Welcome to the Nexus of Ethics, Psychology, Morality, Philosophy and Health Care

Welcome to the nexus of ethics, psychology, morality, technology, health care, and philosophy

Wednesday, October 28, 2015

The predictive brain and the “free will” illusion

Dirk De Ridder, Jan Verplaetse and Sven Vanneste
Front. Psychol., 30 April 2013

Here is an excerpt:

From an evolutionary point of our experience of “free will” can best be approached by the development of flexible behavioral decision making (Brembs, 2011). Predators can very easily take advantage of deterministic flight reflexes by predicting future prey behavior (Catania, 2009). The opposite, i.e., random behavior is unpredictable but highly inefficient. Thus learning mechanisms evolved to permit flexible behavior as a modification of reflexive behavioral strategies (Brembs, 2011). In order to do so, not one, but multiple representations and action patterns should be generated by the brain, as has already been proposed by von Helmholtz. He found the eye to be optically too poor for vision to be possible, and suggested vision ultimately depended on computational inference, i.e., predictions, based on assumptions and conclusions from incomplete data, relying on previous experiences. The fact that multiple predictions are generated could for example explain the Rubin vase illusion, the Necker cube and the many other stimuli studied in perceptual rivalry, even in monocular rivalry. Which percept or action plan is selected is determined by which prediction is best adapted to the environment that is actively explored (Figure 1A). In this sense, predictive selection of the fittest action plan is analogous to the concept of Darwinian selection of the fittest in natural and sexual selection in evolutionary biology, as well as to the Mendelian selection of the fittest allele in genetics and analogous the selection of the fittest quantum state in physics (Zurek, 2009). Bayesian statistics can be used to select the model with the highest updated likelihood based on environmental new information (Campbell, 2011). What all these models have in common is the fact that they describe adaptive mechanisms to an ever changing environment (Campbell, 2011).

The entire article is here.

No comments: