During speech perception, humans constantly sense multiple sources of information. As so, the McGurk effect has been typically conceived as a prototypical example of language multi-modal integration, specifically, it has been used to study audio-visual integration during speech. The McGurk effect arises when in-congruent audio-visual stimuli} are paired and perceived as a different syllable ( auditory /ba/ + visual /ga/ = percept “da” ). We developed a hierarchical computer model, based on self-organizing maps and Hebbian learning to study speech multi-modal integration. Our architecture allows studying the McGurk effect purely on bottom-up processing of audio-visual information. We trained several versions of the model and measured the activation similarity between McGurk alike stimuli and congruent ones by means of mutual information. Our results suggest that the illusory percept arises from the best congruent representation that reduces uncertainty. Furthermore, the reliability of each sensory modality determines the best congruent representation.

We perceive and categorize the world constrained by the restrictions of our sensory and neural systems. Additionally, naming modulates how we categorize the things we see. Here we explore a third influence on visual categorization, we study how the perceived statistical regularities of shapes and colors modulate our experience in categorizing objects. We conducted our analyses in artificial systems. We used computer vision to process pictures of real objects, and artificial neural networks to categorize them. We found that the statistical regularities of different object sets produced either shape or color biases, depending on the nature of the set. Our statistical-based categorization approach presents complementary mechanisms of categorization biases, relevant for a more comprehensive understanding of the linguistic shape bias, the color bias of food, and it let us hypothesize why categorization may vary across populations.

Sensory-related word association differences in Parkinson's Disease for verb processing Free word association tasks are tools to study spontaneous lexical organization in children and adults. Lexical organization resembles a network of words grounded on relevant sensory information; for example, verbs engage the most with motor information. Studying lexical networks in populations with motor disorders, like Parkinson's Disease (PD), will insight into the effect of relevant information deprivation in lexical organization. We applied a free word association task with early-acquisition Spanish nouns as stimuli. Participants consisted of 20 with PD -divided into two groups depending on their cognitive state- and ten healthy older adults for control. Then, we used an artificial neural network to determine the semantic distance between stimuli-response dyads. We found differentiated semantic distances for verb responses in all groups in an inverse u-shaped curve depending on their cognitive and PD state. Our findings suggest that lexical organization and meaning are not crystallized, but adapt from sensory information.