There are many types of cognitive models, and they can range from box-and-arrow diagrams to a set of equations to software programs that interact with the same tools that humans use to complete tasks (e.g., computer mouse and keyboard).
A number of key terms are used to describe the processes involved in the perception, storage, and production of speech.
The unseen psychological events that occur between the arrival of an input signal and the production of speech are the focus of psycholinguistic models.
In box-and-arrow psycholinguistic models, each hypothesized level of representation or processing can be represented in a diagram by a “box,” and the relationships between them by “arrows,” hence the name.
Sometimes (as in the models of Smith, 1973, and Menn, 1978, described later in this paper) the arrows represent processes additional to those shown in boxes.
Some have only one or two boxes between the input and output signals (e.g., Menn, 1978; Smith, 1973), whereas others have multiple boxes representing complex relationships between a number of different information-processing events (e.g., Hewlett, 1990; Hewlett, Gibbon, & Cohen- McKenzie, 1998; Stackhouse & Wells, 1997).
As the following description of several models will illustrate, the nature of this information and thus the type(s) of representation present in the child's knowledge base have captured the attention of researchers for some time.
(Elise Baker et al. Psycholinguistic Models of Speech Development and Their Application to Clinical Practice.
Rather than deriving a mathematical analytical solution to the problem, experimentation with the model is done by changing the parameters of the system in the computer, and studying the differences in the outcome of the experiments.
Cognition takes place by transforming static symbol structures in discrete, sequential steps.
[6] A typical dynamical model is formalized by several differential equations that describe how the system's state changes over time.
Early work in the application of dynamical systems to cognition can be found in the model of Hopfield networks.
This early model was a major step toward a dynamical systems view of human cognition, though many details had yet to be added and more phenomena accounted for.
Once a basic grammar had been learned, the networks could then parse complex sentences by predicting which words would appear next according to the dynamical model.
[12] One proposed mechanism of a dynamical system comes from analysis of continuous-time recurrent neural networks (CTRNNs).
Outputs of the network represent whether the foot is up or down and how much force is being applied to generate torque in the leg joint.
These modules can in theory be combined to create larger circuits that comprise a complete dynamical system.
The first transforms the representation of the agents action into specific patterns of muscle activation that in turn produce forces in the environment.
At the second level of time evolution, behavior can be expressed as a dynamical system represented as a vector field.
In the mediator view, internal states carry information about the environment which is used by the system in obtaining its goals.
Rather than being at odds with traditional cognitive science approaches, dynamical systems are a natural extension of these methods and should be studied in parallel rather than in competition.