Cognitive science is the study of mental representations and processes. A mental representation is a description of information that exists within the mind. A mental process is a procedure for translating information into representations, representations into other representations or representations into action/behavior. Historically, we are well known for making box and arrow diagrams that describe information flow. Let’s build an example. We’ll start with a narrative.
Imagine an adventurous child has escaped their high-chair and begins exploring the house, armed with their trusty screwdriver (not sonic). Of course, there is an exhausted parent nearby in the kitchen struggling to supervise the kid while working for home. The parent gets a quick glimpse at the escaped child slowly working his way towards the electric socket. He shouts, ``Who wants ice cream?'' to get the child’s attention. The child turn, smiles and runs back into the kitchen.
There’s a lot of information being exchanged here. Let’s dissect it starting with the child:
Now for the father,
And back to the child,
What’s cool here is that this simple story illustrates the breadth of cognitive science. Cognitive science is highly interdisciplinary.
In general, we live rich mental lives, which allow us to create and represent our world, and also the worlds that we inhabit from time to time when we read fiction or watch a movie. The interdisciplinary nature of cognitive science is vital to understanding how we build mental models of the world. The aim of this endeavor is to characterize the nature of human knowledge, and how that knowledge is used processed and acquired. This is the MIT Brain and Cognitive Science party line.
In this course, we are going to introduce you to the landscape of cognitive science, focusing on the kinds of questions we ask, the data we collect to answer these questions, the theories we build to based on these data and the computational models we use to implement these theories. A fundamental belief in cognitive science is that computational modelling can be used to evaluate theories, to generate new hypotheses and to guide the collection of new data.