Whatever Next? Predictive Brains, Situated Agents, and the Future of Cognitive Science.

Citations in the corpus

• Theory of Mind: A Neural Prediction Problem (2013, positive): “…predictive coding models require at least two classes of neurons: neurons that formulate predictions for sensory inputs (“predictor” neurons, also called “representation” neurons; Summerfield et al., 2008; Clark, 2013), and neurons that respond to deviations from the predictions (“error” neurons).”
• Time-Agnostic Prediction: Predicting Predictable Video Frames (2018, background): “Prediction is thought to be fundamental to intelligence (Bar, 2009; Clark, 2013; Hohwy, 2013).”
• Like a Baby: Visually Situated Neural Language Acquisition (2018, unknown): “More generally, the idea of predictive coding holds that the mind forms expectations before perception occurs (see Clark, 2013, for a review).”
• A review of predictive coding algorithms. (2017, background): “Predictive coding suggests one way in which the brain might apply constraints in order to solve the inverse problem of perception (Bubic, von Cramon, & Schubotz, 2010; Clark, 2013; Huang & Rao, 2011; Rao & Ballard, 1999; Spratling, 2014a).”
• Cognitive Penetrability of Perception in the Age of Prediction: Predictive Systems are Penetrable Systems (2015, background): “As argued by Andy Clark in his recent synthesis (Clark 2013), Bthe sheer breadth of application [of predictive coding] is striking.”
• Answering Schrödinger's question: A free-energy formulation (2017, unknown): “, scientific prediction errors) over time [7,34].”
• A nice surprise? Predictive processing and the active pursuit of novelty (2018, background): “…knowledge, while lower ones would specialize in predictions that involve letter-level knowledge, still lower ones in predictions that turn on stroke-level knowledge, and so 1 Predictive Processing (Clark 2013a, b, c) is also known as hierarchical predictive coding and as ‘active inference’.”
• Affective Value in the Predictive Mind (2017, background): “There is some debate about the extent to which such an imperative to maximize prediction error reduction and PEM are one and the same thing (Clark 2013; Froese and Ikegami 2013; Little and Sommer 2013).”
• Hamiltonian Generative Networks (2019, background): “This is true for intelligence in both biological (Friston, 2009; 2010; Clark, 2013) and artificial (Hafner et al., 2019; Battaglia et al., 2013; Watter et al., 2015; Watters et al., 2019) systems.”
• Precise minds in uncertain worlds: predictive coding in autism. (2014, background): “The assumed mechanism is, again, precision (Clark, 2013a).”