Opinion
Approaches to cognitive modeling
Letting structure emerge: connectionist and dynamical systems approaches to cognition

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Connectionist and dynamical systems approaches explain human thought, language and behavior in terms of the emergent consequences of a large number of simple noncognitive processes. We view the entities that serve as the basis for structured probabilistic approaches as abstractions that are occasionally useful but often misleading: they have no real basis in the actual processes that give rise to linguistic and cognitive abilities or to the development of these abilities. Although structured probabilistic approaches can be useful in determining what would be optimal under certain assumptions, we propose that connectionist, dynamical systems, and related approaches, which focus on explaining the mechanisms that give rise to cognition, will be essential in achieving a full understanding of cognition and development.

Section snippets

Emergence of structure in cognition

Emergence is ubiquitous in nature: consider the complex structure of an anthill. It can have a complex architecture, with a complex network of passageways leading from deep underground to 7.5 m into the sky. One might suppose that ants possess a blueprint for creating such structures, but something far simpler is in play [1]. Ants are sensitive to certain gasses within their nests; when these gasses build up they move grains of dirt to the outside. This activity lets the gasses escape and has

The A-not-B error: absence of a hypothesis or emergent consequence of the dynamics of motor behavior?

The A-not-B task was introduced by Piaget [11] to measure the development of the object concept: the belief that objects exist independent of one's own actions. In the canonical form of the task (Figure 1), after searching for an object at one location, then seeing it hidden at a new location, 8–10-month-old infants reach back to that first location, whereas older infants reach correctly to the new location. Although the A-not-B task has not been an explicit focus of research within the

Connectionist vs. structured probabilistic approaches to semantic cognition

We consider next a domain that both approaches have addressed, that of semantic cognition. Under the structured probabilistic approach [9], the acquisition of semantic knowledge is viewed as the inductive problem of deciding which of several alternative conceptual structures is most likely to have generated the observed properties of a set of items in a domain. This computation requires specification of considerable initial knowledge: (i) knowledge of the hypothesis space, the space of possible

Conclusion

Far from being functionally equivalent or simply different levels of description, different theoretical frameworks lead to different conclusions about the nature of cognitive development, the kind of questions that a cognitive theory should address, and how explanations of different domains of behavior should be unified. The structured probabilistic approach takes the stand that it is crucial to specify the goal of cognitive processes at an abstract, computational or competence level of

Glossary

Connectionism
An approach to modeling cognition based on the idea that the knowledge underlying cognitive activity is stored in the connections among neurons. In connectionist models, knowledge is acquired by using an experience-driven connection adjustment rule to alter the strengths of connections among neuron-like processing units.
Dynamical field theory
Originally formulated as a theory of movement preparation, in which movement parameters are represented by distributions of activation defined

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