The frontal eye field as a prediction map

Crapse, Trinity B.; Sommer, Marc A.

doi:10.1016/s0079-6123(08)00656-0

Cited by 36 publications

(28 citation statements)

References 28 publications

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“…As reviewed by Hamker et al, different computational models of visual stability incorporate varying types of dynamic receptive fields. Remapping in the frontal eye fields (FEF), for example, has been linked to the development of a predictive map of the expected consequences of the saccade [21,26] while remapping in the posterior parietal cortex has been linked to post-saccadic updating of visual working memory [27]. Two of the articles in this special issue [20,21] review the recent attempts to directly link remapping circuits to behaviour by disrupting the flow of information within the primate brain.…”

Section: Neural Mechanisms Of Visual Stability: Remapping and Spatiotopymentioning

confidence: 99%

Visual stability

Melcher

2011

Phil. Trans. R. Soc. B

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Our vision remains stable even though the movements of our eyes, head and bodies create a motion pattern on the retina. One of the most important, yet basic, feats of the visual system is to correctly determine whether this retinal motion is owing to real movement in the world or rather our own selfmovement. This problem has occupied many great thinkers, such as Descartes and Helmholtz, at least since the time of Alhazen. This theme issue brings together leading researchers from animal neurophysiology, clinical neurology, psychophysics and cognitive neuroscience to summarize the state of the art in the study of visual stability. Recently, there has been significant progress in understanding the limits of visual stability in humans and in identifying many of the brain circuits involved in maintaining a stable percept of the world. Clinical studies and new experimental methods, such as transcranial magnetic stimulation, now make it possible to test the causal role of different brain regions in creating visual stability and also allow us to measure the consequences when the mechanisms of visual stability break down.

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Section: Neural Mechanisms Of Visual Stability: Remapping and Spatiotopymentioning

confidence: 99%

Visual stability

Melcher

2011

Phil. Trans. R. Soc. B

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“…This account recasts the brain as a Bayesian inference machine [15]. Perception relies on probabilistic models at each level of cortical hierarchy [8], [9], [11], [16].…”

Section: Introductionmentioning

confidence: 99%

Surprised at All the Entropy: Hippocampal, Caudate and Midbrain Contributions to Learning from Prediction Errors

et al. 2012

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Influential concepts in neuroscientific research cast the brain a predictive machine that revises its predictions when they are violated by sensory input. This relates to the predictive coding account of perception, but also to learning. Learning from prediction errors has been suggested for take place in the hippocampal memory system as well as in the basal ganglia. The present fMRI study used an action-observation paradigm to investigate the contributions of the hippocampus, caudate nucleus and midbrain dopaminergic system to different types of learning: learning in the absence of prediction errors, learning from prediction errors, and responding to the accumulation of prediction errors in unpredictable stimulus configurations. We conducted analyses of the regions of interests' BOLD response towards these different types of learning, implementing a bootstrapping procedure to correct for false positives. We found both, caudate nucleus and the hippocampus to be activated by perceptual prediction errors. The hippocampal responses seemed to relate to the associative mismatch between a stored representation and current sensory input. Moreover, its response was significantly influenced by the average information, or Shannon entropy of the stimulus material. In accordance with earlier results, the habenula was activated by perceptual prediction errors. Lastly, we found that the substantia nigra was activated by the novelty of sensory input. In sum, we established that the midbrain dopaminergic system, the hippocampus, and the caudate nucleus were to different degrees significantly involved in the three different types of learning: acquisition of new information, learning from prediction errors and responding to unpredictable stimulus developments. We relate learning from perceptual prediction errors to the concept of predictive coding and related information theoretic accounts.

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“…We have proposed elsewhere that predictive operations, grounded in such probabilistic inference, are implemented in the primate visuosaccadic system for the purpose of constructing a stable transaccadic percept [50]. At the center of the model are CD and shifting RFs of the FEF, and we propose they together constitute an inferential architecture that engages in predictive coding.…”

Section: Introductionmentioning

confidence: 99%

Corollary discharge circuits in the primate brain

Crapse

Sommer

2008

Current Opinion in Neurobiology

140

106

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Movements are necessary to engage the world, but every movement results in sensorimotor ambiguity. Self-movements cause changes to sensory inflow as well as changes in the positions of objects relative to motor effectors (eyes and limbs). Hence the brain needs to monitor self-movements, and one way this is accomplished is by routing copies of movement commands to appropriate structures. These signals, known as corollary discharge (CD), enable compensation for sensory consequences of movement and preemptive updating of spatial representations. Such operations occur with a speed and accuracy that implies a reliance on prediction. Here we review recent CD studies and find that they arrive at a shared conclusion: CD contributes to prediction for the sake of sensorimotor harmony. IntroductionImagine a monkey leaping through the forest canopy. As it moves, branches brush against its skin, leaves rustle at its hands and feet, and patterns of light and shade alternate across its eyes ( Figure 1a). In principle, the monkey should be startled by these sensory events. The activation of its skin receptors could be interpreted as due to an insect landing on its leg and the sounds and shadows as due to a predator looming. Surprisingly, the monkey does not find these sensory events alarming; they are expected, partly because the monkey has access to an internal report of its own movements called corollary discharge (CD).Each of the monkey's movements is initiated by motor commands that travel from movement areas of the brain out to the periphery to contract the appropriate muscles. Neural copies of the movement commands -the CD signals -are issued simultaneously and travel in the opposite direction, impinging upon sensory brain areas (Figure 1b). The CD information tells the sensory areas about the upcoming movements and allows them to prepare for the sensory consequences of the movement. As a result, our monkey in the forest is not surprised by the brush of the branch, the rustle of the leaves, or the change in shade. Were the monkey at rest or moving passively -for example, sitting on a branch that sways in the breeze -the same sensory events would be startling indeed.As a theoretical concept, CD has a rich history [1]. Behavioral and psychophysical evidence for CD has accumulated over a century and received a significant boon in 1950 when two teams of researchers working independently and on different continents arrived at the same conclusion: Motor and sensory systems require reciprocal coordination, implying that motor signals travel to sensory structures [2,3]. Now, decades later, we know the motor-to-sensory signal to be quite ubiquitous as demonstrated by a wealth of direct physiological evidence collected from a menagerie of species [4][5][6]. In this review we examine recent behavioral and physiological studies of CD in primates and place an emphasis on its role in prediction. We close by considering computational treatments of the concept. CD and human behaviorHuman psychophysical studies have provided much insight...

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The frontal eye field as a prediction map

Cited by 36 publications

References 28 publications

Visual stability

Visual stability

Surprised at All the Entropy: Hippocampal, Caudate and Midbrain Contributions to Learning from Prediction Errors

Corollary discharge circuits in the primate brain

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