Context Matters for Social-Emotional Learning: Examining Variation in Program Impact by Dimensions of School Climate

This paper examines whether three dimensions of school climate—leadership, accountability, and safety/respect—moderated the impacts of the INSIGHTS program on students’ social‐emotional, behavioral, and academic outcomes. Twenty‐two urban schools and N = 435 low‐income racial/ethnic minority students were enrolled in the study and received intervention services across the course of 2 years, in both kindergarten and first grade. Intervention effects on math and reading achievement were larger for students enrolled in schools with lower overall levels of leadership, accountability, and safety/respect at baseline. Program impacts on disruptive behaviors were greater in schools with lower levels of accountability at baseline; impacts on sustained attention were greater in schools with lower levels of safety/respect at baseline. Implications for Social‐Emotional Learning program implementation, replication, and scale‐up are discussed.     

Learning General Phonological Rules From Distributional Information: A Computational Model

Phonological rules create alternations in the phonetic realizations of related words. These rules must be learned by infants in order to identify the phonological inventory, the morphological structure, and the lexicon of a language. Recent work proposes a computational model for the learning of one kind of phonological alternation, allophony (Peperkamp, Le Calvez, Nadal, & Dupoux, 2006). This paper extends the model to account for learning of a broader set of phonological alternations and the formalization of these alternations as general rules. In Experiment 1, we apply the original model to new data in Dutch and demonstrate its limitations in learning nonallophonic rules. In Experiment 2, we extend the model to allow it to learn general rules for alternations that apply to a class of segments. In Experiment 3, the model is further extended to allow for generalization by context; we argue that this generalization must be constrained by linguistic principles.     

Making Probabilistic Relational Categories Learnable

Theories of relational concept acquisition (e.g., schema induction) based on structured intersection discovery predict that relational concepts with a probabilistic (i.e., family resemblance) structure ought to be extremely difficult to learn. We report four experiments testing this prediction by investigating conditions hypothesized to facilitate the learning of such categories. Experiment 1 showed that changing the task from a category‐learning task to choosing the “winning” object in each stimulus greatly facilitated participants' ability to learn probabilistic relational categories. Experiments 2 and 3 further investigated the mechanisms underlying this “who's winning” effect. Experiment 4 replicated and generalized the “who's winning” effect with more natural stimuli. Together, our findings suggest that people learn relational concepts by a process of intersection discovery akin to schema induction, and that any task that encourages people to discover a higher order relation that remains invariant over members of a category will facilitate the learning of putatively probabilistic relational concepts.     

Non‐Bayesian Noun Generalization in 3‐ to 5‐Year‐Old Children: Probing the Role of Prior Knowledge in the Suspicious Coincidence Effect

It is unclear how children learn labels for multiple overlapping categories such as “Labrador,” “dog,” and “animal.” Xu and Tenenbaum (2007a) suggested that learners infer correct meanings with the help of Bayesian inference. They instantiated these claims in a Bayesian model, which they tested with preschoolers and adults. Here, we report data testing a developmental prediction of the Bayesian model—that more knowledge should lead to narrower category inferences when presented with multiple subordinate exemplars. Two experiments did not support this prediction. Children with more category knowledge showed broader generalization when presented with multiple subordinate exemplars, compared to less knowledgeable children and adults. This implies a U‐shaped developmental trend. The Bayesian model was not able to account for these data, even with inputs that reflected the similarity judgments of children. We discuss implications for the Bayesian model, including a combined Bayesian/morphological knowledge account that could explain the demonstrated U‐shaped trend.     

Development of Different Forms of Skill Learning Throughout the Lifespan

The acquisition of complex motor, cognitive, and social skills, like playing a musical instrument or mastering sports or a language, is generally associated with implicit skill learning (SL). Although it is a general view that SL is most effective in childhood, and such skills are best acquired if learning starts early, this idea has rarely been tested by systematic empirical studies on the developmental pathways of SL from childhood to old age. In this paper, we challenge the view that childhood and early school years are the prime time for skill learning by tracking age‐related changes in performance in three different paradigms of SL. We collected data from participants between 7 and 87 years for (1) a Serial Reaction Time Task (SRT) testing the learning of motor sequences, (2) an Artificial Grammar Learning (AGL) task testing the extraction of regularities from auditory sequences, and (3) Probabilistic Category Learning in the Weather Prediction task (WP), a non‐sequential categorization task. Results on all three tasks show that adolescence and adulthood are the most efficient periods for skill learning, since instead of becoming less and less effective with age, SL improves from childhood into adulthood and then later declines with aging.     

Word Meanings Evolve to Selectively Preserve Distinctions on Salient Dimensions

Words refer to objects in the world, but this correspondence is not one‐to‐one: Each word has a range of referents that share features on some dimensions but differ on others. This property of language is called underspecification. Parts of the lexicon have characteristic patterns of underspecification; for example, artifact nouns tend to specify shape, but not color, whereas substance nouns specify material but not shape. These regularities in the lexicon enable learners to generalize new words appropriately. How does the lexicon come to have these helpful regularities? We test the hypothesis that systematic backgrounding of some dimensions during learning and use causes language to gradually change, over repeated episodes of transmission, to produce a lexicon with strong patterns of underspecification across these less salient dimensions. This offers a cultural evolutionary mechanism linking individual word learning and generalization to the origin of regularities in the lexicon that help learners generalize words appropriately.     

Learning to Manipulate and Categorize in Human and Artificial Agents

This study investigates the acquisition of integrated object manipulation and categorization abilities through a series of experiments in which human adults and artificial agents were asked to learn to manipulate two‐dimensional objects that varied in shape, color, weight, and color intensity. The analysis of the obtained results and the comparison of the behavior displayed by human and artificial agents allowed us to identify the key role played by features affecting the agent/environment interaction, the relation between category and action development, and the role of cognitive biases originating from previous knowledge.