Structural coding versus free-energy predictive coding

Abstract

Focusing on visual perceptual organization, this article contrasts the free-energy (FE) version of predictive coding (a recent Bayesian approach) to structural coding (a long-standing representational approach). Both use freeenergy minimization as metaphor for processing in the brain, but their formal elaborations of this metaphor are fundamentally different. FE predictive coding formalizes it by minimization of prediction errors, whereas structural coding formalizes it by minimization of the descriptive complexity of predictions. Here, both sides are evaluated. A conclusion regarding competence is that FE predictive coding uses a powerful modeling technique, but that structural coding has more explanatory power. A conclusion regarding performance is that FE predictive coding-though more detailed in its account of neurophysiological data-provides a less compelling cognitive architecture than that of structural coding, which, for instance, supplies formal support for the computationally powerful role it attributes to neuronal synchronization.

Expectation and attention increase the integration of top-down and bottom-up signals in perception through different pathways

“…Non-Bayesian models of hierarchical perceptual inference may also be consistent with our association of the IMs in the HFT paradigm with the integration of higher-level, more abstract, and semantically meaningful signals, with lower-level features extracted from the sensory input. Such a representational approach is set forward, for example, in the structural coding theory of perception [45], which also incorporates task-relevant attention into its view of hierarchical visual processing. Nevertheless, in structural coding, top-down signals (or hypotheses) are understood to be constructed on the fly from the sensory data, rather than reflecting expectations derived from past experience.…”

The Joint Effects of Spatial Cueing and Transcranial Direct Current Stimulation on Visual Acuity

“…Such a change in connectivity may involve not only forward but also feedback connections to early visual areas. According to predictive coding models (revised by Van der Helm, 2016 ), human brain corrects error in a cascade of processing. Namely, higher-level cortical systems attempt to predict the inputs to lower-level systems.…”

Dubious Claims about Simplicity and Likelihood: Comment on Pinna and Conti (2019)

“…At one end of this spectrum are Bayesian approaches like Friston's free-energy predictive coding approach [18]. This approach claims to have high explanatory power, but in fact, hardly goes beyond data accommodation, hardly produces falsifiable predictions, and suffers from computational intractability (see, e.g., [19][20][21][22][23]). In my view, it therefore qualifies as what Chomsky called an "analogic guess", that is, it "creates the illusion of a rigorous scientific theory with very broad scope" [24] (p. 32).…”

“…As a matter of fact, for alleged support, they all referred consistently to either Chater [46] or MacKay [55], or to both. These sources are discussed next (for more details, see in [17,22,56,57]).…”