Belief states as a framework to explain extra-retinal influences in visual cortex

Nienborg, Hendrikje; Roelfsema, Pieter R.

doi:10.1016/j.conb.2014.10.013

Cited by 25 publications

(32 citation statements)

References 69 publications

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“…The ongoing debate regarding the time course of cognitive penetration in vision emphasises a need to combine both spatial (e.g., fMRI) and temporal (e.g., EEG, MEG) information sources. Furthermore, work in non-human primates can make valuable contributions to this debate; however future studies using simultaneous recordings in both higher-level regions and V1 sites are needed to assess both the origins and targets of top-down effects (Nienborg & Roelfsema, 2015). …”

Section: Discussionmentioning

confidence: 99%

“…Given the inconsistencies in detecting effects at this time-scale (Rauss et al, 2011) there may also be methodological challenges to be addressed. One possibility is that the effects of very early top-down modulation on V1 may be mostly evident in the subtle tuning of cells’ functional properties (Gilbert & Li, 2013; Nienborg & Roelfsema, 2015), as opposed to net effects of increased neural/BOLD activity that are documented in higher cortical regions to indicate the origins of top-down effects. Careful consideration of how to detect these potentially subtler effects in human studies is needed.…”

Section: The Timescale Of Top-down Penetration In Visionmentioning

confidence: 99%

See 1 more Smart Citation

Predictions penetrate perception: Converging insights from brain, behaviour and disorder

O’Callaghan

Kveraga

Shine

et al. 2017

Consciousness and Cognition

146

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It is argued that during ongoing visual perception, the brain is generating top-down predictions to facilitate, guide and constrain the processing of incoming sensory input. Here we demonstrate that these predictions are drawn from a diverse range of cognitive processes, in order to generate the richest and most informative prediction signals. This is consistent with a central role for cognitive penetrability in visual perception. We review behavioural and mechanistic evidence that indicate a wide spectrum of domains—including object recognition, contextual associations, cognitive biases and affective state—that can directly influence visual perception. We combine these insights from the healthy brain with novel observations from neuropsychiatric disorders involving visual hallucinations, which highlight the consequences of imbalance between top-down signals and incoming sensory information. Together, these lines of evidence converge to indicate that predictive penetration, be it cognitive, social or emotional, should be considered a fundamental framework that supports visual perception.

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Section: Discussionmentioning

confidence: 99%

Section: The Timescale Of Top-down Penetration In Visionmentioning

confidence: 99%

Predictions penetrate perception: Converging insights from brain, behaviour and disorder

O’Callaghan

Kveraga

Shine

et al. 2017

Consciousness and Cognition

146

View full text Add to dashboard Cite

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“…Notably, the present results do not speak to the complementarity of the phase/power influences in the auditory and frontoparietal networks. Some studies have suggested that top-down interactions between cognitive processes and sensory regions determine the patterns of sensory encoding in sensory cortices (49,50), and future work is required to investigate the possibility that the two mechanisms described here are part of the same large-scale process.…”

Section: Sensory and Decision-related Origins Of Prestimulus Influencesmentioning

confidence: 97%

Prestimulus influences on auditory perception from sensory representations and decision processes

Kayser

McNair

Kayser

2016

Proc. Natl. Acad. Sci. U.S.A.

116

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The qualities of perception depend not only on the sensory inputs but also on the brain state before stimulus presentation. Although the collective evidence from neuroimaging studies for a relation between prestimulus state and perception is strong, the interpretation in the context of sensory computations or decision processes has remained difficult. In the auditory system, for example, previous studies have reported a wide range of effects in terms of the perceptually relevant frequency bands and state parameters (phase/ power). To dissociate influences of state on earlier sensory representations and higher-level decision processes, we collected behavioral and EEG data in human participants performing two auditory discrimination tasks relying on distinct acoustic features. Using single-trial decoding, we quantified the relation between prestimulus activity, relevant sensory evidence, and choice in different taskrelevant EEG components. Within auditory networks, we found that phase had no direct influence on choice, whereas power in taskspecific frequency bands affected the encoding of sensory evidence. Within later-activated frontoparietal regions, theta and alpha phase had a direct influence on choice, without involving sensory evidence. These results delineate two consistent mechanisms by which prestimulus activity shapes perception. However, the timescales of the relevant neural activity depend on the specific brain regions engaged by the respective task.perception | oscillatory brain activity | EEG | single-trial decoding | prestimulus effects S ensory percepts depend not only on the environmental inputs but also on the internal brain state before stimulus presentation (1). Many studies have shown that the accuracy and speed of sensory performance change with the power and timing (phase) of rhythmic activity during a prestimulus period (2, 3). Studies in the auditory system, for example, have demonstrated that performance in detecting sounds and gaps in noise, or the discrimination of lexical stimuli, varies with the power and phase of rhythmic activity between about 1 and 12 Hz (4-9).Although the collective evidence makes a strong case that prestimulus state shapes the processing and perceptual consequences of sensory inputs, the functional interpretation of these findings in the context of specific sensory computations or higher cognitive processes has remained difficult (7, 10, 11). Electrophysiological studies in animals have described the state dependency of firing rates relative to cortical oscillations (12-15). Hence, it is tempting to interpret the reported prestimulus effects in neuroimaging studies as direct evidence for a link between the neural gain of early sensory cortices and perception. However, this is difficult for two reasons. First, previous studies have used different behavioral protocols (detection and discrimination) and stimuli (tones in silence or noise, gaps in noise, or speech), and each has implied different frequency bands and state parameters as relevant (from 1 to 12 Hz, repo...

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“…Cohen and Maunsell [8 •• , 9, 10, 11] indeed showed that the estimates obtained using this approach in a change-detection task while recording from neurons in area V4, predicted behavioral performance (accuracy) on single trials. An analogous approach can be taken to infer a ‘choice axis’ [12, 13] which is closely related to measuring choice probabilities across a population of sensory neurons [14, 15]. The projection of the population activity on a single trial onto this choice axis yields an estimate of a decision-related variable.…”

Section: Inferring Internal Variables and Their Influence On Neuralmentioning

confidence: 99%

“…Knowledge of the inferred variables’ values and their corresponding ‘tuning curves’ may allow a functional interpretation of them, for example as the influence of anesthesia [4 •• ], attentional state [8 •• , 20, 21, 22], motor activity [23], or in terms of their sensory representation, e.g. as beliefs about the outside world [15, 24, 25 •• , 26, 27, 28]. Even though this approach requires large neural data sets (but not prohibitively large [29]), we believe that it will be an extremely productive one (see Box 2 for an illustration).…”

Section: Inferring Internal Variables and Their Influence On Neuralmentioning

confidence: 99%

Characterizing and interpreting the influence of internal variables on sensory activity

Lange

Haefner

2017

Current Opinion in Neurobiology

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The concept of a tuning curve has been central for our understanding of how the responses of cortical neurons depend on external stimuli. Here, we describe how the influence of unobserved internal variables on sensory responses, in particular correlated neural variability, can be understood in a similar framework. We suggest that this will lead to deeper insights into the relationship between stimulus, sensory responses, and behavior. We review related recent work and discuss its implication for distinguishing feedforward from feedback influences on sensory responses, and for the information contained in those responses.

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Belief states as a framework to explain extra-retinal influences in visual cortex

Cited by 25 publications

References 69 publications

Predictions penetrate perception: Converging insights from brain, behaviour and disorder

Predictions penetrate perception: Converging insights from brain, behaviour and disorder

Prestimulus influences on auditory perception from sensory representations and decision processes

Characterizing and interpreting the influence of internal variables on sensory activity

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