IEEE Computational Intelligence Society 4/12/2006 11/100 Models can be analyzed in ways people cannot In the following, I specifically refer to neural
{"Image DocumentName": "How (I like) to build Cognitive Models", "Image ImageDescription": "I like to be able to relate them to the brain, so neurally plausible models are preferred -- neural nets. The model should be a working model of the actual task, rather than a cartoon version of it. Of course, the model should nevertheless be simplifying (i.e. it should be constrained to the essential features of the problem at hand): Do we really need to model the (supposed) translation invariance and size invariance of biological perception? As far as I can tell, NO! Then, take the model as is and fit the experimental data: 0 fitting parameters is preferred over 1, 2 , or 3. IEEE Computational Intelligence Society 4/12/2006.", "Image XResolution": "37", "Image YResolution": "37", "Image ResolutionUnit": "Pixels/Centimeter", "Image Software": "SlidePlayer", "Image YCbCrPositioning": "Centered"}