Cortical and subcortical brain regions involved in rule-based category learning

Filoteo, J. Vincent; Maddox, CA W. Todd; Simmons, Alan N.; Ing, A. David; Cagigas, Xavier E.; Matthews, Scott C.; Paulus, Martin P.

doi:10.1097/00001756-200502080-00007

Cited by 76 publications

(74 citation statements)

References 15 publications

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“…Contrasting mean BOLD activation on test trials composed of previously unseen I.PCo/I.PRo stimulus combinations with previously learned pairs revealed activation in a fronto-parietal network associated with rule-based category learning (Filoteo et al, 2005;Seger and Cincotta, 2006, Soto et al, 2013) and decisional uncertainty (DeGuits and D'Esposito, 2007Seger et al, 2015;Davis et al, 2017). This novelty-sensitive network included clusters in dorsomedial PFC, dorsolateral PFC, and superior parietal lobule.…”

Section: Cc-by-nc-ndmentioning

confidence: 99%

Model-based fMRI Reveals Dissimilarity Processes Underlying Base Rate Neglect

O’Bryan

Worthy

Livesey

et al. 2017

Preprint

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The inverse base-rate effect (IBRE) describes an apparent irrationality in human decision making whereby people tend to ignore category base rates and choose rarer options when classifying ambiguous stimuli. According to associative learning theories, people choose rare categories for ambiguous stimuli because rare cues draw more attention. Alternatively, inferential theories predict that people choose rare categories because ambiguous stimuli contrast more with well-established rules that predict membership in the common category. In this experiment, we used multi-voxel pattern analysis to decode which features of ambiguous stimuli, common or rare, participants are activating during an fMRI version of the IBRE task. Participants learned to predict four hypothetical diseases based on pairings of face, object, and scene cues. Objects and scenes were diagnostic cues, and predicted either a common or rare disease. Prior to the task, we collected independent localizer scans to characterize activation patterns associated with each of the different visual cues in object-selective cortex. During a test phase, ambiguous trials were presented in which a cue for the rare disease was paired with a cue for the common disease. We show that individuals engage qualitatively distinct neural processes when making rare versus common responses: choosing the rare category involved activation of cues associated with the common category. Consistent with inferential theories of base-rate neglect, our findings suggest that this surprising behavior involves a deliberative mechanism not explained by purely associative models.

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Section: Cc-by-nc-ndmentioning

confidence: 99%

Model-based fMRI Reveals Dissimilarity Processes Underlying Base Rate Neglect

O’Bryan

Worthy

Livesey

et al. 2017

Preprint

View full text Add to dashboard Cite

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“…Dopamine neurons also code for the degree of uncertainty or risk about the predicted reward during the time period preceding the reward (Fiorillo et al, 2003). categorization tasks (Cincotta & Seger, 2007;Filoteo et al, 2005;Seger & Cincotta, 2005, 2006Tricomi et al, 2006). Parkinson's disease, which particularly affects the head of the caudate (Dauer & Przedborski, 2003), impairs learning via feedback but not learning via observation , Smith & McDowall, 2006.…”

Section: Feedback Processing In the Executive And Motivational Loopsmentioning

confidence: 99%

How do the basal ganglia contribute to categorization? Their roles in generalization, response selection, and learning via feedback

Seger

2008

Neuroscience & Biobehavioral Reviews

265

252

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This article examines how independent corticostriatal loops linking basal ganglia with cerebral cortex contribute to visual categorization. The first aspect of categorization discussed is the role of the visual corticostriatal loop, which connects the visual cortex and the body/tail of the caudate, in mapping visual stimuli to categories, including evaluating the degree to which this loop may generalize across individual category members. The second aspect of categorization discussed is the selection of appropriate actions or behaviors on the basis of category membership, and the role of the visual corticostriatal loop output and the motor corticostriatal loop, which connects motor planning areas with the putamen, in action selection. The third aspect of categorization discussed is how categories are learned with the aid of feedback linked dopaminergic projections to the basal ganglia. These projections underlie corticostriatal synaptic plasticity across the basal ganglia, and also serve as input to the executive and motivational corticostriatal loops that play a role in strategic use of feedback.Categorization of people (friend or foe), objects (food or nonfood), and environments (dangerous or safe) is vital for survival in the world. The process of categorization involves both knowledge of category structure and linkage of category membership to behavior. Categorical knowledge must be sufficient to allow the organism to correctly classify each member. Categorization requires an appropriate level of generalization: generalization needs to be sufficient to correctly identify category members that are encountered for the first time, but limited so that nonmembers are not included. Categorization processes must also link category members to appropriate behaviors. For example, take the problem of identifying whether a fruit is good to eat. The organism must have a category of edible fruit acquired from past experience (e.g., the blackberries consumed last summer), and will ideally generalize so that related items are also considered edible (e.g., berries that somewhat differ in size or color), but not over generalize to fruits that are sufficiently novel that they might not be edible (e.g., holly berries). Then the categories must be linked to appropriate behaviors (e.g., ingestion of the berries categorized as safe, and avoidance of the others).To learn new categories, there must be plasticity that allows learning of both representations (acquiring new categories, extending or tuning already acquired categories), and new links between categories and behaviors (both learning new behaviors, and extending previously learned behaviors to new categories). Traditional cognitive psychology approaches to categorization have emphasized the how category structure is represented and learned. This approach has led to a rich literature examining learning of many different forms of category

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“…In addition to the uncertainty about the encoding of negative versus positive prediction errors, there is also uncertainty about the precise regions in which activity correlates with prediction errors: Brain regions in which activity correlates with prediction errors have been reported in the ventral striatum (nucleus accumbens and/or putamen; Abler et al, 2006;Knutson & Wimmer, 2007;McClure et al, 2003;O'Doherty et al, 2004;Rolls et al, 2008;Seymour et al, 2007;Seymour et al, 2004), the dorsal striatum (Delgado, Miller, Inati, & Phelps, 2005;Filoteo et al, 2005;Haruno & Kawato, 2006), regions of the prefrontal cortex, including orbitofrontal, ventrolateral, and dorsolateral (M. X Cohen, 2007;O'Doherty, Dayan, et al, 2003;Ramnani, Elliott, Athwal, & Passingham, 2004;Rolls et al, 2008), the midbrain (Aron et al, 2004;Murray et al, 2008), and the cerebellum (O'Doherty, Dayan, et al, 2003). One possibility is that prediction errors are signaled to different networks in the brain depending on the task (i.e., how those prediction errors are used); another possibility is that anatomical differences across studies are related to intersubject and interstudy variability and statistical thresholding.…”

Section: Neural Activity Correlates With Components Of Computational mentioning

confidence: 99%

Neurocomputational mechanisms of reinforcement-guided learning in humans: A review

Cohen

2008

Cognitive, Affective, & Behavioral Neuroscience

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Our world is filled with uncertainty and limited resources. Nearly every decision we face is clouded in uncertainty-uncertainty about the consequences of our decisions, uncertainty about whether others will obtain resources before we do, uncertainty about how different individuals will respond in similar contexts. Fortunately, we are often faced with the same or similar decision problems repeatedly, providing the opportunity to learn from our previous decisions and outcomes and to dynamically adapt decision-making strategies. The ability to use reinforcements flexibly to modify goal-directed behavior in the service of maximizing rewards and minimizing punishments is termed reinforcement learning. Theories and computational models of reinforcement learning provide a biologically and mathematically grounded framework within which to characterize, predict, and understand the b behavioral, cognitive, and neural bases of reward-guided decision making. The brain is highly adept at reinforcement learning, and although the neural processes that support this adaptation are being discovered, much remains unknown about the mechanisms by which rewards and punishments are used by the brain to optimize and guide decision making. Recent research in neuroscience and computational modeling suggests that theories of reinforcement learning provide a useful framework within which to study the behavioral and neural mechanisms of reward-based learning and decision making Camerer, 2003;Dayan & Balleine, 2002;Egelman, Person, & Montague, 1998;Montague & Berns, 2002;O'Doherty, Hampton, & Kim, 2007;Schultz, Dayan, & Montague, 1997). This literature seems to be in its infancy and yet is making noteworthy strides in chart acterizing the brain systems and neural computations that support reinforcement learning.Imaging and single-unit recording studies have demonstrated that reinforcements elicit increases in neural activity in several brain regions-notably, those in midbrain-striatal-frontal cortical circuits. Dopamine appears to be critically involved in such processes, although its precise function is still debated (Berridge, 2007;Montague, Hyman, & Cohen, 2004;Redgrave & Gurney, 2006;Schultz, 1998). Generally, changes in brain activity strongly correlate with reinforcements and their predictors, with some regions exhibiting increases in activity following rewards, as compared with losses, and some regions exhibiting increases in activity following losses, as compared with rewards. One might ask the simple question, why? Why should these brain circuits "care about" reinforcements? If the reinforcement already occurred, why continue processing and evaluatr ing its relative reward value after it has been consumed or received? The straightforward and intuitive answer is that reinforcements are useful for guiding future decisions and t actions. That is, reinforcements provide information that can be used to adjust behavior to maximize future rewards f when similar conditions or decision problems arise. Most of the neuroscience studies of rein...

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Cortical and subcortical brain regions involved in rule-based category learning

Cited by 76 publications

References 15 publications

Model-based fMRI Reveals Dissimilarity Processes Underlying Base Rate Neglect

Model-based fMRI Reveals Dissimilarity Processes Underlying Base Rate Neglect

How do the basal ganglia contribute to categorization? Their roles in generalization, response selection, and learning via feedback

Neurocomputational mechanisms of reinforcement-guided learning in humans: A review

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