending our understanding of human learning in general.

Separating the effect of reward from corrective feedback during learning in patients with Parkinson's disease

Parkinson's disease (PD) is associated with procedural learning deficits. Nonetheless, studies have demonstrated that reward-related learning is comparable between patients with PD and controls (Bódi et al., Brain, 132(9), 2385-2395, 2009; Frank, Seeberger, & O'Reilly, Science, 306(5703), 1940-1943, 2004; Palminteri et al., Proceedings of the National Academy of Sciences of the United States of America, 106(45), 19179-19184, 2009). However, because these studies do not separate the effect of reward from the effect of practice, it is difficult to determine whether the effect of reward on learning is distinct from the effect of corrective feedback on learning. Thus, it is unknown whether these group differences in learning are due to reward processing or learning in general. Here, we compared the performance of medicated PD patients to demographically matched healthy controls (HCs) on a task where the effect of reward can be examined separately from the effect of practice. We found that patients with PD showed significantly less reward-related learning improvements compared to HCs. In addition, stronger learning of rewarded associations over unrewarded associations was significantly correlated with smaller skin-conductance responses for HCs but not PD patients. These results demonstrate that when separating the effect of reward from the effect of corrective feedback, PD patients do not benefit from reward.

Dorsal striatum mediates deliberate decision making, not late-stage, stimulus-response learning

We investigated a controversy regarding the role of the dorsal striatum (DS) in deliberate decision-making versus late-stage, stimulus-response learning to the point of automatization. Participants learned to associate abstract images with right or left button presses explicitly before strengthening these associations through stimulus-response trials with (i.e., Session 1) and without (i.e., Session 2) feedback. In Session 1, trials were divided into response-selection and feedback events to separately assess decision versus learning processes. Session 3 evaluated stimulus-response automaticity using a location Stroop task. DS activity correlated with response-selection and not feedback events in Phase 1 (i.e., Blocks 1-3), Session 1. Longer response times (RTs), lower accuracy, and greater intertrial variability characterized Phase 1, suggesting deliberation. DS activity extinguished in Phase 2 (i.e., Blocks 4-12), Session 1, once RTs, response variability, and accuracy stabilized, though stimulus-response automatization continued. This was signaled by persisting improvements in RT and accuracy into Session 2. Distraction between Sessions 1 and 2 briefly reintroduced response uncertainty, and correspondingly, significant DS activity reappeared in Block 1 of Session 2 only. Once stimulus-response associations were again refamiliarized and deliberation unnecessary, DS activation disappeared for Blocks 2-8, Session 2. Interference from previously learned right or left button responses with incongruent location judgments in a location Stroop task provided evidence that automaticity of stimulus-specific button-press responses had developed by the end of Session 2. These results suggest that DS mediates decision making and not late-stage learning, reconciling two, independently evolving and well-supported literatures that implicate DS in different cognitive functions. Hum Brain Mapp 38:6133-6156, 2017. © 2017 Wiley Periodicals, Inc.

Aging and a genetic KIBRA polymorphism interactively affect feedback- and observation-based probabilistic classification learning

Probabilistic category learning involves complex interactions between the hippocampus and striatum that may depend on whether acquisition occurs via feedback or observation. Little is known about how healthy aging affects these processes. We tested whether age-related behavioral differences in probabilistic category learning from feedback or observation depend on a genetic factor known to influence individual differences in hippocampal function, the KIBRA gene (single nucleotide polymorphism rs17070145). Results showed comparable age-related performance impairments in observational as well as feedback-based learning. Moreover, genetic analyses indicated an age-related interactive effect of KIBRA on learning: among older adults, the beneficial T-allele was positively associated with learning from feedback, but negatively with learning from observation. In younger adults, no effects of KIBRA were found. Our results add behavioral genetic evidence to emerging data showing age-related differences in how neural resources relate to memory functions, namely that hippocampal and striatal contributions to probabilistic category learning may vary with age. Our findings highlight the effects genetic factors can have on differential age-related decline of different memory functions.

Base-rate sensitivity through implicit learning

Two experiments assessed the contributions of implicit and explicit learning to base-rate sensitivity. Using a factorial design that included both implicit and explicit learning disruptions, we tested the hypothesis that implicit learning underlies base-rate sensitivity from experience (and that explicit learning contributes comparatively little). Participants learned to classify two categories of simple stimuli (bar graph heights) presented in a 3:1 base-rate ratio. Participants learned either from “observational” training to disrupt implicit learning or “response” training which supports implicit learning. Category label feedback on each trial was followed either immediately or after a 2.5 second delay by onset of a working memory task intended to disrupt explicit reasoning about category membership feedback. Decision criterion values were significantly larger following response training, suggesting that implicit learning underlies base-rate sensitivity. Disrupting explicit processing had no effect on base-rate learning as long as implicit learning was supported. These results suggest base-rate sensitivity develops from experience primarily through implicit learning, consistent with separate learning systems accounts of categorization.

Do infants retain the statistics of a statistical learning experience? Insights from a developmental cognitive neuroscience perspective

Statistical structure abounds in language. Human infants show a striking capacity for using statistical learning (SL) to extract regularities in their linguistic environments, a process thought to bootstrap their knowledge of language. Critically, studies of SL test infants in the minutes immediately following familiarization, but long-term retention unfolds over hours and days, with almost no work investigating retention of SL. This creates a critical gap in the literature given that we know little about how single or multiple SL experiences translate into permanent knowledge. Furthermore, different memory systems with vastly different encoding and retention profiles emerge at different points in development, with the underlying memory system dictating the fidelity of the memory trace hours later. I describe the scant literature on retention of SL, the learning and retention properties of memory systems as they apply to SL, and the development of these memory systems. I propose that different memory systems support retention of SL in infant and adult learners, suggesting an explanation for the slow pace of natural language acquisition in infancy. I discuss the implications of developing memory systems for SL and suggest that we exercise caution in extrapolating from adult to infant properties of SL.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'.

Learning from observation, feedback, and intervention in linear and non-linear task environments

This multiple-cue judgment study investigates whether we can manipulate the judgment strategy and increase accuracy in linear and non-linear cue-criterion environments just by changing the training mode. Three experiments show that accuracy in simple linear additive task environments are improved with feedback training and intervention training, while accuracy in complex multiplicative tasks are improved with observational training. The observed interaction effect suggests that the training mode invites different strategies that are adjusted as a function of experience to the demands from the underlying cue-criterion structure. Thus, feedback and the intervention training modes invite cue abstraction, an effortful but successful strategy in combination with simple linear task structures, and observational training invites exemplar memory processes, a simple but successful strategy in combination with complex non-linear task structures. The study discusses adaptive cognition and the implication of the different training modes across a life span and for clinical populations.

Putamen Activation Represents an Intrinsic Positive Prediction Error Signal for Visual Search in Repeated Configurations

We investigated fMRI responses to visual search targets appearing at locations that were predicted by the search context. Based on previous work in visual category learning we expected an intrinsic reward prediction error signal in the putamen whenever the target appeared at a location that was predicted with some degree of uncertainty. Comparing target appearance at locations predicted with 50% probability to either locations predicted with 100% probability or unpredicted locations, increased activation was observed in left posterior putamen and adjacent left posterior insula. Thus, our hypothesis of an intrinsic prediction error-like signal was confirmed. This extends the observation of intrinsic prediction error-like signals, driven by intrinsic rather than extrinsic reward, to memory-driven visual search.

Volumetric and shape analysis of the thalamus and striatum in amnestic mild cognitive impairment

Alterations in brain structures, including progressive neurodegeneration, are a hallmark in patients with Alzheimer's disease (AD). However, pathological mechanisms, such as the accumulation of amyloid and the proliferation of tau, are thought to begin years, even decades, before the initial clinical manifestations of AD. In this study, we compare the brain anatomy of amnestic mild cognitive impairment patients (aMCI, n = 16) to healthy subjects (CS, n = 22) using cortical thickness, subcortical volume, and shape analysis, which we believe to be complimentary to volumetric measures. We were able to replicate "classical" cortical thickness alterations in aMCI in the hippocampus, amygdala, putamen, insula, and inferior temporal regions. Additionally, aMCI showed significant thalamic and striatal shape differences. We observed higher global amyloid deposition in aMCI, a significant correlation between striatal displacement and global amyloid, and an inverse correlation between executive function and right-hemispheric thalamic displacement. In contrast, no volumetric differences were detected in thalamic, striatal, and hippocampal regions. Our results provide new evidence for early subcortical neuroanatomical changes in patients with aMCI, which are linked to cognitive abilities and amyloid deposition. Hence, shape analysis may aid in the identification of structural biomarkers for identifying individuals at highest risk of conversion to AD.

Contributions of the hippocampus to feedback learning

Humans learn about the world in a variety of manners, including by observation, by associating cues in the environment, and via feedback. Across species, two brain structures have been predominantly involved in these learning processes: the hippocampus--supporting learning via observation and paired association--and the striatum--critical for feedback learning. This simple dichotomy, however, has recently been challenged by reports of hippocampal engagement in feedback learning, although the role of the hippocampus is not fully understood. The purpose of this experiment was to characterize the hippocampal response during feedback learning by manipulating varying levels of memory interference. Consistent with prior reports, feedback learning recruited the striatum and midbrain. Notably, feedback learning also engaged the hippocampus. The level of activity in these regions was modulated by the degree of memory interference, such that the greatest activation occurred during the highest level of memory interference. Importantly, the accuracy of information learned via feedback correlated with hippocampal activation and was reduced by the presence of high memory interference. Taken together, these findings provide evidence of hippocampal involvement in feedback learning by demonstrating both its relevance for the accuracy of information learned via feedback and its susceptibility to interference.

Multiple stages of learning in perceptual categorization: evidence and neurocomputational theory

Virtually all current theories of category learning assume that humans learn new categories by gradually forming associations directly between stimuli and responses. In information-integration category-learning tasks, this purported process is thought to depend on procedural learning implemented via dopamine-dependent cortical-striatal synaptic plasticity. This article proposes a new, neurobiologically detailed model of procedural category learning that, unlike previous models, does not assume associations are made directly from stimulus to response. Rather, the traditional stimulus-response (S-R) models are replaced with a two-stage learning process. Multiple streams of evidence (behavioral, as well as anatomical and fMRI) are used as inspiration for the new model, which synthesizes evidence of multiple distinct cortical-striatal loops into a neurocomputational theory. An experiment is reported to test a priori predictions of the new model that: (1) recovery from a full reversal should be easier than learning new categories equated for difficulty, and (2) reversal learning in procedural tasks is mediated within the striatum via dopamine-dependent synaptic plasticity. The results confirm the predictions of the new two-stage model and are incompatible with existing S-R models.

A Comparison of the neural correlates that underlie rule-based and information-integration category learning

The influential competition between verbal and implicit systems (COVIS) model proposes that category learning is driven by two competing neural systems-an explicit, verbal, system, and a procedural-based, implicit, system. In the current fMRI study, participants learned either a conjunctive, rule-based (RB), category structure that is believed to engage the explicit system, or an information-integration category structure that is thought to preferentially recruit the implicit system. The RB and information-integration category structures were matched for participant error rate, the number of relevant stimulus dimensions, and category separation. Under these conditions, considerable overlap in brain activation, including the prefrontal cortex, basal ganglia, and the hippocampus, was found between the RB and information-integration category structures. Contrary to the predictions of COVIS, the medial temporal lobes and in particular the hippocampus, key regions for explicit memory, were found to be more active in the information-integration condition than in the RB condition. No regions were more activated in RB than information-integration category learning. The implications of these results for theories of category learning are discussed. Hum Brain Mapp 37:3557-3574, 2016. © 2016 Wiley Periodicals, Inc.

Escalating risk and the moderating effect of resistance to peer influence on the P200 and feedback-related negativity

Young people frequently socialize together in contexts that encourage risky decision making, pointing to a need for research into how susceptibility to peer influence is related to individual differences in the neural processing of decisions during sequentially escalating risk. We applied a novel analytic approach to analyze EEG activity from college-going students while they completed the Balloon Analogue Risk Task (BART), a well-established risk-taking propensity assessment. By modeling outcome-processing-related changes in the P200 and feedback-related negativity (FRN) sequentially within each BART trial as a function of pump order as an index of increasing risk, our results suggest that analyzing the BART in a progressive fashion may provide valuable new insights into the temporal neurophysiological dynamics of risk taking. Our results showed that a P200, localized to the left caudate nucleus, and an FRN, localized to the left dACC, were positively correlated with the level of risk taking and reward. Furthermore, consistent with our hypotheses, the rate of change in the FRN was higher among college students with greater self-reported resistance to peer influence.

Generalization in category learning: the roles of representational and decisional uncertainty

Effective generalization in a multiple-category situation involves both assessing potential membership in individual categories and resolving conflict between categories while implementing a decision bound. We separated generalization from decision bound implementation using an information integration task in which category exemplars varied over two incommensurable feature dimensions. Human subjects first learned to categorize stimuli within limited training regions, and then, during fMRI scanning, they also categorized transfer stimuli from new regions of perceptual space. Transfer stimuli differed both in distance from the training region prototype and distance from the decision bound, allowing us to independently assess neural systems sensitive to each. Across all stimulus regions, categorization was associated with activity in the extrastriate visual cortex, basal ganglia, and the bilateral intraparietal sulcus. Categorizing stimuli near the decision bound was associated with recruitment of the frontoinsular cortex and medial frontal cortex, regions often associated with conflict and which commonly coactivate within the salience network. Generalization was measured in terms of greater distance from the decision bound and greater distance from the category prototype (average training region stimulus). Distance from the decision bound was associated with activity in the superior parietal lobe, lingual gyri, and anterior hippocampus, whereas distance from the prototype was associated with left intraparietal sulcus activity. The results are interpreted as supporting the existence of different uncertainty resolution mechanisms for uncertainty about category membership (representational uncertainty) and uncertainty about decision bound (decisional uncertainty).

Dissociation of category-learning systems via brain potentials

Behavioral, neuropsychological, and neuroimaging evidence has suggested that categories can often be learned via either an explicit rule-based (RB) mechanism critically dependent on medial temporal and prefrontal brain regions, or via an implicit information-integration (II) mechanism relying on the basal ganglia. In this study, participants viewed sine-wave gratings (Gabor patches) that varied on two dimensions and learned to categorize them via trial-by-trial feedback. Two different stimulus distributions were used; one was intended to encourage an explicit RB process and the other an implicit II process. We monitored brain activity with scalp electroencephalography (EEG) while each participant: (1) passively observed stimuli represented of both distributions; (2) categorized stimuli from one distribution, and, 1 week later; (3) categorized stimuli from the other distribution. Categorization accuracy was similar for the two distributions. Subtractions of Event-Related Potentials (ERPs) for correct and incorrect trials were used to identify neural differences in RB and II categorization processes. We identified an occipital brain potential that was differentially modulated by categorization condition accuracy at an early latency (150-250 ms), likely reflecting the degree of holistic processing. A stimulus-locked Late Positive Complex (LPC) associated with explicit memory updating was modulated by accuracy in the RB, but not the II task. Likewise, a feedback-locked P300 ERP associated with expectancy was correlated with performance only in the RB, but not the II condition. These results provide additional evidence for distinct brain mechanisms supporting RB vs. implicit II category learning and use.

Neural networks involved in learning lexical-semantic and syntactic information in a second language

The present study used functional magnetic resonance imaging (fMRI) to investigate the neural correlates of language acquisition in a realistic learning environment. Japanese native speakers were trained in a miniature version of German prior to fMRI scanning. During scanning they listened to (1) familiar sentences, (2) sentences including a novel sentence structure, and (3) sentences containing a novel word while visual context provided referential information. Learning-related decreases of brain activation over time were found in a mainly left-hemispheric network comprising classical frontal and temporal language areas as well as parietal and subcortical regions and were largely overlapping for novel words and the novel sentence structure in initial stages of learning. Differences occurred at later stages of learning during which content-specific activation patterns in prefrontal, parietal and temporal cortices emerged. The results are taken as evidence for a domain-general network supporting the initial stages of language learning which dynamically adapts as learners become proficient.

How may the basal ganglia contribute to auditory categorization and speech perception?

Listeners must accomplish two complementary perceptual feats in extracting a message from speech. They must discriminate linguistically-relevant acoustic variability and generalize across irrelevant variability. Said another way, they must categorize speech. Since the mapping of acoustic variability is language-specific, these categories must be learned from experience. Thus, understanding how, in general, the auditory system acquires and represents categories can inform us about the toolbox of mechanisms available to speech perception. This perspective invites consideration of findings from cognitive neuroscience literatures outside of the speech domain as a means of constraining models of speech perception. Although neurobiological models of speech perception have mainly focused on cerebral cortex, research outside the speech domain is consistent with the possibility of significant subcortical contributions in category learning. Here, we review the functional role of one such structure, the basal ganglia. We examine research from animal electrophysiology, human neuroimaging, and behavior to consider characteristics of basal ganglia processing that may be advantageous for speech category learning. We also present emerging evidence for a direct role for basal ganglia in learning auditory categories in a complex, naturalistic task intended to model the incidental manner in which speech categories are acquired. To conclude, we highlight new research questions that arise in incorporating the broader neuroscience research literature in modeling speech perception, and suggest how understanding contributions of the basal ganglia can inform attempts to optimize training protocols for learning non-native speech categories in adulthood.

Toward a dual-learning systems model of speech category learning

More than two decades of work in vision posits the existence of dual-learning systems of category learning. The reflective system uses working memory to develop and test rules for classifying in an explicit fashion, while the reflexive system operates by implicitly associating perception with actions that lead to reinforcement. Dual-learning systems models hypothesize that in learning natural categories, learners initially use the reflective system and, with practice, transfer control to the reflexive system. The role of reflective and reflexive systems in auditory category learning and more specifically in speech category learning has not been systematically examined. In this article, we describe a neurobiologically constrained dual-learning systems theoretical framework that is currently being developed in speech category learning and review recent applications of this framework. Using behavioral and computational modeling approaches, we provide evidence that speech category learning is predominantly mediated by the reflexive learning system. In one application, we explore the effects of normal aging on non-speech and speech category learning. Prominently, we find a large age-related deficit in speech learning. The computational modeling suggests that older adults are less likely to transition from simple, reflective, unidimensional rules to more complex, reflexive, multi-dimensional rules. In a second application, we summarize a recent study examining auditory category learning in individuals with elevated depressive symptoms. We find a deficit in reflective-optimal and an enhancement in reflexive-optimal auditory category learning. Interestingly, individuals with elevated depressive symptoms also show an advantage in learning speech categories. We end with a brief summary and description of a number of future directions.

Procedural-based category learning in patients with Parkinson's disease: impact of category number and category continuity

Previously we found that Parkinson's disease (PD) patients are impaired in procedural-based category learning when category membership is defined by a nonlinear relationship between stimulus dimensions, but these same patients are normal when the rule is defined by a linear relationship (Maddox and Filoteo, 2001; Filoteo et al., 2005a,b). We suggested that PD patients' impairment was due to a deficit in recruiting “striatal units” to represent complex nonlinear rules. In the present study, we further examined the nature of PD patients' procedural-based deficit in two experiments designed to examine the impact of (1) the number of categories, and (2) category discontinuity on learning. Results indicated that PD patients were impaired only under discontinuous category conditions but were normal when the number of categories was increased from two to four. The lack of impairment in the four-category condition suggests normal integrity of striatal medium spiny cells involved in procedural-based category learning. In contrast, and consistent with our previous observation of a nonlinear deficit, the finding that PD patients were impaired in the discontinuous condition suggests that these patients are impaired when they have to associate perceptually distinct exemplars with the same category. Theoretically, this deficit might be related to dysfunctional communication among medium spiny neurons within the striatum, particularly given that these are cholinergic neurons and a cholinergic deficiency could underlie some of PD patients' cognitive impairment.

The visual corticostriatal loop through the tail of the caudate: circuitry and function

Although high level visual cortex projects to a specific region of the striatum, the tail of the caudate, and participates in corticostriatal loops, the function of this visual corticostriatal system is not well understood. This article first reviews what is known about the anatomy of the visual corticostriatal loop across mammals, including rodents, cats, monkeys, and humans. Like other corticostriatal systems, the visual corticostriatal system includes both closed loop components (recurrent projections that return to the originating cortical location) and open loop components (projections that terminate in other neural regions). The article then reviews what previous empirical research has shown about the function of the tail of the caudate. The article finally addresses the possible functions of the closed and open loop connections of the visual loop in the context of theories and computational models of corticostriatal function.

Coding of level of ambiguity within neural systems mediating choice

Data from previous neuroimaging studies exploring neural activity associated with uncertainty suggest varying levels of activation associated with changing degrees of uncertainty in neural regions that mediate choice behavior. The present study used a novel task that parametrically controlled the amount of information hidden from the subject; levels of uncertainty ranged from full ambiguity (no information about probability of winning) through multiple levels of partial ambiguity, to a condition of risk only (zero ambiguity with full knowledge of the probability of winning). A parametric analysis compared a linear model in which weighting increased as a function of level of ambiguity, and an inverted-U quadratic models in which partial ambiguity conditions were weighted most heavily. Overall we found that risk and all levels of ambiguity recruited a common "fronto-parietal-striatal" network including regions within the dorsolateral prefrontal cortex, intraparietal sulcus, and dorsal striatum. Activation was greatest across these regions and additional anterior and superior prefrontal regions for the quadratic function which most heavily weighs trials with partial ambiguity. These results suggest that the neural regions involved in decision processes do not merely track the absolute degree ambiguity or type of uncertainty (risk vs. ambiguity). Instead, recruitment of prefrontal regions may result from greater degree of difficulty in conditions of partial ambiguity: when information regarding reward probabilities important for decision making is hidden or not easily obtained the subject must engage in a search for tractable information. Additionally, this study identified regions of activity related to the valuation of potential gains associated with stimuli or options (including the orbitofrontal and medial prefrontal cortices and dorsal striatum) and related to winning (including orbitofrontal cortex and ventral striatum).

A matched filter hypothesis for cognitive control

The prefrontal cortex exerts top-down influences on several aspects of higher-order cognition by functioning as a filtering mechanism that biases bottom-up sensory information toward a response that is optimal in context. However, research also indicates that not all aspects of complex cognition benefit from prefrontal regulation. Here we review and synthesize this research with an emphasis on the domains of learning and creative cognition, and outline how the appropriate level of cognitive control in a given situation can vary depending on the organism's goals and the characteristics of the given task. We offer a matched filter hypothesis for cognitive control, which proposes that the optimal level of cognitive control is task-dependent, with high levels of cognitive control best suited to tasks that are explicit, rule-based, verbal or abstract, and can be accomplished given the capacity limits of working memory and with low levels of cognitive control best suited to tasks that are implicit, reward-based, non-verbal or intuitive, and which can be accomplished irrespective of working memory limitations. Our approach promotes a view of cognitive control as a tool adapted to a subset of common challenges, rather than an all-purpose optimization system suited to every problem the organism might encounter.

Hippocampal BOLD response during category learning predicts subsequent performance on transfer generalization

To test a prediction of our previous computational model of cortico-hippocampal interaction (Gluck and Myers [1993, 2001]) for characterizing individual differences in category learning, we studied young healthy subjects using an fMRI-adapted category-learning task that has two phases, an initial phase in which associations are learned through trial-and-error feedback followed by a generalization phase in which previously learned rules can be applied to novel associations (Myers et al. [2003]). As expected by our model, we found a negative correlation between learning-related hippocampal responses and accuracy during transfer, demonstrating that hippocampal adaptation during learning is associated with better behavioral scores during transfer generalization. In addition, we found an inverse relationship between Blood Oxygenation Level Dependent (BOLD) activity in the striatum and that in the hippocampal formation and the orbitofrontal cortex during the initial learning phase. Conversely, activity in the dorsolateral prefrontal cortex, orbitofrontal cortex and parietal lobes dominated over that of the hippocampal formation during the generalization phase. These findings provide evidence in support of theories of the neural substrates of category learning which argue that the hippocampal region plays a critical role during learning for appropriately encoding and representing newly learned information so that that this learning can be successfully applied and generalized to subsequent novel task demands.

The Good, the Bad, or the Useful? The Examination of the Relationship between the Feedback-related Negativity (FRN) and Long-term Learning Outcomes

According to the reinforcement learning account of the error-related negativity (ERN), the ERN is a manifestation of a signal generated in ACC as a consequence of a phasic decrease in the activity of the mesencephalic dopamine system occurring when the monitoring system evaluates events as worse than expected. This signal is also hypothesized to be used to modify behavior to ascertain that future events will have better outcomes. It is therefore expected that this signal be correlated with learning outcomes. We report a study designed to examine the extent to which the ERN is related to learning outcomes within a paired-associates learning task. The feedback-related negativity (FRN) elicited by stimuli that indicated to the participants whether their response was correct or not was examined both according the degree to which the associates were learned in the session and according to whether participants recalled the associations on the next day. The results of the spatio-temporal PCA indicate that, whereas the process giving rise to the negative feedback elicited a FRN whose amplitude was not correlated with long-term learning outcomes, positive feedback was associated with a FRN-like activity, which was correlated with the learning outcomes. Another ERP component that follows the FRN temporally and shares its spatial distribution was found associated with long-term learning outcomes. Our findings shed light on the functional significance of the feedback-related ERP components and are discussed within the framework of the reinforcement learning ERN hypothesis.

The cost of selective attention in category learning: developmental differences between adults and infants

Selective attention plays an important role in category learning. However, immaturities of top-down attentional control during infancy coupled with successful category learning suggest that early category learning is achieved without attending selectively. Research presented here examines this possibility by focusing on category learning in infants (6-8months old) and adults. Participants were trained on a novel visual category. Halfway through the experiment, unbeknownst to participants, the to-be-learned category switched to another category, where previously relevant features became irrelevant and previously irrelevant features became relevant. If participants attend selectively to the relevant features of the first category, they should incur a cost of selective attention immediately after the unknown category switch. Results revealed that adults demonstrated a cost, as evidenced by a decrease in accuracy and response time on test trials as well as a decrease in visual attention to newly relevant features. In contrast, infants did not demonstrate a similar cost of selective attention as adults despite evidence of learning both to-be-learned categories. Findings are discussed as supporting multiple systems of category learning and as suggesting that learning mechanisms engaged by adults may be different from those engaged by infants.

Brain activity across the development of automatic categorization: a comparison of categorization tasks using multi-voxel pattern analysis

Previous evidence suggests that relatively separate neural networks underlie initial learning of rule-based and information-integration categorization tasks. With the development of automaticity, categorization behavior in both tasks becomes increasingly similar and exclusively related to activity in cortical regions. The present study uses multi-voxel pattern analysis to directly compare the development of automaticity in different categorization tasks. Each of the three groups of participants received extensive training in a different categorization task: either an information-integration task, or one of two rule-based tasks. Four training sessions were performed inside an MRI scanner. Three different analyses were performed on the imaging data from a number of regions of interest (ROIs). The common patterns analysis had the goal of revealing ROIs with similar patterns of activation across tasks. The unique patterns analysis had the goal of revealing ROIs with dissimilar patterns of activation across tasks. The representational similarity analysis aimed at exploring (1) the similarity of category representations across ROIs and (2) how those patterns of similarities compared across tasks. The results showed that common patterns of activation were present in motor areas and basal ganglia early in training, but only in the former later on. Unique patterns were found in a variety of cortical and subcortical areas early in training, but they were dramatically reduced with training. Finally, patterns of representational similarity between brain regions became increasingly similar across tasks with the development of automaticity.

Striatal and hippocampal entropy and recognition signals in category learning: simultaneous processes revealed by model-based fMRI

Category learning is a complex phenomenon that engages multiple cognitive processes, many of which occur simultaneously and unfold dynamically over time. For example, as people encounter objects in the world, they simultaneously engage processes to determine their fit with current knowledge structures, gather new information about the objects, and adjust their representations to support behavior in future encounters. Many techniques that are available to understand the neural basis of category learning assume that the multiple processes that subserve it can be neatly separated between different trials of an experiment. Model-based functional magnetic resonance imaging offers a promising tool to separate multiple, simultaneously occurring processes and bring the analysis of neuroimaging data more in line with category learning's dynamic and multifaceted nature. We use model-based imaging to explore the neural basis of recognition and entropy signals in the medial temporal lobe and striatum that are engaged while participants learn to categorize novel stimuli. Consistent with theories suggesting a role for the anterior hippocampus and ventral striatum in motivated learning in response to uncertainty, we find that activation in both regions correlates with a model-based measure of entropy. Simultaneously, separate subregions of the hippocampus and striatum exhibit activation correlated with a model-based recognition strength measure. Our results suggest that model-based analyses are exceptionally useful for extracting information about cognitive processes from neuroimaging data. Models provide a basis for identifying the multiple neural processes that contribute to behavior, and neuroimaging data can provide a powerful test bed for constraining and testing model predictions.

Category learning increases discriminability of relevant object dimensions in visual cortex

Learning to categorize objects can transform how they are perceived, causing relevant perceptual dimensions predictive of object category to become enhanced. For example, an expert mycologist might become attuned to species-specific patterns of spacing between mushroom gills but learn to ignore cap textures attributable to varying environmental conditions. These selective changes in perception can persist beyond the act of categorizing objects and influence our ability to discriminate between them. Using functional magnetic resonance imaging adaptation, we demonstrate that such category-specific perceptual enhancements are associated with changes in the neural discriminability of object representations in visual cortex. Regions within the anterior fusiform gyrus became more sensitive to small variations in shape that were relevant during prior category learning. In addition, extrastriate occipital areas showed heightened sensitivity to small variations in shape that spanned the category boundary. Visual representations in cortex, just like our perception, are sensitive to an object's history of categorization.

Striatal activations signal prediction errors on confidence in the absence of external feedback

Research on the neural bases of learning has mainly focused on reinforcement learning where the central role of the dopaminergic system is well established. However, in everyday life many decisions are not followed by feedback, in which case humans have been shown to code the most probable outcome into memory. We used functional magnetic resonance imaging (fMRI) to examine the neural basis of internally generated signals on correctness and decision confidence in the complete absence of feedback in a categorization task. During test trials after observational training activation in dopaminergic target regions was modulated by the correctness of the answer similarly as during feedback-based training. Moreover, activation in the nucleus accumbens and putamen was correlated with the prediction error on confidence as estimated by a reinforcement learning model. In this model subjective confidence ratings acquired after each trial served as outcome measure. Activation in the striatum therefore follows a similar pattern in response to prediction errors on confidence as it does during reinforcement learning in response to reward prediction errors, but with respect to internally generated signals based on knowledge of the structure of the environment. Furthermore, ventral striatal activation decreased with stimulus novelty, which might support the allocation of attention to unfamiliar stimuli. These results provide a parsimonious account for the neural bases of learning, indicating overlapping neural substrates of reinforcement learning and learning when outcome information has to be internally constructed.