Psychological implicit motives construct as an emergent fractal attractor from intermittent neurophysiological responses: simulation and entropy-like characterization

Implicit Motives (IM) are non-conscious needs that drive humanbehavior towards the achievement of incentives that are affectivelyincited. Repeated affective experiences providing satisfying rewardshave been held responsible of the Implicit Motives building. Re-sponses to rewarding experiences have a biological basis via close con-nections with neurophysiological systems controlling neurohormonerelease. We propose an iteration random function system acting ina metric space to model experience-reward interactions. This modelis based on key facts of IM theory reported in a broad number ofstudies. The model shows how (random) responses produced by in-termittent random experiences create a well-defined probability distri-bution on an attractor, thus providing an insight into the underlyingmechanism leading to the emergence of IM psychological structures.Implicit Motives robustness and resilience properties appear theoret-ically explained by the model. The model also provides uncertaintyentropy-like parameters to characterize the Implicit Motives whichhopefully might be useful, beyond the mere theoretical frame, whenused in combination with neurophysiological methods.