A Hierarchy of Temporal Receptive Windows in Human Cortex

By Uri Hasson, Eunice Yang, Ignacio Vallines, David J. Heeger, N. Rubin. 2008

855 citations recorded by the source corpus.

Real-world events unfold at different time scales and, therefore, cognitive and neuronal processes must likewise occur at different time scales. We present a novel procedure that identifies brain regions responsive to sensory information accumulated over different time scales. We measured functional magnetic resonance imaging activity while observers viewed silent films presented forward, backward, or piecewise-scrambled in time. Early visual areas (e.g., primary visual cortex and the motion-sensitive area MT+) exhibited high response reliability regardless of disruptions in temporal structure. In contrast, the reliability of responses in several higher brain areas, including the superior temporal sulcus (STS), precuneus, posterior lateral sulcus (LS), temporal parietal junction (TPJ), and frontal eye field (FEF), was affected by information accumulated over longer time scales. These regions showed highly reproducible responses for repeated forward, but not for backward or piecewise-scrambled presentations. Moreover, these regions exhibited marked differences in temporal characteristics, with LS, TPJ, and FEF responses depending on information accumulated over longer durations (∼36 s) than STS and precuneus (∼12 s). We conclude that, similar to the known cortical hierarchy of spatial receptive fields, there is a hierarchy of progressively longer temporal receptive windows in the human brain.

Subfields: Cognitive Neuroscience

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Citations in the corpus

Continual Lifelong Learning with Neural Networks: A Review (2018, background): “Evidence on cortical function has shown that neural activity in multiple brain areas results from the combination of bottom-up sensory drive, top-down feedback, and prior knowledge and expectations (Heeger 2017). In this setting, complex neurodynamic behaviour can emerge from the dense interaction o”
Lifelong learning of human actions with deep neural network self-organization (2017, background): “…shape and optic-flow features with increasing complexity, from low- to high-level representations of the visual stimuli (Giese & Poggio, 2003; Hasson et al., 2008); and (iii) input-driven self-organization is crucial for the cortex to tune the neurons according to the distribution of the…”
Emergence of multimodal action representations from neural network self-organization (2017, background): “After the training is completed, neurons in higher-level layers will encode prototype sequence-selective snapshots of visual input, following the assumption that the recognition of actions must be selective for temporal order (Giese & Poggio, 2003; Hasson et al., 2008).”
Self-Organization of Spatio-Temporal Hierarchy via Learning of Dynamic Visual Image Patterns on Action Sequences (2015, methodology): “The spatial hierarchy of the receptive fields for the MSTNN is analogous to the organization observed in the mammalian visual cortex [17], and there is evidence for the assumed temporal hierarchy of progressively slower time-scale dynamics of the MSTNN in the human visual cortex [18].”
Towards a Mathematical Theory of Cortical Micro-circuits (2009, result): “In addition, due to the temporal nature of HTM, this arrangement corresponds to a temporal hierarchy analogous to the kind reported by Hasson and colleagues [37].”
Topographic mapping of a hierarchy of temporal receptive windows using a narrated story (2011, methodology): “In addition, we compared the responses to the intact story with time-reversed responses to the backward story (Hasson et al., 2008).”
Theory of cortical function (2017, background): “The half-squaring can be approximated by rectification with a high threshold (64, 65).”
Functional Magnetic Resonance Imaging Phase Synchronization as a Measure of Dynamic Functional Connectivity (2012, positive): “For SBPS, since the alternative hypothesis has a known mean phase equal to zero, the most suitable parametric test is the V test (Zar, 1999).”
Shared computational principles for language processing in humans and deep language models (2022, background): “In natural language, each word receives its full meaning based on the preceding words 42–44 .”
A Hierarchy of Time-Scales and the Brain (2008, neutral): “The principle of a temporal hierarchy provides a theoretical framework for experiments in systems neuroscience. The predictions based on this account could be addressed by making time-scale an experimental factor. For visual areas, Hasson e”
Speaker–listener neural coupling underlies successful communication (2010, background): “Moreover, we recently demonstrated that some high-order brain areas, such as the TPJ and the parietal lobule, have the capacity to accumulate information over many seconds (8).”
How the brain might work: a hierarchical and temporal model for learning and recognition (2008, background): “Recently, Hasson et al [42] studied the nature of temporal hierarchies using functional magnetic resonance imaging.”
Cortical representation of the constituent structure of sentences (2011, background): “Discussion In the visual system, parametric manipulations of the stimuli using both spatial scrambling (24) and temporal scrambling (25) have played an instrumental role in understanding how pictures are encoded.”