Using biologically plausible neural models to specify the functional and neural mechanisms of visual search

Humphreys, Glyn W.; Allen, Harriet A.; Mavritsaki, Eirini

doi:10.1016/s0079-6123(09)17609-4

Cited by 7 publications

(8 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Visual search requires an interaction between bottom-up processes responding to local feature differences between visual elements, and top-down processes that set excitatory expectations for targets as well as inhibiting irrelevant distractors (Treisman and Kanwisher, 1998). Both excitatory guidance of search to targets and rejection of irrelevant distractors are thus required (Humphreys et al, 2009). Separate networks of areas in parietal and occipital cortex have been linked to the top-down prioritisation of target features and the active suppression of distractors (Mavritsaki et al, 2010).…”

Section: Discussionmentioning

confidence: 99%

Evidence for a general face salience signal in human amygdala

et al. 2011

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Evidence for a general face salience signal in human amygdala

et al. 2011

View full text Add to dashboard Cite

“…It is fairly easy to show that maximizing salience is the same as maximizing Bayesian surprise (Friston et al, 2012a). This is important because it links salience in the context of active inference with salience in the theoretical (Humphreys et al, 2009) and empirical literature (Shen et al, 2011; Wardak et al, 2011). Here, we will focus on the principle of maximum mutual information.…”

Section: Action Perception and Controlmentioning

confidence: 99%

What is value—accumulated reward or evidence?

Friston¹,

Adams²,

Montague³

2012

Front. Neurorobot.

View full text Add to dashboard Cite

Why are you reading this abstract? In some sense, your answer will cast the exercise as valuable—but what is value? In what follows, we suggest that value is evidence or, more exactly, log Bayesian evidence. This implies that a sufficient explanation for valuable behavior is the accumulation of evidence for internal models of our world. This contrasts with normative models of optimal control and reinforcement learning, which assume the existence of a value function that explains behavior, where (somewhat tautologically) behavior maximizes value. In this paper, we consider an alternative formulation—active inference—that replaces policies in normative models with prior beliefs about the (future) states agents should occupy. This enables optimal behavior to be cast purely in terms of inference: where agents sample their sensorium to maximize the evidence for their generative model of hidden states in the world, and minimize their uncertainty about those states. Crucially, this formulation resolves the tautology inherent in normative models and allows one to consider how prior beliefs are themselves optimized in a hierarchical setting. We illustrate these points by showing that any optimal policy can be specified with prior beliefs in the context of Bayesian inference. We then show how these prior beliefs are themselves prescribed by an imperative to minimize uncertainty. This formulation explains the saccadic eye movements required to read this text and defines the value of the visual sensations you are soliciting.

show abstract

“…In brief, we will see that agents engage actively with their sensorium and must be equipped with prior beliefs that salient features of the world will disclose themselves, or be discovered by active sampling. This leads to a natural explanation for exploratory behavior and visual search strategies, of the sort studied in psychology and psychophysics (Gibson, 1979; Itti and Koch, 2001; Humphreys et al, 2009; Itti and Baldi, 2009; Shires et al, 2010; Shen et al, 2011; Wurtz et al, 2011). Crucially, this behavior is an emergent property of minimizing surprise about sensations and their causes.…”

Section: Introductionmentioning

confidence: 99%

Perceptions as Hypotheses: Saccades as Experiments

Friston¹,

Adams²,

Perrinet³

et al. 2012

Front. Psychology

365

349

View full text Add to dashboard Cite

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-organized behavior: namely, the imperative to minimize 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 minimization. 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 assimilated into beliefs about the world.

show abstract

Using biologically plausible neural models to specify the functional and neural mechanisms of visual search

Cited by 7 publications

References 32 publications

Evidence for a general face salience signal in human amygdala

Evidence for a general face salience signal in human amygdala

What is value—accumulated reward or evidence?

Perceptions as Hypotheses: Saccades as Experiments

Contact Info

Product

Resources

About