Individual differences and cross-situational consistency of dyadic social behavior

A new theoretical analysis of individual differences and cross-situational consistency of behavior is proposed. The authors hypothesized that the social behavior of mice (Mus musculus) is determined by individual differences among animals in behavior emitted (i.e., actor effects), in behavior elicited from social partners (partner effects), and by unique responses of one animal to another (relationship effects). Each effect represents a distinct facet of individual differences with different psychological meaning; likewise, the cross-situational consistency of each effect has a distinct psychological meaning. Individual differences in behavior emitted were observed, and these actor effects were consistent longitudinally. Individual differences in behavior elicited from social partners were observed, and these partner effects were also consistent longitudinally. Unique responses to specific social partners also determined behavior but were inconsistent longitudinally. The theoretical importance of reconceptualizing the concepts of individual differences and cross-situational consistency in behavior is discussed. ((c) 2005 APA, all rights reserved).     

2.5-year-olds use cross-situational consistency to learn verbs under referential uncertainty

Recent evidence shows that children can use cross-situational statistics to learn new object labels under referential ambiguity (e.g., Smith & Yu, 2008). Such evidence has been interpreted as support for proposals that statistical information about word-referent co-occurrence plays a powerful role in word learning. But object labels represent only a fraction of the vocabulary children acquire, and arguably represent the simplest case of word learning based on observations of world scenes. Here we extended the study of cross-situational word learning to a new segment of the vocabulary, action verbs, to permit a stronger test of the role of statistical information in word learning. In two experiments, on each trial 2.5-year-olds encountered two novel intransitive (e.g., "She's pimming!"; Experiment 1) or transitive verbs (e.g., "She's pimming her toy!"; Experiment 2) while viewing two action events. The consistency with which each verb accompanied each action provided the only source of information about the intended referent of each verb. The 2.5-year-olds used cross-situational consistency in verb learning, but also showed significant limits on their ability to do so as the sentences and scenes became slightly more complex. These findings help to define the role of cross-situational observation in word learning.     

Mothers with depressive symptoms: cross-situational consistency and temporal stability of their parenting behavior

In the present study, it has been hypothesized that anxious and depressed mothers may show less typical behavior in a laboratory observational setting because they may be more affected by unfamiliarity and the social evaluative meaning of the situation. They may thus show low consistency across laboratory and home assessments. A sample of 101 mother-infant pairs participated in the longitudinal study. Laboratory and home observations of mother-infant interaction episodes were conducted at the infant ages of 4, 8, and 12 months. Consistent with expectations, at 4 months the association between laboratory- and home-assessed maternal sensitivity was significantly moderated by maternal depression/anxiety: in highly anxious/depressed mothers the association between laboratory- and home-assessed sensitivity was weak, whereas a strong association was found in mothers with low anxiety/depression. At 8 months this interaction was still marginally significant and at 12 months it had disappeared. Thus, mothers with depressive symptoms showed less consistent behavior across the two settings of the first assessment wave, and with increased familiarity with the procedures of the longitudinal study, their behavioral consistency increased and no longer differed from that of well mothers.     

A failure to replicate the Bem and Allen study of individual differences in cross-situational consistency

Bem and Allen (1974) purportedly found evidence that, by using self-report measures of cross-situational consistency as moderator variables, it was possible to substantially increase the size of correlation coefficients computed among measures of each of two personality traits. The present study was undertaken to (a) replicate the Bem and Allen finding on a larger set of personality traits, (b) determine if the results hold differentially for self-report, other-report, and objective personality measures, and (c) compare different methods for dividing subjects into high and low consistent groups. One hundred twelve subjects (64 men and 48 women) were divided into high and low consistent groups using three different methods. Within each group correlations were computed among and between 6 self-report measures, 10 other-report measures, and when possible a few objective measures, for each of eight traits. In general, none of the methods of consistency classification replicated the Bem and Allen finding. In cases where the high consistent group had a larger average correlation among the measures than their low consistent counterparts, the size of that difference was negligible. Moreover, there were about as many instances in which the low consistent group actually had larger average correlations. Finally, there was essentially no agreement among the various consistency classification methods in dividing subjects into high and low consistent subsamples.     

The effect of individual differences in act prototypicality judgments on trait-behaviour correlations and cross-situational consistency

Individuals differ in their perceptions of actprototypicality. This study examined whether incorporating such individual differences enhances trait-behaviour correlations and provides stronger evidence for cross-situational consistency. Three hundred and fifty-three subjects rated the dominance prototypicality of 100 acts, indicated how often they performed each of these acts, and provided trait ratings of how dominant they were in general. There were substantial and reliable individual differences in prototypicality judgements over a 4–5 month period. A variety of weighting schemes were used to incorporate these individual differences, but none dramatically increased the trait-behaviour correlation. Similarly, incorporating individual differences did not increase the magnitude of cross-situational consistency correlations. However, incorporating individual differences did enhance the pattern of trait-behaviour and consistency correlations from less prototypical to highly prototypical acts. Differences in perceptions of act prototypicality thus do not affect the magnitude of the correlations that can be obtained, but they are useful in revealing theoretically meaningful patterns of relationships.     

Modeling cross-situational word-referent learning: prior questions

Both adults and young children possess powerful statistical computation capabilities--they can infer the referent of a word from highly ambiguous contexts involving many words and many referents by aggregating cross-situational statistical information across contexts. This ability has been explained by models of hypothesis testing and by models of associative learning. This article describes a series of simulation studies and analyses designed to understand the different learning mechanisms posited by the 2 classes of models and their relation to each other. Variants of a hypothesis-testing model and a simple or dumb associative mechanism were examined under different specifications of information selection, computation, and decision. Critically, these 3 components of the models interact in complex ways. The models illustrate a fundamental tradeoff between amount of data input and powerful computations: With the selection of more information, dumb associative models can mimic the powerful learning that is accomplished by hypothesis-testing models with fewer data. However, because of the interactions among the component parts of the models, the associative model can mimic various hypothesis-testing models, producing the same learning patterns but through different internal components. The simulations argue for the importance of a compositional approach to human statistical learning: the experimental decomposition of the processes that contribute to statistical learning in human learners and models with the internal components that can be evaluated independently and together.     

The cross-situational validity of risk as a value

Demand characteristics and risk are confounded on the choice dilemma items. Thus, it is difficult using that instrument to determine if individuals view themselves as more risky than others because of a cultural value of risk or instructional demand characteristics. The present study asked Ss to make judgments for themselves and for others on either the choice dilemmas (with risk-oriented or risk-neutral instructions) or on one of three other measures of risk taking (behavior prediction scale, risk of aversive consequences, zero expected value bets) which have exhibited the risky shift but do not have the instructional problems of the choice dilemmas. Only on the choice dilemmas did Ss view themselves as being more risky. On all other measures, Ss viewed themselves as either equal to or more conservative than others. These findings cast doubt on the validity of value type theories as general explanations for the risky shift.     

A probabilistic computational model of cross-situational word learning

Words are the essence of communication: They are the building blocks of any language. Learning the meaning of words is thus one of the most important aspects of language acquisition: Children must first learn words before they can combine them into complex utterances. Many theories have been developed to explain the impressive efficiency of young children in acquiring the vocabulary of their language, as well as the developmental patterns observed in the course of lexical acquisition. A major source of disagreement among the different theories is whether children are equipped with special mechanisms and biases for word learning, or their general cognitive abilities are adequate for the task. We present a novel computational model of early word learning to shed light on the mechanisms that might be at work in this process. The model learns word meanings as probabilistic associations between words and semantic elements, using an incremental and probabilistic learning mechanism, and drawing only on general cognitive abilities. The results presented here demonstrate that much about word meanings can be learned from naturally occurring child-directed utterances (paired with meaning representations), without using any special biases or constraints, and without any explicit developmental changes in the underlying learning mechanism. Furthermore, our model provides explanations for the occasionally contradictory child experimental data, and offers predictions for the behavior of young word learners in novel situations.     

Social cues modulate the representations underlying cross-situational learning

Because children hear language in environments that contain many things to talk about, learning the meaning of even the simplest word requires making inferences under uncertainty. A cross-situational statistical learner can aggregate across naming events to form stable word-referent mappings, but this approach neglects an important source of information that can reduce referential uncertainty: social cues from speakers (e.g., eye gaze). In four large-scale experiments with adults, we tested the effects of varying referential uncertainty in cross-situational word learning using social cues. Social cues shifted learners away from tracking multiple hypotheses and towards storing only a single hypothesis (Experiments 1 and 2). In addition, learners were sensitive to graded changes in the strength of a social cue, and when it became less reliable, they were more likely to store multiple hypotheses (Experiment 3). Finally, learners stored fewer word-referent mappings in the presence of a social cue even when given the opportunity to visually inspect the objects for the same amount of time (Experiment 4). Taken together, our data suggest that the representations underlying cross-situational word learning of concrete object labels are quite flexible: In conditions of greater uncertainty, learners store a broader range of information.     

Learning times for large lexicons through cross-situational learning

Cross-situational learning is a mechanism for learning the meaning of words across multiple exposures, despite exposure-by-exposure uncertainty as to a word's true meaning. Doubts have been expressed regarding the plausibility of cross-situational learning as a mechanism for learning human-scale lexicons in reasonable timescales under the levels of referential uncertainty likely to confront real word learners. We demonstrate mathematically that cross-situational learning facilitates the acquisition of large vocabularies despite significant levels of referential uncertainty at each exposure, and we provide estimates of lexicon learning times for several cross-situational learning strategies. This model suggests that cross-situational word learning cannot be ruled out on the basis that it predicts unreasonably long lexicon learning times. More generally, these results indicate that there is no necessary link between the ability to learn individual words rapidly and the capacity to acquire a large lexicon.     

Infants rapidly learn word-referent mappings via cross-situational statistics

First word learning should be difficult because any pairing of a word and scene presents the learner with an infinite number of possible referents. Accordingly, theorists of children's rapid word learning have sought constraints on word-referent mappings. These constraints are thought to work by enabling learners to resolve the ambiguity inherent in any labeled scene to determine the speaker's intended referent at that moment. The present study shows that 12- and 14-month-old infants can resolve the uncertainty problem in another way, not by unambiguously deciding the referent in a single word-scene pairing, but by rapidly evaluating the statistical evidence across many individually ambiguous words and scenes.     

Exploring the robustness of cross-situational learning under Zipfian distributions

Cross-situational learning has recently gained attention as a plausible candidate for the mechanism that underlies the learning of word-meaning mappings. In a recent study, Blythe and colleagues have studied how many trials are theoretically required to learn a human-sized lexicon using cross-situational learning. They show that the level of referential uncertainty exposed to learners could be relatively large. However, one of the assumptions they made in designing their mathematical model is questionable. Although they rightfully assumed that words are distributed according to Zipf's law, they applied a uniform distribution of meanings. In this article, Zipf's law is also applied to the distribution of meanings, and it is shown that under this condition, cross-situational learning can only be plausible when referential uncertainty is sufficiently small. It is concluded that cross-situational learning is a plausible learning mechanism but needs to be guided by heuristics that aid word learners with reducing referential uncertainty.     

Rapid word learning under uncertainty via cross-situational statistics

There are an infinite number of possible word-to-word pairings in naturalistic learning environments. Previous proposals to solve this mapping problem have focused on linguistic, social, representational, and attentional constraints at a single moment. This article discusses a cross-situational learning strategy based on computing distributional statistics across words, across referents, and, most important, across the co-occurrences of words and referents at multiple moments. We briefly exposed adults to a set of trials that each contained multiple spoken words and multiple pictures of individual objects; no information about word-picture correspondences was given within a trial. Nonetheless, over trials, subjects learned the word-picture mappings through cross-trial statistical relations. Different learning conditions varied the degree of within-trial reference uncertainty, the number of trials, and the length of trials. Overall, the remarkable performance of learners in various learning conditions suggests that they calculate cross-trial statistics with sufficient fidelity and by doing so rapidly learn word-referent pairs even in highly ambiguous learning contexts.     

Developmental and cross-situational stability in infant pigtailed macaque temperament

We assessed developmental stability and context generalizability of temperament in pigtailed macaques (Macaca nemestrina) from the University of Washington Infant Primate Research Lab. A principal components analysis condensed 6 behavioral measures into 2 components, interpreted as reactivity and boldness. Changes in these measures over the 1st 10 months of development showed a trend toward calmer and bolder behavior with age, with significant individual variation in the pattern of change. Boldness showed a quadratic pattern of change, whereas reactivity decreased linearly. We also studied the relationship between temperament and response to a novelty probe. The magnitude of the response to the novelty probe decreased slightly over time, and boldness and reactivity in a familiar setting did not predict these changes in response to novelty. In a 2nd principal components analysis, reactivity to novelty represented a distinct aspect of temperament. Our results demonstrate developmental changes and context dependency in macaque behavior.     

Propose but verify: Fast mapping meets cross-situational word learning

We report three eyetracking experiments that examine the learning procedure used by adults as they pair novel words and visually presented referents over a sequence of referentially ambiguous trials. Successful learning under such conditions has been argued to be the product of a learning procedure in which participants provisionally pair each novel word with several possible referents and use a statistical-associative learning mechanism to gradually converge on a single mapping across learning instances [e.g., Yu, C., & Smith, L. B. (2007). Rapid word learning under uncertainty via cross-situational statistics. Psychological Science, 18(5), 414–420]. We argue here that successful learning in this setting is instead the product of a one-trial procedure in which a single hypothesized word-referent pairing is retained across learning instances, abandoned only if the subsequent instance fails to confirm the pairing – more a ‘fast mapping’ procedure than a gradual statistical one. We provide experimental evidence for this propose-but-verify learning procedure via three experiments in which adult participants attempted to learn the meanings of nonce words cross-situationally under varying degrees of referential uncertainty. The findings, using both explicit (referent selection) and implicit (eye movement) measures, show that even in these artificial learning contexts, which are far simpler than those encountered by a language learner in a natural environment, participants do not retain multiple meaning hypotheses across learning instances. As we discuss, these findings challenge ‘gradualist’ accounts of word learning and are consistent with the known rapid course of vocabulary learning in a first language.     

Detailed behavioral analysis as a window into cross-situational word learning

Recent research has demonstrated that word learners can determine word-referent mappings by tracking co-occurrences across multiple ambiguous naming events. The current study addresses the mechanisms underlying this capacity to learn words cross-situationally. This replication and extension of Yu and Smith (2007) investigates the factors influencing both successful cross-situational word learning and mis-mappings. Item analysis and error patterns revealed that the co-occurrence structure of the learning environment as well as the context of the testing environment jointly affected learning across observations. Learners also adopted an exclusion strategy, which contributed conjointly with statistical tracking to performance. Implications for our understanding of the processes underlying cross-situational word learning are discussed.