scholarly journals Categorical Decision Making and Category Learning in Parietal and Prefrontal Cortices

2016 ◽  
Vol 16 (12) ◽  
pp. 1301
Author(s):  
David Freedman
Keyword(s):  
Decision Making ◽  
Category Learning ◽  
Prefrontal Cortices

scholarly journals Thalamic afferents to prefrontal cortices from ventral motor nuclei in decision‐making

2018 ◽  
Vol 49 (5) ◽  
pp. 646-657 ◽  
Author(s):  
Bianca Sieveritz ◽  
Marianela García‐Muñoz ◽  
Gordon W. Arbuthnott
Keyword(s):  
Decision Making ◽  
Motor Nuclei ◽  
Prefrontal Cortices

scholarly journals A Canonical Laminar Neocortical Circuit Whose Bottom-Up, Horizontal, and Top-Down Pathways Control Attention, Learning, and Prediction

2021 ◽  
Vol 15 ◽  
Author(s):  
Stephen Grossberg
Keyword(s):  
Decision Making ◽  
Working Memory ◽  
Cerebral Cortex ◽  
Speech Perception ◽  
Category Learning ◽  
Top Down ◽  
Human Cerebral Cortex ◽  
Bottom Up ◽  
3D Vision

All perceptual and cognitive circuits in the human cerebral cortex are organized into layers. Specializations of a canonical laminar network of bottom-up, horizontal, and top-down pathways carry out multiple kinds of biological intelligence across different neocortical areas. This article describes what this canonical network is and notes that it can support processes as different as 3D vision and figure-ground perception; attentive category learning and decision-making; speech perception; and cognitive working memory (WM), planning, and prediction. These processes take place within and between multiple parallel cortical streams that obey computationally complementary laws. The interstream interactions that are needed to overcome these complementary deficiencies mix cell properties so thoroughly that some authors have noted the difficulty of determining what exactly constitutes a cortical stream and the differences between streams. The models summarized herein explain how these complementary properties arise, and how their interstream interactions overcome their computational deficiencies to support effective goal-oriented behaviors.

scholarly journals Dynamic and causal contribution of parietal circuits to perceptual decisions during category learning

2018 ◽  
Author(s):  
Lin Zhong ◽  
Yuan Zhang ◽  
Chunyu A. Duan ◽  
Jingwei Pan ◽  
Ning-long Xu
Keyword(s):  
Decision Making ◽  
Category Learning ◽  
Posterior Parietal Cortex ◽  
Causal Link ◽  
Sensory Stimuli ◽  
Neuronal Populations ◽  
Decision Performance ◽  
Posterior Parietal

AbstractMaking perceptual decisions to categorize unknown sensory stimuli is a fundamental cognitive function, known as category learning. The posterior parietal cortex (PPC), although has been intensively studied for its role in decision-making and other cognitive functions, its causal link with behavior remains controversial. Here we combine in vivo two-photon imaging, circuit manipulation and auditory psychophysics behavior in mice to probe the role of PPC and its connectivity with sensory cortex in decision-making during category learning. We show that PPC neuronal populations exhibit coding dynamics characteristic of category learning, showing representations for both new sensory stimuli and prior learned categories. Circuit-specific perturbations of PPC and its projections to auditory cortex impaired decision performance specifically for categorizing new auditory stimuli. These data reveal a dynamic and causal role of the parietal-auditory circuit in decision-making, integrating prior knowledge to guide categorical decisions on new sensory stimuli.

scholarly journals One-shot category learning in human infants

2019 ◽  
Author(s):  
Vladislav Ayzenberg ◽  
Stella F. Lourenco
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Category Learning ◽  
State Of The Art ◽  
Human Learning ◽  
Skeletal Structure ◽  
Human Infants ◽  
Object Structure ◽  
Object Categories ◽  
Object Experience

State-of-the-art artificial neural networks (ANNs) require enormous amounts of data to learn object categories. By contrast, human learning is fast and efficient. Most impressive is our capacity for ‘one-shot learning’, in which experience with a single exemplar permits inferences about a larger class of objects. This remarkable feat of categorization is integral to decision making but, surprisingly, remains poorly understood. Here we tested whether invariant object structure—namely, an object’s internal skeleton—supports one-shot category learning in human infants, a population with limited object experience and language. Across two experiments, 6- to 12-month-olds (Mage = 9.29 months; N = 82) were habituated to a single, never-before-seen object. They were then tested with objects that differed from the habituated object in their external features and either matched or mismatched in their skeletal structure. We found that infants only dishabituated to objects with different skeletons, as predicted if objects with the same skeleton belonged to the same class of objects. By contrast, two different ANN architectures (AlexNet and ResNet-50), trained with millions of either curated (ImageNet) or variable (Stylized-ImageNet) images, failed to categorize objects under the same conditions. Taken together, these findings suggest that single exemplar categorization reflects an early-developing sensitivity of the human visual system to perceptually invariant object structure.

scholarly journals Identifying the neural dynamics of category decisions with computational model-based fMRI

2020 ◽  
Author(s):  
Emily Heffernan ◽  
Juliana Daphne Adema ◽  
Michael Louis Mack
Keyword(s):  
Decision Making ◽  
Category Learning ◽  
Computational Models ◽  
Brain Regions ◽  
Neural Dynamics ◽  
Neural Activation ◽  
Neural Signals ◽  
Neural Representations ◽  
Model Predictions ◽  
The Brain

Successful categorization requires a careful coordination of attention, representation, and decision making. Comprehensive theories that span levels of analysis are key to understanding the computational and neural dynamics of categorization. Here, we build on recent work linking neural representations of category learning to computational models to investigate how category decision making is driven by neural signals across the brain. We uniquely combine functional magnetic resonance imaging with drift diffusion and exemplar-based categorization models to show that trial-by-trial fluctuations in neural activation from regions of occipital, cingulate, and lateral prefrontal cortices are linked to category decisions. Notably, only lateral prefrontal cortex activation was associated with exemplar-based model predictions of trial-by-trial category evidence. We propose that these brain regions underlie distinct functions that contribute to successful category learning.

scholarly journals Desirability, availability, credit assignment, category learning, and attention: Cognitive-emotional and working memory dynamics of orbitofrontal, ventrolateral, and dorsolateral prefrontal cortices

2018 ◽  
Vol 2 ◽  
pp. 239821281877217 ◽  
Author(s):  
Stephen Grossberg
Keyword(s):  
Working Memory ◽  
Category Learning ◽  
Posterior Parietal Cortex ◽  
Spatial Working Memory ◽  
Temporal Cortex ◽  
Brain Regions ◽  
Credit Assignment ◽  
Temporal Area ◽  
Dorsolateral Prefrontal ◽  
Prefrontal Cortices

Background: The prefrontal cortices play an essential role in cognitive-emotional and working memory processes through interactions with multiple brain regions. Methods: This article further develops a unified neural architecture that explains many recent and classical data about prefrontal function and makes testable predictions. Results: Prefrontal properties of desirability, availability, credit assignment, category learning, and feature-based attention are explained. These properties arise through interactions of orbitofrontal, ventrolateral prefrontal, and dorsolateral prefrontal cortices with the inferotemporal cortex, perirhinal cortex, parahippocampal cortices; ventral bank of the principal sulcus, ventral prearcuate gyrus, frontal eye fields, hippocampus, amygdala, basal ganglia, hypothalamus, and visual cortical areas V1, V2, V3A, V4, middle temporal cortex, medial superior temporal area, lateral intraparietal cortex, and posterior parietal cortex. Model explanations also include how the value of visual objects and events is computed, which objects and events cause desired consequences and which may be ignored as predictively irrelevant, and how to plan and act to realise these consequences, including how to selectively filter expected versus unexpected events, leading to movements towards, and conscious perception of, expected events. Modelled processes include reinforcement learning and incentive motivational learning; object and spatial working memory dynamics; and category learning, including the learning of object categories, value categories, object-value categories, and sequence categories, or list chunks. Conclusion: This article hereby proposes a unified neural theory of prefrontal cortex and its functions.

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