A computational cognitive model explores the essence of cognition and various cognitive functionalities through developing detailed, process-based understanding by specifying corresponding computation models.
Sylvain Baillet discusses various aspects of cognitive computation models
Computational model is a mathematical model using computation to study complex systems. Typically one sets up a simulation with the desired parameters and lets the computer run. One then looks at the output to interpret the behavior of the model.
Computational cognitive models are computational models used in the field of cognitive science. Models in cognitive science can be generally categorized into computational, mathematical or verbal-conceptual models. At present, computational modeling appears to be the most promising approach in many respects and offers more flexibility and expressive power than other approaches.
Computational models are mostly process based theories i.e., they try to answer how human performance comes about and by what psychological mechanism.
Among the different computational cognitive models, neural networks are by far the most commonly used connectionist model today. The prevailing connectionist approach today was originally known as parallel distributed processing (PDP). It is an artificial neural network approach that stressed the parallel nature of neural processing, and the distributed nature of neural representation. It is now common to fully equate PDP and connectionism.
Neural networks were inspired by the structure of human brains. One particular variant of the neural network is called the recurrent neural network. It embodies the philosophy that learning requires remembering. Recurrent neural networks are also becoming a popular computational model for cognitive sciences.
Try it yourself: Play with a neural network to see how it works. A Neural Network Playground
Real World Example: Optimizing Teaching
Cognitive Computational models are used to do tasks that are hard or impossible to do with a lab experiments e.g., too many people are involved for the experiment to be feasible. For example, let us assume a teacher wants to teach 100 problems to children in class. But due to time constraint she can only teach 30 problems to the students. The teacher would preferably like to select 30 problems such that the children learn the most i.e., perform well on all 100 problems. Note that, there are 100C30 = 2.93e25 possible question sets. To evaluate the question sets, the teacher would need to teach each dataset to one group of children and evaluate their performance. Let there be 30 children in each class then the total number of children required would be 30 X 2.93e25 = 8.81e26 which is large. This makes identifying an optimal question set infeasible.
This task can be simplified if a cognitive computational model of children for that particular task can be devised. Then the teacher only needs to test the question sets on the cognitive model to figure out which one is the best. This saves a lot of time and is feasible.
- For more information about our project on optimizing human learning with machine teaching: OMELET
- “Introduction to Computational Cognitive Modeling” by Ron Sun talks in detail about computational cognitive models.
- Some resources related to neural networks can be found here
- More resources for cognitive computational modeling can be found from the Knowledge and Concepts Laboratory