simultaneous-data-collection-fmri-fnirs-measurements-using-whole-head

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Functional near-infrared spectroscopy (fNIRS) is a portable neuroimaging methodology, more robust to motion and more cost-effective than functional ...

Yale University School of Medicine

University of Connecticut

Yale University

Word learning entails the mapping of an auditory word-form to its appropriate grammatical category (e.g., noun, verb, adjective), but before that mapping can occur, the naïve learner must infer which of the myriad of possible referents of that word was intended by the speaker. This creates a computational explosion of referential ambiguity referred to as the gavagai problem. In a set of corpus analyses of parent-directed speech to young infants, we describe the distributional information available to early word learners, with a focus on nouns and adjectives that refer to whole objects and object properties. And in two experiments on word-learning in adults spanning seven different distributional conditions, we document how variations in the ratio of novel labels for objects and properties affect the robustness of word learning. Our results suggest that the language input to 6- to 20-month-olds is robustly populated with high-frequency object words and high-frequency property words, but their co-occurrence is sparse. Although this distributional information slightly favors object words over property words, a more plausible account of the whole-object bias in early word learning is the inability to encode the details of an object/event during rapid naming. Our results from adults, presented with novel labels for multi-referent objects in a cross-situational statistical learning paradigm, also reveal this whole-object bias as well as the absence of property-label generalization to novel objects, even when the distribution of labels is shifted almost exclusively to property words. These results are discussed in terms of the relative ease of mapping auditory word-forms to whole objects vs. object properties, thereby limiting the combinatorics of the gavagai problem, especially in infants with immature encoding and memory representation abilities.

A distributional perspective on the gavagai problem in early word learning.

Understanding language neurobiology in early childhood is essential for characterizing the developmental structural and functional changes that lead to the mature adult language network. In the last two decades, the field of language neurodevelopment has received increasing attention, particularly given the rapid advances in the implementation of neuroimaging techniques and analytic approaches that allow detailed investigations into the developing brain across a variety of cognitive domains. These methodological and analytical advances hold the promise of developing early markers of language outcomes that allow diagnosis and clinical interventions at the earliest stages of development. Here, we argue that findings in language neurobiology need to be integrated within an approach that captures the dynamic nature and inherent variability that characterizes the developing brain and the interplay between behavior and (structural and functional) neural patterns. Accordingly, we describe a framework for understanding language neurobiology in early development, which minimally requires an explicit characterization of the following core domains: i) computations underlying language learning mechanisms, ii) developmental patterns of change across neural and behavioral measures, iii) environmental variables that reinforce language learning (e.g., the social context), and iv) brain maturational constraints for optimal neural plasticity, which determine the infant's sensitivity to learning from the environment. We discuss each of these domains in the context of recent behavioral and neuroimaging findings and consider the need for quantitatively modeling two main sources of variation: individual differences or trait-like patterns of variation and within-subject differences or state-like patterns of variation. The goal is to enable models that allow prediction of language outcomes from neural measures that take into account these two types of variation. Finally, we examine how future methodological approaches would benefit from the inclusion of more ecologically valid paradigms that complement and allow generalization of traditional controlled laboratory methods.

Article	Year
A distributional perspective on the gavagai problem in early word learning. Cognition\| PubMed ID: 33853740	2021
Towards a model of language neurobiology in early development. Brain and language\| PubMed ID: 34894429	2022
Multiple pathways to developmental continuity in infant cognition. Trends in cognitive sciences\| PubMed ID: 37321924	2023

Richard N. Aslin

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