Gaze-informed modeling of preference learning and prediction.

Gaze-informed modeling of preference learning and prediction.
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偏好学习和预测的注视建模。

DOI:
10.1037/npe0000107
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发表时间:
2019
期刊:
and Economics
影响因子:
--
通讯作者:
Krajbich, Ian
Krajbich, Ian
中科院分区:
--
文献类型:
--
作者:
Smith, Stephanie M.;Krajbich, Ian

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了解他人的喜好是在社会中有效运作所必需的基本技能。然而,这种行为背后的过程在很大程度上还没有得到研究。在这里,我们的目标是描述这一过程,使用眼球跟踪和计算模型来研究人们,同时他们估计另一个人的电影偏好。在研究的前半部分,受试者在猜测后立即得到反馈,而在后半部分,受试者被呈现四个随机的上半年结果,以帮助他们进行当前的估计。从各种学习模型中,我们确定了两个最符合受试者行为和眼动的学习模型:K-近邻和选美比赛。这些结果表明,尽管一些人试图形成对其他人偏好的高维表征,但另一些人只是简单地遵循平均意见。这些策略可以通过观察一个人的眼球运动来区分。结果还表明,受试者有能力在他们的估计中适当地加权反馈。(数据库记录(C)2019 APA,保留所有权利)
Learning other people’s preferences is a basic skill required to function effectively in society. However, the process underlying this behavior has been left largely unstudied. Here we aimed to characterize this process, using eye-tracking and computational modeling to study people while they estimated another person’s film preferences. In the first half of the study, subjects received immediate feedback after their guess, whereas in the second half, subjects were presented with four random first-half outcomes to aid them with their current estimation. From a variety of learning models, we identified two that best fit subjects’ behavior and eye movements: k-nearest neighbor and beauty contest. These results indicate that although some people attempt to form a high-dimensional representation of other people’s preferences, others simply go with the average opinion. These strategies can be distinguished by looking at a person’s eye movements. The results also demonstrate subjects’ ability to appropriately weight feedback in their estimates.(PsycINFO Database Record (c) 2019 APA, all rights reserved)
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