scholarly journals Perceptual learning viewed as a statistical modeling process - Is it all overfitting?

2011 ◽  
Vol 11 (11) ◽  
pp. 11-11
Author(s):  
D. Sagi ◽  
H. Harris
Actuators ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 68 ◽  
Author(s):  
Takuya Taniguchi ◽  
Loïc Blanc ◽  
Toru Asahi ◽  
Hideko Koshima ◽  
Pierre Lambert

Mechanically responsive materials are promising as next-generation actuators for soft robotics, but have scarce reports on the statistical modeling of the actuation behavior. This research reports on the development and modeling of the photomechanical bending behavior of hybrid silicones mixed with azobenzene powder. The photo-responsive hybrid silicone bends away from the light source upon light irradiation when a thin paper is attached on the hybrid silicone. The time courses of bending behaviors were fitted well with exponential models with a time variable, affording fitting constants at each experimental condition. These fitted parameters were further modeled using the analysis of variance (ANOVA). Cubic models were proposed for both the photo-bending and unbending processes, which were parameterized by the powder ratio and the light intensity. This modeling process allows such photo-responsive materials to be controlled as actuators, and will possibly be effective for engineering mechanically responsive materials.


Author(s):  
Susanna Makela ◽  
Yajuan Si ◽  
Andrew Gelman

This chapter argues that it is wasteful to do a large, expensive poll and then just report a few percentages. Statistical modeling allows researchers to make the most effective use of available data, and graphs make it possible to convey more information more directly, both to general audiences and to specialists. Graphs are an invaluable tool at each step of the modeling process: exploring raw data, building and refining the model, and understanding and communicating the results are all made easier with graphs. In addition, graphical methods can be useful to survey researchers to understand weighting and other aspects of survey construction and analysis. The chapter includes several examples.


Author(s):  
Martin Chavant ◽  
Alexis Hervais-Adelman ◽  
Olivier Macherey

Purpose An increasing number of individuals with residual or even normal contralateral hearing are being considered for cochlear implantation. It remains unknown whether the presence of contralateral hearing is beneficial or detrimental to their perceptual learning of cochlear implant (CI)–processed speech. The aim of this experiment was to provide a first insight into this question using acoustic simulations of CI processing. Method Sixty normal-hearing listeners took part in an auditory perceptual learning experiment. Each subject was randomly assigned to one of three groups of 20 referred to as NORMAL, LOWPASS, and NOTHING. The experiment consisted of two test phases separated by a training phase. In the test phases, all subjects were tested on recognition of monosyllabic words passed through a six-channel “PSHC” vocoder presented to a single ear. In the training phase, which consisted of listening to a 25-min audio book, all subjects were also presented with the same vocoded speech in one ear but the signal they received in their other ear differed across groups. The NORMAL group was presented with the unprocessed speech signal, the LOWPASS group with a low-pass filtered version of the speech signal, and the NOTHING group with no sound at all. Results The improvement in speech scores following training was significantly smaller for the NORMAL than for the LOWPASS and NOTHING groups. Conclusions This study suggests that the presentation of normal speech in the contralateral ear reduces or slows down perceptual learning of vocoded speech but that an unintelligible low-pass filtered contralateral signal does not have this effect. Potential implications for the rehabilitation of CI patients with partial or full contralateral hearing are discussed.


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