TY - CHAP
A2 - Metzinger, Thomas K.
A2 - Wiese, Wanja
AB - The general question of our paper concerns the relationship between Bayesian models of cognition and predictive processing, and whether predictive processing can provide explanatory insight over and above Bayesian models. Bayesian models have been gaining influence in neuroscience and the cognitive sciences since they are able to predict human behavior with high accuracy. Models based on a Bayesian optimal observer are fitted on behavioral data. A good fit is hence interpreted as human subjects “behaving” in a Bayes’ optimal fashion. However, these models are performance-oriented and do not specify which processes could give rise to the observed behavior. Here, David Marr’s (Marr 1982) levels of analysis can help understand the relationship between performance- and process-oriented models or explanations. Bayesian models are situated at the computational level since they specify what the system (in this case the brain) does and why it does it in this manner. Although Bayesian models can constrain the search space for hypotheses at the algorithmic level, they do not provide a precise solution about how a system realizes the observed behavior. Here predictive processing can shed more light on the underlying principles. Predictive processing provides a unifying functional theory of cognition and can thus i) provide an answer at the algorithmic level by answering how the brain realizes cognition, ii) can aid in the interpretation of neurophysiological findings at the implementational level.
AU - Harkness, Dominic L.
AU - Keshava, Ashima
CY - Frankfurt am Main
DO - 10.15502/9783958573178
KW - Predictive processing, Explanation, Bayesian models, Unification, Marr
LA - English
PB - MIND Group
PY - 2017
SE - 16
SN - 9783958573178
ST - Moving from the What to the How and Where – Bayesian Models and Predictive Processing
T2 - Philosophy and Predictive Processing
TI - Moving from the What to the How and Where – Bayesian Models and Predictive Processing
UR - https://predictive-mind.net/papers/moving-from-the-what-to-the-how-and-where-bayesian-models-and-predictive-processing
ID - 16