Navigating the Affordance Landscape: Feedback Control as a Process Model of Behavior and Cognition.

By Giovanni Pezzulo, Paul Cisek. 2016

379 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Social Psychology

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Citations in the corpus

Towards Continual Reinforcement Learning: A Review and Perspectives (2020, background): “…the agent-environment boundary is an open concept because the space of affordances (Khetarpal, Ahmed, Comanici, Abel, and Precup, 2020b) might evolve over time (Pezzulo and Cisek, 2016) and may only emerge as a result of agent-environment interaction (Gibson, 1977; Heft, 1989; Chemero, 2003).”
Inductive biases for deep learning of higher-level cognition (2020, background): “A human-centric version of this viewpoint is the psychological theory of affordances (Gibson, 1977; Cisek, 2007; Pezzulo and Cisek, 2016) that can be linked to predictive state representations in reinforcement learning: what can we do with an object? What are the consequences of these actions? Learn”
Ecological-Enactive Cognition as engaging with a field of relevant affordances (2018, background): “…recent years, changes in body and brain gained a lot of attention in research on what is called ‘the anticipating brain’ (see e.g. Friston 2010, 2011; Allen and Friston, 2016; Allen and Gallagher 2016; Kiebel, Daunizeau, and Friston 2008; Cisek 2007; Cisek and Kalaska 2010; Pezzulo and Cisek 2016).”
Direct-fit to nature: an evolutionary perspective on biological (and artificial) neural networks (2019, background): “…the traditional, strongly representational, computational approach evoked resentment and skepticism in the ﬁeld, which took the position that without workable computational models, the argument in favor of direct perception seemed vague and unscientiﬁc (Ullman, 1980; cf. Pezzulo and Cisek, 2016).”
Conscious Processing and the Global Neuronal Workspace Hypothesis (2020, background): “Working memory operations can thereby execute the successive mental steps required to solve a task and permit mental simulations, i.e., subjects can navigate through sequences of working memory states to explore the future consequences of actions (Pezzulo and Cisek, 2016).”
Hierarchical Active Inference: A Theory of Motivated Control (2018, background): “Higher hierarchical levels contextualize this inference, finessing outcome prediction based on additional (semantic or episodic) information as well as on long-term action consequences [41] and future affordances [42]; for example, choosing a restaurant in anticipation of satiating hunger.”