Is the perceived present a predictive model of the objective present?

White, Peter A.

doi:10.1080/13506285.2018.1530322

Cited by 11 publications

(9 citation statements)

References 243 publications

(361 reference statements)

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“…One way the brain might compensate for neural delays is through prediction (3)(4)(5)(6). In support of this idea, predictable visual stimuli have been found to be represented in the visual system with a shorter latency than unpredictable stimuli in cats (7), macaques (8), and humans (9).…”

mentioning

confidence: 89%

“…This interplay between predictive and postdictive mechanisms has a perceptual parallel in the flash-lag effect, a visual illusion in which a static object flashed in alignment with a moving object is perceived to lag behind the moving object (2). Although convergent evidence suggests the flash-lag effect is due to predictive motion extrapolation mechanisms (3,6,19), the flash-lag effect reverses when motion after the flash reverses direction, prompting Eagleman and Sejnowski (17) to propose an account of this illusion in terms of postdiction. In the related flash-grab illusion (20), in which an object flashed on a reversing background is mislocalized, the flashed object is similarly mislocalized in the direction of motion after the flash.…”

Section: Significancementioning

confidence: 99%

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Predictions drive neural representations of visual events ahead of incoming sensory information

Blom

Feuerriegel

Johnson

et al. 2020

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

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The transmission of sensory information through the visual system takes time. As a result of these delays, the visual information available to the brain always lags behind the timing of events in the present moment. Compensating for these delays is crucial for functioning within dynamic environments, since interacting with a moving object (e.g., catching a ball) requires real-time localization of the object. One way the brain might achieve this is via prediction of anticipated events. Using time-resolved decoding of electroencephalographic (EEG) data, we demonstrate that the visual system represents the anticipated future position of a moving object, showing that predictive mechanisms activate the same neural representations as afferent sensory input. Importantly, this activation is evident before sensory input corresponding to the stimulus position is able to arrive. Finally, we demonstrate that, when predicted events do not eventuate, sensory information arrives too late to prevent the visual system from representing what was expected but never presented. Taken together, we demonstrate how the visual system can implement predictive mechanisms to preactivate sensory representations, and argue that this might allow it to compensate for its own temporal constraints, allowing us to interact with dynamic visual environments in real time.

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mentioning

confidence: 89%

Section: Significancementioning

confidence: 99%

Predictions drive neural representations of visual events ahead of incoming sensory information

Blom

Feuerriegel

Johnson

et al. 2020

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

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“…Persistence of a visual stimulus maintains newly formed IPLs whose continuous re-activations maintain the percept. Natural retrograde extrapolation from inter-LINKed spines for generating perceptons fulfills the expectation that an operation of perception will have a mechanism for extrapolation (White, 2018).…”

Section: Mechanism For First-person Perceptmentioning

confidence: 63%

A new explanation for flash-lag effect using a testable mechanism for first- person inner sensation of perception

Vadakkan¹

2022

Preprint

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When a flash is briefly presented in a specific location adjacent to the path of a uniformly moving object, the former is perceived to lag the latter. Different experimental conditions carried out to qualify this flash-lag effect (FLE) effect have led to several seemingly unrelated first-person reports from which several constraints are available to synthesize a testable mechanism for FLE. Using a derived mechanism for the generation of first-person internal sensation of perception, present work provides new interconnected explanations for disparate findings associated with FLE. By verifying testable predictions arising from the hypothesized mechanism of first-person inner sensations, it may become possible to understand timescale-matched neurobiological changes responsible for perception.

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“…Specifically, the process of fixating on a rapidly moving target also needs an explicit temporal prediction mechanism. 27 For further support of the working hypothesis that our perception of the present likely involves essential components of prediction or extrapolation in the forward direction of time, see Nijhawan (1994), Nijhawan (2008), Grush (2007), Grush (2008), Changizi et al (2008) and White (2018).…”

Section: Fig 13mentioning

confidence: 98%

A time-causal and time-recursive scale-covariant scale-space representation of temporal signals and past time

Lindeberg

2023

Biol Cybern

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This article presents an overview of a theory for performing temporal smoothing on temporal signals in such a way that: (i) temporally smoothed signals at coarser temporal scales are guaranteed to constitute simplifications of corresponding temporally smoothed signals at any finer temporal scale (including the original signal) and (ii) the temporal smoothing process is both time-causal and time-recursive, in the sense that it does not require access to future information and can be performed with no other temporal memory buffer of the past than the resulting smoothed temporal scale-space representations themselves. For specific subsets of parameter settings for the classes of linear and shift-invariant temporal smoothing operators that obey this property, it is shown how temporal scale covariance can be additionally obtained, guaranteeing that if the temporal input signal is rescaled by a uniform temporal scaling factor, then also the resulting temporal scale-space representations of the rescaled temporal signal will constitute mere rescalings of the temporal scale-space representations of the original input signal, complemented by a shift along the temporal scale dimension. The resulting time-causal limit kernel that obeys this property constitutes a canonical temporal kernel for processing temporal signals in real-time scenarios when the regular Gaussian kernel cannot be used, because of its non-causal access to information from the future, and we cannot additionally require the temporal smoothing process to comprise a complementary memory of the past beyond the information contained in the temporal smoothing process itself, which in this way also serves as a multi-scale temporal memory of the past. We describe how the time-causal limit kernel relates to previously used temporal models, such as Koenderink’s scale-time kernels and the ex-Gaussian kernel. We do also give an overview of how the time-causal limit kernel can be used for modelling the temporal processing in models for spatio-temporal and spectro-temporal receptive fields, and how it more generally has a high potential for modelling neural temporal response functions in a purely time-causal and time-recursive way, that can also handle phenomena at multiple temporal scales in a theoretically well-founded manner. We detail how this theory can be efficiently implemented for discrete data, in terms of a set of recursive filters coupled in cascade. Hence, the theory is generally applicable for both: (i) modelling continuous temporal phenomena over multiple temporal scales and (ii) digital processing of measured temporal signals in real time. We conclude by stating implications of the theory for modelling temporal phenomena in biological, perceptual, neural and memory processes by mathematical models, as well as implications regarding the philosophy of time and perceptual agents. Specifically, we propose that for A-type theories of time, as well as for perceptual agents, the notion of a non-infinitesimal inner temporal scale of the temporal receptive fields has to be included in representations of the present, where the inherent nonzero temporal delay of such time-causal receptive fields implies a need for incorporating predictions from the actual time-delayed present in the layers of a perceptual hierarchy, to make it possible for a representation of the perceptual present to constitute a representation of the environment with timing properties closer to the actual present.

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Is the perceived present a predictive model of the objective present?

Cited by 11 publications

References 243 publications

Predictions drive neural representations of visual events ahead of incoming sensory information

Predictions drive neural representations of visual events ahead of incoming sensory information

A new explanation for flash-lag effect using a testable mechanism for first- person inner sensation of perception

A time-causal and time-recursive scale-covariant scale-space representation of temporal signals and past time

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