Monolingual and bilingual infants' attention to talking faces: evidence from eye-tracking and Bayesian modeling.

Introduction: A substantial amount of research from the last two decades suggests that infants' attention to the eyes and mouth regions of talking faces could be a supporting mechanism by which they acquire their native(s) language(s). Importantly, attentional strategies seem to be sensitive to three types of constraints: the properties of the stimulus, the infants' attentional control skills (which improve with age and brain maturation) and their previous linguistic and non-linguistic knowledge. The goal of the present paper is to present a probabilistic model to simulate infants' visual attention control to talking faces as a function of their language learning environment (monolingual vs.

Visual attention modulates the transition from fine-grained, serial processing to coarser-grained, more parallel processing: A computational modeling study.

During reading acquisition, beginning readers transition from serial to more parallel processing. The acquisition of word specific knowledge through orthographic learning is critical for this transition. However, the processes by which orthographic representations are acquired and fine-tuned as learning progresses are not well understood.

Probabilistic modeling of orthographic learning based on visuo-attentional dynamics.

How is orthographic knowledge acquired? In line with the self-teaching hypothesis, most computational models assume that phonological recoding has a pivotal role in orthographic learning. However, these models make simplifying assumptions on the mechanisms involved in visuo-orthographic processing. Against evidence from eye movement data during orthographic learning, they assume that orthographic information on novel words is immediately available and accurately encoded after a single exposure.

Atypical viewing position effect in developmental dyslexia: A behavioural and modelling investigation.

The probability of recognizing a word depends on the position of fixation during processing. In typical readers, the resulting word-recognition curves are asymmetrical, showing a left-of-centre optimal viewing position (OVP). First, we report behavioural results from dyslexic participants who show atypical word-recognition curves characterized by the OVP being right of centre with recognition probability being higher on the rightmost than on the leftmost letters.

Speakers are able to categorize vowels based on tongue somatosensation.

Auditory speech perception enables listeners to access phonological categories from speech sounds. During speech production and speech motor learning, speakers' experience matched auditory and somatosensory input. Accordingly, access to phonetic units might also be provided by somatosensory information.

Modeling Sensory Preference in Speech Motor Planning: A Bayesian Modeling Framework.

Experimental studies of speech production involving compensations for auditory and somatosensory perturbations and adaptation after training suggest that both types of sensory information are considered to plan and monitor speech production. Interestingly, individual sensory preferences have been observed in this context: subjects who compensate less for somatosensory perturbations compensate more for auditory perturbations, and . We propose to integrate this sensory preference phenomenon in a model of speech motor planning using a probabilistic model in which speech units are characterized both in auditory and somatosensory terms.

Modeling the length effect for words in lexical decision: The role of visual attention.

The word length effect in Lexical Decision (LD) has been studied in many behavioral experiments but no computational models has yet simulated this effect. We use a new Bayesian model of visual word recognition, the BRAID model, that simulates expert readers' performance. BRAID integrates an attentional component modeled by a Gaussian probability distribution, a mechanism of lateral interference between adjacent letters and an acuity gradient, but no phonological component.

Computer simulations of coupled idiosyncrasies in speech perception and speech production with COSMO, a perceptuo-motor Bayesian model of speech communication.

The existence of a functional relationship between speech perception and production systems is now widely accepted, but the exact nature and role of this relationship remains quite unclear. The existence of idiosyncrasies in production and in perception sheds interesting light on the nature of the link. Indeed, a number of studies explore inter-individual variability in auditory and motor prototypes within a given language, and provide evidence for a link between both sets.

Reanalyzing neurocognitive data on the role of the motor system in speech perception within COSMO, a Bayesian perceptuo-motor model of speech communication.

While neurocognitive data provide clear evidence for the involvement of the motor system in speech perception, its precise role and the way motor information is involved in perceptual decision remain unclear. In this paper, we discuss some recent experimental results in light of COSMO, a Bayesian perceptuo-motor model of speech communication. COSMO enables us to model both speech perception and speech production with probability distributions relating phonological units with sensory and motor variables.

The complementary roles of auditory and motor information evaluated in a Bayesian perceptuo-motor model of speech perception.

There is a consensus concerning the view that both auditory and motor representations intervene in the perceptual processing of speech units. However, the question of the functional role of each of these systems remains seldom addressed and poorly understood. We capitalized on the formal framework of Bayesian Programming to develop COSMO (Communicating Objects using Sensory-Motor Operations), an integrative model that allows principled comparisons of purely motor or purely auditory implementations of a speech perception task and tests the gain of efficiency provided by their Bayesian fusion.

Optimal speech motor control and token-to-token variability: a Bayesian modeling approach.

The remarkable capacity of the speech motor system to adapt to various speech conditions is due to an excess of degrees of freedom, which enables producing similar acoustical properties with different sets of control strategies. To explain how the central nervous system selects one of the possible strategies, a common approach, in line with optimal motor control theories, is to model speech motor planning as the solution of an optimality problem based on cost functions. Despite the success of this approach, one of its drawbacks is the intrinsic contradiction between the concept of optimality and the observed experimental intra-speaker token-to-token variability.

A Bayesian computational model for online character recognition and disability assessment during cursive eye writing.

This research involves a novel apparatus, in which the user is presented with an illusion inducing visual stimulus. The user perceives illusory movement that can be followed by the eye, so that smooth pursuit eye movements can be sustained in arbitrary directions. Thus, free-flow trajectories of any shape can be traced.

Integrate, yes, but what and how? A computational approach of sensorimotor fusion in speech.

We consider a computational model comparing the possible roles of "association" and "simulation" in phonetic decoding, demonstrating that these two routes can contain similar information in some "perfect" communication situations and highlighting situations where their decoding performance differs. We conclude that optimal decoding should involve some sort of fusion of association and simulation in the human brain.

Bayesian action-perception computational model: interaction of production and recognition of cursive letters.

In this paper, we study the collaboration of perception and action representations involved in cursive letter recognition and production. We propose a mathematical formulation for the whole perception-action loop, based on probabilistic modeling and bayesian inference, which we call the Bayesian Action-Perception (BAP) model. Being a model of both perception and action processes, the purpose of this model is to study the interaction of these processes.

Common Bayesian models for common cognitive issues.

How can an incomplete and uncertain model of the environment be used to perceive, infer, decide and act efficiently? This is the challenge that both living and artificial cognitive systems have to face. Symbolic logic is, by its nature, unable to deal with this question. The subjectivist approach to probability is an extension to logic that is designed specifically to face this challenge.