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一种用于识别理想刺激的通用识别理论模型。

A general recognition theory model for identifying an ideal stimulus.

机构信息

University of California, Santa Barbara, Santa Barbara, CA, USA.

出版信息

Atten Percept Psychophys. 2022 Oct;84(7):2408-2421. doi: 10.3758/s13414-022-02513-3. Epub 2022 Jun 14.

Abstract

A probabilistic, multidimensional model is described that accounts for sensory and hedonic ratings that are collected from the same experiment. The model combines a general recognition theory model of the sensory ratings with Coombs' unfolding model of the hedonic ratings. The model uses sensory ratings to build a probabilistic, multidimensional representation of the sensory experiences elicited by exposure to each stimulus, and it also builds a similar representation of the hypothetical ideal stimulus in this same space. It accounts for hedonic ratings by measuring differences between the presented stimulus and the imagined ideal on each rated sensory dimension. Therefore, it provides precise estimates of the sensory qualities of the ideal on all rated sensory dimensions. The model is tested successfully against data from a new experiment.

摘要

本文描述了一个概率多维模型,该模型可以解释从同一实验中收集到的感官和愉悦评价。该模型将感官评价的通用识别理论模型与 Coombs 的愉悦评价展开模型相结合。该模型使用感官评价来构建对每个刺激物暴露所产生的感官体验的概率多维表示,并且它还在相同空间中构建了对假设的理想刺激物的类似表示。它通过测量呈现的刺激物与每个评价感官维度上想象的理想刺激物之间的差异来解释愉悦评价。因此,它可以精确地估计理想刺激物在所有评价感官维度上的感官质量。该模型在一项新实验的数据中得到了成功的验证。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39b4/9481514/38d3f8069af0/13414_2022_2513_Fig1_HTML.jpg

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