The global landscape of cognition: hierarchical aggregation as an organizational principle of human cortical networks and functions

By P. Taylor, J. Nicholas Hobbs, Javier Burroni, Hava T. Siegelmann. 2015

118 citations recorded by the source corpus.

Though widely hypothesized, limited evidence exists that human brain functions organize in global gradients of abstraction starting from sensory cortical inputs. Hierarchical representation is accepted in computational networks and tentatively in visual neuroscience, yet no direct holistic demonstrations exist in vivo. Our methods developed network models enriched with tiered directionality, by including input locations, a critical feature for localizing representation in networks generally. Grouped primary sensory cortices defined network inputs, displaying global connectivity to fused inputs. Depth-oriented networks guided analyses of fMRI databases (~17,000 experiments;~1/4 of fMRI literature). Formally, we tested whether network depth predicted localization of abstract versus concrete behaviors over the whole set of studied brain regions. For our results, new cortical graph metrics, termed network-depth, ranked all databased cognitive function activations by network-depth. Thus, we objectively sorted stratified landscapes of cognition, starting from grouped sensory inputs in parallel, progressing deeper into cortex. This exposed escalating amalgamation of function or abstraction with increasing network-depth, globally. Nearly 500 new participants confirmed our results. In conclusion, data-driven analyses defined a hierarchically ordered connectome, revealing a related continuum of cognitive function. Progressive functional abstraction over network depth may be a fundamental feature of brains and is observed in artificial networks.

Subfields: Cognitive Neuroscience, Neuroscience, Cognitive Science & Psychology

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Citations in the corpus

Continual Lifelong Learning with Neural Networks: A Review (2018, background): “In a hierarchical processing regime, neural detectors have increasingly large spatio-temporal receptive fields to encode information over larger spatial and temporal scales (Taylor et al., 2015; Hasson et al., 2008).”
Lifelong learning of human actions with deep neural network self-organization (2017, background): “A hierarchical organization is consistent with neurophysiological evidence for increasingly large spatiotemporal receptive windows in the human cortex (Taylor et al., 2015; Hasson et al., 2008; Lerner et al., 2011), where simple features manifest in 250”
Emergence of multimodal action representations from neural network self-organization (2017, background): “This is consistent with neurophysiological evidence supporting increasingly large temporal receptive windows in the mammalian cortex (Hasson et al., 2008; Lerner et al., 2011; Taylor et al., 2015), where neurons in higher areas encode information accumulated over longer timescales.”
Multimodal Language Analysis with Recurrent Multistage Fusion (2018, background): “Recent advances in cognitive neuroscience have demonstrated the existence of multistage aggregation across human cortical networks and functions (Taylor et al., 2015), particularly during the integration of multisensory information (Parisi et al., 2017).”
Lifelong Learning of Action Representations with Deep Neural Self-Organization (2017, result): “This processing scheme is in agreement with neurophysiological studies supporting the presence of functional hierarchies with increasingly large spatial and temporal receptive fields along cortical pathways (Taylor et al. 2015; Hasson et al. 2008).”
Large-Scale Gradients in Human Cortical Organization. (2018, unknown): “When selecting seeds in the different primary sensorimotor areas, gradients of decreasing connectivity strength from each of these seeds converge in multimodal integration areas, such as the anterior cingulate cortex and the occipitotemporal junction, and finally reach transmodal regions of the defa”
Hierarchical consciousness: the Nested Observer Windows model (2024, background): “…observer window is depicted as progressively increased opacity for each lower level and for the arrow toward it influence over one or more systems within the same spatiotemporal scale (Felleman and Van Essen 1991, Riesenhuber and Poggio 1999, Luria 2012, Murray et al. 2014, Taylor et al. 2015).”