Retrospective Attention Gates Discrete Conscious Access to Past Sensory Stimuli

Thibault, Louis; Berg, Ronald van den; Cavanagh, Patrick; Sergent, Claire

doi:10.1371/journal.pone.0148504

Cited by 45 publications

(63 citation statements)

References 37 publications

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“…Norman et al 2014;Faivre et al 2014; see also Ansorge et al 2014 for a review). On the other hand, several studies have shown that percepts which were not consciously registered by the subjects can later be brought into consciousness by attentional cues directed at the location of the stimulus after its disappearance (Kentridge 2013;Sergent et al 2013;Thibault et al 2016). This is important, as it lends support to the PEM idea that, by modulating the probability of the bottom-up signals, attention determines how much influence their probability should have on the posterior.…”

Section: The Problem Of Unconscious Perceptionmentioning

confidence: 93%

Fame in the predictive brain: a deflationary approach to explaining consciousness in the prediction error minimization framework

Dołęga

Dewhurst

2020

Synthese

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The proposal that probabilistic inference and unconscious hypothesis testing are central to information processing in the brain has been steadily gaining ground in cognitive neuroscience and associated fields. One popular version of this proposal is the new theoretical framework of predictive processing or prediction error minimization (PEM), which couples unconscious hypothesis testing with the idea of 'active inference' and claims to offer a unified account of perception and action. Here we will consider one outstanding issue that still looms large at the core of the PEM framework: the lack of a clear criterion for distinguishing conscious states from unconscious ones. In order to fulfill the promise of becoming a unifying framework for describing and modeling cognition, PEM needs to be able to differentiate between conscious and unconscious mental states or processes. We will argue that one currently popular view, that the contents of conscious experience are determined by the 'winning hypothesis' (i.e. the one with the highest posterior probability, which determines the behavior of the system), falls short of fully accounting for conscious experience. It ignores the possibility that some states of a system can control that system's behavior even though they are apparently not conscious (as evidenced by e.g. blindsight or subliminal priming). What follows from this is that the 'winning hypothesis' view does not provide a complete account of the difference between conscious and unconscious states in the probabilistic brain. We show how this problem (and some other related problems) for the received view can be resolved by augmenting PEM with Daniel Dennett's multiple drafts model of consciousness. This move is warranted by the similar roles that attention and internal competition play in both the PEM framework and the multiple drafts model. SyntheseKeywords Predictive processing · Active inference · Consciousness · Multiple drafts · Prediction error minimization · Generative model · Control model · Attention · Illusionism Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included

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Section: The Problem Of Unconscious Perceptionmentioning

confidence: 93%

Fame in the predictive brain: a deflationary approach to explaining consciousness in the prediction error minimization framework

Dołęga

Dewhurst

2020

Synthese

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“…In a series of experiments, my colleagues and I tested whether conscious perception could be triggered by retrospective attention [27,28]. Participants had to detect faint grids of oriented lines (Gabor patches) presented to the left or to the right of fixation, in one of two circles (figure 2a).…”

Section: Retro-perception: Perceiving the Pastmentioning

confidence: 99%

The offline stream of conscious representations

Sergent

2018

Phil. Trans. R. Soc. B

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When do we become conscious of a stimulus after its presentation? We would all agree that this necessarily takes time and that it is not instantaneous. Here, I would like to propose not only that conscious access is relative to the external stimulation, but also that it can flexibly from external stimulation; it can process some information 'offline', if and when it becomes relevant. Thus, in contrast with initial sensory processing, conscious experience might not strictly follow the sequence of events in the environment. In this article, I will review gathering evidence in favour of this proposition. I will argue that it offers a coherent framework for explaining a great variety of observations in the domain of perception, sensory memory and working memory: the psychological refractory period, the attentional blink, post-dictive phenomena, iconic memory, latent working memory and the newly described retro-perception phenomenon. I will integrate this proposition to the global neuronal workspace model and consider possible underlying brain mechanisms. Finally, I will argue that this capacity to process information 'offline' might have made conscious processing evolutionarily advantageous in spite of its sluggishness and capacity limitations.This article is part of the theme issue 'Perceptual consciousness and cognitive access'.

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“…The interpretation of these parameters as products of separable psychological processes determining the quality and quantity of representation has led researchers to make claims of "discreteness" not only for working memory (W. Zhang & Luck, 2008) but also iconic (sensory) memory (Pratte, 2018) and even conscious visual perception (Thibault, van den Berg, Cavanagh, & Sergent, 2016;Asplund et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Theory of neural coding predicts an upper bound on estimates of memory variability.

Taylor¹,

Bays²

2020

Psychological Review

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Observers reproducing elementary visual features from memory after a short delay produce errors consistent with the encoding-decoding properties of neural populations. While inspired by electrophysiological observations of sensory neurons in cortex, the population coding account of these errors is based on a mathematical idealization of neural response functions that abstracts away most of the heterogeneity and complexity of real neuronal populations. Here we examine a more physiologically grounded model based on the tuning of a large set of neurons recorded in macaque V1, and show that key predictions of the idealized model are preserved.Both models predict long-tailed distributions of error when memory resources are taxed, as observed empirically in behavioral experiments and commonly approximated with a mixture of normal and uniform error components. Specifically, for an idealized homogeneous neural population, the width of the fitted normal distribution cannot exceed the average tuning width of the component neurons, and this also holds to a good approximation for more biologically realistic populations. Examining eight published studies of orientation recall, we find a consistent pattern of results suggestive of a median tuning width of approximately 20 degrees, which compares well with neurophysiological observations. The finding that estimates of variability obtained by the normal-plus-uniform mixture method are bounded from above leads us to reevaluate previous studies that interpreted a saturation in width of the normal component as evidence for fundamental limits on the precision of perception, working memory and long-term memory.

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Retrospective Attention Gates Discrete Conscious Access to Past Sensory Stimuli

Cited by 45 publications

References 37 publications

Fame in the predictive brain: a deflationary approach to explaining consciousness in the prediction error minimization framework

Fame in the predictive brain: a deflationary approach to explaining consciousness in the prediction error minimization framework

The offline stream of conscious representations

Theory of neural coding predicts an upper bound on estimates of memory variability.

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