Search

An effective way to quantify metacognitive abilities is to ask participants to estimate their confidence in the accuracy of their response during a cognitive task. A recent meta-analysis1raised the issue that most assessments of metacognitive abilities in schizophrenia spectrum disorders may be confounded with cognitive deficits, which are known to be present in this population. Therefore, it remains unclear whether the reported metacognitive deficits are metacognitive in nature, or rather inherited from cognitive deficits. Arbitrating between these two possibilities requires equating task performance between experimental groups. Here, we aimed to characterize metacognitive performance among individuals with schizophrenia across three tasks (visual detection, familiarity, recollection) using a within-subject design, while controlling experimentally for intra-individual task performance and statistically for between-subject task performance. In line with our hypotheses, we found no metacognitive deficit for visual detection and familiarity judgements. While we expected metacognition for recollection to be specifically impaired among individuals with schizophrenia, we found evidence in favor of an absence of a deficit in that domain also. The clinical relevance of our findings is discussed in light of a hierarchical framework of metacognition.

International audience

We show that sleep deprivation in otherwise normal and healthy adults can be detected through machine-learning analysis of vocal recordings. Importantly, we used fully generic acoustic features, derived from auditory models, together with our own machine learning interpretation method, derived from neuroscience. Sleep deprivation impacted two broad types of acoustic features: one related to speech rhythms, the other related to the timbre of the voice. Such features plausibly reflect two independent physiological processes: one explicit, the cognitive control of speech production, and the other implicit, the inflammation of the vocal apparatus. Crucially, the relative balance of the two processes varied widely across individuals, consistent with the known but unexplained variability in responses to sleep deprivation. Overall, our results suggest that the voice may be used as a “sleep stethoscope” to characterize the individual effects of sleep deprivation. Moreover, the method we applied is fully general and could be adapted to any future investigation of vocal biomarkers using machine-learning techniques.Author summarySleep deprivation has an ever-increasing impact on individuals and societies, from accidents to chronic conditions costing billions to health systems. Yet, to date, there is no quick and objective test for sleep deprivation. We show that sleep deprivation can be detected at the individual level with voice recordings, outlining future cost-effective and non-invasive “sleep stethoscopes”. Importantly, we focused on interpretability, which identified two independent physiological effects of sleep deprivation: a change in prosody, related to cognitive control, and a change in timbre, related to inflammation. This also revealed a striking variability in individual reactions to the same deprivation. The neuroscientific framework we developed, combining auditory models and machine learning, is freely available and could be adapted to any vocal biomarker.

International audience; This study investigated the potential for the development of novel perceptual experiences through sustained training with a sensory augmentation device. We developed (1) a new geomagnetic sensory augmentation device, the NaviEar, and (2) a battery of tests for automaticity in the use of the device. The NaviEar translates head direction toward north into continuous sound according to a “wind coding” principle. To facilitate automatization of use, its design is informed by considerations of the embodiment of spatial orientation and multi-sensory integration, and it uses a sensory coding scheme derived from means for auditory perception of wind direction that is common in sailing because it is easy to understand and use. The test battery assesses different effects of automaticity (interference, rigidity of responses, and dynamic integration) assuming that automaticity is a necessary criterion to show the emergence of perceptual feel, that is, an augmented experience with perceptual phenomenal quality. We measured performance in simple training tasks, administered the tests for automaticity, and assessed subjective reports through a questionnaire. Results suggest that the NaviEar is easy and comfortable to use and has a potential for applications in real-world situations. Despite high usability, however, a 5-day training with the NaviEar did not reach levels of automaticity that are indicative of perceptual feel. We propose that the test battery for automaticity may be used as a benchmark test for iterative research on perceptual experiences in sensory augmentation and sensory substitution.

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.

International audience; Key points: In quiet, envelope tracking in the low amplitude modulation range (

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

The way the visual system processes different scales of spatial information has been widely studied, highlighting the dominant role of global over local processing. Recent studies addressing how the auditory system deals with local–global temporal information suggest a comparable processing scheme, but little is known about how this organization is modulated by long-term musical training, in particular regarding musical sequences. Here, we investigate how non-musicians and expert musicians detect local and global pitch changes in short hierarchical tone sequences structured across temporally-segregated triplets made of musical intervals (local scale) forming a melodic contour (global scale) varying either in one direction (monotonic) or both (non-monotonic). Our data reveal a clearly distinct organization between both groups. Non-musicians show global advantage (enhanced performance to detect global over local modifications) and global-to-local interference effects (interference of global over local processing) only for monotonic sequences, while musicians exhibit the reversed pattern for non-monotonic sequences. These results suggest that the local–global processing scheme depends on the complexity of the melodic contour, and that long-term musical training induces a prominent perceptual reorganization that reshapes its initial global dominance to favour local information processing. This latter result supports the theory of “analytic” processing acquisition in musicians.

International audience; Background:A long-lasted goal in psycholinguistics is to identify the acoustic cues underlying phonetic percepts. The reverse correlation method offers an agnostic approach to explore internal representations of phonemes by relating listeners’ responses in a phoneme-in-noise task with the exact time-frequency representation of the tested noises. The outcome is called auditory classification images (ACIs): A fine-grained time-frequency map of the acoustic cues listeners relied upon. Here, we focus on the effect of the statistics of the background noises with respect to the efficiency and robustness of the method. We used three different noise types that have a flat long-term spectrum, but differ in the amount of temporal envelope fluctuations: (1) White noise, (2) white noise low-pass filtered in the modulation power spectrum (MPS) domain, and (3) bump noise.Methods:We conducted a consonant-in-noise discrimination task using the words /aba/ and /ada/. The speech samples were uttered by a male speaker taken from the French Logatome speech corpus. Each participant performed a total of 4000 /aba/-/ada/ categorizations in each of the three noise conditions. During the task, the signal-to-noise ratio (SNR) was varied to target a score of 70.7%. Subsequently, ACIs were derived using the reverse correlation method, revealing the time-frequency regions where the noises systematically affected the participants’ discrimination. Further, we computed ACIs using subsets of trials to evaluate the fidelity and speed of convergence of these "partial ACIs" with respect to the ACI obtained using all trials.Results:For both participants and for the three test noise types, we succeeded to find stable ACIs which, in line with previous evaluations that used another pair of /aba/-/ada/ utterances, reveal a high-weighting of the speech information between the first and second formants in the consonant-vowel transitions. Our analysis of partial ACIs shows that the three noise types required a different number of conducted trials to converge to a stable ACI, with a faster convergence for the MPS and bump noises compared with the use of white noises.Our analyses suggest that the reverse correlation method applied to a consonant-in-noise discrimination task converges more quickly to a stable result, the ACI, when the background noises contain dominant components in the modulation frequency range between 0 and 40 Hz, which is the case for MPS and bump noises. The prominent envelope fluctuations in this range lead to more systematic confusion errors compared to white noise and, therefore, to higher prediction accuracy and more robust reverse correlation results.

It is well known that ageing and noise exposure are important causes of sensorineural hearing loss, and can result in damage of the outer hair cells or other structures of the inner ear, including synaptic damage to the auditory nerve (AN), i.e., cochlear synaptopathy (CS). Despite the suspected high prevalence of CS among people with self-reported hearing difficulties but seemingly normal hearing, conventional hearing-aid algorithms do not compensate for the functional deficits associated with CS. Here, we present and evaluate a number of auditory signal-processing strategies designed to maximally restore AN coding for listeners with CS pathologies. We evaluated our algorithms in subjects with and without suspected age-related CS to assess whether physiological and behavioural markers associated with CS can be improved. Our data show that after applying our algorithms, envelope-following responses and perceptual amplitude-modulation sensitivity were consistently enhanced in both young and older listeners. Speech-in-noise intelligibility showed small improvements after processing but mostly for young normal-hearing participants, with median improvements of up to 8.3%. Since our hearing-enhancement strategies were designed to optimally drive the AN fibres, they were able to improve temporal-envelope processing for listeners both with and without suspected CS. Our proposed algorithms can be rapidly executed and can thus extend the application range of current hearing aids and hearables, while leaving sound amplification unaffected.

International audience; To guide behavior, perceptual systems must operate on intrinsically ambiguous sensory input. Observers are usually able to acknowledge the uncertainty of their perception, but in some cases, they critically fail to do so. Here, we show that a physiological correlate of ambiguity can be found in pupil dilation even when the observer is not aware of such ambiguity. We used a well-known auditory ambiguous stimulus, known as the tritone paradox, which can induce the perception of an upward or downward pitch shift within the same individual. In two experiments, behavioral responses showed that listeners could not explicitly access the ambiguity in this stimulus, even though their responses varied from trial to trial. However, pupil dilation was larger for the more ambiguous cases. The ambiguity of the stimulus for each listener was indexed by the entropy of behavioral responses, and this entropy was also a significant predictor of pupil size. In particular, entropy explained additional variation in pupil size independent of the explicit judgment of confidence in the specific situation that we investigated, in which the two measures were decoupled. Our data thus suggest that stimulus ambiguity is implicitly represented in the brain even without explicit awareness of this ambiguity.

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.

Stochastic sounds are useful to probe auditory memory, as they require listeners to learn unpredictable and novel patterns under controlled experimental conditions. Previous studies using white noise or random click trains have demonstrated rapid auditory learning for instances of such a class of sounds. Here, we tested stochastic sounds that enabled parametrical control of spectrotemporal complexity: tone clouds. Tone clouds were defined as broadband combinations of tone pips at randomized frequencies and onset times. Varying the density of tones covered a perceptual range from random melodies to noise. Results showed that listeners could detect repeating patterns in tone clouds at all tested densities, with sparse tone clouds being the easiest. A model estimating amplitude modulation within cochlear filters showed that repetition detection was correlated with the amount of amplitude modulation at lower rates. Rapid learning of individual tone clouds was also observed, again for all densities. Tone clouds thus provide a tool to probe auditory learning in a variety of task-difficulty settings, which could be useful for clinical or neurophysiological studies. They also show that rapid auditory learning operates over the full range of spectrotemporal complexity typical of natural sounds, essentially from melodies to noise.

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.

International audience

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).

International audience; Frequency modulation (FM) is assumed to be detected through amplitude modulation (AM) created by cochlear filtering for modulation rates above 10 Hz and carrier frequencies (f c) above 4 kHz. If this is the case, a model of modulation perception based on the concept of AM filters should predict masking effects between AM and FM. To test this, masking effects of sinusoidal AM on sinusoidal FM detection thresholds were assessed on normal-hearing listeners as a function of FM rate, f c , duration, AM rate, AM depth, and phase difference between FM and AM. The data were compared to predictions of a computational model implementing an AM filter-bank. Consistent with model predictions, AM masked FM with some AM-masking-AM features (broad tuning and effect of AM-masker depth). Similar masking was predicted and observed at f c ¼ 0.5 and 5 kHz for a 2 Hz AM masker, inconsistent with the notion that additional (e.g., temporal fine-structure) cues drive slow-rate FM detection at low f c. However, masking was lower than predicted and, unlike model predictions, did not show beating or phase effects. Broadly, the modulation filter-bank concept successfully explained some AM-masking-FM effects, but could not give a complete account of both AM and FM detection.

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.

Visual confidence is the observers’ estimate of their precision in one single perceptual decision. Ultimately, however, observers often need to judge their confidence over a task in general rather than merely on one single decision. Here, we measured the global confidence acquired across multiple perceptual decisions. Participants performed a dual task on two series of oriented stimuli. The perceptual task was an orientation-discrimination judgment. The metacognitive task was a global confidence judgment: observers chose the series for which they felt they had performed better in the perceptual task. We found that choice accuracy in global confidence judgments improved as the number of items in the series increased, regardless of whether the global confidence judgment was made before (prospective) or after (retrospective) the perceptual decisions. This result is evidence that global confidence judgment was based on an integration of confidence information across multiple perceptual decisions rather than on a single one. Furthermore, we found a tendency for global confidence choices to be influenced by response times, and more so for recent perceptual decisions than earlier ones in the series of stimuli. Using model comparison, we found that global confidence is well described as a combination of noisy estimates of sensory evidence and position-weighted response-time evidence. In summary, humans can integrate information across multiple decisions to estimate global confidence, but this integration is not optimal, in particular because of biases in the use of response-time information. Supplementary Information The online version contains supplementary material available at 10.3758/s13423-020-01869-7.

International audience; Background noise strongly penalizes auditory perception of speech in humans or vocalizations in animals. Despite this, auditory neurons are still able to detect communications sounds against considerable levels of background noise. We collected neuronal recordings in cochlear nucleus (CN), inferior colliculus (IC), auditory thalamus, and primary and secondary auditory cortex in response to vocalizations presented either against a stationary or a chorus noise in anesthetized guinea pigs at three signal-to-noise ratios (SNRs; -10, 0, and 10 dB). We provide evidence that, at each level of the auditory system, five behaviors in noise exist within a continuum, from neurons with high-fidelity representations of the signal, mostly found in IC and thalamus, to neurons with high-fidelity representations of the noise, mostly found in CN for the stationary noise and in similar proportions in each structure for the chorus noise. The two cortical areas displayed fewer robust responses than the IC and thalamus. Furthermore, between 21% and 72% of the neurons (depending on the structure) switch categories from one background noise to another, even if the initial assignment of these neurons to a category was confirmed by a severe bootstrap procedure. Importantly, supervised learning pointed out that assigning a recording to one of the five categories can be predicted up to a maximum of 70% based on both the response to signal alone and noise alone.Significance Statement In daily situations, humans and animals are faced with various background noises in which they have to detect behaviorally salient signals. Noise resistance is often viewed as an emergent property of cortical networks, but only a few studies have characterized the relative contribution of cortical and subcortical neurons. Our results demonstrate that the neuronal resistance to noise is distributed along the auditory system with a more important fraction of robust neurons in subcortical structures compared with auditory cortex, and is relatively well predictable based on the responses to the signal alone and the noise alone. Our results also suggest that noise-invariant representations of communication sounds coexist with accurate noise representations, which are detected as early as the cochlear nucleus (CN).

The decline of speech intelligibility in presbycusis can be regarded as resulting from the combined contribution of two main groups of factors: (1) audibility-related factors and (2) age-related factors. In particular, there is now an abundant scientific literature on the crucial role of suprathreshold auditory abilities and cognitive functions, which have been found to decline with age even in the absence of audiometric hearing loss. However, researchers investigating the direct effect of aging in presbycusis have to deal with the methodological issue that age and peripheral hearing loss covary to a large extent. In the present study, we analyzed a dataset of consonant-identification scores measured in quiet and in noise for a large cohort (n = 459, age = 42–92) of hearing-impaired (HI) and normal-hearing (NH) listeners. HI listeners were provided with a frequency-dependent amplification adjusted to their audiometric profile. Their scores in the two conditions were predicted from their pure-tone average (PTA) and age, as well as from their Extended Speech Intelligibility Index (ESII), a measure of the impact of audibility loss on speech intelligibility. We relied on a causal-inference approach combined with Bayesian modeling to disentangle the direct causal effects of age and audibility on intelligibility from the indirect effect of age on hearing loss. The analysis revealed that the direct effect of PTA on HI intelligibility scores was 5 times higher than the effect of age. This overwhelming effect of PTA was not due to a residual audibility loss despite amplification, as confirmed by a ESII-based model. More plausibly, the marginal role of age could be a consequence of the relatively little cognitively-demanding task used in this study. Furthermore, the amount of variance in intelligibility scores was smaller for NH than HI listeners, even after accounting for age and audibility, reflecting the presence of additional suprathreshold deficits in the latter group. Although the non-sense-syllable materials and the particular amplification settings used in this study potentially restrict the generalization of the findings, we think that these promising results call for a wider use of causal-inference analysis in audiology, e.g., as a way to disentangle the influence of the various cognitive factors and suprathreshold deficits associated to presbycusis.

The success of human cooperation crucially depends on mechanisms enabling individuals to detect unreliability in their conspecifics. Yet, how such epistemic vigilance is achieved from naturalistic sensory inputs remains unclear. Here we show that listeners’ perceptions of the certainty and honesty of other speakers from their speech are based on a common prosodic signature. Using a data-driven method, we separately decode the prosodic features driving listeners’ perceptions of a speaker’s certainty and honesty across pitch, duration and loudness. We find that these two kinds of judgments rely on a common prosodic signature that is perceived independently from individuals’ conceptual knowledge and native language. Finally, we show that listeners extract this prosodic signature automatically, and that this impacts the way they memorize spoken words. These findings shed light on a unique auditory adaptation that enables human listeners to quickly detect and react to unreliability during linguistic interactions., It remains unclear whether and how listeners can infer speakers’ reliability from the sound of their voice. Here, the authors show that listeners from several languages rely on a common prosodic signature to infer whether speakers are lying or doubting.

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

International audience; This study presents insights into the discrimination of two consonants presented in vowel-consonant-vowel (VCV) words embedded in speech-shaped noise (SSN) by adopting an auditory model that uses a modulation filter bank front-end followed by either of two speech back-end decision modules from the literature. These decision modules have been validated in the past for the discrimination of sentences in closed-and open-sets. Our analysis is focused on the discrimination cues available to the model, evaluating whether these cues might be further used to simulate listener-dependent performance. For that purpose we will rely on a reverse correlation approach by comparing the noise representations that lead to the choice of one or the other consonant.

Sensing the movement of fast objects within our visual environments is essential for controlling actions. It requires online estimation of motion direction and speed. We probed human speed representation using ocular tracking of stimuli of different statistics. First, we compared ocular responses to single drifting gratings (DGs) with a given set of spatiotemporal frequencies to broadband motion clouds (MCs) of matched mean frequencies. Motion energy distributions of gratings and clouds are point-like, and ellipses oriented along the constant speed axis, respectively. Sampling frequency space, MCs elicited stronger, less variable, and speed-tuned responses. DGs yielded weaker and more frequency-tuned responses. Second, we measured responses to patterns made of two or three components covering a range of orientations within Fourier space. Early tracking initiation of the patterns was best predicted by a linear combination of components before nonlinear interactions emerged to shape later dynamics. Inputs are supralinearly integrated along an iso-velocity line and sublinearly integrated away from it. A dynamical probabilistic model characterizes these interactions as an excitatory pooling along the iso-velocity line and inhibition along the orthogonal “scale” axis. Such crossed patterns of interaction would appropriately integrate or segment moving objects. This study supports the novel idea that speed estimation is better framed as a dynamic channel interaction organized along speed and scale axes.

International audience

International audience; In order to succeed in everyday auditory environments, listeners must often distinguish between multiple plausible interpretations of an ambiguous signal. Generally, listeners are aware of this ambiguity, but certain classes of ambiguous stimuli seem to inhibit conscious access to ambiguity. We used a well-known ambiguous stimulus consisting of two Shepard tones containing octave-spaced components presented one after the other. We measured listeners’ ability to access ambiguity in this stimulus, both explicitly through behavioral responses and implicitly through pupil dilation. In two experiments, listeners judged the direction of the change between the two tones, while pupil diameter was continuously measured. In experiment 1, we varied the degree of ambiguity in the stimulus by varying the size of the interval between tones. In experiment 2, in order to ensure that the observed effects were not due to interval size itself, we compared Shepard tones to harmonic complex tones, presenting the same interval sizes for each tone type. In both experiments, the ambiguous 6-semitone case for the Shepard tones evoked first-order behavioral variability but high explicit confidence. Pupil dilation was elevated for this condition relative to other conditions in both experiments. More specifically, we could correlate trial-by-trial pupil dilation with subsequent response entropy, even when participants were not aware of producing variable responses. This suggests that implicit sensitivity to ambiguity, measured through pupil dilation, may occur without explicit awareness of this ambiguity.

International audience; Studies on loudness judgments for time-varying sounds consistently show a primacy effect: The beginning of a sound is more important for the judgment of its overall loudness than later temporal parts. The mechanisms that were proposed as an explanation of this pattern of temporal loudness weights encompass a wide range of concepts. Ordering the proposed explanations from early auditory mechanisms to supra-modal cognitive processes, the primacy effect has been linked to a) the onset peak of auditory nerve neurons, b) capture of auditory attention to the sound onset, c) temporal integration of evidence at a decisional stage (sequential sampling), and d) serial position effects in a memory system. This talk discusses each of the alternative concepts in the light of the available data, and identifies gaps in the literature that need to be filled in order to decide between the alternatives. Also, the predictions of current loudness models concerning temporal weights are evaluated.

International audience; Although extensively studied for many years, defining the timbre of musical sounds remains unclear and somewhatcontroversial. We here address this question by using representations of sounds inspired by auditory cortical processes - so-called spectro-temporal modulation representations - as front-end representations to interpretable metric learning techniques modelling human dissimilarity ratings. We present a meta-analysis of 17 published experiments on the perception of musical instrument timbre. The results reveal that these studies are only partly replicable. Interestingly, we observed that spectro-temporal modulations embed relevant information to model human dissimilarity ratings of musical instruments sounds. Thanks to an interpretable distance metric learning technique, the results strikingly suggest that humans use both generic and context-driven acoustical cues defining the different facets of musical instrument timbre. This study hereby provides a unique overview of 17 historical studies on timbre and points the limitation of the traditional dimensional analyses. We further propose a new way to investigate the acoustical correlates of timbre. The proposed methodology hence opens avenues to link acoustical representations to high-level human judgements.

International audience; 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. 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 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. 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.

Summary Visual perception is not instantaneous. It takes a few milliseconds for light to be transduced in photoreceptors and tens of milliseconds more for neuronal spikes to occur at successive levels of the visual hierarchy. Moreover, the latency of responses varies across the visual field and the cortical hierarchy. In peripheral compared to central vision, the impulse response of primates’ ganglion cells has a higher peak occurring at shorter latency [ 1 ], and yet humans’ primary visual cortex is activated later [ 2 ]. The diversity of these physiological results makes it difficult to predict when is the perceived present [ 3 ], especially when events are presented across the visual field. This question cannot be directly addressed with paradigms traditionally used to investigate perceptual latencies. In particular, response times are not suitable because they are influenced by decisional mechanisms, motor-related processing or compensatory mechanisms [ 4 , 5 ]. Likewise, temporal order judgements between foveal and peripheral stimuli are not suitable because humans overweigh foveal information when making perceptual decisions [ 6 ]. Here we explicitly asked participants to estimate when a stimulus is perceived within a fixed duration temporal interval, rather than relative to another stimulus presented in its near temporal proximity. We show that the perceived time of a visual event depends on its position in the visual field. We find a large bias to report events earlier when they were presented in the periphery.

International audience; Languages tend to license segmental contrasts where they are maximally perceptible, i.e. where more perceptual cues to the contrast are available. For strident fricatives, the most salient cues to the presence of voicing are low-frequency energy concentrations and fricative duration, as voiced fricatives are systematically shorter than voiceless ones. Cross-linguistically, the voicing contrast is more frequently realized word-initially than word-finally, as for obstruents. We investigate the phonetic underpinnings of this asymmetric behavior at the word edges, focusing on the availability of durational cues to the contrast in the two positions. To assess segmental duration, listeners rely on temporal markers, i.e. jumps in acoustic energy which demarcate segmental boundaries, thereby facilitating duration discrimination. We conducted an acoustic analysis of wordinitial and word-final strident fricatives in American English. We found that temporal markers are sharper at the left edge of word-initial fricatives than at the right edge of word-final fricatives, in terms of absolute value of the intensity slope, in the high-frequency region. These findings allow us to make predictions about the availability of durational cues to the voicing contrast in the two positions.

Summary The visual responses of neurons in the primary visual cortex (V1) are influenced by the animal’s position in the environment [1, 2, 3, 4, 5]. V1 responses encode positions that co-fluctuate with those encoded by place cells in hippocampal area CA1 [2, 5]. This correlation might reflect a common influence of non-visual spatial signals on both areas. Place cells in CA1, indeed, do not rely only on vision; their place preference depends on the physical distance traveled [6, 7, 8, 9, 10, 11] and on the phase of the 6–9 Hz theta oscillation [12, 13]. Are V1 responses similarly influenced by these non-visual factors? We recorded V1 and CA1 neurons simultaneously while mice performed a spatial task in a virtual corridor by running on a wheel and licking at a reward location. By changing the gain that couples the wheel movement to the virtual environment, we found that ∼20% of V1 neurons were influenced by the physical distance traveled, as were ∼40% of CA1 place cells. Moreover, the firing rate of ∼24% of V1 neurons was modulated by the phase of theta oscillations recorded in CA1 and the response profiles of ∼7% of V1 neurons shifted spatially across the theta cycle, analogous to the phase precession observed in ∼37% of CA1 place cells. The influence of theta oscillations on V1 responses was more prominent in putative layer 6. These results reveal that, in a familiar environment, sensory processing in V1 is modulated by the key non-visual signals that influence spatial coding in the hippocampus., Graphical Abstract, Highlights • We compared responses of mouse V1 and CA1 during navigation in virtual reality • Similar to CA1 neurons, V1 neurons are influenced by physical distance traveled • V1 responses are modulated by theta oscillations recorded in hippocampus • Positions encoded by V1 shift spatially with the phase of the theta oscillation, Fournier et al. compare responses of hippocampus and visual cortex in mice navigating a virtual reality. They find that, similar to hippocampus, activity in visual cortex is influenced by the physical distance traveled in an environment. In addition, theta oscillations modulate the activity of visual cortical neurons and their position coding.

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

To best interact with the external world, humans are often required to consider the quality of their actions. Sometimes the environment furnishes rewards or punishments to signal action efficacy. However, when such feedback is absent or only partial, we must rely on internally generated signals to evaluate our performance (i.e., metacognition). Yet, very little is known about how humans form such judgements of sensorimotor confidence. Do they monitor their performance? Or do they rely on cues to sensorimotor uncertainty to infer how likely it is they performed well? We investigated motor metacognition in two visuomotor tracking experiments, where participants followed an unpredictably moving dot cloud with a mouse cursor as it followed a random trajectory. Their goal was to infer the underlying target generating the dots, track it for several seconds, and then report their confidence in their tracking as better or worse than their average. In Experiment 1, we manipulated task difficulty with two methods: varying the size of the dot cloud and varying the stability of the target’s velocity. In Experiment 2, the stimulus statistics were fixed and duration of the stimulus presentation was varied. We found similar levels of metacognitive sensitivity in all experiments, with the temporal analysis revealing a recency effect, where error later in the trial had a greater influence on the sensorimotor confidence. In sum, these results indicate humans predominantly monitor their tracking performance, albeit inefficiently, to judge sensorimotor confidence.HighlightsParticipants consciously reflected on their tracking performance with some accuracySensorimotor confidence was influenced by recent errorsExpectations of task difficulty did not play a large role in sensorimotor confidenceMetacognitive sensitivity of binary confidence judgements on continuous performance can be quantified with standard non-parametric techniques

The COVID-19 pandemic poses a historical challenge to society. The profusion of data requires machine learning to improve and accelerate COVID-19 diagnosis, prognosis and treatment. However, a global and open approach is necessary to avoid pitfalls in these applications.

International audience; Natural soundscapes correspond to the acoustical patterns produced by biological and geophysical sound sources at different spatial and temporal scales for a given habitat. This pilot study aims to characterize the temporal-modulation information available to humans when perceiving variations in soundscapes within and across natural habitats. This is addressed by processing soundscapes from a previous study [Krause, Gage, and Joo. (2011). Landscape Ecol. 26, 1247] via models of human auditory processing extracting modulation at the output of cochlear filters. The soundscapes represent combinations of elevation, animal, and vegetation diversity in four habitats of the biosphere reserve in the Sequoia National Park (Sierra Nevada, USA). Bayesian statistical analysis and support vector machine classifiers indicate that: (i) amplitude-modulation (AM) and frequency-modulation (FM) spectra distinguish the soundscapes associated with each habitat; and (ii) for each habitat, diurnal and seasonal variations are associated with salient changes in AM and FM cues at rates between about 1 and 100 Hz in the low (1-3 kHz) audio-frequency range. Support vector machine classifications further indicate that soundscape variations can be classified accurately based on these perceptually inspired representations.

Perceptual decisions are accompanied by feelings of confidence that reflect the likelihood that the decision was correct. Here we aim to clarify the relationship between perception and confidence by studying the same perceptual task across three different confidence contexts. Human observers were asked to categorize the source of sequentially presented visual stimuli. Each additional stimulus provided evidence for making more accurate perceptual decisions, and better confidence judgements. We show that observers’ ability to set appropriate evidence accumulation bounds for perceptual decisions is strongly predictive of their ability to make accurate confidence judgements. When observers were not permitted to control their exposure to evidence, they imposed covert bounds on their perceptual decisions but not on their confidence decisions. This partial dissociation between decision processes is reflected in behaviour and pupil dilation. Together, these findings suggest a confidence-regulated accumulation-to-bound process that controls perceptual decision-making even in the absence of explicit speed-accuracy trade-offs., Feelings of confidence reflect the likelihood that decisions are correct. Here the authors show that confidence taps partially dissociable evidence from that used for perceptual decisions, and that, rather than passively monitoring, confidence controls the depth of sensory information processing.

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.

Spectrotemporal modulations (STM) are essential features of speech signals that make them intelligible. While their encoding has been widely investigated in neurophysiology, we still lack a full understanding of how STMs are processed at the behavioral level and how cochlear hearing loss impacts this processing. Here, we introduce a novel methodological framework based on psychophysical reverse correlation deployed in the modulation space to characterize the mechanisms underlying STM detection in noise. We derive perceptual filters for young normal-hearing and older hearing-impaired individuals performing a detection task of an elementary target STM (a given product of temporal and spectral modulations) embedded in other masking STMs. Analyzed with computational tools, our data show that both groups rely on a comparable linear (band-pass)-nonlinear processing cascade, which can be well accounted for by a temporal modulation filter bank model combined with cross-correlation against the target representation. Our results also suggest that the modulation mistuning observed for the hearing-impaired group results primarily from broader cochlear filters. Yet, we find idiosyncratic behaviors that cannot be captured by cochlear tuning alone, highlighting the need to consider variability originating from additional mechanisms. Overall, this integrated experimental-computational approach offers a principled way to assess suprathreshold processing distortions in each individual and could thus be used to further investigate interindividual differences in speech intelligibility.

International audience; The large heterogeneity observed among individuals with similar audiograms for understanding speech in noisy environments still remains poorly understood. Supra-threshold sensory mechanisms, i.e. that operate above detection threshold, likely play a prominent role in these interindividual differences, but a precise view of where and how distortions arise along the auditory processing hierarchy is lacking. Spectrotemporal modulations (STM) - noise signals whose envelope is jointly modulated in time and frequency - offer a unified mathematical framework to probe these supra-threshold processes as they are recruited with speech signals. Yet, at present, it remains unknown how STMs are processed by the human auditory system, and how this processing varies between individuals. In order to provide a clear computational understanding of STM processing for both normal-hearing (NH) and hearing-impaired (HI) individuals, we developed a novel methodological framework that combines psychophysical reverse-correlation experiments deployed in the modulation domain with simulations from current supra-threshold auditory models. In this talk, we will present recent results collected in various tasks where both normal-hearing and hearing-impaired listeners detect or discriminate specific target STMs embedded in other masking STMs. We will show which aspects of their results can be accounted for by frequency selectivity and temporal-envelope based mechanisms, and which cannot, highlighting the need to consider additional mechanisms. Finally, we will discuss how, by focusing on the origins of the variability that remains unaccounted for by these models, this joint experimental-modeling approach could actually be used to pinpoint the peripheral and central components underlying supra-threshold hearing distortions in each individual.

Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at ) contained 145 datasets with data from more than 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects., NIH National Institute of Mental Health (NIMH)

1AbstractPriors and payoffs are known to affect perceptual decision-making, but little is understood about how they influence confidence judgments. For optimal perceptual decision-making, both priors and payoffs should be considered when selecting a response. However, for confidence to reflect the probability of being correct in a perceptual decision, priors should affect confidence but payoffs should not. To experimentally test whether human observers follow this normative behavior, we conducted an orientation-discrimination task with varied priors and payoffs, probing both perceptual and metacognitive decision-making. We then examined the placement of discrimination and confidence criteria according to several plausible Signal Detection Theory models. In the normative model, observers use the optimal discrimination criterion (i.e., the criterion that maximizes expected gain) and confidence criteria that shift with the discrimination criterion that maximizes accuracy (i.e., are not affected by payoffs). No observer was consistent with this model, with the majority exhibiting non-normative confidence behavior. One subset of observers ignored both priors and payoffs for confidence, always fixing the confidence criteria around the neutral discrimination criterion. The other group of observers incorrectly incorporated payoffs into their confidence by always shifting their confidence criteria with the same gains-maximizing criterion used for discrimination. Such metacognitive mistakes could have negative consequences outside the laboratory setting, particularly when priors or payoffs are not matched for all the possible decision alternatives.

International audience; Metaperception is the self-monitoring and self-control of one's own perception. Perceptual confidence is the prototypical example of metaperception. Perceptual confidence refers to the ability to judge whether a perceptual decision is correct. We argue that metaperception is not limited to confidence but includes other judgments such as the estimation of familiarity and the aesthetic experience of sensory events. Perceptual confidence has recently received a surge of interests due in particular to the design of careful psychophysical experiments and powerful computational models. In psychophysics, the use of confidence ratings is the dominant methodology, but other paradigms are available, including the confidence forced choice. In this latter paradigm, participants are presented with two stimuli, make perceptual decisions about these stimuli, and then choose which decision is more likely to be correct. One benefit of confidence forced choice is that it disregards confidence biases to focus on confidence sensitivity. Confidence forced choice might also be a paradigm that will allow us to establish whether confidence is estimated serially or in parallel to the perceptual decision.

International audience; Recent works suggest that the global-local organizational principles of auditory processing are comparable to that of visual processing, with a superior efficiency as well as stronger interferences of global over local processing. Yet, the factors governing auditory local-global processing organization remain poorly understood. Here, we introduce a novel paradigm that attempts to better characterize local-global temporal processing taking into account musical expertise. The ability of non-musicians as well as expert musicians to detect local and/or global pitch changes occurring in short hierarchical sequences is assessed. Importantly, melodic structures of different complexity are considered, in which the changes can occur at various temporal positions and over different scales, thus forcing listeners to attend to the whole sequences. Built in a signal detection theory framework, this experimental paradigm provides ideal conditions to assess potential global-local interference effects and tease apart differences in processing efficiencies vs. response strategies. On average, our results demonstrate a clear distinction between the local-global processing organization of non-musicians and musicians. While non-musicians show clear global advantage and global-to-local interference ef- fects, in line with previous observations, this pattern is reversed in musicians. Yet, we observe important interindividual differences within each group, with some non-musicians exhibiting a global-local organization similar to that of expert listeners. Overall, these results provide a detailed characterization of how the different temporal processing scales are re-organized following long-term musical training, with a reversal of the initial global predominance to provide mu- sicians with enhanced analytic listening abilities. More interestingly, they suggest that this reorganization is mediated by both increased general auditory abilities as well as the acquisition of novel discrimination strategies. Overall, the present study yields new insights regarding the organization of our perceptual system to deal with auditory information over different temporal scales, and shows how this can be reshaped through experience and training.