The Development of Communication Across Timescales

Piazza, Elise A.; Nencheva, Mira L.; Lew‐Williams, Casey

doi:10.1177/09637214211037665

Cited by 6 publications

(4 citation statements)

References 46 publications

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“…Second, we measured pupil size synchrony -the alignment in pupillary responses across participants -which has been shown in prior studies to be positively associated with attention to or similar processing of a stimulus (Hasson et al, 2004(Hasson et al, , 2008Kang & Wheatley, 2017;Nencheva et al, 2021;Piazza, Cohen, et al, 2021;Piazza, Nencheva, et al, 2021). This allowed us to probe whether infants find more vs. less frequent transitions more engaging.…”

Section: Current Investigationmentioning

confidence: 99%

Infants track the statistics of emotion transitions in the home

Nencheva,

Peng,

Tamir

et al. 2024

Preprint

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Predicting others' feelings is a superpower that enables efficient social interactions. How do infants learn which emotions precede and follow each other? We propose that infants develop this ability by tuning into the dynamics of their socio-emotional environment. If so, we expect that the way in which infants process emotion transitions will reflect both general patterns seen in adults as well as local statistics of observed emotion transitions. We measured 4-10-month-old U.S. infants' (N=70) pupillary responses to emotion transitions and surveyed primary caregivers on the frequency of their own emotion transitions. As expected, infants were attuned to adult patterns of emotion transitions, showing greater pupillary synchrony for frequent transitions. They were also sensitive to their caregiver’s specific transition frequencies, exhibiting similar pupillary responses to infants whose caregivers show similar patterns. These findings suggest that infants learn about emotion dynamics by observing statistical patterns in the people around them.

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Section: Current Investigationmentioning

confidence: 99%

Infants track the statistics of emotion transitions in the home

Nencheva,

Peng,

Tamir

et al. 2024

Preprint

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“…The input to young learners reflects these complex goals. Child-directed input is multidimensional, incorporating a diverse set of communicative cues across multiple modalities, and this multidimensional input is highly variable over time and across individuals, communities and cultures Bergelson, Casillas, et al, 2019;Casillas et al, 2020;Holler & Levinson, 2019;Kosie & Lew-Williams, 2023;Piazza et al, 2021;Ryskin & Fang, 2021;Schatz et al, 2022;Suarez-Rivera et al, 2022a;Yu & Smith, 2012). There is no "onesize-fits-all" characterization of human input, and any model of learning (language learning included) needs to account for and be robust to this massive variation.…”

Section: Introductionmentioning

confidence: 99%

Models of human learning should capture the complexity of natural communication

Kosie,

Nencheva,

Junge

et al. 2024

Preprint

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Children do not learn language from language alone. Instead, children learn from social interactions with multidimensional communicative cues that occur dynamically across timescales. A wealth of research using in-lab experiments and brief audio recordings has made progress in explaining early cognitive and communicative development, but these approaches are limited in their ability to capture the rich diversity of children’s early experience. Large language models represent a powerful approach for understanding how language can be learned from massive amounts of textual (and in some cases visual) data, but they have near-zero access to the actual, lived complexity of children’s everyday input. We assert the need for more descriptive research that densely samples the natural dynamics of children’s everyday communicative environments in order to grasp the long-stand mystery of how young children learn, including their language development. With the right multimodal data, researchers will be able to go beyond large language models to build developmentally-grounded efficient communication models that truly take into account the complexity of children’s diverse environments.

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“…Beyond differences between the timescales and contexts of laboratory and real-world task activities, there are important domains of human activity that are themselves complexly organized with component processes operating over quite different timescales despite interacting and depending on one another. An example is the development and deployment of language communication skills as described by Piazza et al (2021 , p. 459):…”

Section: Introductionmentioning

confidence: 99%

Integrating cognition in the laboratory with cognition in the real world: the time cognition takes, task fidelity, and finding tasks when they are mixed together

Carr,

Arrington,

Fitzpatrick

2023

Front. Psychol.

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It is now possible for real-life activities, unfolding over their natural range of temporal and spatial scales, to become the primary targets of cognitive studies. Movement toward this type of research will require an integrated methodological approach currently uncommon in the field. When executed hand in hand with thorough and ecologically valid empirical description, properly developed laboratory tasks can serve as model systems to capture the essentials of a targeted real-life activity. When integrated together, data from these two kinds of studies can facilitate causal analysis and modeling of the mental and neural processes that govern that activity, enabling a fuller account than either method can provide on its own. The resulting account, situated in the activity’s natural environmental, social, and motivational context, can then enable effective and efficient development of interventions to support and improve the activity as it actually unfolds in real time. We believe that such an integrated multi-level research program should be common rather than rare and is necessary to achieve scientifically and societally important goals. The time is right to finally abandon the boundaries that separate the laboratory from the outside world.

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The Development of Communication Across Timescales

Cited by 6 publications

References 46 publications

Infants track the statistics of emotion transitions in the home

Infants track the statistics of emotion transitions in the home

Models of human learning should capture the complexity of natural communication

Integrating cognition in the laboratory with cognition in the real world: the time cognition takes, task fidelity, and finding tasks when they are mixed together

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