Prediction error and repetition suppression have distinct effects on neural representations of visual information

Tang, Matthew; Smout, Cooper; Arabzadeh, Ehsan; Mattingley, Jason B.

doi:10.7554/elife.33123

Cited by 74 publications

(110 citation statements)

References 87 publications

(148 reference statements)

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“…Numerous subsequent functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), and electroencephalography (EEG) studies have reported modulations of RS by the probability of a repetition 45,46,58–67 (i.e., expectation) (but see Refs. 68–70). Also consistent with PP, and seemingly at odds with neural adaptation accounts, is evidence that unexpected repetitions evoke greater neural responses than frequent alternations 37 .…”

Section: Hypothesis 1: Error‐signaling Neural Responses To Sensory Stmentioning

confidence: 99%

“…4 for a recent review). Although some studies have reported that expected stimulus representations are dampened, 69,79,90,92,105,[109][110][111] others have found that this global suppression is attributable to the suppression of neurons tuned away from the expected stimulus feature, while the representation of the expected stimulus is actually sharpened c . 78,88,112 Interestingly, Marques et al 113 recently found that while backward inputs from rodent lateromedial visual area (LM) to V1 c When interpreting these results, it should be noted that these decoding analyses of global BOLD activity or singleunit responses did not segregate activity from the proposed error unit and expectation unit populations.…”

Section: Hypothesis 1: Summary and Conclusionmentioning

confidence: 99%

“…Finally, recent studies provide evidence of prediction error updating perceptual hypotheses to improve future predictions. 69,158 For example, Tang et al 69 applied a forward encoding model to EEG data in order to measure the orientation selectivity of visual signals in response to pairs of oriented Gabors. Unexpected orientations led to increased orientation selectivity soon after stimulus presentation and this effect reemerged at later time points, consistent with an updating of sensory expectations.…”

Section: Evidence Of Prediction In Sensory Processingmentioning

confidence: 99%

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Evaluating the neurophysiological evidence for predictive processing as a model of perception

Walsh

McGovern

Clark

et al. 2020

Annals of the New York Academy of Sciences

239

172

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For many years, the dominant theoretical framework guiding research into the neural origins of perceptual experience has been provided by hierarchical feedforward models, in which sensory inputs are passed through a series of increasingly complex feature detectors. However, the long‐standing orthodoxy of these accounts has recently been challenged by a radically different set of theories that contend that perception arises from a purely inferential process supported by two distinct classes of neurons: those that transmit predictions about sensory states and those that signal sensory information that deviates from those predictions. Although these predictive processing (PP) models have become increasingly influential in cognitive neuroscience, they are also criticized for lacking the empirical support to justify their status. This limited evidence base partly reflects the considerable methodological challenges that are presented when trying to test the unique predictions of these models. However, a confluence of technological and theoretical advances has prompted a recent surge in human and nonhuman neurophysiological research seeking to fill this empirical gap. Here, we will review this new research and evaluate the degree to which its findings support the key claims of PP.

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Section: Hypothesis 1: Error‐signaling Neural Responses To Sensory Stmentioning

confidence: 99%

Section: Hypothesis 1: Summary and Conclusionmentioning

confidence: 99%

Section: Evidence Of Prediction In Sensory Processingmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating the neurophysiological evidence for predictive processing as a model of perception

Walsh

McGovern

Clark

et al. 2020

Annals of the New York Academy of Sciences

239

172

View full text Add to dashboard Cite

show abstract

“…Second, subjects participated in an orthogonal fixation task, which did not require top-down attention on the face images. Third, reported effects of stimulus predictability on the magnitude of RS are inconsistent across studies, stimuli, and measurements (Kaliukhovich DA and R Vogels 2011;Kovacs G et al 2013;Tang MF et al 2018;Vinken K et al 2018). Thus, while the prediction error account for RS is an appealing hypothesis, it does explain the presence of RS during our long-lagged paradigm.…”

Section: Theoretical Implicationsmentioning

confidence: 80%

Diverse temporal dynamics of repetition suppression revealed by intracranial recordings in human ventral temporal cortex

Rangarajan¹,

Jacques

Knight

et al. 2019

Preprint

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186/200)Repeated stimulus presentations commonly produce decreased neural responses -a phenomenon known as repetition suppression (RS) or adaptation -in ventral temporal cortex (VTC) in humans and nonhuman primates. However, the temporal features of RS in human VTC are not well understood. To fill this gap in knowledge, we utilized the precise spatial localization and high temporal resolution of electrocorticography (ECoG) from 9 human subjects implanted with intracranial electrodes in VTC. Subjects viewed non-repeated and repeated images of faces with (long-lagged) intervening stimuli between repeats. We report three main findings: (i) robust RS occurs for VTC activity in high-frequency broadband (HFB), but not lower frequency bands, (ii) RS is associated with lower peak magnitude, lower total responses of the HFB signal, and earlier peak responses, and (iii) RS effects occur early within initial stimulus processing and persist for the entire stimulus duration. We discuss these findings in the context of early and late components of visual perception, as well as theoretical models of RS in which these ECoG data empirically support timing predictions stemming from the facilitation model of RS in human VTC for the first time.

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“…To characterise the neural correlates of decision-making, we recorded brain activity using electroencephalography (EEG). We were primarily interested in measuring feature-specific brain responses to presented motion signals using population-tuning modelling of brain activity (31)(32)(33)(34)(35)(36).…”

Section: Introductionmentioning

confidence: 99%

Stimulus reliability automatically biases temporal integration of discrete perceptual targets

Rangelov

West

Mattingley

2019

Preprint

Self Cite

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Decision making is a ubiquitous cognitive process that determines choice behaviour. In recent years there has been increased interest in how information about multiple discrete sensory events are combined in support of single, integrated decisions. Previous studies have shown that integrative decision-making is biased in favour of more reliable stimuli. As reliability-weighted integration typically mimics statistically optimal integration, it remains unclear whether reliability biases are automatic or strategic. To dissociate reliability-weighting and optimal decisions, we developed a task that required participants to monitor two successive epochs containing brief, suprathreshold coherent motion signals which varied in their reliability. Rather than judging the individual target motion directions, however, participants had to reproduce the average motion direction of the two targets. Using mixture distribution modelling and linear regression to model behavioural data, we found robust biases in favour of the more reliable stimulus, despite the fact that unbiased responses were optimal in our paradigm. Using population-tuning modelling to characterise feature specific brain activity recorded using electroencephalography, we observed robust and sustained feature-specific responses to target signals in both epochs. Using the same method, we were able to capture the temporal dynamics of integrated decision-making by characterising tuning to the average motion direction. Critically, the tuning profiles to the average motion direction exhibited biases in favour of the more reliable signal, in keeping with the modelled behavioural responses. Taken together, our findings reveal that temporal integration of discrete sensory events is automatically and suboptimally weighted according to stimulus reliability.

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Prediction error and repetition suppression have distinct effects on neural representations of visual information

Cited by 74 publications

References 87 publications

Evaluating the neurophysiological evidence for predictive processing as a model of perception

Evaluating the neurophysiological evidence for predictive processing as a model of perception

Diverse temporal dynamics of repetition suppression revealed by intracranial recordings in human ventral temporal cortex

Stimulus reliability automatically biases temporal integration of discrete perceptual targets

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