The ability to form relational categories for objects that share few features in common is a hall... more The ability to form relational categories for objects that share few features in common is a hallmark of human cognition. However until recently, neuroimaging research largely focused solely on how people acquire categories defined by features. In the current electroencephalography (EEG) study, we examine how relational and feature-based category learning compare in well-matched learning tasks. Building on a previous functional magnetic resonance imaging study by our laboratory, we capitalise on the rich temporal information offered by EEG. Focusing on the neural dynamics of how people learn category memberships over individual trials in an experimental task, we investigate how these single trial dynamics modulate computational estimates from decision-making modelling frameworks. Specifically, by sorting participants' individual trials by their position in the experimental sequence we observe striking relationships between EEG dynamics (e.g., frontal theta oscillations and P300 component) and feature-based and relational categorisation behaviour.
The ability to form relational categories for objects that share few features in common is a hall... more The ability to form relational categories for objects that share few features in common is a hallmark of human cognition. However until recently, neuroimaging research largely focused solely on how people acquire categories defined by features. In the current electroencephalography (EEG) study, we examine how relational and feature-based category learning compare in well-matched learning tasks. Building on a previous functional magnetic resonance imaging study by our laboratory, we capitalise on the rich temporal information offered by EEG. Focusing on the neural dynamics of how people learn category memberships over individual trials in an experimental task, we investigate how these single trial dynamics modulate computational estimates from decision-making modelling frameworks. Specifically, by sorting participants’ individual trials by their position in the experimental sequence we observe striking relationships between EEG dynamics (e.g., frontal theta oscillations and P300 comp...
Cerebral cortex (New York, N.Y. : 1991), Apr 29, 2016
The ability to form relational categories for objects that share few features in common is a hall... more The ability to form relational categories for objects that share few features in common is a hallmark of human cognition. For example, anything that can play a preventative role, from a boulder to poverty, can be a "barrier." However, neurobiological research has focused solely on how people acquire categories defined by features. The present functional magnetic resonance imaging study examines how relational and feature-based category learning compare in well-matched learning tasks. Using a computational model-based approach, we observed a cluster in left rostrolateral prefrontal cortex (rlPFC) that tracked quantitative predictions for the representational distance between test and training examples during relational categorization. Contrastingly, medial and dorsal PFC exhibited graded activation that tracked decision evidence during both feature-based and relational categorization. The results suggest that rlPFC computes an alignment signal that is critical for integrati...
The ability to form relational categories for objects that share few features in common is a hall... more The ability to form relational categories for objects that share few features in common is a hallmark of human cognition. However until recently, neuroimaging research largely focused solely on how people acquire categories defined by features. In the current electroencephalography (EEG) study, we examine how relational and feature-based category learning compare in well-matched learning tasks. Building on a previous functional magnetic resonance imaging study by our laboratory, we capitalise on the rich temporal information offered by EEG. Focusing on the neural dynamics of how people learn category memberships over individual trials in an experimental task, we investigate how these single trial dynamics modulate computational estimates from decision-making modelling frameworks. Specifically, by sorting participants' individual trials by their position in the experimental sequence we observe striking relationships between EEG dynamics (e.g., frontal theta oscillations and P300 component) and feature-based and relational categorisation behaviour.
The ability to form relational categories for objects that share few features in common is a hall... more The ability to form relational categories for objects that share few features in common is a hallmark of human cognition. However until recently, neuroimaging research largely focused solely on how people acquire categories defined by features. In the current electroencephalography (EEG) study, we examine how relational and feature-based category learning compare in well-matched learning tasks. Building on a previous functional magnetic resonance imaging study by our laboratory, we capitalise on the rich temporal information offered by EEG. Focusing on the neural dynamics of how people learn category memberships over individual trials in an experimental task, we investigate how these single trial dynamics modulate computational estimates from decision-making modelling frameworks. Specifically, by sorting participants’ individual trials by their position in the experimental sequence we observe striking relationships between EEG dynamics (e.g., frontal theta oscillations and P300 comp...
Cerebral cortex (New York, N.Y. : 1991), Apr 29, 2016
The ability to form relational categories for objects that share few features in common is a hall... more The ability to form relational categories for objects that share few features in common is a hallmark of human cognition. For example, anything that can play a preventative role, from a boulder to poverty, can be a "barrier." However, neurobiological research has focused solely on how people acquire categories defined by features. The present functional magnetic resonance imaging study examines how relational and feature-based category learning compare in well-matched learning tasks. Using a computational model-based approach, we observed a cluster in left rostrolateral prefrontal cortex (rlPFC) that tracked quantitative predictions for the representational distance between test and training examples during relational categorization. Contrastingly, medial and dorsal PFC exhibited graded activation that tracked decision evidence during both feature-based and relational categorization. The results suggest that rlPFC computes an alignment signal that is critical for integrati...
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Papers by Josue Giron