The Predictive Brain: A Modular View of Brain and Cognitive Function?

Abstract

Modularity is arguably one of the most influential theses guiding research on brain and cognitive function since phrenology. This paper considers the following question: is modularity entailed by recent Bayesian models of brain and cognitive function, especially the predictive processing framework? It starts by considering three of the most well-articulated arguments for the view that modularity and predictive processing work well together. It argues that all three kinds of arguments for modularity come up short, albeit for different reasons. The analysis in this paper, although formulated in the context of predictive processing, speaks to broader issues with how to understand the relationship between functional segregation and integration and the reciprocal architecture of the predictive brain. These conclusions have implications for how to study brain and cognitive function. Specifically, when cognitive neuroscience works within an acyclic Markov decision scheme, adopted by most Bayesian models of brain and cognitive function, it may very well be methodologically misguided. This speaks to an increasing tendency within the cognitive neurosciences to emphasise recurrent and reciprocal neuronal processing captured within newly emerging dynamical causal modelling frameworks. The conclusions also suggest that functional integration is an organising principle of brain and cognitive function.

Is the Free-Energy Principle a Formal Theory of Semantics? From Variational Density Dynamics to Neural and Phenotypic Representations

“…We find approaches in the biological sciences and in the neuroscience less committed to the view of cognition as a representational process taking place within the boundaries of the brain [ 54 , 55 , 56 , 57 , 58 ]. These views include perceptual and motor control theory [ 59 , 60 ]; robotics [ 61 ]; cybernetics [ 62 , 63 , 64 , 65 ]; and, arguably, the free-energy principle and active inference [ 56 , 57 , 66 ].…”

Markov blankets in the brain

“…An important limitation of this paradigm is that it typically only considers a single level of description, neglecting the rich intrinsic and extrinsic dynamics across regions and microcircuits. In addition, the philosophical assumptions of modular perspectives on neuronal organisation have been criticised (Friston 2002;Colombo, 2013;Palecek, 2017;George and Sunny, 2019;Hipólito and Kirchhoff, 2019). In short, this calls for a more nuanced treatment of partitions and functional interactions.…”

“…A growing literature leverages the Markov blanket construct (Pearl 1988) to formalise dynamic coupling in physical and biological systems , Hipólito, 2019, Ramstead et al, 2018a, Ramstead et al, 2019a, Palacios et al, 2020, Kirchhoff et al, 2018. This construct is a description of the dependencies within and between random dynamical systems -like the brain -that sets a boundary between the inside and outside of each system.…”

Bayes, predictive processing, and the cognitive architecture of motor control

“…Aside from some discussion about whether modularity is compatible with the hierarchical approach (Drayson, 2017;Hipolito & Kirchhoff, 2019), or whether the predictive processing framework entails cognitive penetration (Lupyan & Clark, 2015;Vance & Stokes, 2017;Vetter & Newen, 2014), there has been little systematic discussion of the hierarchical picture's upshot for overall cognitive architecture. 2 On the standard construal of the hierarchical approach, it is tempting to see the mind as a continuous hierarchy, with each level of the hierarchy predicting activity at the one below it, out to the furthest extremities of perception and the motor system.…”