著者

岡本 安晴

出版者

日本情報教育学会

雑誌

情報教育ジャーナル (ISSN:24326321)

巻号頁・発行日

vol.1, no.1, pp.70-77, 2018 (Released:2018-12-04)

参考文献数

16

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心理学における量的研究法と質的研究法の現在のような区別は消失し,両研究法は情報通信技術(ICT)を活用した研究法に統合されると考えられる.心理学およびその関連領域におけるICT的研究法の歴史を振り返り,未来の心理学におけるその可能性について考える.ICT において創造的な若者を育成するために,Pythonプログラミングを教えることを提案する.

著者

岡本 安晴

出版者

日本基礎心理学会

雑誌

基礎心理学研究 (ISSN:02877651)

巻号頁・発行日

pp.38.2, (Released:2019-06-21)

参考文献数

39

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The up-down method of adaptive psychophysical measurement uses binary response categories, e.g., “stronger” and “weaker.” This study proposes that ratings using three response categories, e.g., “stronger,” “do not know,” and “weaker,” or four response categories, e.g., “stronger,” “probably stronger,” “probably weaker,” and “weaker,” should be used instead. Simulation experiments showed that the proposed methods were superior to the standard up-down method. Comparisons were made with respect to the root mean square error (RMSE). First, in the case of two response categories, the RMSEs of estimates made using a stochastic model were smaller than those derived using the standard arithmetic method based on simple averaging, except in one extreme case. Hence, comparison of two, three, and four response categories was made with respect to estimates made using stochastic models. The RMSEs of estimates of the point of subjective equality using three or four response categories were smaller than those using two response categories. The RMSEs of estimates of model slope parameters, where a just noticeable difference was calculated as a ratio of the parameter, were smaller with three or four response categories than with two response categories, except in two extreme cases.

著者

吉野 諒三 山岸 侯彦 足立 浩平 岡本 安晴 森 周司 伊東 三四 中川 正宣 寺井 あすか 坂本 佳陽 千野 直仁 浅川 伸一 椎名 乾平 竹村 和久

出版者

公益社団法人 日本心理学会

雑誌

日本心理学会大会発表論文集

巻号頁・発行日

vol.72, pp.WS058, 2008

著者

岡本 安晴

出版者

日本基礎心理学会

雑誌

基礎心理学研究 (ISSN:02877651)

巻号頁・発行日

vol.30, no.1, pp.44-55, 2011-09-30 (Released:2016-12-01)

被引用文献数

1

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Bayesian analysis is applied to experimental data to effectively exploit information by the up-down method. Comparing Bayesian analysis to the standard one, which estimates the point of subjective equality (PSE) by averaging part of the comparison stimuli, confirms the two methods do not differ in terms of the PSE estimation. However, the standard analysis estimates only the PSE, whereas Bayesian analysis can also estimate a just noticeable difference (JND). Estimates of the PSE and JND determine a psychometric function. These results reveal that the Bayesian analysis is useful and superior to the standard analysis.