Distributed Neural Systems Support Flexible Attention Updating during Category Learning

Weichart, Emily R.; Evans, Daniel; Galdo, Matthew; Bahg, Giwon; Turner, B. E.

doi:10.1162/jocn_a_01882

Cited by 3 publications

(1 citation statement)

References 117 publications

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“…One computational model we discuss here is the adaptive attention representation model (AARM; Galdo et al, 2022; Turner, 2019; Weichart, Evans, et al, 2022; Weichart, Galdo, et al, 2022), which is derived from other exemplar-based models of categorization (Estes, 1986; Medin & Schaffer, 1978; Nosofsky, 1986). In AARM (as well as other models in this class), attention is represented as a vector containing the amount of attention for each dimension of information.…”

Section: Optimizing For a Learner’s Goalsmentioning

confidence: 99%

Cognitive Inertia: Cyclical Interactions Between Attention and Memory Shape Learning

Turner,

Sloutsky

2024

Curr Dir Psychol Sci

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In explaining how humans selectively attend, common frameworks often focus on how attention is allocated relative to an idealized allocation based on properties of the task. However, these perspectives often ignore different types of constraints that could help explain why attention was allocated in a particular way. For example, many computational models of learning are well equipped to explain how attention should ideally be allocated to minimize errors within the task, but these models often assume all features are perfectly encoded or that the only learning goal is to maximize accuracy. In this article, we argue for a more comprehensive view by using computational modeling to understand the complex interactions that occur between selective attention and memory. Our central thesis is that although selective attention directs attention to relevant dimensions, relevance can be established only through memories of previous experiences. Hence, attention is initially used to encode features and create memories, but thereafter, attention operates selectively on the basis of what is kept in memory. Through this lens, deviations from ideal performance can still be viewed as goal-directed selective attention, but the orientation of attention is subject to the constraints of the individual learner.

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Section: Optimizing For a Learner’s Goalsmentioning

confidence: 99%

Cognitive Inertia: Cyclical Interactions Between Attention and Memory Shape Learning

Turner,

Sloutsky

2024

Curr Dir Psychol Sci

Self Cite

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Dissociable Roles of the Dorsolateral and Ventromedial Prefrontal Cortex in Human Categorization

Broschard,

Turner,

Tranel

et al. 2024

J. Neurosci.

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Models of human categorization predict the prefrontal cortex (PFC) serves a central role in category learning. The dorsolateral prefrontal cortex (dlPFC) and ventromedial prefrontal cortex (vmPFC) have been implicated in categorization; however, it is unclear whether both are critical for categorization and whether they support unique functions. We administered three categorization tasks to patients with PFC lesions (mean age = 69.6 years; 5 men, 5 women) to examine how prefrontal subregions contribute to categorization. These included a rule-based (RB) task that was solved via a unidimensional rule, an information integration (II) task that was solved by combining information from two stimulus dimensions, and a deterministic/probabilistic (DP) task with stimulus features that had varying amounts of category-predictive information. Compared to healthy comparison participants, both patient groups had impaired performance. Impairments in the dlPFC patients were largest during the RB task, whereas impairments in the vmPFC patients were largest during the DP task. A hierarchical model was fit to the participants’ data to assess learning deficits in the patient groups. PFC damage was correlated with a regularization term that limited updates to attention after each trial. Our results suggest that the PFC, as a whole, is important for learning to orient attention to relevant stimulus information. The dlPFC may be especially important for rule-based learning, whereas the vmPFC may be important for focusing attention on deterministic (highly diagnostic) features and ignoring less predictive features. These results support overarching functions of the dlPFC in executive functioning and the vmPFC in value-based decision making.Significance StatementCategory learning creates flexible memory representations that easily generalize to novel situations. Although it is generally established that the prefrontal cortex is central to categorization, it is unclear how different prefrontal subregions contribute to learning. Separate literatures have implicated both the dorsolateral prefrontal cortex (dlPFC) and the ventromedial prefrontal cortex (vmPFC) in categorization, but there has been little effort to bridge these literatures. The current study is the first to examine categorization in patients with lesions centered in the dlPFC and vmPFC. We found that, as a whole, the PFC orients attention to relevant stimulus information. The dlPFC is important for rule-based learning, whereas the vmPFC is important for focusing attention on highly diagnostic features and ignoring less predictive features.

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Strategy Optimization, Behavioral Shifts, and Task Engagement During Rat Category Learning

Broschard,

Kim,

Freeman

2024

Preprint

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Distributed Neural Systems Support Flexible Attention Updating during Category Learning

Cited by 3 publications

References 117 publications

Cognitive Inertia: Cyclical Interactions Between Attention and Memory Shape Learning

Cognitive Inertia: Cyclical Interactions Between Attention and Memory Shape Learning

Dissociable Roles of the Dorsolateral and Ventromedial Prefrontal Cortex in Human Categorization

Strategy Optimization, Behavioral Shifts, and Task Engagement During Rat Category Learning

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