A large body of evidence suggests that an approximate number sense allows humans to estimate numerosity in sensory scenes. This ability is widely observed in humans, including those without formal mathematical training. Despite this, many outstanding questions remain about the nature of the numerosity representation in the brain. Specifically, it is not known whether approximate numbers are represented as scalar estimates of numerosity or, alternatively, as probability distributions over numerosity. In the present study, we used a multisensory decision task to distinguish these possibilities. We trained human subjects to decide whether a test stimulus had a larger or smaller numerosity compared with a fixed reference. Depending on the trial, the numerosity was presented as either a sequence of visual flashes or a sequence of auditory tones, or both. To test for a probabilistic representation, we varied the reliability of the stimulus by adding noise to the visual stimuli. In accordance with a probabilistic representation, we observed a significant improvement in multisensory compared with unisensory trials. Furthermore, a trial-by-trial analysis revealed that although individual subjects showed strategic differences in how they leveraged auditory and visual information, all subjects exploited the reliability of unisensory cues. An alternative, nonprobabilistic model, in which subjects combined cues without regard for reliability, was not able to account for these trial-by-trial choices. These findings provide evidence that the brain relies on a probabilistic representation for numerosity decisions.
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http://dx.doi.org/10.1152/jn.00787.2014 | DOI Listing |
J Neurosci Methods
January 2025
School of Electrical and Computer Engineering, Gallogly College of Engineering, University of Oklahoma, Norman, OK 73019, USA.
Background: Recent advances in multimodal signal analysis enable the identification of subtle drug-induced anomalies in sleep that traditional methods often miss.
New Method: We develop and introduce the Dynamic Representation of Multimodal Activity and Markov States (DREAMS) framework, which embeds explainable artificial intelligence (XAI) techniques to model hidden state transitions during sleep using tensorized EEG, EMG, and EOG signals from 22 subjects across three age groups (18-29, 30-49, and 50-66 years). By combining Tucker decomposition with probabilistic Hidden Markov Modeling, we quantified age-specific, temazepam-induced hidden states and significant differences in transition probabilities.
Heliyon
January 2025
Centre for Tactile Internet with Human-in-the-Loop (CeTI), 6G Life, Technische Universität Dresden, Germany.
Recent research has highlighted a notable confidence bias in the haptic sense, yet its impact on learning relative to other senses remains unexplored. This online study investigated learning behaviour across visual, auditory, and haptic modalities using a probabilistic selection task on computers and mobile devices, employing dynamic and ecologically valid stimuli to enhance generalisability. We analysed reaction time as an indicator of confidence, alongside learning speed and task accuracy.
View Article and Find Full Text PDFElife
January 2025
Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, United States.
The basal ganglia (BG) are an evolutionarily conserved and phylogenetically old set of sub-cortical nuclei that guide action selection, evaluation, and reinforcement. The entopeduncular nucleus (EP) is a major BG output nucleus that contains a population of GABA/glutamate cotransmitting neurons (EP) that specifically target the lateral habenula (LHb) and whose function in behavior remains mysterious. Here, we use a probabilistic switching task that requires an animal to maintain flexible relationships between action selection and evaluation to examine when and how GABA/glutamate cotransmitting neurons contribute to behavior.
View Article and Find Full Text PDFThe development of diffusion models, such as Glide, DALLE 2, Imagen, and Stable Diffusion, marks a significant advancement in generative AI for image synthesis. In this paper, we introduce a novel framework for synthesizing intrinsic connectivity networks (ICNs) by utilizing the nonlinear capabilities of denoising diffusion probabilistic models (DDPMs). This approach builds upon and extends traditional linear methods, such as independent component analysis (ICA), which are commonly used in neuroimaging studies.
View Article and Find Full Text PDFOpen Mind (Camb)
January 2025
Department of Computer Science, University of Toronto, Toronto, Canada.
The lexicon is an evolving symbolic system that expresses an unbounded set of emerging meanings with a limited vocabulary. As a result, words often extend to new meanings. Decades of research have suggested that word meaning extension is non-arbitrary, and recent work formalizes this process as cognitive models of semantic chaining whereby emerging meanings link to existing ones that are semantically close.
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