It is argued that working memory limitations are best defined in terms of the complexity of relations that can be processed in parallel. Relational complexity is related to processing loads in problem solving, and discriminates between higher animal species, as well as between children of different ages. Complexity is defined by the number of dimensions, or sources of variation, that are related. A unary relation has one argument and one source of variation, because its argument can be instantiated in only one way at a time. A binary relation has two arguments, and two sources of variation, because two argument instantiations are possible at once. Similarly, a ternary relation is three dimensional, a quaternary relation is four dimensional, and so on. Dimensionality is related to number of chunks, because both attributes on dimensions and chunks are independent units of information of arbitrary size. Empirical studies of working memory limitations indicate a soft limit which corresponds to processing one quaternary relation in parallel. More complex concepts are processed by segmentation or conceptual chunking. Segmentation entails breaking tasks into components which do not exceed processing capacity, and which are processed serially. Conceptual chunking entails "collapsing" representations to reduce their dimensionality and consequently their processing load, but at the cost of making some relational information inaccessible. Parallel distributed processing implementations of relational representations show that relations with more arguments entail a higher computational cost, which corresponds to empirical observations of higher processing loads in humans. Empirical evidence is presented that relational complexity discriminates between higher species...

The agent-based approach emphasizes the importance of learning through organism-environment interaction. This approach is part of a recent trend in computational models of learning and development toward studying autonomous organisms that are embedded in virtual or real environments. In this paper we introduce the concepts of online and offline sampling and highlight the role of online sampling in agent-based models. After comparing the strengths of each approach for modeling particular developmental phenomena and research questions, we describe a recent agent-based model of infant causal perception. We conclude by discussing some of the present limitations of agent-based models and suggesting how these challenges may be addressed. © 2001 Academic Press Computational models of learning and development are playing an increasingly critical role in child development research (Cassidy, 1990;

Cognitive development on certain tasks (e.g., the balance scale task) is characterized by periods of relative stable, rule-like but suboptimal behavior that occur in a fixed order and alternate with short transition periods. Many models have been developed to capture the developmental phenomena associated with the balance scale task. However, most of these models do not account for important phenomena as discontinuous transitions or rely on questionable assumptions, and none of the models is able to predict improvement in behavior without feedback. We propose a computational model that is implemented in ACT-R and is based on the evaluation of success of applied knowledge structures combined with a mechanism to construct new knowledge by searching for differences in the presented balance scale problems. This model accounts for the empirical phenomena, including learning without feedback as is common in developmental tasks.

How have connectionist models informed the study of development? This paper considers three contributions from specific models. First, connectionist models have proven useful for exploring nonlinear dynamics and emergent properties, and their role in nonlinear developmental trajectories, critical periods and developmental disorders. Second, connectionist models have informed the study of the representations that lead to behavioral dissociations. Third, connectionist models have provided insight into neural mechanisms, and why different brain regions are specialized for different functions. Connectionist and dynamic systems approaches to development have differed, with connectionist approaches focused on learning processes and representations in cognitive tasks, and dynamic systems approaches focused on mathematical characterizations of physical elements of the system and their interactions with the environment. The two approaches also share much in common, such as their emphasis on continuous, nonlinear processes and their broad application to a range of behaviors.

At root, the systematicity debate over classical versus connectionist explanations for cognitive architecture turns on quantifying the degree to which human cognition is systematic. We introduce into the debate recent psychological data that provides strong support for the purely structure-based generalizations claimed by Fodor and Pylyshyn (1988). We then show, via simulation, that two widely used connectionist models (feedforward and simple recurrent networks) do not capture the same degree of generalization as human subjects. However, we show that this limitation is overcome by tensor networks that support relational processing. Distribution of cognitive behaviour In the search for the essential components of cognitive

. We present an alternative model of human cognitive development on the balance scale task. Study of this task has inspired a wide range of human and computational work. The task requires that children predict the outcome of placing a discrete number of weights at various distances on either side of a fulcrum. Our model, which features the symbolic learning algorithm C4.5 as a transition mechanism, exhibits regularities found in the human data including orderly stage progression, U-shaped development, and the torque difference effect. Unlike previous successful models of the task, the current model uses a single free parameter, is not restricted in the size of the balance scale that it can accommodate, and does not require the assumption of a highly structured output representation or a training environment biased towards weight or distance information. The model makes a number of predictions differing from those of previous computational efforts. Keywords: Cognitive development, bala...

The violation-of-expectation paradigm investigates infants' physical knowledge by exploiting their tendency to look longer at events that are surprising, unexpected, or physically impossible. The current simulation study examines the role of prediction as a fundamental component of infants' expectations in physical-knowledge studies. A recurrent network is presented with a computer-animated version of Baillargeon’s “car study ” (1986; Baillargeon & DeVos, 1991), in which a car rolls down a ramp and behind a screen. After learning to predict the outcome of a training event, the model is then tested on possible and impossible events from the same study. During testing, the model successfully predicts only superficial features of the test events. These results are used to argue for the necessity of prior physical knowledge, and perhaps also a built-in capacity for mental representation, in order for a prediction system to work.

In this paper we discuss a computational cognitive model of children’s poor performance on pronoun interpretation (the so-called Delay of Principle B Effect, or DPBE). This cognitive model is based on a theoretical account that attributes the DPBE to children’s inability as hearers to also take into account the speaker’s perspective. The cognitive model predicts that child hearers are unable to do so because their speed of linguistic processing is too limited to perform this second step in interpretation. We tested this hypothesis empirically in a psycholinguistic study, in which we slowed down the speech rate to give children more time for interpretation, and in a computational simulation study. The results of the two studies confirm the predictions of our model. Moreover, these studies show that embedding a theory of linguistic competence in a cognitive architecture allows for the generation of detailed and testable predictions with respect to linguistic performance.

The capacity for infants to form mental representations of hidden or occluded objects can be decomposed into two tasks: one process that identifies salient objects and a second complementary process that identifies salient locations. This functional decomposition is supported by the distinction between dorsal and ventral extrastriate visual processing in the primate visual system. This approach is illustrated by presenting an eye-movement model that incorporates both dorsal and ventral processing streams and by using the model to simulate infants ’ reactions to possible and impossible events from an infant looking-time study (R. Baillargeon, “Representing the existence and the location of hidden objects: object permanence in 6- and 8-month-old infants”, Cognition, 23, pp. 21–41, 1986.). As expected, the model highlights how the dorsal system is sensitive to the location of a key feature in these events (i.e. the location of an obstacle), whereas the ventral system responds equivalently to the possible and impossible events. These results are used to help explain infants’reactions in looking-time studies. Keywords: Object representations; Dorsal–ventral model; Infant perception 1.

Abstract For various domains in proportional reasoning cognitive development is characterized as a progression through a series of increasingly complex rules. A multiplicative relationship between two task features, such as weight and distance information of blocks placed at both sides of the fulcrum of a balance scale, appears difficult to discover. During development, children change their beliefs about the balance scale several times: from a focus on the weight dimension (Rule I) to occasionally considering the distance dimension (Rule II), guessing (Rule III), and applying multiplication (Rule IV; Siegler, 1981). Because of the detailed empirical findings the balance scale task has become a benchmark task for computational models of proportional reasoning. In this article, we present a large empirical study (N = 420) of which the findings provide a challenge for computational models. The effect of feedback and the effect of individually adapted training items on rule transition were tested for children using Rule I or Rule II. Presenting adapted training items initiates belief revision for Rule I but not for Rule II. The experience of making mistakes (by providing feedback) induces a change for both Rule I and Rule II. However, a delayed posttest shows that these changes are preserved after 2 weeks only for children using Rule I. We conclude that the transition from Rule I to Rule II differs from the transition from Rule II to a more complex rule. Concerning these empirical findings, we will review performance of computational models and the implications for a future belief revision model.