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Our binocular world is seamlessly assembled from two retinal images that remain segregated until the cerebral cortex. Despite the coherence of this input, there is often an imbalance between the strength of these connections in the brain. ‘Eye dominance’ provides a measure of the perceptual dominance of one eye over the other. Theoretical models suggest that eye dominance is related to reciprocal inhibition between monocular units in the primary visual cortex, the first location where the binocular input is combined. As the specific inhibitory interactions in the binocular visual system critically depend on the presence of visual input, we sought to test the role of inhibition by measuring the concentrations of inhibitory (GABA) neurotransmitters during monocular visual stimulation of the dominant and the non-dominant eye. GABA-levels were acquired in V1 using a combined functional magnetic resonance imaging (fMRI) and magnetic resonance spectroscopy (MRS) sequence on a 7-Tesla MRI scanner. Individuals with stronger eye dominance had a greater difference in GABAergic inhibition between the eyes. This relationship was present only when the visual system was actively processing sensory input and was not present at rest. We provide the first evidence that imbalances in GABA levels during ongoing sensory processing are related to eye dominance in the human visual cortex. This provides strong support to the view that intracortical inhibition underlies normal eye dominance.SIGNIFICANCE STATEMENTWhat we see is shaped by excitation and inhibition in our brain. We investigated how eye dominance, the perceptual preference of one eye’s input over the other, is related to levels of inhibitory neurotransmitter GABA during monocular visual stimulation. GABAergic inhibition is related to eye dominance, but only when the visual system is actively processing sensory input. This provides key support for the view that imbalances in visual competition that are observed in the normal visual system arise from an inability of GABA signalling to suppress the stronger sensory representation.

Several deep neural networks have recently been shown to generate activations similar to those of the brain in response to the same input. These algorithms, however, remain largely implausible: they require (1) extraordinarily large amounts of data, (2) unobtainable supervised labels, (3) textual rather than raw sensory input, and / or (4) implausibly large memory (e.g. thousands of contextual words). These elements highlight the need to identify algorithms that, under these limitations, would suffice to account for both behavioral and brain responses. Focusing on the issue of speech processing, we here hypothesize that self-supervised algorithms trained on the raw waveform constitute a promising candidate. Specifically, we compare a recent self-supervised architecture, Wav2Vec 2.0, to the brain activity of 412 English, French, and Mandarin individuals recorded with functional Magnetic Resonance Imaging (fMRI), while they listened to ~1h of audio books. Our results are four-fold. First, we show that this algorithm learns brain-like representations with as little as 600 hours of unlabelled speech -- a quantity comparable to what infants can be exposed to during language acquisition. Second, its functional hierarchy aligns with the cortical hierarchy of speech processing. Third, different training regimes reveal a functional specialization akin to the cortex: Wav2Vec 2.0 learns sound-generic, speech-specific and language-specific representations similar to those of the prefrontal and temporal cortices. Fourth, we confirm the similarity of this specialization with the behavior of 386 additional participants. These elements, resulting from the largest neuroimaging benchmark to date, show how self-supervised learning can account for a rich organization of speech processing in the brain, and thus delineate a path to identify the laws of language acquisition which shape the human brain., Accepted to NeurIPS 2022

International audience; Amplitude modulation (AM) and frequency modulation (FM) provide crucial auditory information. If FM is encoded as AM, it should be possible to give a unified account of AM and FM perception both in terms of response consistency and performance. These two aspects of behavior were estimated for normal-hearing participants using a constant-stimuli, forced-choice detection task repeated twice with the same stimuli (double pass). Sinusoidal AM or FM with rates of 2 or 20 Hz were applied to a 500-Hz pure-tone carrier and presented at detection threshold. All stimuli were masked by a modulation noise. Percent agreement of responses across passes and percent-correct detec- tion for the two passes were used to estimate consistency and performance, respectively. These data were simulated using a model implementing peripheral processes, a central modulation filterbank, an additive internal noise, and a template-matching device. Different levels of internal noise were required to reproduce AM and FM data, but a single level could account for the 2- and 20-Hz AM data. As for FM, two levels of internal noise were needed to account for detection at slow and fast rates. Finally, the level of internal noise yielding best predictions increased with the level of the modulation-noise masker. Overall, these results suggest that different sources of internal variability are involved for AM and FM detection at low audio frequencies.

International audience; Perceptual confidence is an evaluation of the validity of perceptual decisions. While there is behavioural evidence that confidence evaluation differs from perceptual decision-making, disentangling these two processes remains a challenge at the neural level. Here, we examined the electrical brain activity of human participants in a protracted perceptual decision-making task where observers tend to commit to perceptual decisions early whilst continuing to monitor sensory evidence for evaluating confidence. Premature decision commitments were revealed by patterns of spectral power overlying motor cortex, followed by an attenuation of the neural representation of perceptual decision evidence. A distinct neural representation was associated with the computation of confidence, with sources localised in the superior parietal and orbitofrontal cortices. In agreement with a dissociation between perception and confidence, these neural resources were recruited even after observers committed to their perceptual decisions, and thus delineate an integral neural circuit for evaluating perceptual decision confidence.

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Perceptual confidence is an evaluation of the validity of our perceptual decisions. We present here a complete generative model that describes how confidence judgments result from some confidence evidence. The model that generates confidence evidence has two main parameters, confidence noise and confidence boost. Confidence noise reduces the sensitivity to the confidence evidence, and confidence boost accounts for information used for confidence judgment which was not used for the perceptual decision. The opposite effect of these two parameters creates a problem of confidence parameters indeterminacy, where the confidence in a perceptual decision is the same in spite of differences in confidence noise and confidence boost. When confidence is estimated for multiple stimulus strengths, both of these parameters can be recovered, thus allowing us to estimate whether confidence is generated using the same primary information that was used for the perceptual decision or some secondary information. We also describe a novel measure of confidence efficiency relative to the ideal confidence observer, as well as the estimate of one type of confidence bias. Finally, we apply the model to the confidence forced-choice paradigm, a paradigm that provides objective estimates of confidence, and we discuss how each parameter of the model can be recovered using this paradigm. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Our visual experience appears uniform across the visual field, despite the poor resolution of peripheral vision. This may be because we do not notice that we are missing details in the periphery of our visual field and believe that peripheral vision is just as rich as central vision. In other words, the uniformity of the visual scene could be explained by a metacognitive bias. We deployed a confidence forced-choice method to measure metacognitive performance in peripheral as compared to central vision. Participants judged the orientation of gratings presented in central and peripheral vision, and reported whether they thought they were more likely to be correct in the perceptual decision for the central or for the peripheral stimulus. Observers were underconfident in the periphery: higher sensory evidence in the periphery was needed to equate confidence choices between central and peripheral perceptual decisions. When performance on the central and peripheral tasks was matched, observers were still more confident in their ability to report the orientation of the central gratings over the one of the peripheral gratings. In a second experiment, we measured metacognitive sensitivity, as the difference in perceptual sensitivity between perceptual decisions that are chosen with high confidence and decisions that are chosen with low confidence. Results showed that metacognitive sensitivity is lower when participants compare central to peripheral perceptual decisions compared to when they compare peripheral to peripheral or central to central perceptual decisions. In a third experiment, we showed that peripheral underconfidence does not arise because observers based confidence judgments on stimulus size or contrast range rather than on perceptual performance. Taken together, results indicate that humans are impaired in comparing central with peripheral perceptual performance, but metacognitive biases cannot explain our impression of uniformity, as this would require peripheral overconfidence.

Sensory adaptation is a useful tool to identify the links between perceptual effects and neural mechanisms. Even though motion adaptation is one of the earliest and most documented aftereffects, few studies have investigated the perception of direction and speed of the aftereffect at the same time, that is the perceived velocity. Using a novel experimental paradigm, we simultaneously recorded the perceived direction and speed of leftward or rightward moving random dots before and after adaptation. For the adapting stimulus, we chose a horizontally-oriented broadband grating moving upward behind a circular aperture. Because of the aperture problem, the interpretation of this stimulus is ambiguous, being consistent with multiple velocities, and yet it is systematically perceived as moving at a single direction and speed. Here we ask whether the visual system adapts to the multiple velocities of the adaptor or to just the single perceived velocity. Our results show a strong repulsion aftereffect, away from the adapting velocity (downward and slower), that increases gradually for faster test stimuli as long as these stimuli include some velocities that match some of the ambiguous ones of the adaptor. In summary, the visual system seems to adapt to the multiple velocities of an ambiguous stimulus even though a single velocity is perceived. Our findings can be well described by a computational model that assumes a joint encoding of direction and speed and that includes an extended adaptation component that can represent all the possible velocities of the ambiguous stimulus.

The optimal template for signal detection in white additive noise is the signal itself: the ideal observer matches each stimulus against this template and selects the stimulus associated with largest match. In the noisy ideal observer, internal noise is added to the decision variable returned by the template. While the ideal observer represents an unrealistic approximation to the human visual process, the noisy ideal observer may be applicable under certain experimental conditions. For template values constrained to lie within a specified range, theory predicts that the template associated with a noisy ideal observer should be a clipped image of the signal, a result which we demonstrate analytically using variational calculus. It is currently unknown whether the human process conforms to theory. We report a targeted analysis of the theoretical prediction for an experimental protocol that maximizes template-matching on the part of human participants. We find indicative evidence to support the theoretical expectation when internal noise is compared across participants, but not within each participant. Our results indicate that implicit knowledge about internal variability in different individuals is reflected by their detection templates; no implicit knowledge is retained for internal-noise fluctuations experienced by a given participant during data collection. The results also indicate that template encoding is constrained by the dynamic range of weight specification, rather than the range of output values transduced by the template-matching process.

This paper proposes Shared Component Analysis (SCA) as an alternative to Principal Component Analysis (PCA) for the purpose of dimensionality reduction of neuroimaging data. The trend towards larger numbers of recording sensors, pixels or voxels leads to richer data, with finer spatial resolution, but it also inflates the cost of storage and computation and the risk of overfitting. PCA can be used to select a subset of orthogonal components that explain a large fraction of variance in the data. This implicitly equates variance with relevance, and for neuroimaging data such as electroencephalography (EEG) or magnetoencephalography (MEG) that assumption may be inappropriate if (latent) sources of interest are weak relative to competing sources. SCA instead assumes that components that contribute to observable signals on multiple sensors are of likely interest, as may be the case for deep sources within the brain as a result of current spread. In SCA, steps of normalization and PCA are applied iteratively, linearly transforming the data such that components more widely shared across channels appear first in the component series. The paper explains the motivation, defines the algorithm, evaluates the outcome, and sketches a wider strategy for dimensionality reduction of which this algorithm is an example. SCA is intended as a plug-in replacement for PCA for the purpose of dimensionality reduction.

BackgroundMany scientific fields now use machine-learning tools to assist with complex classification tasks. In neuroscience, automatic classifiers may be useful to diagnose medical images, monitor electrophysiological signals, or decode perceptual and cognitive states from neural signals. However, such tools often remain black-boxes: they lack interpretability. A lack of interpretability has obvious ethical implications for clinical applications, but it also limits the usefulness of these tools to formulate new theoretical hypotheses.New methodWe propose a simple and versatile method to help characterize the information used by a classifier to perform its task. Specifically, noisy versions of training samples or, when the training set is unavailable, custom-generated noisy samples, are fed to the classifier. Multiplicative noise, so-called “bubbles”, or additive noise are applied to the input representation. Reverse correlation techniques are then adapted to extract either the discriminative information, defined as the parts of the input dataset that have the most weight in the classification decision, and represented information, which correspond to the input features most representative of each category.ResultsThe method is illustrated for the classification of written numbers by a convolutional deep neural network; for the classification of speech versus music by a support vector machine; and for the classification of sleep stages from neurophysiological recordings by a random forest classifier. In all cases, the features extracted are readily interpretable.Comparison with Existing MethodsQuantitative comparisons show that the present method can match state-of-the art interpretation methods for convolutional neural networks. Moreover, our method uses an intuitive and well-established framework in neuroscience, reverse correlation. It is also generic: it can be applied to any kind of classifier and any kind of input data.ConclusionsWe suggest that the method could provide an intuitive and versatile interface between neuroscientists and machine-learning tools.HighlightsThe heuristics of black-box classifiers can be probed with noisy inputsThe relevant features can be visualised in the input representation spaceThe method applies to any kind of data such as 2D images or 1D time seriesIt applies to any classifier such as deep neural networks, support vector machines, random forests

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Learning about new sounds is essential for cochlear-implant and normal-hearing listeners alike, with the additional challenge for implant listeners that spectral resolution is severely degraded. Here, a task measuring the rapid learning of slow or fast stochastic temporal sequences [Kang, Agus, and Pressnitzer (2017). J. Acoust. Soc. Am. 142, 2219-2232] was performed by cochlear-implant (N = 10) and normal-hearing (N = 9) listeners, using electric or acoustic pulse sequences, respectively. Rapid perceptual learning was observed for both groups, with highly similar characteristics. Moreover, for cochlear-implant listeners, an additional condition tested ultra-fast electric pulse sequences that would be impossible to represent temporally when presented acoustically. This condition also demonstrated learning. Overall, the results suggest that cochlear-implant listeners have access to the neural plasticity mechanisms needed for the rapid perceptual learning of complex temporal sequences.

Introduction Lack of sleep drastically affects many aspects of human behavior. The early detection of sleepiness is thus a major challenge for health and security reasons. Here we investigated the effect of sleep deprivation on the acoustic properties of human speech. Methods Twenty-four participants were sleep deprived for two days (two successive nights with only 3 hours of sleep). They were recorded reading a short text aloud before and after sleep deprivation. An auditory model, based on spectro-temporal modulations, was used to analyse the acoustic properties of their speech and served as a front-end to machine-learning classifiers. Results Results showed that sleepiness could be accurately detected with individually-trained classifiers. However,we were not able to fit a generic classifier for all participants. As we relied on an auditory-inspired model,we could identify and interpret the acoustic features impacted by sleep deprivation. Again,no simple diagnostic feature could be easily identified in the group- level analyses of the speech signals. We therefore developed a novel probing method, combining signal detection theory and noise activation of the classifier, to understand what made the classifier successful for each participant. This led to a diagnostic map for each participant, specifying which frequency region and modulation rates were impacted by sleep deprivation for this particular individual Conclusion In addition to suggesting a practical machine learning algorithm to detect sleep deprivation, combining our probing method with considerations about voice production could help uncover the physiological impact of sleep deprivation at the level of each individual. Support

Listeners experience speech as a sequence of discrete words. However, the real input is a continuously varying acoustic signal that blends words and phonemes into one another. Here we recorded two-hour magnetoencephalograms from 21 subjects listening to stories, in order to investigate how the brain concurrently solves three competing demands: 1) processing overlapping acoustic-phonetic information while 2) keeping track of the relative order of phonemic units and 3) maintaining individuated phonetic information until successful word recognition. We show that the human brain transforms speech input, roughly at the rate of phoneme duration, along a temporally-defined representational trajectory. These representations, absent from the acoustic signal, are active earlier when phonemes are predictable than when they are surprising, and are sustained until lexical ambiguity is resolved. The results reveal how phoneme sequences in natural speech are represented and how they interface with stored lexical items. One sentence summary The human brain keeps track of the relative order of speech sound sequences by jointly encoding content and elapsed processing time

International audience; Identifying causes solely from observations can be particularly challenging when i) the factors under investigation are difficult to manipulate independently from one-another and ii) observations are high-dimensional. To address this issue, we introduce "Back-to-Back" regression (B2B), a linear method designed to efficiently estimate, from a set of correlated factors, those that most plausibly account for multidimensional observations. First, we prove the consistency of B2B, its links to other linear approaches, and show how it can provide a robust, unbiased and interpretable scalar estimate for each factor. Second, we use a variety of simulated data to show that B2B can outperform forward modeling ("encoding"), backward modeling ("decoding") as well as crossdecomposition modeling (i.e.. canonical correlation analysis and partial least squares) on causal identification when the factors and the observations are not orthogonal. Finally, we apply B2B to a hundred magneto-encephalography recordings and to a hundred functional Magnetic Resonance Imaging recordings acquired while subjects performed a one hour reading task. B2B successfully disentangles the respective contribution of collinear factors such as word length, word frequency in the early visual and late associative cortical responses respectively. B2B compared favorably to other standard techniques on this disentanglement. We discuss how the speed and the generality of B2B sets promising foundations to help identify the causal contributions of covarying factors from high-dimensional observations.

Classification images have become popular tools in psychophysics, yet difficulties associated with their interpretation have often hindered their application. Alternative methods for characterizing perceptual filters have been proposed, and the discussion has often focussed on the degree to which classification images are optimal statistical estimators of system components (e.g. kernels). This technical note argues that those difficulties become irrelevant once the tool is situated within a data-driven interpretational framework. Within this framework, classification images and their nonlinear derivatives are understood not as transparent estimates of system components, but instead as transparent descriptors of data structure. The many pitfalls associated with the former approach, and the power of the latter, are demonstrated via combination of counter-intuitive computer simulations with empirical examples from published literature. A change in perspective over the manner in which this tool is understood and utilized may lead to a more productive engagement with this methodology.

International audience; Each perceptual decision is commonly attached to a judgment of confidence in the uncertainty of that decision. Confidence is classically defined as the estimate of the posterior probability of the decision to be correct, given the evidence. Here we argue that correctness is neither a valid normative statement of what observers should be doing after their perceptual decision nor a proper descriptive statement of what they actually do. Instead, we propose that perceivers aim at being self-consistent with themselves. We present behavioral evidence obtained in two separate psychophysical experiments that human observers achieve that aim. In one experiment adaptation led to aftereffects, and in the other prior stimulus occurrences were manipulated. We show that confidence judgments perfectly follow changes in perceptual reports and response times, regardless of the nature of the bias. Although observers are able to judge the validity of their percepts, they are oblivious to how biased these percepts are. Focusing on self-consistency rather than correctness leads us to interpret confidence as an estimate of the reliability of one's perceptual decision rather than a distance to an unattainable truth.

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The world teems with complex sounds that animals have to interpret in order to survive. To do so, their brain must represent the richness of the sounds' acoustic structure, from simple to high-order features. Understanding how it does it, however, remains filled with challenges. In this thesis, these questions were explored through a new technical prism, namely functional UltraSound imaging (fUSi). First, fUSi was used to investigate with a high fidelity the topographical organization of the auditory system, as well as its connectivity with other brain areas. Second, it provided fundamental clues for our understanding of how natural sounds are encoded in the auditory cortex, and hints at the human particularities for speech processing. Last, it gave us access to non-continuous topographical encoding, with the example of spatial localization. Through these three aspects, we exposed the different spatially organized modules of processing that overlap within a single brain area.; Le monde extérieur regorge de sons complexes, que chaque animal doit interpréter afin de survivre. Pour ce faire, leur cerveau se doit de représenter toute la richesse de la structure acoustique de ces sons, jusque dans leurs propriétés les plus complexes. Dans cette thèse, cette question est explorée à travers un nouveau prisme, l'imagerie fonctionnelle ultrasonore (fUS). Dans un premier temps, l'imagerie fUS est utilisée pour étudier avec une haute fidélité l'organisation topographique du système auditif, ainsi que ses connexions avec d'autres aires cérébrales. Dans un deuxième temps, elle permet d'explorer des aspects fondamentaux de la façon dont le cortex auditif encode les sons naturels, ainsi que les spécificités humaines pour le traitement du langage. Enfin, elle révèle des formes topographiques mais non continues d'encodage, avec l'exemple de la localisation spatiale des sons. À travers ces trois aspects sont révélés les différents modules de traitement de l'information auditive, spatialement organisés, qui se superposent au sein d'une aire cérébrale unique.

The world teems with complex sounds that animals have to interpret in order to survive. To do so, their brain must represent the richness of the sounds' acoustic structure, from simple to high-order features. Understanding how it does it, however, remains filled with challenges. In this thesis, these questions were explored through a new technical prism, namely functional UltraSound imaging (fUSi). First, fUSi was used to investigate with a high fidelity the topographical organization of the auditory system, as well as its connectivity with other brain areas. Second, it provided fundamental clues for our understanding of how natural sounds are encoded in the auditory cortex, and hints at the human particularities for speech processing. Last, it gave us access to non-continuous topographical encoding, with the example of spatial localization. Through these three aspects, we exposed the different spatially organized modules of processing that overlap within a single brain area., Le monde extérieur regorge de sons complexes, que chaque animal doit interpréter afin de survivre. Pour ce faire, leur cerveau se doit de représenter toute la richesse de la structure acoustique de ces sons, jusque dans leurs propriétés les plus complexes. Dans cette thèse, cette question est explorée à travers un nouveau prisme, l'imagerie fonctionnelle ultrasonore (fUS). Dans un premier temps, l'imagerie fUS est utilisée pour étudier avec une haute fidélité l'organisation topographique du système auditif, ainsi que ses connexions avec d'autres aires cérébrales. Dans un deuxième temps, elle permet d'explorer des aspects fondamentaux de la façon dont le cortex auditif encode les sons naturels, ainsi que les spécificités humaines pour le traitement du langage. Enfin, elle révèle des formes topographiques mais non continues d'encodage, avec l'exemple de la localisation spatiale des sons. À travers ces trois aspects sont révélés les différents modules de traitement de l'information auditive, spatialement organisés, qui se superposent au sein d'une aire cérébrale unique.

Temporal attention enhances the perceptual representation of a stimulus at a particular point in time. The number of possible attentional episodes in a given period is limited, but whether observers’ confidence reflects such limitations is still unclear. To investigate this issue, we adapted an “Attentional Blink” paradigm, presenting observers with a rapid visual stream of letters containing two targets cued for subsequent perceptual reports and confidence judgments. We found three main results. First, when two targets fell within the same attentional episode, the second target underwent a strong under-confidence bias. In other words, confidence neglected that a single attentional episode can benefit to both targets. Second, despite this initial bias, confidence was strongly correlated with response probability. Third, as confidence was yoked to the evidence used in perceptual reports, it remains blind to delays in response selection for the second target. Notably, the second target was often mistaken with a later item associated with higher confidence. These results suggest that confidence does not perfectly evaluate the limits of temporal attention in challenging situations.

An outstanding challenge for consciousness research is to characterize the neural signature of conscious access independently of any decisional processes. Here we present a model-based approach that uses inter-trial variability to identify the brain dynamics associated with stimulus processing. We demonstrate that, even in the absence of any task or behavior, the electroencephalographic response to auditory stimuli shows bifurcation dynamics around 250–300 milliseconds post-stimulus. Namely, the same stimulus gives rise to late sustained activity on some trials, and not on others. This late neural activity is predictive of task-related reports, and also of reports of conscious contents that are randomly sampled during task-free listening. Source localization further suggests that task-free conscious access recruits the same neural networks as those associated with explicit report, except for frontal executive components. Studying brain dynamics through variability could thus play a key role for identifying the core signatures of conscious access, independent of report., Current knowledge on the neural basis of consciousness mostly relies on situations where people report their perception. Here, the authors provide evidence for the idea that bifurcation in brain dynamics reflects conscious perception independent of report.

What has been previously experienced can systematically affect human perception in the present. We designed a novel psychophysical experiment to measure the perceptual effects of adapting to dynamically changing stimulus statistics. Observers are presented with a series of oriented Gabor patches and are asked occasionally to judge the orientation of highly ambiguous test patches. We developed a computational model to quantify the influence of past stimuli presentations on the observers' perception of test stimuli over multiple timescales and to show that this influence is distinguishable from simple response biases. The experimental results reveal that perception is attracted toward the very recent past and simultaneously repulsed from stimuli presented at short to medium timescales and attracted to presentations further in the past. All effects differ significantly both on their relative strength and their respective duration. Our model provides a structured way of quantifying serial effects in psychophysical experiments, and it could help experimenters in identifying such effects in their data and distinguish them from less interesting response biases.

Does the moment when an event is perceived depends on where it is presented? To measure when participants perceived events, they were first familiarized with trial duration, by watching the hand of a clock rotating. Then, the hand was removed, and stimuli were presented at a random time from the trial onset. Participants indicated the location where the hand would have been when the stimulus was presented. The stimuli’s eccentricity, the appearance, and location of the spatial features of the clock were varied. The targets were reported earlier if they were presented in spatial proximity to the clock outline, even when it was not presented during the trial. The effect was replicated with stimuli presented at the same distance from fixation but at different distances from the spatial features. In summary, the time of an event is perceived earlier if it is presented near attended features in the visual scene.

Between 1 to 5 out of 100 people worldwide has never experienced normotypic vision due to a condition called amblyopia, and about 1 out of 4000 suffer from inherited retinal dystrophies that progressively lead them to blindness. While a wide range of technologies and therapies are being developed to restore vision, a fundamental question still remains unanswered: would the adult visual brain retain a sufficient plastic potential to learn how to see after a prolonged period of abnormal visual experience? In this review we summarize studies showing that the visual brain of sighted adults retains a type of developmental plasticity, called homeostatic plasticity, and this property has been recently exploited successfully for adult amblyopia recover. Next, we discuss how the brain circuits reorganizes when visual stimulation is partially restored by means of a bionic eye in late blinds with Retinitis Pigmentosa. The primary visual cortex in these patients slowly became activated by the artificial visual stimulation, indicating that sight restoration therapies can rely on a considerable degree of spared plasticity in adulthood., 28 pages, 2 figures

Adaptive decision-making requires precise monitoring of decision quality in light of both sensory uncertainty and the variability inherent in cognitive functions. Such monitoring, or metacognitive reasoning, can be assessed by relating subjective confidence in a perceptual decision to objective accuracy. Selective attention is a known modulator of sensory processing, and reliable metacognitive access to attention may be the key to cope with the variability of the environment. The present dissertation investigates the temporal construction of visual confidence during and after the allocation of selective attention either to a point in time (temporal attention) or to a point in space (spatial attention). In both the temporal and spatial domain, we observe that attention constrains metacognitive ability, both during and after allocation. The robust temporal binding observed in the present thesis between attention and metacognition induces dissociations between confidence and accuracy when attention is misallocated. The empirical results presented in this work highlight a systematic inability to integrate the temporal dynamics of selective attention into metacognitive judgments., La dynamique du monde qui nous entoure nécessite sans cesse d'adapter nos décisions à son incertitude latente. Cette incertitude définit autant notre perception que le fonctionnement même de nos fonctions cognitives. La « métacognition » d’un individu - la manière dont il raisonne sur ses propres perceptions - peut être étudiée en comparant sa confiance à la qualité objective de ses décisions perceptives. Parce que l’attention sélective est une source importante de modulation sensorielle, une bonne métacognition des effets de l’attention sur la perception semble primordiale. La façon dont la confiance émerge du processus d’orientation de l'attention, et se développe ensuite dans l'espace et le temps, fait l'objet de cette thèse. Nous y décrivons notamment la solide dépendance que la confiance cultive à l'égard de l’attention visuelle, une dépendance qui subsiste à chaque étape du processus attentionnel. Les travaux expérimentaux présentés dans cette thèse suggèrent ainsi une dépendance si forte qu’une orientation erronée de l’attention passe souvent inaperçue au niveau métacognitif. Ces résultats témoignent de l'incapacité de la confiance à prendre en compte certaines des limites temporelles de l'attention sélective.

International audience; Objectives: The aim of this study was to investigate whether short-term inverse occlusion, combined with moderate physical exercise, could promote the recovery of visual acuity and stereopsis in a group of adult anisometropic amblyopes. Methods: Ten adult anisometropic patients underwent six brief (2 h) training sessions over a period of 4 weeks. Each training session consisted in the occlu-sion of the amblyopic eye combined with physical exercise (intermittent cycling on a stationary bike). Visual acuity (measured with ETDRS charts), stereoacuity (measured with the TNO test), and sensory eye dominance (measured with binocular rivalry) were tested before and after each training session, as well as in follow-up visits performed 1 month, 3 months, and 1 year after the end of the training. Results: After six brief (2 h) training sessions, visual acuity improved in all 10 patients (0.15 AE 0.02 LogMar), and six of them also recovered stereopsis. The improvement was preserved for up to 1 year after training. A pilot experiment suggested that physical activity might play an important role for the recovery of visual acuity and stereopsis. Conclusions: Our results suggest a noninvasive training strategy for adult human amblyopia based on an inverse-occlusion procedure combined with physical exercise.

International audience; Filters are commonly used to reduce noise and improve data quality. Filter theory is part of a scientist's training, yet the impact of filters on interpreting data is not always fully appreciated. This paper reviews the issue, explains what is a filter, what problems are to be expected when using them, how to choose the right filter, or how to avoid filtering by using alternative tools. Time-frequency analysis shares some of the same problems that filters have, particularly in the case of wavelet transforms. We recommend reporting filter characteristics with sufficient details, including a plot of the impulse or step response as an inset.

Navigating a busy street while talking on the phone is trivial for a majority of the population. However, how the brain extracts the relevant information from the ever-changing and cluttered acoustic environment to produce the appropriate behavior remains poorly understood. The proposed thesis investigates neural basis of the extraction of relevant information in complex, continuous streams for goal-directed behavior using a combination technique linking electrophysiology to psychophysics. Humans and ferrets performed a similar change detection task that consisted of reporting changes in the underlying statistics of a toned cloud. The brain electrical activity was recorded (scalp level for humans and cell level for ferrets) while subjects engaged in the task. Overall, we find area-specific cortical responses: change-related responses are generalized along the cortical pathway. In addition, task engagement strongly modulates the frontal cortex where decision-related responses are found., Se déplacer dans une rue tout en discutant au téléphone est une action triviale pour une majorité de la population. Cependant, comment le cerveau parvient à extraire les informations pertinentes d'un environnement acoustique aussi complexe afin de produire un comportement adéquat reste encore actuellement peu compris. La thèse présentée explore les bases neurales de l'extraction d'informations pertinentes dans un flux complexe et continu afin de produire un comportement. Les sujets (humains et furets) détectent des changements dans les statistiques de nuages de tons pendant que leur activité électrique cérébrale (sur le scalp, pour le furet) est enregistrée. Les différentes zones du cortex enregistrées présentent des activités spécifiques : les réponses liées au changement se généralisent le long de la hiérarchie corticale. De plus, l'engagement dans la tâche module spécifiquement les réponses frontales au changement et à la décision.

Over the past few years, the field of visual social cognition and face processing has been dramatically impacted by a series of data-driven studies employing computer-graphics tools to synthesize arbitrary meaningful facial expressions. In the auditory modality, reverse correlation is traditionally used to characterize sensory processing at the level of spectral or spectro-temporal stimulus properties, but not higher-level cognitive processing of e.g. words, sentences or music, by lack of tools able to manipulate the stimulus dimensions that are relevant for these processes. Here, we present an open-source audio-transformation toolbox, called CLEESE, able to systematically randomize the prosody/melody of existing speech and music recordings. CLEESE works by cutting recordings in small successive time segments (e.g. every successive 100 milliseconds in a spoken utterance), and applying a random parametric transformation of each segment’s pitch, duration or amplitude, using a new Python-language implementation of the phase-vocoder digital audio technique. We present here two applications of the tool to generate stimuli for studying intonation processing of interrogative vs declarative speech, and rhythm processing of sung melodies.

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Perception deals with temporal sequences of events, like series of phonemes for audition, dynamic changes in pressure for touch textures, or moving objects for vision. Memory processes are thus needed to make sense of the temporal patterning of sensory information. Recently, we have shown that auditory temporal patterns could be learned rapidly and incidentally with repeated exposure [Kang et al., 2017]. Here, we tested whether rapid incidental learning of temporal patterns was specific to audition, or if it was a more general property of sensory systems. We used a same behavioral task in three modalities: audition, touch, and vision, for stimuli having identical temporal statistics. Participants were presented with sequences of acoustic pulses for audition, motion pulses to the fingertips for touch, or light pulses for vision. Pulses were randomly and irregularly spaced, with all inter-pulse intervals in the sub-second range and all constrained to be longer than the temporal acuity in any modality. This led to pulse sequences with an average inter-pulse interval of 166 ms, a minimum inter-pulse interval of 60 ms, and a total duration of 1.2 s. Results showed that, if a random temporal pattern re-occurred at random times during an experimental block, it was rapidly learned, whatever the sensory modality. Moreover, patterns first learned in the auditory modality displayed transfer of learning to either touch or vision. This suggests that sensory systems may be exquisitely tuned to incidentally learn re-occurring temporal patterns, with possible cross-talk between the senses.

Using a same-different discrimination task, it has been shown that discrimination performance for sequences of complex tones varying just detectably in pitch is less dependent on sequence length (1, 2, or 4 elements) when the tones contain resolved harmonics than when they do not [Cousineau, Demany, and Pessnitzer (2009). J. Acoust. Soc. Am. 126, 3179-3187]. This effect had been attributed to the activation of automatic frequency-shift detectors (FSDs) by the shifts in resolved harmonics. The present study provides evidence against this hypothesis by showing that the sequence-processing advantage found for complex tones with resolved harmonics is not found for pure tones or other sounds supposed to activate FSDs (narrow bands of noise and wide-band noises eliciting pitch sensations due to interaural phase shifts). The present results also indicate that for pitch sequences, processing performance is largely unrelated to pitch salience per se: for a fixed level of discriminability between sequence elements, sequences of elements with salient pitches are not necessarily better processed than sequences of elements with less salient pitches. An ideal-observer model for the same-different binary-sequence discrimination task is also developed in the present study. The model allows the computation of d' for this task using numerical methods.

Contrast is the most fundamental property of images. Consequently, any comprehensive model of biological vision must incorporate this attribute and provide a veritable description of its impact on visual perception. Current theoretical and computational models predict that vision should modify its characteristics at low contrast: for example, it should become broader (more lowpass) to protect from noise, as often demonstrated by individual neurons. We find that the opposite is true for human discrimination of elementary image elements: vision becomes sharper, not broader, as contrast approaches threshold levels. Furthermore, it suffers from increased internal variability at low contrast and it transitions from a surprisingly linear regime at high contrast to a markedly nonlinear processing mode in the low-contrast range. These characteristics are hard-wired in that they happen on a single trial without memory or expectation. Overall, the empirical results urge caution when attempting to interpret human vision from the standpoint of optimality and related theoretical constructs. Direct measurements of this phenomenon indicate that the actual constraints derive from intrinsic architectural features, such as the co-existence of complex-cell-like and simple-cell-like components. Small circuits built around these elements can indeed account for the empirical results, but do not appear to operate in a manner that conforms to optimality even approximately. More generally, our results provide a compelling demonstration of how far we still are from securing an adequate computational account of the most basic operations carried out by human vision., Author summary We can view cortex from two fundamentally different perspectives: a powerful device for performing optimal inference, or an assembly of biological components not built for achieving statistical optimality. The former approach is attractive thanks to its elegance and potentially wide applicability, however the basic facts of human pattern vision do not support it. Instead, they indicate that the idiosyncratic behaviour produced by visual cortex is primarily dictated by its hardware components. The output of these components can be steered towards optimality by our cognitive apparatus, but only to a marginal extent. We conclude that current theories of visually-guided behaviour are at best inadequate, calling for a rebalanced view of the roles played by theoretical and experimental thinking about this function.

Significance In speech, social evaluations of a speaker’s dominance or trustworthiness are conveyed by distinguishing, but little-understood, pitch variations. This work describes how to combine state-of-the-art vocal pitch transformations with the psychophysical technique of reverse correlation and uses this methodology to uncover the prosodic prototypes that govern such social judgments in speech. This finding is of great significance, because the exact shape of these prototypes, and how they vary with sex, age, and culture, is virtually unknown, and because prototypes derived with the method can then be reapplied to arbitrary spoken utterances, thus providing a principled way to modulate personality impressions in speech., Human listeners excel at forming high-level social representations about each other, even from the briefest of utterances. In particular, pitch is widely recognized as the auditory dimension that conveys most of the information about a speaker’s traits, emotional states, and attitudes. While past research has primarily looked at the influence of mean pitch, almost nothing is known about how intonation patterns, i.e., finely tuned pitch trajectories around the mean, may determine social judgments in speech. Here, we introduce an experimental paradigm that combines state-of-the-art voice transformation algorithms with psychophysical reverse correlation and show that two of the most important dimensions of social judgments, a speaker’s perceived dominance and trustworthiness, are driven by robust and distinguishing pitch trajectories in short utterances like the word “Hello,” which remained remarkably stable whether male or female listeners judged male or female speakers. These findings reveal a unique communicative adaptation that enables listeners to infer social traits regardless of speakers’ physical characteristics, such as sex and mean pitch. By characterizing how any given individual’s mental representations may differ from this generic code, the method introduced here opens avenues to explore dysprosody and social-cognitive deficits in disorders like autism spectrum and schizophrenia. In addition, once derived experimentally, these prototypes can be applied to novel utterances, thus providing a principled way to modulate personality impressions in arbitrary speech signals.

The use of simple stimuli has been tremendously beneficial to understand the basic properties of primary sensory systems. \ud However, interacting with the natural environment leads to complex stimulations of our senses. \ud Here we focus on the estimation of visual speed, a critical information for the survival of many animal species as they monitor moving prey or approaching dangers. In mammals, and in particular in primates, speed information is conceived to be represented by a set of channels sensitive to different spatial and temporal characteristics of the optic flow [1-5]. \ud However, it is still largely unknown how the brain could accurately infer the speed of complex natural scenes from this set of spatiotemporal channels [6-14]. \ud As complex stimuli, we chose a set of well-controlled moving naturalistic textures called 'Compound Motion Clouds' (CMC) [15, 16] that simultaneously activate multiple spatiotemporal channels. \ud We found that CMC stimuli that have the same physical speed are perceived moving at different speeds depending on which channels are activated. \ud Thanks to a computational model, we show that the activity in a given channel is both boosted and weakened following a systematic pattern over neighboring channels. \ud This pattern of interactions can be understood as a combination of two components oriented in speed (consistent with a slow-speed prior) and scale (sharpening of similar features). Interestingly, the interaction along scale implements a lateral inhibition principle that is usually found to operate in early sensory processing. \ud These results further promote the idea that lateral inhibition along a variety of dimensions is a canonical principle of perceptual processing. \ud Overall, the speed-scale normalization mechanism may reflect the natural tendency of the visual system to integrate complex inputs into one coherent percept.

International audience; As eye movements are mostly automatic and overtly generated to attain visual goals, individuals have a poor metacognitive knowledge of their own eye movements. We present an exploratory study on the effects of real-time continuous auditory feedback generated by eye movements. We considered both a tracking task and a production task where smooth pursuit eye movements (SPEM) can be endogenously generated. In particular, we used a visual paradigm which enables to generate and control SPEM in the absence of a moving visual target. We investigated whether real-time auditory feedback of eye movement dynamics might improve learning in both tasks, through a training protocol over 8 days. The results indicate that real-time sonification of eye movements can actually modify the oculomotor behavior, and reinforce intrinsic oculomotor perception. Nevertheless, large inter-individual differences were observed preventing us from reaching a strong conclusion on sensorimotor learning improvements.

When different images are presented to the eyes, the brain is faced with ambiguity, causing perceptual bistability: visual perception continuously alternates between the monocular images, a phenomenon called binocular rivalry. Many models of rivalry suggest that its temporal dynamics depend on mutual inhibition among neurons representing competing images. These models predict that rivalry should be different in autism, which has been proposed to present an atypical ratio of excitation and inhibition [the E/I imbalance hypothesis; Rubenstein & Merzenich, 2003]. In line with this prediction, some recent studies have provided evidence for atypical binocular rivalry dynamics in autistic adults. In this study, we examined if these findings generalize to autistic children. We developed a child‐friendly binocular rivalry paradigm, which included two types of stimuli, low‐ and high‐complexity, and compared rivalry dynamics in groups of autistic and age‐ and intellectual ability‐matched typical children. Unexpectedly, the two groups of children presented the same number of perceptual transitions and the same mean phase durations (times perceiving one of the two stimuli). Yet autistic children reported mixed percepts for a shorter proportion of time (a difference which was in the opposite direction to previous adult studies), while elevated autistic symptomatology was associated with shorter mixed perception periods. Rivalry in the two groups was affected similarly by stimulus type, and consistent with previous findings. Our results suggest that rivalry dynamics are differentially affected in adults and developing autistic children and could be accounted for by hierarchical models of binocular rivalry, including both inhibition and top‐down influences. Autism Res 2017. ©2017 The Authors Autism Research published by Wiley Periodicals, Inc. on behalf of International Society for Autism Research Autism Res 2017, 10: 1096–1106. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

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N.F. was an EPFL fellow co-funded by Marie Skłodowska-Curie. C.L. was funded by the project ECSPLAIN (European Research Council under the Seventh Framework Programme, FPT/2007-2013, grant agreement no. 338866). A.A. was a Blavatnik fellow and a Royal society Kohn international fellow. We thank the Association for the Scientific Study of Consciousness for travel funding, and we are grateful to Liad Mudrik, Lavi Secundo, Andrés Canales-Johnson, and Ophelia Deroy for valuable discussions and helpful comments on the article.; International audience; Over the last 30 years, our understanding of the neurocognitive bases of consciousness has improved, mostly through studies employing vision. While studying consciousness in the visual modality presents clear advantages, we believe that a comprehensive scientific account of subjective experience must not neglect other exteroceptive and interoceptive signals as well as the role of multisensory interactions for perceptual and self-consciousness. Here, we briefly review four distinct lines of work which converge in documenting how multisensory signals are processed across several levels and contents of consciousness. Namely, how multisensory interactions occur when consciousness is prevented because of perceptual manipulations (i.e. subliminal stimuli) or because of low vigilance states (i.e. sleep, anesthesia), how interactions between exteroceptive and interoceptive signals give rise to bodily self-consciousness, and how multisensory signals are combined to form metacognitive judgments. By describing the interactions between multisensory signals at the perceptual, cognitive, and metacognitive levels, we illustrate how stepping out the visual comfort zone may help in deriving refined accounts of consciousness, and may allow cancelling out idiosyncrasies of each sense to delineate supramodal mechanisms involved during consciousness.

Continuous flash suppression (CFS) is a psychophysical technique where a rapidly changing Mondrian pattern viewed by one eye suppresses the target in the other eye for several seconds. Despite the widespread use of CFS to study unconscious visual processes, the temporal tuning of CFS suppression is currently unknown. In the present study we used spatiotemporally filtered dynamic noise as masking stimuli to probe the temporal characteristics of CFS. Surprisingly, we find that suppression in CFS peaks very prominently at approximately 1 Hz, well below the rates typically used in CFS studies (10 Hz or more). As well as a strong bias to low temporal frequencies, CFS suppression is greater for high spatial frequencies and increases with increasing masker contrast, indicating involvement of parvocellular/ventral mechanisms in the suppression process. These results are reminiscent of binocular rivalry, and unifies two phenomenon previously thought to require different explanations.

International audience; The growing availability of large, multidimensional data sets has created demand for high-performance, interactive visualization tools. VisPy leverages the GPU to provide fast, interactive, and beautiful visualizations in a high-level API. Here we introduce the main features, architecture, and techniques used in VisPy.

Moving objects generate motion information at different scales, which are processed in the visual system with a bank of spatiotemporal frequency channels. It is not known how the brain pools this information to reconstruct object speed and whether this pooling is generic or adaptive; that is, dependent on the behavioral task. We used rich textured motion stimuli of varying bandwidths to decipher how the human visual motion system computes object speed in different behavioral contexts. We found that, although a simple visuomotor behavior such as short-latency ocular following responses takes advantage of the full distribution of motion signals, perceptual speed discrimination is impaired for stimuli with large bandwidths. Such opposite dependencies can be explained by an adaptive gain control mechanism in which the divisive normalization pool is adjusted to meet the different constraints of perception and action.

Accuracy in estimating an object’s global motion over time is not only affected by the noise in visual motion information but also by the spatial limitation of the local motion analyzers (aperture problem). Perceptual and oculomotor data demonstrate that during the initial stages of the motion information processing, 1D motion cues related to the object’s edges have a dominating influence over the estimate of the object’s global motion. However, during the later stages, 2D motion cues related to terminators (edge-endings) progressively take over, leading to a final correct estimate of the object’s global motion. Here, we propose a recursive extension to the Bayesian framework for motion processing ( Weiss, Simoncelli, & Adelson, 2002 ) cascaded with a model oculomotor plant to describe the dynamic integration of 1D and 2D motion information in the context of smooth pursuit eye movements. In the recurrent Bayesian framework, the prior defined in the velocity space is combined with the two independent measurement likelihood functions, representing edge-related and terminator-related information, respectively to obtain the posterior. The prior is updated with the posterior at the end of each iteration step. The maximum-a posteriori (MAP) of the posterior distribution at every time step is fed into the oculomotor plant to produce eye velocity responses that are compared to the human smooth pursuit data. The recurrent model was tuned with the variance of pursuit responses to either “pure” 1D or “pure” 2D motion. The oculomotor plant was tuned with an independent set of oculomotor data, including the effects of line length (i.e. stimulus energy) and directional anisotropies in the smooth pursuit responses. The model not only provides an accurate qualitative account of dynamic motion integration but also a quantitative account that is close to the smooth pursuit response across several conditions (three contrasts and three speeds) for two human subjects.

1st French Conference in Computational Neuroscience, Pont a Mousson, FRANCE, OCT, 2006; International audience; The quality of the representation of an object's motion is limited by the noise in the sensory input as well as by an intrinsic ambiguity due to the spatial limitation of the visual motion analyzers (aperture problern). Perceptual and oculomotor data demonstrate that motion processing of extended objects is initially dominated by the local ID motion cues, related to the object's edges and orthogonal to them, whereas 2D information, related to terminators (or edge-endings), takes progressively over and leads to the final correct representation of global motion. A Bayesian framework accounting for the sensory noise and general expectancies for object velocities has proven successful in explaining several experimental findings concerning early motion processing [Weiss, Y., Adelson, E., 1998. Slow and smooth: a Bayesian theory for the combination of local motion signals in human vision. MIT Technical report, A.I. Memo 1624]. In particular, these models provide a qualitative account for the initial bias induced by the ID motion cue. However, a complete functional model, encompassing the dynamical evolution of object motion perception, including the integration of different motion cues, is still lacking. Here we outline several experimental observations concerning human smooth pursuit of moving objects and more particularly the time course of its initiation phase, which reflects the ongoing motion integration process. In addition, we propose a recursive extension of the Bayesian model, motivated and constrained by our oculomotor data, to describe the dynamical integration of ID and 2D motion information. We compare the model predictions for object motion tracking with human oculomotor recordings. (C) 2007 Elsevier Ltd. All rights reserved.

International audience; Sleep and plasticity are highly interrelated, as sleep slow oscillations and sleep spindles are associated with consolidation of Hebbian-based processes. However, in adult humans, visual cortical plasticity is mainly sustained by homeostatic mechanisms, for which the role of sleep is still largely unknown. Here, we demonstrate that non-REM sleep stabilizes homeostatic plasticity of ocular dominance induced in adult humans by short-term monocular deprivation: the counterintuitive and otherwise transient boost of the deprived eye was preserved at the morning awakening (>6 hr after deprivation). Subjects exhibiting a stronger boost of the deprived eye after sleep had increased sleep spindle density in frontopolar electrodes, suggesting the involvement of distributed processes. Crucially, the individual susceptibility to visual homeostatic plasticity soon after deprivation correlated with the changes in sleep slow oscillations and spindle power in occipital sites, consistent with a modulation in early occipital visual cortex. Editor's evaluation Menicucci and colleagues investigated the potential role of sleep in the homeostatic plasticity of ocular dominance in adult humans. This is a careful study that should be of broad interest to those studying adult cortical plasticity, particularly in vision. The study shows that sleep can maintain the changes in ocular dominance obtained after applying an eye patch on the dominant eye for two hours, which contrasts with the rapid decline of these changes during quiet wake in darkness. The authors further report correlations between sleep oscillations and the magnitude of the plasticity effect. Overall, these results implicate sleep in a new form of plasticity.

Prominent models of time perception assume a reset of the timing mechanism with an explicit onset of the interval to be timed. Here we investigated the accuracy and precision of temporal estimations when the duration does not have such an explicit onset. Participants were tracking a disc moving on a circular path with varying speeds, and estimated the duration of one full revolution before the stimulus stopped. The onset of that revolution was either cued (explicit), or undetermined until the stimulus stopped (implicit). Reproduced duration was overestimated for short and underestimated for long durations, and variability of the estimates scaled with the duration in both temporal conditions. However, the bias was more pronounced in the implicit condition. In addition, if the stimulus path was partially occluded, duration of the occluded motion was correctly estimated. In a second experiment, we compared the precision in the explicit and implicit conditions by asking participants to discriminate the duration of one revolution before the stimulus stopped to that of a static stimulus presentation in a forced-choice task. Sensitivity of discrimination was worse in the implicit onset condition, but surprisingly, still comparable to the explicit condition. In summary, the estimates follow principles described in prospective timing paradigms, although not knowing beforehand when to start timing decreases sensitivity of temporal estimations. Since in naturalistic contexts, we often do not know in advance which durations might be relevant to estimate, the simple task presented here could become a valuable tool for testing models of temporal estimation.

International audience; PURPOSE. Retinitis pigmentosa is a family of genetic diseases inducing progressive photoreceptor degeneration. There is no cure for retinitis pigmentosa, but prospective therapeutic strategies are aimed at restoring or substituting retinal input. Yet, it is unclear whether the visual cortex of retinitis pigmentosa patients retains plasticity to react to the restored visual input. METHODS. To investigate short-term visual cortical plasticity in retinitis pigmentosa, we tested the effect of short-term (2 hours) monocular deprivation on sensory ocular dominance (measured with binocular rivalry) in a group of 14 patients diagnosed with retinitis pigmentosa with a central visual field sparing greater than 208 in diameter. RESULTS. After deprivation most patients showed a perceptual shift in ocular dominance in favor of the deprived eye (P < 0.001), as did control subjects, indicating a level of visual cortical plasticity in the normal range. The deprivation effect correlated negatively with visual acuity (r ¼ À0.63, P ¼ 0.015), and with the amplitude of the central 188 focal electroretinogram (r ¼ À0.68, P ¼ 0.015) of the deprived eye, revealing that in retinitis pigmentosa stronger visual impairment is associated with higher plasticity. CONCLUSIONS. Our results provide a new tool to assess the ability of retinitis pigmentosa patients to adapt to altered visual inputs, and suggest that in retinitis pigmentosa the adult brain has sufficient short-term plasticity to benefit from prospective therapies.