Word-Object Learning via Visual Exploration in Space (WOLVES): A neural process model of cross-situational word learning

Don't throw the associative baby out with the Bayesian bathwater: Children are more associative when reasoning retrospectively under information processing demands

Causal reasoning is a fundamental cognitive ability that enables individuals to learn about the complex interactions in the world around them. However, the mechanisms that underpin causal reasoning are not well understood. For example, it remains unresolved whether children's causal inferences are best explained by Bayesian inference or associative learning. The two experiments and computational models reported here were designed to examine whether 5- and 6-year-olds will retrospectively reevaluate objects-that is, adjust their beliefs about the causal status of some objects presented at an earlier point in time based on the observed causal status of other objects presented at a later point in time-when asked to reason about 3 and 4 objects and under varying degrees of information processing demands. Additionally, the experiments and models were designed to determine whether children's retrospective reevaluations were best explained by associative learning, Bayesian inference, or some combination of both. The results indicated that participants retrospectively reevaluated causal inferences under minimal information-processing demands (Experiment 1) but failed to do so under greater information processing demands (Experiment 2) and that their performance was better captured by an associative learning mechanism, with less support for descriptions that rely on Bayesian inference. RESEARCH HIGHLIGHTS: Five- and 6-year-old children engage in retrospective reevaluation under minimal information-processing demands (Experiment 1). Five- and 6-year-old children do not engage in retrospective reevaluation under more extensive information-processing demands (Experiment 2). Across both experiments, children's retrospective reevaluations were better explained by a simple associative learning model, with only minimal support for a simple Bayesian model. These data contribute to our understanding of the cognitive mechanisms by which children make causal judgements.

Infants' attention during cross-situational word learning: Environmental variability promotes novelty preference

Infants as young as 14 months can track cross-situational statistics between sets of words and objects to acquire word-referent mappings. However, in naturalistic word learning situations, words and objects occur with a host of additional information, sometimes noisy, present in the environment. In this study, we tested the effect of this environmental variability on infants' word learning. Fourteen-month-old infants (N = 32) were given a cross-situational word learning task with additional gestural, prosodic, and distributional cues that occurred reliably or variably. In the reliable cue condition, infants were able to process this additional environmental information to learn the words, attending to the target object during test trials. But when the presence of these cues was variable, infants paid greater attention to the gestural cue during training and subsequently switched preference to attend more to novel word-object mappings rather than familiar ones at test. Environmental variation may be key to enhancing infants' exploration of new information.

Neural dynamic foundations of a theory of higher cognition: the case of grounding nested phrases

Because cognitive competences emerge in evolution and development from the sensory-motor domain, we seek a neural process account for higher cognition in which all representations are necessarily grounded in perception and action. The challenge is to understand how hallmarks of higher cognition, productivity, systematicity, and compositionality, may emerge from such a bottom-up approach. To address this challenge, we present key ideas from Dynamic Field Theory which postulates that neural populations are organized by recurrent connectivity to create stable localist representations. Dynamic instabilities enable the autonomous generation of sequences of mental states. The capacity to apply neural circuitry across broad sets of inputs that emulates the function call postulated in symbolic computation emerges through coordinate transforms implemented in neural gain fields. We show how binding localist neural representations through a shared index dimension enables conceptual structure, in which the interdependence among components of a representation is flexibly expressed. We demonstrate these principles in a neural dynamic architecture that represents and perceptually grounds nested relational and action phrases. Sequences of neural processing steps are generated autonomously to attentionally select the referenced objects and events in a manner that is sensitive to their interdependencies. This solves the problem of 2 and the massive binding problem in expressions such as "the small tree that is to the left of the lake which is to the left of the large tree". We extend earlier work by incorporating new types of grammatical constructions and a larger vocabulary. We discuss the DFT framework relative to other neural process accounts of higher cognition and assess the scope and challenges of such neural theories.

Four- and six-year-old children track a single meaning with both familiar and unfamiliar referents when the referent is clear: More evidence for propose-but-verify

In word learning, learners need to identify the referent of words by leveraging the fact that the same word may co-occur with different sets of objects. This raises the question, what do children remember from "in the moment" that they can use for cross-situational learning? Furthermore, do children represent pictures of familiar animals versus drawings of non-existent novel objects as potential referents differently? This study examined these questions by creating learning scenarios with only two potential referents, requiring the least amount of memory to represent all co-present objects. Across three experiments (n > 250) with 4- and 6-year-old children, children reliably selected the intended referent from learning at test, though the learning of novel objects was better than familiar objects. When asked for a co-present object, children of all ages in the study performed at chance in all of the conditions. We discuss the developmental differences in cross-situational word learning capabilities with regard to representing different stimuli as potential referents. Importantly, all children used a propose-but-verify procedure for learning novel words even in the simplest of the learning scenarios given repeated exposure.

The effects of speaker and exemplar variability in children's cross-situational word learning

Cross-situational word learning (XSWL) - children's ability to learn words by tracking co-occurrence statistics of words and their referents over time - has been identified as a fundamental mechanism underlying lexical learning. However, it is unknown whether children can acquire new words when faced with variable input in XSWL paradigms, such as varying object exemplars and variable speakers. In the present study, we examine the separate and combined effects of exemplar and speaker variability on XSWL in typically developing English-speaking monolingual children. Results revealed that variability in speakers and exemplars did not facilitate or hinder XSWL performance. However, input that varied in both speakers and exemplars simultaneously did hinder children's word learning. Results from this work suggest that XSWL mechanisms may support categorization and generalization beyond word-object associations, but that accommodating multiple forms of variable input may incur costs. Overall, this research provides new theoretical insights into how fundamental mechanisms of word learning scale to more complex and naturalistic forms of input.

Associative learning or Bayesian inference? Revisiting backwards blocking reasoning in adults

Causal reasoning is a fundamental cognitive ability that enables humans to learn about the complex interactions in the world around them. However, the cognitive mechanisms that underpin causal reasoning are not well understood. For instance, there is debate over whether Bayesian inference or associative learning best captures causal reasoning in human adults. The two experiments and computational models reported here were designed to examine whether adults engage in one form of causal inference called backwards blocking reasoning, whether the presence of potential distractors affects performance, and how adults' ratings align with the predictions of different computational models. The results revealed that adults engaged in backwards blocking reasoning regardless of whether distractor objects are present and that their causal judgements supported the predictions of a Bayesian model but not the predictions of two different associative learning models. Implications of these results are discussed.

Formal theories clarify the complex: Generalizing a neural process account of the interaction of visual exploration and word learning in infancy

The interaction of visual exploration and auditory processing is central to early cognitive development, supporting object discrimination, categorization, and word learning. Research has shown visual-auditory interactions to be complex, created from multiple processes and changing over multiple timescales. To better understand these interactions, we generalize a formal neural process model of early word learning to two studies examining how words impact 9- to 22-month-olds' attention to novelty. These simulations clarify the origin and nature of previously demonstrated effects of labels on visual exploration and the basis of mutual exclusivity effects in word learning. We use our findings to discuss key questions for this special section: what makes a good theory and how should formal theories interface with empirical paradigms and findings?

What have we learned from 15 years of research on cross-situational word learning? A focused review

In 2007 and 2008, Yu and Smith published their seminal studies on cross-situational word learning (CSWL) in adults and infants, showing that word-object-mappings can be acquired from distributed statistics despite in-the-moment uncertainty. Since then, the CSWL paradigm has been used extensively to better understand (statistical) word learning in different language learners and under different learning conditions. The goal of this review is to provide an entry-level overview of findings and themes that have emerged in 15 years of research on CSWL across three topic areas (mechanisms of CSWL, CSWL across different learner and task characteristics) and to highlight the questions that remain to be answered.

Intraobject and extraobject memory binding across early development

The ability to bind, or link, different aspects of an experience in memory undergoes protracted development across childhood. Most studies of memory binding development have assessed extraobject binding between an object and some external element such as another object, whereas little work has examined the development of intraobject binding, such as between shape and color features within the same object. In this work, we investigate the development of intra- and extraobject memory binding in five-year-olds, eight-year-olds, and young adults with a memory interference paradigm. Between two experiments, we manipulate whether stimuli are presented as coherent objects (Experiment 1: n5-year-olds = 32, 19 males, 13 females; n8-year-olds = 30, 15 males, 15 females; nadults = 30, 15 males, 15 females), requiring intraobject binding between shape and color features, or as spatially separated features (Experiment 2: n5-year-olds = 24, 16 males, 8 females; n8-year-olds = 41, 19 males, 22 females; nadults = 31, 13 males, 18 females), requiring extraobject binding. To estimate the contributions of different binding structures to performance, we present a novel computational model that mathematically instantiates the memory binding, forgetting, and retrieval processes we hypothesize to underlie performance on the task. The results provide evidence of substantial developmental improvements in both intraobject and extraobject binding of shape and color features between 5 and 8 years of age, as well as stronger intraobject compared with extraobject binding of features in all age groups. These findings provide key insights into memory binding across early development. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

What Children with Developmental Language Disorder Teach Us About Cross-Situational Word Learning

Children with developmental language disorder (DLD) served as a test case for determining the role of extant vocabulary knowledge, endogenous attention, and phonological working memory abilities in cross‐situational word learning. First‐graders (M_age = 7 years; 3 months), 44 with typical development (TD) and 28 with DLD, completed a cross‐situational word‐learning task comprised six cycles, followed by retention tests and independent assessments of attention, memory, and vocabulary. Children with DLD scored lower than those with TD on all measures of learning and retention, a performance gap that emerged in the first cycle of the cross‐situational protocol and that we attribute to weaknesses in initial encoding. Over cycles, children with DLD learned words at a similar rate as their TD peers but they were less flexible in their strategy use, demonstrating a propose‐but‐verify approach but never a statistical aggregation approach. Also, they drew upon different mechanisms to support their learning. Attention played a greater role for the children with DLD, whereas extant vocabulary size played a greater role for the children with TD. Children navigate the problem space of cross‐situational learning via varied routes. This conclusion is offered as motivation for theorists to capture all learners, not just the most typical ones.

Computational approaches to understanding interaction and development

Audio-visual recording and location tracking produce enormous quantities of digital data with which researchers can document children's everyday interactions in naturalistic settings and assessment contexts. Machine learning and other computational approaches can produce replicable, automated measurements of these big behavioral data. The economies of scale afforded by repeated automated measurements offer a potent approach to investigating linkages between real-time behavior and developmental change. In our work, automated measurement of audio from child-worn recorders-which quantify the frequency of child and adult speech and index its phonemic complexity-are paired with ultrawide radio tracking of children's location and interpersonal orientation. Applications of objective measurement indicate the influence of adult behavior in both expert ratings of attachment behavior and ratings of autism severity, suggesting the role of dyadic factors in these "child" assessments. In the preschool classroom, location/orientation measures provide data-driven measures of children's social contact, fertile ground for vocal interactions. Both the velocity of children's movement toward one another and their social contact with one another evidence homophily: children with autism spectrum disorder, other developmental disabilities, and typically developing children were more likely to interact with children in the same group even in inclusive preschool classrooms designed to promote interchange between all children. In the vocal domain, the frequency of peer speech and the phonemic complexity of teacher speech predict the frequency and phonemic complexity of children's own speech over multiple timescales. Moreover, children's own speech predicts their assessed language abilities across disability groups, suggesting how everyday interactions facilitate development.

The infant's view redefines the problem of referential uncertainty in early word learning

The learning of first object names is deemed a hard problem due to the uncertainty inherent in mapping a heard name to the intended referent in a cluttered and variable world. However, human infants readily solve this problem. Despite considerable theoretical discussion, relatively little is known about the uncertainty infants face in the real world. We used head-mounted eye tracking during parent-infant toy play and quantified the uncertainty by measuring the distribution of infant attention to the potential referents when a parent named both familiar and unfamiliar toy objects. The results show that infant gaze upon hearing an object name is often directed to a single referent which is equally likely to be a wrong competitor or the intended target. This bimodal gaze distribution clarifies and redefines the uncertainty problem and constrains possible solutions.