Neural Correlates of Rule-Based and Information-Integration Visual Category Learning

Nomura, Emi; Maddox, W. Todd; Filoteo, J. Vincent; Ing, A. David; Gitelman, Darren R.; Parrish, Todd B.; Mesulam, Marek-Marsel; Reber, Paul J.

doi:10.1093/cercor/bhj122

Cited by 201 publications

(251 citation statements)

References 45 publications

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“…Indeed, the current study extends prior fMRI work implicating HPC processes in classification learning (22,(51)(52)(53) to demonstrate the influence of goal-relevant selective attention in HPC-based conceptual representations. These results add support for the theoretical proposal from the episodic memory literature that the HPC adaptively builds knowledge representations across episodes (11,12).…”

Section: Discussionsupporting

confidence: 73%

Dynamic updating of hippocampal object representations reflects new conceptual knowledge

Mack

Love

Preston

2016

Proc. Natl. Acad. Sci. U.S.A.

207

248

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Concepts organize the relationship among individual stimuli or events by highlighting shared features. Often, new goals require updating conceptual knowledge to reflect relationships based on different goal-relevant features. Here, our aim is to determine how hippocampal (HPC) object representations are organized and updated to reflect changing conceptual knowledge. Participants learned two classification tasks in which successful learning required attention to different stimulus features, thus providing a means to index how representations of individual stimuli are reorganized according to changing task goals. We used a computational learning model to capture how people attended to goal-relevant features and organized object representations based on those features during learning. Using representational similarity analyses of functional magnetic resonance imaging data, we demonstrate that neural representations in left anterior HPC correspond with model predictions of concept organization. Moreover, we show that during early learning, when concept updating is most consequential, HPC is functionally coupled with prefrontal regions. Based on these findings, we propose that when task goals change, object representations in HPC can be organized in new ways, resulting in updated concepts that highlight the features most critical to the new goal.category learning | attention | computational modeling | hippocampus | fMRI C oncepts are organizing principles that define how items or events are similar to one another. Goals are critical to shaping concepts, by emphasizing some shared features over others. When goals change, previously experienced events may be organized in new ways, resulting in an updated concept that highlights the features most critical to the new goal. For instance, consider purchasing a home. One must learn which features make for the most desirable home. A young couple seeking a cosmopolitan lifestyle may organize potential houses based on trendy features like exposed brick walls, a wet bar, and room for vintage record collections. However, with the news of a baby on the way, the couple's goals are likely to shift. After pouring through parenting books and web forums to learn what makes for a child-friendly home, they may look at those previously seen potential homes in a different light. Instead, family-oriented features such as whether or not a home has a bathtub, is within walking distance to a park, and is in a well-respected school district may matter more resulting in a reorganization of which homes are a good buy. At the core of this example are the fundamental challenges we face in flexible goaldirected learning. When learning new concepts (e.g., child-friendly instead of a trendy house), attention changes focus to different information and items that were conceptually dissimilar (e.g., two houses with and without a wet bar) may become more similar (e.g., they both are close to a park) and vice versa (1). Understanding how conceptual knowledge is created and updated during learning is a cen...

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Section: Discussionsupporting

confidence: 73%

Dynamic updating of hippocampal object representations reflects new conceptual knowledge

Mack

Love

Preston

2016

Proc. Natl. Acad. Sci. U.S.A.

207

248

View full text Add to dashboard Cite

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“…For example, it is clear that aspects of category learning are mediated by a hippocampal learning system (Foerde, Knowlton, & Poldrack, 2006;Love & Gureckis, 2007), and we have shown that this system may be involved in certain aspects of rule learning (Nomura et al, 2007). However, the present experiment was designed to differentially tap the rule and procedural systems.…”

Section: Discussionmentioning

confidence: 92%

“…The rule and procedural systems rely on distinct neural substrates. The rule system is implemented by a circuit involving dorsolateral prefrontal cortex, anterior cingulate and the head of the caudate nucleus (Ashby & Maddox, 2005;Filoteo et al, 2005;Love & Gureckis, 2007;Seger & Cincotta, 2005, whereas the procedural system is implemented by a circuit involving inferotemporal cortex and the posterior caudate nucleus (Ashby et al, 1998;Nomura et al, 2007;Seger & Cincotta, 2005;Wilson, 1995).…”

Section: Introductionmentioning

confidence: 99%

When more is less: Feedback effects in perceptual category learning

et al. 2008

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Rule-based and information-integration category learning were compared under minimal and full feedback conditions. Rule-based category structures are those for which the optimal rule is verbalizable. Information-integration category structures are those for which the optimal rule is not verbalizable. With minimal feedback subjects are told whether their response was correct or incorrect, but are not informed of the correct category assignment. With full feedback subjects are informed of the correctness of their response and are also informed of the correct category assignment. An examination of the distinct neural circuits that subserve rule-based and information-integration category learning leads to the counterintuitive prediction that full feedback should facilitate rulebased learning but should also hinder information-integration learning. This prediction was supported in the experiment reported below. The implications of these results for theories of learning are discussed.

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“…First, a recent II neuroimaging study failed to report taskrelated activation in the putamen (Nomura et al, 2007). Instead, significant striatal activation was limited to the caudate nucleus.…”

Section: Discussionmentioning

confidence: 99%

“…For example, a number of studies have reported that patients with basal ganglia dysfunction are impaired in II tasks (Filoteo, Maddox, & Davis, 2001;Filoteo, Maddox, Salmon, & Song, 2005) and neuroimaging studies of II learning have reported significant learning-related striatal activation (Cincotta & Seger, 2007;DeGutis & D'Esposito, 2007;Nomura et al, 2007;Seger & Cincotta, 2002). In addition, a large literature implicates the striatum in visual discrimination learning and category learning in nonhuman animals (for a review, see Ashby & Ennis, 2006).…”

Section: Introductionmentioning

confidence: 99%

Response processes in information–integration category learning

Spiering¹,

Ashby²

2008

Neurobiology of Learning and Memory

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Much recent evidence suggests that human category learning is mediated by multiple systems. Evidence suggests that at least one of these depends on procedural learning within the basal ganglia. Information-integration categorization tasks are thought to load heavily on this procedural learning system. The results of several previous studies were interpreted to suggest that response positions are learned in information-integration tasks. This hypothesis was tested in two experiments. Experiment 1 showed that information-integration category learning was slowed but not disrupted when the spatial location of the responses varied randomly across trials. Experiment 2 showed that information-integration learning was impaired if category membership was signaled by responding to a yes/no question and the category label had no consistent spatial location. These results suggest that information-integration category learning does not require consistent response locations. In these experiments, a consistent association between a category and a response feature was sufficient. The implication of these results for the neurobiology of information-integration category learning is discussed.

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Neural Correlates of Rule-Based and Information-Integration Visual Category Learning

Cited by 201 publications

References 45 publications

Dynamic updating of hippocampal object representations reflects new conceptual knowledge

Dynamic updating of hippocampal object representations reflects new conceptual knowledge

When more is less: Feedback effects in perceptual category learning

Response processes in information–integration category learning

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