Theories and tools to measure the efficiency of neural codes have been important in understanding neural responses to external stimuli. However, similar principles to describe the efficiency of brain responses to tasks demanding higher-order cognitive processes remain underdeveloped. A key domain to study such efficiency is learning, where patterns of activity across the entire brain provide insight into the principles governing the acquisition of knowledge about objects or concepts. We propose a mathematical framework for studying the efficiency of spatially embedded whole-brain states reflected in functional MRI , and demonstrate its utility in describing how human subjects learn the values of novel objects . We find that compared to slow learners, quick learners use a smaller ambient embedding to simultaneously encode higher dimensional patterns of neural responses to stimuli, providing a notion of effective codingmost associated with rapid learning. Furthermore, we identify which regions form the strongest neurophysiological drivers of these differences in learning rate, and complement our region-based approach with a voxel-level approach to uncover structure in finer-scale responses. Finally, for a full investigation of complementary geometric approaches, we verify that quick learners develop more assortative responses to stimuli: that is, whole-brain responses that are more easily distinguishable from one another. Our work offers a suite of geometric measures to represent a notion of efficient coding for higher-order cognitive processes, and provide insight into the dimensionality of neural responses characteristic of the successful optimization of reward, that are applicable to the study of cognitive performance more broadly.
A fundamental cognitive process is the ability to map objects into meaningful categories depending on the task at hand. Exactly how such mental constructs emerge and what kind of space best embeds this mapping remains incompletely understood. Here we develop tools to quantify the space URLanization of such a mapping, thereby providing a framework for studying the geometric representations of neural responses as reflected in functional MRI . Considering how human subjects learn the values of novel objects , we show that quick learners have a higher dimensional geometric representation than slow learners, and hence more easily distinguishable whole-brain responses to objects of different value. Furthermore, we find that quick learners display a more compact embedding of the task-relevant information, where the ratio of task-relevant dimension to embedding dimension is consistent with a greater efficiency of cognitive coding. Lastly, we investigate the neurophysiological drivers of high dimensional patterns at both regional and voxel levels, and complete our study with a complementary test of the distinguishability of associated whole-brain responses . Our results demonstrate a URLanization of neural responses characteristic of learning, and offer a suite of geometric measures applicable to the study of efficient coding in higher-order cognitive processes more broadly.
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Theories and tools to measure the efficiency of neural codes have been important in understanding neural responses to external stimuli. However, similar principles to describe the efficiency of brain responses to tasks demanding higher-order cognitive processes remain underdeveloped. A key domain to study such efficiency is learning, where patterns of activity across the entire brain provide insight into the principles governing the acquisition of knowledge about objects or concepts. We propose a mathematical
A fundamental cognitive process is the ability to map objects into meaningful categories depending on the task at hand. Exactly how such mental constructs emerge and what kind of space best embeds this mapping remains incompletely understood. Here we develop tools to quantify the space URLanization of such a mapping, thereby providing a
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efficiency of spatially embedded whole-brain states
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, and demonstrate its utility in describing
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. We find that compared to
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quick learners use a smaller ambient embedding to simultaneously encode higher dimensional patterns of neural responses to stimuli, providing a notion of effective codingmost associated with rapid learning. Furthermore, we identify which regions form the strongest
and hence more easily distinguishable whole-brain responses to objects of different value. Furthermore, we find that quick learners display a more compact embedding of the task-relevant information, where the ratio of task-relevant dimension to embedding dimension is consistent with a greater efficiency of cognitive coding. Lastly, we investigate the
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these differences in learning rate, and complement our region-based approach with a voxel-level approach to uncover structure in finer-scale responses. Finally, for a full investigation of complementary geometric approaches, we verify that quick learners develop more assortative responses to stimuli: that is,
high dimensional patterns at both regional and voxel levels, and complete our study with a complementary test of the distinguishability of associated
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that are more easily distinguishable from one another. Our work offers a suite of geometric measures to represent a notion of efficient coding for higher-order cognitive processes, and provide insight into the dimensionality of
. Our results demonstrate a URLanization of
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the successful optimization of reward, that are
learning, and offer a suite of geometric measures
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cognitive performance
efficient coding in higher-order cognitive processes