Speaker–listener neural coupling underlies successful communication

By Greg J. Stephens, Lauren J. Silbert, Uri Hasson. 2010

907 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Social Psychology, Neuroscience, Cognitive Science & Psychology

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Citations in the corpus

Experience Grounds Language (2020, methodology): “A.M. Turing (Computing Machinery and Intelligence 1950) In the language community, this paradigm has been described under the “Speaker-Listener” model (Stephens et al., 2010), and a rich theory to describe this computationally is being actively developed under the Rational Speech Act Model (Frank…”
Topographic mapping of a hierarchy of temporal receptive windows using a narrated story (2011, methodology): “A transcript of the story is provided in Stephens et al. (2010).”
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜 (2021, background): “The fact that human-human communication is a jointly constructed activity [29, 128] is most clearly true in co-situated spoken or signed communication, but we use the same facilities for producing language that is intended for audiences not co-present with us (readers, listeners, watchers at a dista”
How do forward models work? And why would you want them? (2013, neutral): “A different type of evidence comes from Stephens et al. (2010), who correlated cortical BOLD signal changes between speakers and listeners during the course of a narrative.”
Functional Magnetic Resonance Imaging Phase Synchronization as a Measure of Dynamic Functional Connectivity (2012, neutral): “We also computed the Euclidean distance between IPS and ISC time series for each voxel to localize the differences between the two approaches.”
The role of prediction in social neuroscience (2012, background): “In a social interactive setting, i.e., during natural verbal communication, Stephens et al. (2010) found that spatiotemporal brain activity of the speaker and the listener became synchronized, and the greater this coupling, the greater the understanding.”
20 years of the default mode network: a review and synthesis (2023, background): “64 In summary, the DMN plays a role in the semantic and narrative aspects of speech processing, as well as the predictive and anticipatory aspects of language comprehension.”
Reintegrating the Study of Accuracy Into Social Cognition Research (2011, result): “Dovetailing with this initial finding, a subsequent study demonstrated that functional coupling between target brain activity and perceiver brain activity in both ESS and MSAS predicts perceivers’ comprehension of stories told by targets (Stephens et al., 2010).”
Consequences of the Now-or-Never bottleneck for signed versus spoken languages (2015, background): “In particular, Stephens et al. (2010) observed close synchrony between neural activations in speakers and listeners in early auditory areas.”
Data-Efficient Mutual Information Neural Estimator (2019, background): “The efficacy of this transmission, in terms of listener comprehension, is predicted by speaker–listener neural synchrony and synchrony among listeners [43].”
The Ghost in the Machine has an American accent: value conflict in GPT-3 (2022, background): “How we relate words to other words and sentences in a text has as much to do with our sociocultural experience as with the grammatical rules of the language we are using[23, 24].”