Connectionism and Learning: A Web of Development

I have chosen connectionism and its potential ability to model various learning processes in the brain by a multi-disciplinary approach that combines many different theoretical approaches that have recently been given a big boost with advances in technology. The basic principles that define the connectionism model involve a sense of biological realism that combines interconnected networks that form a more complex network that could explain the processes within the human brain, as well as also serve as a model that could also be used to develop non-human networks such as AI for instance. Although it is not necessarily clear how this research might be relevant to my career goals at present, the research seems to be developing so fast that knowledge of this subject could be entirely relevant within the next five years.

Dalege, J., van den Berg, H., Borsboom, D., Conner, M., & van der Mas, H. (2016). Toward a Formalized Account of Attitudes: The Causal Attitude Network (CAN) Model. Psychological Review, 2-21.

Mayor, J., Gomez, P., Chang, F., & Lupyan, G. (2014). Connectionism coming of age: legacy and future challenges. Frontiers in Psychology. doi:10.3389/fpsyg.2014.00187

Nelson, R. (2013). Expanding the Role of Connectionism in SLA theory. Language Learning, 1-33.

Plaut, D., & Vande Velde, A. (2017). Statistical Learning of Parts and Wholes: A Neural Network Approach. Journal of Experimental... (2015). A Prospective Framework for the Design of Ideal Artificial Moral Agents: Insights from the Science of Heroism in Human. Minds and Machines, 57-71.
This article was chosen because it provides a rich and detailed history of how this model developed, as well as some discussion of where the model could go in terms of advancement into the future. In 1986, Rumelhart and McClelland took the cognitive science community by storm with the Parallel Distributed Processing (PDP) framework; which sought to construct at the algorithmic level models of cognition that were compatible with their implementation in the biological substrate (Mayor, Gomez, Chang, & Lupyan, 2014). After walking through some of the obsticles that the theory has so far embraced, it talks about its key challenge, learning abstract structural representations, and how there are many gaps that need to be filled before this model could explain complex intelligence.

Dalege, J., van den Berg, H., Borsboom, D., Conner, M., & van der Mas, H. (2016). Toward a Formalized Account of Attitudes: The Causal Attitude Network (CAN) Model. Psychological Review, 2-21.

This article evaluates the possibility of the CAN model to explain the reactions that people have to events, as well as the interactions between these interactions. For example, when a person jumps at the sight of a snake, they are not undergoing a rational discussion of the threat of…