Some observations I came across in a cognition class:
-> We can perform a visual lookup (identify the circle colored differently from these other circles in this group of circles) in O(1) time. Or if we are asked to locate a friend from an array of people (array fits in field-of-view), we can identify said friend in constant time
-> By nature, we classify objects based on how we use them. So a table and a chair have the same physical 4-legged, flat-top structure. But we differentiate them because we use them differently:
So, my guess :
-> A highly trained decision tree allowing us to perform classification of objects in our environment based on their use. (the training set is whatever is in our field of view and as such we are bombarded with large amounts of data). A hebbian-rule based ANN for training.
For dealing with visual stimulus at-least, I would bet that this is the model we are using right now.
Also, our classifier seems to be operating in parallel on all the objects available in our FOV.
-> We can perform a visual lookup (identify the circle colored differently from these other circles in this group of circles) in O(1) time. Or if we are asked to locate a friend from an array of people (array fits in field-of-view), we can identify said friend in constant time
-> By nature, we classify objects based on how we use them. So a table and a chair have the same physical 4-legged, flat-top structure. But we differentiate them because we use them differently:
So, my guess :
-> A highly trained decision tree allowing us to perform classification of objects in our environment based on their use. (the training set is whatever is in our field of view and as such we are bombarded with large amounts of data). A hebbian-rule based ANN for training.
For dealing with visual stimulus at-least, I would bet that this is the model we are using right now.
Also, our classifier seems to be operating in parallel on all the objects available in our FOV.