Word learning is a complex phenomenon because it is tied to many different behaviors that are linked to multiple perceptual and cognitive systems. Further, recent research suggests that the course of word learning builds from effects at the level of individual referent selection or noun generalization decisions that accumulate on a moment-to-moment timescale and structure subsequent word learning behaviors. Thus, what is needed for any unified theory of word learning is 1) an account of how individual decisions are made across different contexts, including the details of how objects are encoded, represented, and selected in the course of a word learning behavior; and 2) a mechanism that builds on these individual, contextually specific decisions. Here, the authors present a Dynamic Neural Field (DNF) Model that captures processes at both the second-to-second and developmental timescales and provides a process-based account of how individual behaviors accumulate to create development. Simulations illustrate how the model captures multiple word learning behaviors such as comprehension, production, novel noun generalization (in yes/no or forced choice tasks), referent selection, and learning of hierarchical nominal categories. They also discuss how the model ties developments in these tasks to developments in object perception, working memory, and the representation and tracking of objects in space. Finally, the authors review empirical work testing novel predictions of the model regarding the roles of competition and selection in forced-choice and yes/no generalization tasks and the role of space in early name-object binding.

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 examples. Two experiments did not support this prediction. Children with more category knowledge showed broader generalization when presented with multiple subordinate examples, 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.

Marr’s seminal work laid out a program of research by specifying key questions for cognitive science at different levels of analysis. Because Dynamic Systems Theory focuses on time and interdependence of components DST research programs come to very different conclusions regarding the nature of cognitive change. We review a specific DST approach to cognitive-level processes: Dynamic Field Theory. We review research applying dynamic field theory to several cognitive-level processes: object permanence, naming hierarchical categories, and inferring intent, that demonstrate the difference in understanding of behavior and cognition that results from a DST perspective. These point to a central challenge for cognitive science research as defined by Marr—emergence. We argue that appreciating emergence raises questions about the utility of computational level analyses and opens the door to insights concerning the origin of novel forms of behavior and thought (e.g., a new chess strategy). We contend this is one of the most fundamental questions about cognition and behavior.

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The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology -namely, Behaviorism and evolutionary psychology -that set aside mechanistic explanations or make use of optimality assumptions. Through these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls that have plagued previous theoretical movements.