利用神经网络反转认知模型,从定点推断偏好。

IF 2.3 2区 心理学 Q2 PSYCHOLOGY, EXPERIMENTAL
Evan M. Russek, Frederick Callaway, Thomas L. Griffiths
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引用次数: 0

摘要

从个人可观察到的行为中推断其偏好是辅助决策技术开发的关键一步。虽然神经网络等机器学习模型原则上可用于推断,但训练此类模型需要大量数据。在这里,我们提出了一种认知模型生成模拟数据以增强有限的人类数据的方法。利用这些数据,我们训练一个神经网络来反转模型,从而有可能从行为中推断出偏好。我们展示了如何利用这种方法从人们选择食物时的眼球运动来推断他们对这些食物赋予的价值。首先,我们证明了神经网络可以推断出模型用于生成模拟固定动作的潜在偏好;其次,模拟数据有利于预训练网络,以便从真实固定动作中预测人类报告的偏好。与仅从选择中推断偏好相比,这种方法在预测偏好方面略有改进,而且还能在做出选择之前进行预测。总之,我们的研究结果表明,结合使用神经网络和模型模拟训练数据,是开发推断人类偏好技术的一种很有前途的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Inverting Cognitive Models With Neural Networks to Infer Preferences From Fixations

Inverting Cognitive Models With Neural Networks to Infer Preferences From Fixations

Inferring an individual's preferences from their observable behavior is a key step in the development of assistive decision-making technology. Although machine learning models such as neural networks could in principle be deployed toward this inference, a large amount of data is required to train such models. Here, we present an approach in which a cognitive model generates simulated data to augment limited human data. Using these data, we train a neural network to invert the model, making it possible to infer preferences from behavior. We show how this approach can be used to infer the value that people assign to food items from their eye movements when choosing between those items. We demonstrate first that neural networks can infer the latent preferences used by the model to generate simulated fixations, and second that simulated data can be beneficial in pretraining a network for predicting human-reported preferences from real fixations. Compared to inferring preferences from choice alone, this approach confers a slight improvement in predicting preferences and also allows prediction to take place prior to the choice being made. Overall, our results suggest that using a combination of neural networks and model-simulated training data is a promising approach for developing technology that infers human preferences.

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来源期刊
Cognitive Science
Cognitive Science PSYCHOLOGY, EXPERIMENTAL-
CiteScore
4.10
自引率
8.00%
发文量
139
期刊介绍: Cognitive Science publishes articles in all areas of cognitive science, covering such topics as knowledge representation, inference, memory processes, learning, problem solving, planning, perception, natural language understanding, connectionism, brain theory, motor control, intentional systems, and other areas of interdisciplinary concern. Highest priority is given to research reports that are specifically written for a multidisciplinary audience. The audience is primarily researchers in cognitive science and its associated fields, including anthropologists, education researchers, psychologists, philosophers, linguists, computer scientists, neuroscientists, and roboticists.
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