Attention, Uncertainty, and Free-Energy

By H. Feldman, Karl J. Friston. 2010

1,311 citations recorded by the source corpus.

We suggested recently that attention can be understood as inferring the level of uncertainty or precision during hierarchical perception. In this paper, we try to substantiate this claim using neuronal simulations of directed spatial attention and biased competition. These simulations assume that neuronal activity encodes a probabilistic representation of the world that optimizes free-energy in a Bayesian fashion. Because free-energy bounds surprise or the (negative) log-evidence for internal models of the world, this optimization can be regarded as evidence accumulation or (generalized) predictive coding. Crucially, both predictions about the state of the world generating sensory data and the precision of those data have to be optimized. Here, we show that if the precision depends on the states, one can explain many aspects of attention. We illustrate this in the context of the Posner paradigm, using the simulations to generate both psychophysical and electrophysiological responses. These simulated responses are consistent with attentional bias or gating, competition for attentional resources, attentional capture and associated speed-accuracy trade-offs. Furthermore, if we present both attended and non-attended stimuli simultaneously, biased competition for neuronal representation emerges as a principled and straightforward property of Bayes-optimal perception.

Subfields: Cognitive Neuroscience

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In Cognitive Neuroscience

Citations in the corpus

Theory of Mind: A Neural Prediction Problem (2013, neutral): “By contrast, attention may cause better speed and accuracy of performance by increasing overall neural responses to attended stimuli (Feldman and Friston, 2010; Friston, 2010; Herrmann et al., 2010; Hillyard et al., 1998; Kok et al., 2012b; Martinez-Trujillo and Treue, 2004; Reynolds and Heeger,…”
A Free Energy Principle for Biological Systems (2012, background): “Domain Process or paradigm Perception • Perceptual categorisation (bird songs) [18] • Novelty and omission-related responses [18] • Perceptual inference (speech) [19] Sensory learning • Perceptual learning (mismatch negativity) [33] Attention • Attention and the Posner paradigm [20] • Attention and ”
Navigating the Affordance Landscape: Feedback Control as a Process Model of Behavior and Cognition. (2016, neutral): “Still, the fact that some levels can have relatively more importance than others can be captured in these systems using mechanisms of gain (or precision-based) control, which essentially weigh prediction errors by their relative importance or reliability [76].”
Cognitive Control (2012, neutral): “The work done by Feldman and Friston [45] directly relates the neuropsychological ideas to the probabilistic view of an environment.”
The brain basis of emotion: A meta-analytic review (2012, neutral): “Feldman and Friston (2010) argue that attention may be the process of optimising the post-synaptic Commentary/Lindquist et al.: The brain basis of emotion 166 BEHAVIORAL AND BRAIN SCIENCES (2012) 35:3 https://doi.org/10.1017/S0140525X11001750 Downloaded from https://www.cambridge.org/core.”
Action Is Enabled by Systematic Misrepresentations (2017, background): “As we will see below, optimizing precision estimates has been interpreted in terms of attentional processing (cf. Friston & Stephan, 2007; Feldman & Friston, 2010).”
The role of prediction in social neuroscience (2012, methodology): “…scheme has been applied to perceptual inference (Kiebel et al., 2009), perceptual learning (Friston, 2008), reinforcement learning (Friston et al., 2009), active inference (Friston et al., 2010), attentional processing (Feldman and Friston, 2010), and sensorimotor control (Wolpert and Miall, 1996).”
Words and the World (2015, background): “Like Feldman and Friston (2010), summarizes computational accounts of hierarchical predictive coding and its relationship to competing theories.”
An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity (2017, background): “It has been shown in the past how networks in the predictive coding framework can perform unsupervised learning, attentional modulations, and action selection (Rao & Ballard, 1999; Feldman & Friston, 2010; Friston, Daunizeau, Kilner, & Kiebel, 2010).”
Whatever next? Predictive brains, situated agents, and the future of cognitive science. (2013, background): “Transposed (in ways we are about to explore) to the neural domain, this makes prediction error into a kind of proxy (Feldman and Friston (2010)) for sensory information itself. Later, when we consider predictive processing in the larger setting of information theory and entropy, we will see that pre”
Attention Reverses the Effect of Prediction in Silencing Sensory Signals (2011, positive): “(B) According to recent models of perceptual inference (Rao 2005; Friston 2009; Feldman and Friston 2010), attention increases the precision of predictions.”
What do we mean by prediction in language comprehension? (2015, background): “…within her generative model, and use this estimate to modulate the degree to which she updates her beliefs (for a given prior distribution and likelihood function) at this level of representation (i.e. “weight” prediction error, for related discussion, see Feldman & Friston, 2010; Friston, 2010).”
Neural Elements for Predictive Coding (2016, background): “Precision is also considered to be broadcast and coordinated more globally, because the regulation of precision is seen as equivalent to the psychological construct of selective attention (Feldman and Friston, 2010).”
The Computational Anatomy of Psychosis (2013, background): “This precision is thought to be encoded by post-synaptic gain of neuronal populations reporting prediction errors – the principal or pyramidal cells of superficial cortical layers (Mumford, 1992; Feldman and Friston, 2010).”
Vanilla PP for Philosophers: A Primer on Predictive Processing (2017, background): “The difference between bottom-up and top-down changes in precision estimates can be linked to the difference between endogenous and exogenous attention (for details, see Feldman and Friston 2010 and Hohwy 2012).”