Editing Cognitive model (section)

==== Language acquisition====
By taking into account the [[Evolutionary developmental biology|evolutionary development]] of the human [[nervous system]] and the similarity of the [[brain]] to other organs, [[Jeffrey Elman|Elman]] proposed that [[language]] and cognition should be treated as a dynamical system rather than a digital symbol processor.<ref>Elman, J. L. (1995). [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.50.7356&rep=rep1&type=pdf Language as a dynamical system]. In R.F. Port and T. van Gelder (Eds.), Mind as motion: Explorations in the Dynamics of Cognition. (pp. 195-223). Cambridge, Massachusetts: MIT Press.</ref>  Neural networks of the type Elman implemented have come to be known as [[Recurrent neural networks|Elman networks]].  Instead of treating language as a collection of static [[Lexicon|lexical]] items and [[grammar]] rules that are learned and then used according to fixed rules, the dynamical systems view defines the [[lexicon]] as regions of state space within a dynamical system. Grammar is made up of [[attractor]]s and repellers that constrain movement in the state space. This means that representations are sensitive to context, with mental representations viewed as trajectories through mental space instead of objects that are constructed and remain static. Elman networks were trained with simple sentences to represent grammar as a dynamical system. 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.<ref>Elman, J. L. (1991). [https://link.springer.com/content/pdf/10.1007/BF00114844.pdf Distributed representations, simple recurrent networks, and grammatical structure]. Machine Learning, 7, 195-225.</ref>