Whatever next? Predictive brains, situated agents, and the future of cognitive science.

By Andy Clark. Behavioral and Brain Sciences, 2013

3,265 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Social Psychology

View Paper: https://www.semanticscholar.org/paper/69304ba2c5d2bff09f7059916ab0aa117bdbea41

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Citations in the corpus

The graphical brain: Belief propagation and active inference (2017, background): “In particular, we assume that the brain engages in some form of (Bayesian) inference—and can therefore be described as maximizing Bayesian model evidence (Clark, 2013; Friston, Kilner, & Harrison, 2006; Hohwy, 2016; Mumford, 1992).”
The anticipating brain is not a scientist: the free-energy principle from an ecological-enactive perspective (2016, background): “30 The recent literature (Clark 2013) on Helmholtzian interpretations of predictive processing contains a number of commentaries from enactivists that overlap with the arguments of this paper in suggestive ways.”
Implicit stereotypes and the predictive brain: cognition and culture in “biased” person perception (2017, background): “This article explores the nature of implicit stereotypes by examining what is meant by “bias” in the psychological literature on stereotyping, and proposes an explanation of how culture influences implicit cognition through the concept of the “predictive brain” (Clark, 2013).”
Troubles with Bayesianism: An introduction to the psychological immune system (2018, background): “…is an idealization—no particular theorist may hold the exact Imperial Bayes position—though positions reasonably close to it are widely held (e.g., Clark, 2013; Friston, 2012; Hohwy, 2013; Oaksford & Chater, 2009; Tenenbaum et al., 2011).3 Accordingly, my aim is to detail problems with a wide…”
Direct-fit to nature: an evolutionary perspective on biological (and artificial) neural networks (2019, background): “This resonates with the notion of ‘‘predictive coding’’ in neuroscience, which has gained momentum over the past two decades (Rao and Ballard, 1999) and is a central pillar of recent, optimization-oriented theories of brain function (Friston, 2010; Clark, 2013; Heeger, 2017).”
Vanilla PP for Philosophers: A Primer on Predictive Processing (2017, background): “However, we would also strongly encourage readers to deepen their understanding of PP by reading (Clark 2016) and (Hohwy 2013), two excellent first philosophical monographs on this topic.”
Wasserstein Dependency Measure for Representation Learning (2019, background): “[45] which has connections to predictive coding in speech [4, 19], predictive processing in the brain [10, 41, 46] and the free energy principle [20].”
Understanding the origin of information-seeking exploration in probabilistic objectives for control (2021, background): “Developments here follow a similar trajectory to perception, which is now often also understood as fundamentally a process of Bayesian inference (Clark, 2013; Itti & Baldi, 2009; Knill & Pouget, 2004; Sanborn & Chater, 2016), a conceptual heritage which traces back to Helmholtz’s ideas of…”
A Cybernetic Theory of Psychopathology (2018, background): “These are not necessarily conscious questions, but rather assessments carried out by the brain unconsciously as well as consciously, through pervasive mechanisms of predictive coding (Clark, 2013; Friston, 2010).”
The sense of agency – a phenomenological consequence of enacting sensorimotor schemes (2017, background): “Despite its appeal, in proposing a simple computational mechanism underlying sensorimotor self- awareness, which moreover seems to fit naturally into the growing ecology of predictive brain theories (Friston 2010; Clark 2013; Seth 2014), the comparator model is likely neither sufficient nor necessar”
How to Entrain Your Evil Demon (2017, background): “The alternative is a model that sees mind as non-representational, extended, situated, embodied or enactive (Hurley 1998; Noë 2004; Gallagher 2005; Thompson 2007; Clark 2008; Hutto and Myin 2013).”
Predictive Processing: A Canonical Cortical Computation (2018, positive): “Predictive processing as a conceptual framework for understanding brain has a long tradition in the fields of computational and cognitive neuroscience, and has been elegantly summarized elsewhere (Clark, 2013; Koster-Hale and Saxe, 2013).”
Consequences of the Now-or-Never bottleneck for signed versus spoken languages (2015, methodology): “More generally, the notion that perception and action involve representational recoding at a succession of distinct representational levels also fits with a long tradition of theoretical and computational models in cognitive science and computer vision (e.g., Bregman 1990; Marr 1982; Miller et al.…”
The free energy principle for action and perception: A mathematical review (2017, methodology): “ironmental causes of sensory signals, which are continually modied by bottom-up prediction error signals communicating mismatches between predicted and actual signals across hierarchical levels (see [8] for a nice review). In the context of the FEP the R-density is 175 updated using a hierarchical p”