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Perceptual narrowing typically occurs around 6 months of age, and drastically changes an infant's perception of stimuli such as faces or spoken language according to the frequency with which the infant encounters them. It has already been well established that perceptual narrowing improves the sensitivity of infants to frequently encountered stimuli such as same-race faces and their native language while reducing their sensitivity to other-race faces and non-native languages. However, the effect of perceptual narrowing on the combined perception of face and language stimuli is not well understood. Therefore, to investigate the changes in the sensitivity of infants to matches and mismatches between faces and speech which might occur in the course of perceptual narrowing, we tested 3- and 9-month-old German infants using German faces and German spoken sentences which would be familiar to the infants, as well as completely unfamiliar Chinese faces and French spoken sentences. The infants were tested using an intermodal association paradigm, whereby each infant saw sequences of German or Chinese faces, interspersed with German or French spoken sentences. We analyzed the total looking time of infants in conditions where the faces and spoken sentences were congruent (either both familiar, or both unfamiliar), versus incongruent conditions where only the faces or only the sentences were familiar. We found that while the 9-month-olds looked for similar durations in congruent versus incongruent conditions, the 3-month-olds looked significantly longer during congruent conditions versus incongruent conditions, indicating a greater attentiveness to face-speech matches and mismatches prior to the onset of perceptual narrowing.
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http://dx.doi.org/10.1016/j.infbeh.2024.101997 | DOI Listing |
Biol Psychiatry Glob Open Sci
November 2025
Department of Medicine and Surgery, Unit of Neuroscience, University of Parma, Parma, Italy.
Background: Multisensory integration (MSI) enables the brain to combine sensory inputs by defining spatial and temporal boundaries that determine whether stimuli originate from the same event. Among these, the temporal binding window (TBW) specifically refers to the temporal range within which stimuli are perceived as simultaneous and integrated. In adulthood, TBW can be narrowed through short-term perceptual training.
View Article and Find Full Text PDFJASA Express Lett
August 2025
Department of Engineering and Management, University of Padova, Vicenza, 36100,
This article introduces an analytical framework for modeling head-related transfer functions (HRTFs) from a listener-centered perspective. The distinction between strong (or general) HRTFs, aiming for idealized physical acoustic fidelity, and weak (or narrow) HRTFs, prioritizing perceptual adequacy in task-specific contexts, frames the contrast in multiple contrasting definitions and scientific methodologies by drawing inspiration from the debate in artificial intelligence. The proposed formalism adopts a Bayesian structure that models HRTFs through a state-space formulation capturing anatomical, contextual, experiential, and task-related factors: the eHRTF.
View Article and Find Full Text PDFCogn Emot
August 2025
School of Medicine and Psychology, the Australian National University, Canberra, Australia.
How do emotion and motivation affect the breadth of attention? Competing theoretical accounts propose that valence (pleasantness) or motivational intensity (strength of the urge to approach/avoid) drive changes in attentional breadth. Seminal work by Gable and Harmon-Jones (Gable, P. A.
View Article and Find Full Text PDFErgonomics
August 2025
Monash Art, Design and Architecture, Monash University, Melbourne, Australia.
A general focus within reading research is to isolate individual cognitive and perceptual processes for investigation. By doing so, we gradually increase our understanding of the components that collectively enable humans to read. The aim of this article is to create an overview of some of these findings that could inform the selection of typefaces.
View Article and Find Full Text PDFNovel digital images are increasingly created using generative AI tools. However, how well these AI images reflect the color statistics of human-generated ("real") images is unknown. We analyzed hue, chroma, and lightness distributions for image objects and backgrounds created by three generative AI models (Open AI DALL E 2, Stability AI Dreamstudio, and Adobe Firefly) and for real images (Bing image search); =8400 images.
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