Drift in children's categories: when experienced distributions conflict with prior learning

Category Flexibility in Emotion Learning

Learners flexibly update category boundaries to adjust to the range of experiences they encounter. However, little is known about whether the degree of flexibility is consistent across domains. We examined whether categorization of social input, specifically emotions, is afforded more flexibility as compared to other biological input. To address this question, children (6-12 years; 32 female, 37 male; 7 Hispanic or Latino, 62 not Hispanic or Latino; 8 Black or African American, 14 multiracial, 46 White, 1 selected "other") categorized faces morphed from calm to upset and animals morphed from a horse to a cow across task phases that differed in the distribution of stimuli presented. Learners flexibly adjusted both emotion and animal category boundaries according to distributional information, yet children showed more flexibility when updating their category boundaries for emotions. These results provide support for the idea that children-who must adjust to the vast and varied emotional signals of their social partners-respond to social signals dynamically in order to make predictions about the internal states and future behaviors of others.

“What makes this a wug?” Relations among children’s question asking, memory, and categorization of objects

Children ask many questions, but do not always receive answers to the questions they ask. We were interested in whether the act of generating questions, in the absence of an answer, is related to children’s later thinking. Two experiments examined whether children retain the questions they ask in working memory, and whether the type of questions asked relate to their categorization. Four to ten-year-old children (N = 42 in Experiment 1, N = 41 in Experiment 2) were shown 12 novel objects, asked three questions about each, and did not receive answers to their questions. Children recalled their questions in the first experiment and categorized variants of the novel objects in the second experiment. We found that children have robust working memory for their questions, indicating that these questions may relate to their subsequent thinking. Additionally, children generalize category boundaries more narrowly or broadly depending on the type of question they ask, indicating that children’s questions may reflect an underlying bias in how they think about the world. These findings suggest that future research should examine questions in the absence of answers to understand how inquiry affects children’s cognitive development.

Sparse labels, no problems: Infant categorization under challenging conditions

Labeling promotes infants' object categorization even when labels are rare. By 2 years, infants engage in "semi-supervised learning" (SSL), integrating labeled and unlabeled exemplars to learn categories. However, everyday learning contexts pose substantial challenges for infants' SSL. Here, two studies (n = 74, 51% female, 62% non-Hispanic White, 18% multiracial, 8% Asian, 6% Black, M_age  = 27.3 months, collected 2018-2020) implemented a familiarization-novelty preference paradigm assessing 2-year-olds' SSL when (i) exemplars from the target category are interspersed with other objects (Study 1, d = .67) and (ii) multiple categories are learned simultaneously (Study 2, d = .74). The findings indicate 2-year-olds' SSL is robust enough to support object categorization despite substantial challenges posed by everyday learning contexts.

When unsupervised training benefits category learning

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People can learn through unsupervised or supervised means.

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Semi-supervised learning includes both unsupervised and supervised trials.

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Unsupervised trials can help or harm semi-supervised human category learning.

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Unsupervised trials help when aligned with knowledge reflecting category structure.

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Successful semi-supervised training requires assessing learners’ representations.

Humans continuously categorise inputs, but only rarely receive explicit feedback as to whether or not they are correct. This implies that they may be integrating unsupervised information together with their sparse supervised data – a form of semi-supervised learning. However, experiments testing semi-supervised learning are rare, and are bedevilled with conflicting results about whether the unsupervised information affords any benefit. Here, we suggest that one important factor that has been paid insufficient attention is the alignment between subjects’ internal representations of the stimulus material and the experimenter-defined representations that determine success in the tasks. Subjects’ representations are shaped by prior biases and experience, and unsupervised learning can only be successful if the alignment suffices. Otherwise, unsupervised learning might harmfully strengthen incorrect assumptions. To test this hypothesis, we conducted an experiment in which subjects initially categorise items along a salient, but task-irrelevant, dimension, and only recover the correct categories when sufficient feedback draws their attention to the subtle, task-relevant, stimulus dimensions. By withdrawing feedback at different stages along this learning curve, we tested whether unsupervised learning improves or worsens performance when internal stimulus representations and task are sufficiently or insufficiently aligned, respectively. Our results demonstrate that unsupervised learning can indeed have opposing effects on subjects’ learning. We also discuss factors limiting the degree to which such effects can be predicted from momentary performance. Our work implies that predicting and understanding human category learning in particular tasks requires assessment and consideration of the representational spaces that subjects entertain for the materials involved in those tasks. These considerations not only apply to studies in the lab, but could also help improve the design of tutoring systems and instruction.

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Assessing infant category learning is a challenging but vital aspect of studying infant cognition. By employing a familiarization-test paradigm, we straightforwardly measure infants' success in learning a novel category while relying only on their looking behavior. Moreover, the paradigm can directly measure the impact of different auditory signals on the infant categorization across a range of ages. For instance, we assessed how 2-year-olds learn categories in a variety of labeling environments: in our task, 2-year-olds successfully learned categories when all exemplars were labeled or the first two exemplars were labeled, but they failed to categorize when no exemplars were labeled or only the final two exemplars were labeled. To determine infants' success in such tasks, researchers can examine both the overall preference displayed by infants in each condition and infants' pattern of looking over the course of the test phase, using an eye-tracker to provide fine-grained time-course data. Thus, we present a powerful paradigm for identifying the role of language, or any auditory signal, in infants' object category learning.

Probabilistic learning of emotion categories

Although the configurations of facial muscles that humans perceive vary continuously, we often represent emotions as categories. This suggests that, as in other domains of categorical perception such as speech and color perception, humans become attuned to features of emotion cues that map onto meaningful thresholds for these signals given their environments. However, little is known about the learning processes underlying the representation of these salient social signals. In Experiment 1 we test the role of statistical distributions of facial cues in the maintenance of an emotion category in both children (6-8 years old) and adults (18-22 years old). Children and adults learned the boundary between neutral and angry when provided with explicit feedback (supervised learning). However, after we exposed participants to different statistical distributions of facial cues, they rapidly shifted their category boundaries for each emotion during a testing phase. In Experiments 2 and 3, we replicated this finding and also tested the extent to which learners are able to track statistical distributions for multiple actors. Not only did participants form actor-specific categories, but the distributions of facial cues also influenced participants' trait judgments about the actors. Taken together, these data are consistent with the view that the way humans construe emotion (in this case, anger) is not only flexible, but reflects complex learning about the distributions of the myriad cues individuals experience in their social environments. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

A little labeling goes a long way: Semi-supervised learning in infancy

There is considerable evidence that labeling supports infants' object categorization. Yet in daily life, most of the category exemplars that infants encounter will remain unlabeled. Inspired by recent evidence from machine learning, we propose that infants successfully exploit this sparsely labeled input through "semi-supervised learning." Providing only a few labeled exemplars leads infants to initiate the process of categorization, after which they can integrate all subsequent exemplars, labeled or unlabeled, into their evolving category representations. Using a classic novelty preference task, we introduced 2-year-old infants (n = 96) to a novel object category, varying whether and when its exemplars were labeled. Infants were equally successful whether all exemplars were labeled (fully supervised condition) or only the first two exemplars were labeled (semi-supervised condition), but they failed when no exemplars were labeled (unsupervised condition). Furthermore, the timing of the labeling mattered: when the labeled exemplars were provided at the end, rather than the beginning, of familiarization (reversed semi-supervised condition), infants failed to learn the category. This provides the first evidence of semi-supervised learning in infancy, revealing that infants excel at learning from exactly the kind of input that they typically receive in acquiring real-world categories and their names.