Perceptual learning was used as a tool for studying motion perception. The pattern of transfer of learning of luminance- (LM) and contrast-modulated (CM) motion is diagnostic of how their respective processing pathways are integrated. Twenty observers practiced fine direction discrimination with either additive (LM) or multiplicative (CM) mixtures of a dynamic noise carrier and a radially isotropic texture modulator. The temporal frequency was 10 Hz, speed was 10 deg/s, and duration was 400 ms, with feedback. Group 1 pre-tested CM for 2 blocks, trained LM for 16 blocks, and post-tested CM for 6 blocks during 6 sessions on separate days. In Group 2, the LM and CM roles were reversed. The d' improved almost twofold in both groups. There seemed to be full transfer from CM to LM but no significant transfer from LM to CM. The pattern of post-switch improvement was asymmetric as well-no further learning during the LM post-test versus rapid relearning during the CM post-test. These strong asymmetries suggest a dual-pathway architecture with Fourier channels sensitive only to LM signals and non-Fourier channels sensitive to both LM and CM. We hypothesize that the channels tuned for the same motion direction but different carriers are integrated using a MAX operation.
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http://dx.doi.org/10.1167/10.14.11 | DOI Listing |
Mol Autism
January 2025
Department of Special Education, University of Haifa, Haifa, Israel.
Background: Alterations in sensory perception, a core phenotype of autism, are attributed to imbalanced integration of sensory information and prior knowledge during perceptual statistical (Bayesian) inference. This hypothesis has gained momentum in recent years, partly because it can be implemented both at the computational level, as in Bayesian perception, and at the level of canonical neural microcircuitry, as in predictive coding. However, empirical investigations have yielded conflicting results with evidence remaining limited.
View Article and Find Full Text PDFJ Exp Psychol Learn Mem Cogn
December 2024
University of Massachusetts-Amherst, Department of Psychological and Brain Sciences.
Listeners can use both lexical context (i.e., lexical knowledge activated by the word itself) and lexical predictions based on the content of a preceding sentence to adjust their phonetic categories to speaker idiosyncrasies.
View Article and Find Full Text PDFJ Exp Psychol Learn Mem Cogn
December 2024
Technical University of Darmstadt, Institute of Psychology.
The goal of the present investigation was to perform a registered replication of Jones and Macken's (1995b) study, which showed that the segregation of a sequence of sounds to distinct locations reduced the disruptive effect on serial recall. Thereby, it postulated an intriguing connection between auditory stream segregation and the cognitive mechanisms underlying the irrelevant speech effect. Specifically, it was found that a sequence of changing utterances was less disruptive in stereophonic presentation, allowing each auditory object (letters) to be allocated to a unique location (right ear, left ear, center), compared to when the same sounds were played monophonically.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea.
: Accurate volumetric assessment of lung nodules is an essential element of low-dose lung cancer screening programs. Current guidance recommends applying specific thresholds to measured nodule volume to make the following clinical decisions. In reality, however, CT scans often have heterogeneous slice thickness which is known to adversely impact the accuracy of nodule volume assessment.
View Article and Find Full Text PDFJpn J Radiol
January 2025
Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan.
Purpose: Magnetization prepared rapid gradient echo (MPRAGE) is a useful three-dimensional (3D) T1-weighted sequence, but is not a priority in routine brain examinations. We hypothesized that converting 3D MRI localizer (AutoAlign Head) images to MPRAGE-like images with deep learning (DL) would be beneficial for diagnosing and researching dementia and neurodegenerative diseases. We aimed to establish and evaluate a DL-based model for generating MPRAGE-like images from MRI localizers.
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