Position representations of moving objects align with real-time position in the early visual response

Johnson, Philippa Anne; Blom, Tessel; Gaal, Simon van; Feuerriegel, Daniel; Bode, Stefan; Hogendoorn, Hinze

doi:10.7554/elife.82424

Cited by 21 publications

(26 citation statements)

References 84 publications

(121 reference statements)

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“…This, in itself, is non-trivial as smoothly moving stimuli do not evoke the well-defined onset/offset responses that have previously been leveraged to decode the position of ‘apparent motion’ stimuli 16,18 . The fact that we can map the position of smoothly moving objects via a bank of pre-trained static position representations indicates that the position-specific activity patterns evoked by static and dynamic stimuli overlap, at least partially (consistent with 17 ).…”

Section: Resultssupporting

confidence: 68%

Progressive multi-stage extrapolation of predictable motion in human visual cortex

Turner,

Sexton,

Johnson

et al. 2024

Preprint

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Neural processing of sensory information takes time. Consequently, to estimate the current state of the world, the brain must rely on predictive processes - for example, extrapolating the motion of a ball to determine its probable present position. Mounting evidence suggests that extrapolation occurs during early (retinal) processing, however it remains unclear whether extrapolation continues during later-stage (cortical) processing. Moreover, we currently lack a spatially precise characterisation of extrapolation effects in the human brain, with most studies relying on invasive neurophysiological techniques in animals. Here, we address these issues by demonstrating how precise probabilistic maps can be constructed from human EEG recordings. Participants (N = 18) viewed a stimulus moving along a circular trajectory while EEG was recorded. Using LDA classification, we extracted maps of stimulus location over time and found evidence of a widespread temporal shift occurring across distinct processing stages. This accelerated emergence of position representations indicates progressive extrapolation occurring at multiple stages of processing, with representations across the hierarchy shifted closer to real-time. We further show evidence of representational overshoot during early-stage processing following unexpected changes to an object's trajectory, and demonstrate that the observed dynamics can emerge spontaneously in a simulated neural network via spike-timing-dependent plasticity.

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Section: Resultssupporting

confidence: 68%

Progressive multi-stage extrapolation of predictable motion in human visual cortex

Turner,

Sexton,

Johnson

et al. 2024

Preprint

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“…Arguably, the signal contamination stemming from preceding dots may also account for the observed differences between independent pattern estimators, with the sustained influence from preceding dots being more pronounced in the short-ISI pattern estimator blocks, where subsequent stimuli are temporally closer together compared to the long-ISI pattern estimator blocks. Critically, control analysis convincingly demonstrated that this above-chance classification could not be explained by systematic eye movements as the same analysis using the x, y coordinates measured by the eye tracker did not result in above-chance classification (e.g., Chota et al, 2023;Johnson et al, 2023).…”

Section: Within-block Decoding: Increasing the Interval Between Succe...mentioning

confidence: 78%

Neural representations of predicted events: Evidence from time-resolved EEG decoding

Li,

Theeuwes,

van Moorselaar

2024

Preprint

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Through statistical learning, humans are able to extract temporal regularities, using the past to predict the future. Evidence suggests that learning relational structures makes it possible to anticipate the imminent future; yet, the neural dynamics of predicting the future and its time-course remain elusive. To examine whether future representations are denoted in a temporally discounted fashion, we used the high-temporal-resolution of electroencephalography (EEG). Observers were exposed to a fixed sequence of events at four unique spatial positions within the display. Using multivariate pattern analyses trained on independent pattern estimators, we were able to decode the spatial position of dots within full sequences, and within randomly intermixed partial sequences wherein only a single dot was presented. Crucially, within these partial sequences, subsequent spatial positions could be reliably decoded at their expected moment in time. These findings highlight the dynamic weight changes within the assumed spatial priority map and mark the first implementation of EEG to decode predicted, yet critically omitted events.Impact statementUtilizing high-temporal-resolution EEG, the dynamic weight changes of assumed spatial priority map were visualized by decoding the spatial position of expected, yet omitted, events at their expected moment in time.

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“…Mechanisms described in these models are likely to interact with other processes that exploit the spatiotemporal structure of sensory environments. These processes include adaptation (Solomon & Kohn, 2014;Vogels, 2016;Whitmire & Stanley, 2016), surround suppression (Carandini & Heeger, 2012), end-stopping (Rao & Ballard, 1999), as well as retinal circuit mechanisms that rapidly adjust to local luminance levels (Rieke & Rudd, 2009), environmental image structure (Hosoya et al, 2005), periodic stimulation patterns (Schwartz et al, 2007;Schwartz & Berry, 2008) and smooth motion (Berry et al, 1999;Johnson et al, 2023). Systematically investigating these interactions will likely uncover a host of novel predictive phenomena, including feedforward or inherited effects across the visual cortical hierarchy (e.g., Dhruv et al, 2011;Larsson et al, 2016).…”

Section: Consequences Of Cued Expectations On Erpsmentioning

confidence: 99%

Stimulus expectations do not modulate visual event-related potentials in probabilistic cueing designs

Ouden

Zhou

Mepani

et al. 2023

Preprint

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Humans and other animals can learn and exploit repeating patterns that occur within their environments. These learned patterns can be used to form expectations about future sensory events. Several influential predictive coding models have been proposed to explain how learned expectations influence the activity of stimulus- selective neurons in the visual system. These models specify reductions in neural response measures when expectations are fulfilled (termed expectation suppression) and increases following surprising sensory events. However, there is currently scant evidence for expectation suppression in the visual system when confounding factors are taken into account. Effects of surprise have been observed in blood oxygen level dependent (BOLD) signals, but not when using electrophysiological measures. To provide a strong test for expectation suppression and surprise effects we ran a predictive cueing experiment while recording electroencephalographic (EEG) data. Participants (n=48) learned cue-face associations during a training session and were then exposed to these cue-face pairs in a subsequent experiment. Using univariate analyses of face-evoked event-related potentials (ERPs) we did not observe any differences across expected (90% probability), neutral (50%) and surprising (10%) face conditions. Across these comparisons, Bayes factors consistently favoured the null hypothesis throughout the time-course of the stimulus-evoked response. When using multivariate pattern analysis we did not observe above-chance classification of expected and surprising face-evoked ERPs. By contrast, we found robust within- and across-trial stimulus repetition effects. Our findings do not support predictive coding- based accounts that specify reduced prediction error signalling when perceptual expectations are fulfilled. They instead highlight the utility of other types of predictive processing models that describe expectation-related phenomena in the visual system without recourse to prediction error signalling.

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Position representations of moving objects align with real-time position in the early visual response

Cited by 21 publications

References 84 publications

Progressive multi-stage extrapolation of predictable motion in human visual cortex

Progressive multi-stage extrapolation of predictable motion in human visual cortex

Neural representations of predicted events: Evidence from time-resolved EEG decoding

Stimulus expectations do not modulate visual event-related potentials in probabilistic cueing designs

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