TY - JOUR A1 - Krasotkina, Anna A1 - Götz, Antonia A1 - Höhle, Barbara A1 - Schwarzer, Gudrun T1 - Bimodal familiarization re-sensitizes 12-month-old infants to other-race faces JF - Infant behavior & development : an international and interdisciplinary journal N2 - Perceptual narrowing in the domain of face perception typically begins to reduce infants' sensitivity to differences distinguishing other-race faces from approximately 6 months of age. The present study investigated whether it is possible to re-sensitize Caucasian 12-month-old infants to other-race Asian faces through statistical learning by familiarizing them with different statistical distributions of these faces. The familiarization faces were created by generating a morphed continuum from one Asian face identity to another. In the unimodal condition, infants were familiarized with a frequency distribution wherein they saw the midpoint face of the morphed continuum the most frequently. In the bimodal condition, infants were familiarized with a frequency distribution wherein they saw faces closer to the endpoints of the morphed continuum the most frequently. After familiarization, infants were tested on their discrimination of the two original Asian faces. The infants' looking times during the test indicated that infants in the bimodal condition could discriminate between the two faces, while infants in the unimodal condition could not. These findings therefore suggest that 12-month-old Caucasian infants could be re-sensitized to Asian faces by familiarizing them with a bimodal frequency distribution of such faces. KW - Bimodal KW - Unimodal KW - Familiarization KW - Statistical learning KW - Infant KW - Face KW - discrimination Y1 - 2021 U6 - https://doi.org/10.1016/j.infbeh.2020.101502 SN - 0163-6383 SN - 1879-0453 VL - 62 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Blanchard, Gilles A1 - Mücke, Nicole T1 - Optimal rates for regularization of statistical inverse learning problems JF - Foundations of Computational Mathematics N2 - We consider a statistical inverse learning (also called inverse regression) problem, where we observe the image of a function f through a linear operator A at i.i.d. random design points X-i , superposed with an additive noise. The distribution of the design points is unknown and can be very general. We analyze simultaneously the direct (estimation of Af) and the inverse (estimation of f) learning problems. In this general framework, we obtain strong and weak minimax optimal rates of convergence (as the number of observations n grows large) for a large class of spectral regularization methods over regularity classes defined through appropriate source conditions. This improves on or completes previous results obtained in related settings. The optimality of the obtained rates is shown not only in the exponent in n but also in the explicit dependency of the constant factor in the variance of the noise and the radius of the source condition set. KW - Reproducing kernel Hilbert space KW - Spectral regularization KW - Inverse problem KW - Statistical learning KW - Minimax convergence rates Y1 - 2018 U6 - https://doi.org/10.1007/s10208-017-9359-7 SN - 1615-3375 SN - 1615-3383 VL - 18 IS - 4 SP - 971 EP - 1013 PB - Springer CY - New York ER - TY - JOUR A1 - Nixon, Jessie S. A1 - van Rij, Jacolien A1 - Mok, Peggy A1 - Baayen, Harald R. A1 - Chen, Yiya T1 - The temporal dynamics of perceptual uncertainty: eye movement evidence from Cantonese segment and tone perception JF - Journal of memory and language N2 - Two visual world eyetracking experiments investigated how acoustic cue value and statistical variance affect perceptual uncertainty during Cantonese consonant (Experiment 1) and tone perception (Experiment 2). Participants heard low- or high-variance acoustic stimuli. Euclidean distance of fixations from target and competitor pictures over time was analysed using Generalised Additive Mixed Modelling. Distance of fixations from target and competitor pictures varied as a function of acoustic cue, providing evidence for gradient, nonlinear sensitivity to cue values. Moreover, cue value effects significantly interacted with statistical variance, indicating that the cue distribution directly affects perceptual uncertainty. Interestingly, the time course of effects differed between target distance and competitor distance models. The pattern of effects over time suggests a global strategy in response to the level of uncertainty: as uncertainty increases, verification looks increase accordingly. Low variance generally creates less uncertainty, but can lead to greater uncertainty in the face of unexpected speech tokens. (C) 2016 Elsevier Inc. All rights reserved. KW - Discriminative learning KW - Statistical learning KW - Speech perception KW - Cantonese KW - Lexical tone Y1 - 2016 U6 - https://doi.org/10.1016/j.jml.2016.03.005 SN - 0749-596X SN - 1096-0821 VL - 90 SP - 103 EP - 125 PB - Elsevier CY - San Diego ER -