Behavioural evidence for separate mechanisms of audiovisual temporal binding as a function of leading sensory modality

The ability to integrate auditory and visual information is critical for effective perception and interaction with the environment, and is thought to be abnormal in some clinical populations. Several studies have investigated the time window over which audiovisual events are integrated, also called the temporal binding window, and revealed asymmetries depending on the order of audiovisual input (i.e. the leading sense). When judging audiovisual simultaneity, the binding window appears narrower and non-malleable for auditory-leading stimulus pairs and wider and trainable for visual-leading pairs. Here we specifically examined the level of independence of binding mechanisms when auditory-before-visual vs. visual-before-auditory input is bound. Three groups of healthy participants practiced audiovisual simultaneity detection with feedback, selectively training on auditory-leading stimulus pairs (group 1), visual-leading stimulus pairs (group 2) or both (group 3). Subsequently, we tested for learning transfer (crossover) from trained stimulus pairs to non-trained pairs with opposite audiovisual input. Our data confirmed the known asymmetry in size and trainability for auditory–visual vs. visual–auditory binding windows. More importantly, practicing one type of audiovisual integration (e.g. auditory–visual) did not affect the other type (e.g. visual–auditory), even if trainable by within-condition practice. Together, these results provide crucial evidence that audiovisual temporal binding for auditory-leading vs. visual-leading stimulus pairs are independent, possibly tapping into different circuits for audiovisual integration due to engagement of different multisensory sampling mechanisms depending on leading sense. Our results have implications for informing the study of multisensory interactions in healthy participants and clinical populations with dysfunctional multisensory integration

Auditory but Not Audiovisual Cues Lead to Higher Neural Sensitivity to the Statistical Regularities of an Unfamiliar Musical Style

It is still a matter of debate whether visual aids improve learning of music. In a multisession study, we investigated the neural signatures of novel music sequence learning with or without aids (auditory-only: AO, audiovisual: AV). During three training sessions on 3 separate days, participants (nonmusicians) reproduced (note by note on a keyboard) melodic sequences generated by an artificial musical grammar. The AV group (n = 20) had each note color-coded on screen, whereas the AO group (n = 20) had no color indication. We evaluated learning of the statistical regularities of the novel music grammar before and after training by presenting melodies ending on correct or incorrect notes and by asking participants to judge the correctness and surprisal of the final note, while EEG was recorded. We found that participants successfully learned the new grammar. Although the AV group, as compared to the AO group, reproduced longer sequences during training, there was no significant difference in learning between groups. At the neural level, after training, the AO group showed a larger N100 response to lowprobability compared to high-probability notes, suggesting an increased neural sensitivity to statistical properties of the grammar; this effect was not observed in the AV group. Our findings indicate that visual aids might improve sequence reproduction while not necessarily promoting better learning, indicating a potential dissociation between sequence reproduction and learning. We suggest that the difficulty induced by auditory-only input during music training might enhance cognitive engagement, thereby improving neural sensitivity to the underlying statistical properties of the learned material

Computational and Robotic Models of Early Language Development: A Review

We review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. We conclude by discussing future challenges for research, including modeling of large-scale empirical data about language acquisition in real-world environments. Keywords: Early language learning, Computational and robotic models, machine learning, development, embodiment, social interaction, intrinsic motivation, self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J. Horst and J. von Koss Torkildsen, Routledg

Encoding of event timing in the phase of neural oscillations

ime perception is a critical component of conscious experience. To be in synchrony with the environment, the brain must deal not only with differences in the speed of light and sound but also with its computational and neural transmission delays. Here, we asked whether the brain could actively compensate for temporal delays by changing its processing time. Specifically, can changes in neural timing or in the phase of neural oscillation index perceived timing? For this, a lag-adaptation paradigm was used to manipulate participants' perceived audiovisual (AV) simultaneity of events while they were recorded with magnetoencephalography (MEG). Desynchronized AV stimuli were presented rhythmically to elicit a robust 1 Hz frequency-tagging of auditory and visual cortical responses. As participants' perception of AV simultaneity shifted, systematic changes in the phase of entrained neural oscillations were observed. This suggests that neural entrainment is not a passive response and that the entrained neural oscillation shifts in time. Crucially, our results indicate that shifts in neural timing in auditory cortices linearly map participants' perceived AV simultaneity. To our knowledge, these results provide the first mechanistic evidence for active neural compensation in the encoding of sensory event timing in support of the emergence of time awareness