A Feature Selection Approach for Emulating the Structure of Mental Representations

Abstract

In order to develop artificial agents operating in complex ever-changing environments, advanced technical memory systems are required. At this juncture, two central questions are which information needs to be stored and how it is represented. On the other hand, cognitive psychology provides methods to measure the structure of mental representations in humans. But the nature and the characteristics of the underlying representations are largely unknown. We propose to use feature selection methods to determine adequate technical features for approximating the structure of mental representations found in humans. Although this approach does not allow for drawing conclusions transferable to humans, it constitutes an excellent basis for creating technical equivalents of mental representations.

Computational assessment of long-term memory structures from SDA-M related to action sequences

“…The SDA-M software then creates matrices containing the correlations (“ R matrix”) and Euclidean distances (“ D matrix”) between all rows of the Z matrix. The distance values in the D matrix (or, equivalently, the correlation values in the R matrix) contain all information to completely define an individual’s representational structure [38]. The subsequent steps of SDA-M are therefore functions of these matrices.…”

“…According to our experience in practice this usually limits the number of incorporable actions to approximately 10-15 (depending on the time required for each decision), because subjects are rarely willing to perform split procedures lasting much longer than quarter of an hour. In a similar vein [38] stated that the number of actions “should not be chosen higher than 20. Otherwise, the decisions made regarding the similarity of stimuli may become inconsistent”.…”