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Predictive coding

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HYPOTHESIS AND THEORY
17 May 2013
What is Bottom-Up and What is Top-Down in Predictive Coding?
Karsten Raussand Gilles Pourtois

2 Authors

What is Bottom-Up and What is Top-Down in Predictive Coding?
Everyone knows what bottom-up is, and how it is different from top-down. At least one is tempted to think so, given that both terms are ubiquitiously used, but only rarely defined in the psychology and neuroscience literature. In this review, we highlight the problems and limitations of our current understanding of bottom-up and top-down processes, and we propose a reformulation of this distinction in terms of predictive coding.

142,965 views

152 citations

REVIEW
11 December 2012
How Prediction Errors Shape Perception, Attention, and Motivation
Hanneke EM Den Ouden, 1 more and Floris P De Lange

3 Authors

How Prediction Errors Shape Perception, Attention, and Motivation
Prediction errors are a central notion in theoretical models of reinforcement learning, perceptual inference, decision-making and cognition, and prediction error signals have been reported across a wide range of brain regions and experimental paradigms. Here, we will make an attempt to see the forest for the trees, considering the commonalities and differences of reported prediction errors signals in light of recent suggestions that the computation of prediction errors forms a fundamental mode of brain function. We discuss where different types of prediction errors are encoded, how they are generated, and the different functional roles they fulfil. We suggest that while encoding of prediction errors is a common computation across brain regions, the content and function of these error signals can be very different, and are determined by the afferent and efferent connections within the neural circuitry in which they arise.

90,967 views

503 citations

Perceptions as Hypotheses: Saccades as Experiments
ORIGINAL RESEARCH
28 May 2012
Perceptions as Hypotheses: Saccades as Experiments
Karl Friston, 2 more and Michael Breakspear

4 Authors

If perception corresponds to hypothesis testing (Gregory, 1980); then visual searches might be construed as experiments that generate sensory data. In this work, we explore the idea that saccadic eye movements are optimal experiments, in which data are gathered to test hypotheses or beliefs about how those data are caused. This provides a plausible model of visual search that can be motivated from the basic principles of self-organised behaviour: namely, the imperative to minimise the entropy of hidden states of the world and their sensory consequences. This imperative is met if agents sample hidden states of the world efficiently. This efficient sampling of salient information can be derived in a fairly straightforward way, using approximate Bayesian inference and variational free energy minimisation. Simulations of the resulting active inference scheme reproduce sequential eye movements that are reminiscent of empirically observed saccades and provide some counterintuitive insights into the way that sensory evidence is accumulated or simulated into beliefs about the world.

29,875 views

455 citations

HYPOTHESIS AND THEORY
8 May 2012
Free-Energy Minimization and the Dark-Room Problem
Karl Friston, 1 more and Andy Clark

3 Authors

Recent years have seen the emergence of an important new fundamental theory of brain function. This theory brings information-theoretic, Bayesian, neuroscientific, and machine learning approaches into a single framework whose overarching principle is the minimization of surprise (or, equivalently, the maximization of expectation). The most comprehensive such treatment is the ‘free energy minimization’ formulation due to Karl Friston (see e.g. Friston and Stephan (2007), Friston (2010) – see also Thornton (2010), Fiorillo (2010) A recurrent puzzle raised by critics of these models is that biological systems do not seem to avoid surprises. We do not simply seek a dark, unchanging chamber and stay there. This is the ‘Dark Room Problem’. Here, we describe the problem and further unpack the issues to which it speaks. Using the same format as the prologue of Eddington’s Space, Time and Gravitation (Eddington 1920) we present our discussion as a conversation between: An Information THEORIST (Thornton) A PHYSICIST (Friston) A PHILOSOPHER (Clark)

46,909 views

293 citations

HYPOTHESIS AND THEORY
7 May 2012
Predictive Coding Strategies for Developmental Neurorobotics
Jun-Cheol Park, 2 more and Dae-Shik Kim

4 Authors

8,218 views

8 citations