Modeling the Influence of Language Input Statistics on Children's Speech Production

We trained a computational model (the Chunk-Based Learner; CBL) on a longitudinal corpus of child–caregiver interactions in English to test whether one proposed statistical learning mechanism—backward transitional probability—is able to predict children's speech productions with stable accuracy throughout the first few years of development. We predicted that the model less accurately reconstructs children's speech productions as they grow older because children gradually begin to generate speech using abstracted forms rather than specific “chunks” from their speech environment. To test this idea, we trained the model on both recently encountered and cumulative speech input from a longitudinal child language corpus. We then assessed whether the model could accurately reconstruct children's speech. Controlling for utterance length and the presence of duplicate chunks, we found no evidence that the CBL becomes less accurate in its ability to reconstruct children's speech with age.     

The relation between children's conceptual functioning with color and color term acquisition

Young children experience considerable difficulty in learning their first few color terms. One explanation for this difficulty is that initially they lack a conceptual representation of color sufficiently abstract to support word meaning. This hypothesis, that prior to learning color terms children do not represent color as an abstraction, was tested in two experiments using samples of 25- to 39-month-olds and 20- to 32-month-olds. Children's ability to conceptually represent color and their knowledge of color terms were assessed, and a strong association was found between the ability to make inferences based on color and the comprehension of color words. Children who did not comprehend color terms were unsuccessful at a conceptual task that required them to represent color as a property independent of the particular objects that displayed it. The results suggest that the initial absence of an abstract representation of color contributes to the difficulty that young children encounter when first learning color words.     

Birth of an Abstraction: A Dynamical Systems Account of the Discovery of an Elsewhere Principle in a Category Learning Task

Human participants and recurrent (“connectionist”) neural networks were both trained on a categorization system abstractly similar to natural language systems involving irregular (“strong”) classes and a default class. Both the humans and the networks exhibited staged learning and a generalization pattern reminiscent of the Elsewhere Condition (Kiparsky, 1973). Previous connectionist accounts of related phenomena have often been vague about the nature of the networks’ encoding systems. We analyzed our network using dynamical systems theory, revealing topological and geometric properties that can be directly compared with the mechanisms of non‐connectionist, rule‐based accounts. The results reveal that the networks “contain” structures related to mechanisms posited by rule‐based models, partly vindicating the insights of these models. On the other hand, they support the one mechanism (OM), as opposed to the more than one mechanism (MOM), view of symbolic abstraction by showing how the appearance of MOM behavior can arise emergently from one underlying set of principles. The key new contribution of this study is to show that dynamical systems theory can allow us to explicitly characterize the relationship between the two perspectives in implemented models.     

Array training in a categorization task

Two components of categorization, within-category commonalities and between-category distinctiveness, were investigated in a categorization task. Subjects learned three prototype categories composed of moderately high distortions, by observing arrays containing patterns that belonged either to a common prototype category or to three different categories; a third group learned patterns presented one at a time, mirroring the standard paradigm. Following 6 learning blocks, subjects transferred to old patterns and new patterns at low-, medium-, and high-level distortions of the category prototype. The results showed that array training facilitated learning, especially when patterns in the array belonged to the same category. Transfer results showed a strong gradient effect across pattern distortion level for all conditions, with the highest performance obtained following array training on different category patterns and worst in the control condition. Interestingly, the old training patterns were classified worse than new low and no better than medium distortions. Neither this ordering nor the steepness of the gradient across prototype similarity for each condition could be predicted by the generalized context model. A prototype model better captured the steep gradient and ordinal pattern of results, although the overall fits were only slightly better than the exemplar model. The crucial role played by category commonalities and distinctiveness on categorical representations is addressed.     

On the blessing of abstraction

The “blessing of abstraction” refers to the observation that acquiring abstract knowledge sometimes proceeds more quickly than acquiring more speci?c knowledge. This observation can be formalized and reproduced by hierarchical Bayesian models. The key notion is that more abstract layers of the hierarchy have a larger “effective” sample size, because they combine information across multiple speci?c instances lower in the hierarchy. This notion relies on speci?c variables being relatively concentrated around the abstract “overhypothesis”. If the variables are highly dispersed, then the effective sample size for the abstract layers will not be appreciably larger than for the speci?c layers. Moreover, the blessing of abstraction is counterbalanced by the fact that data are more informative about lower levels of the hierarchy, because there is necessarily less stochasticity intervening between speci?c variables and the data. Thus, in certain cases abstract knowledge will be acquired more slowly than speci?c knowledge. This paper reports an experiment that shows how manipulating dispersion can produce both fast and slow acquisition of abstract knowledge in the same paradigm.     

A mechanistic perspective on canonical neural computation

Although it has been argued that mechanistic explanation is compatible with abstraction (i.e., that there are abstract mechanistic models), there are still doubts about whether mechanism can account for the explanatory power of significant abstract models in computational neuroscience. Chirimuuta has recently claimed that models describing canonical neural computations (CNCs) must be evaluated using a non-mechanistic framework. I defend two claims regarding these models. First, I argue that their prevailing neurocognitive interpretation is mechanistic. Additionally, a criterion recently proposed by Levy and Bechtel to legitimize mechanistic abstract models, and also a criterion proposed by Chirimuuta herself aimed to distinguish between causal and non-causal explanation, can be employed to show why these models are explanatory only under this interpretation (as opposed to a purely mathematical or non-causal interpretation). Second, I argue that mechanism is able to account for the special epistemic achievement implied by CNC models. Canonical neural components contribute to an integrated understanding of different cognitive functions. They make it possible for us to explain these functions by describing different mechanisms constituted by common basic components arranged in different ways.     

A new performance-based measure of children’s socioemotional understanding: Psychometric properties of the domino test for socioemotional understanding (DOTSEMU)

Preschoolers were tested on a variety of oddity problems—that is, problems where one object in a set of four differed from the other three on one dimension. These dimensions consisted of colour, size, or form. The children were most accurate on size oddity problems in which one object was bigger than the others. They were almost as accurate on colour oddity problems. Form problems were next, and size oddity problems wherein the odd object was smaller than the other three objects were next. These decalages are interpretable in terms of relational complexity theory and executive function.     

Mere physical distance and integrative agreements: When more space improves negotiation outcomes

We examined the effects of negotiating non-face-to-face with someone that is physically nearby versus faraway on integrative (mutually beneficial) agreements. Across Studies 1 and 2, we found that individuals who negotiated with another person that they believed was physically faraway (several thousand feet away) rather than nearby (a few feet away) attained more integrative agreements (higher joint outcome, more Pareto efficient agreements). In Study 3, we found that the effect of different magnitudes of physical distance between negotiators on integrative agreements depended on negotiators' construal level: individuals who negotiated with another person who was purportedly farther away achieved more integrative agreements when their level of construal was not constrained, but had no effect when they adopted a high-level of construal. The implications for non-face-to-face communication are discussed.