Abstract
In this study, we propose a semi-supervised learning model for decoding of the perceived difficulty of communicated content by older people. Our model is based on mapping of the older people's prefrontal cortex (PFC) activity during their verbal communication onto fine-grained cluster spaces of a working memory (WM) task that induces loads on human's PFC through modulation of its difficulty level. This allows for differential quantification of the observed changes in pattern of PFC activation during verbal communication with respect to the difficulty level of the WM task. We show that such a quantification establishes a reliable basis for categorization and subsequently learning of the PFC responses to more naturalistic contents, such as story comprehension. Our contribution is to present evidence on effectiveness of our method for estimation of the older people's perceived difficulty of the communicated contents during an online storytelling scenario.
Highlights
A DISTINCT attribute of robots in comparison with other media is their physical embodiment which allows for a sense of togetherness [1]
We propose to evaluate the perceived difficulty of communicated contents during verbal communication via cognitive loads that are estimated based on brain activities during simple working memory (WM) tasks
They indicate that our model significantly outperforms the use of such models as support vector machine (SVM), linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA) that were trained based on the same cross-labeled data
Summary
A DISTINCT attribute of robots in comparison with other media is their physical embodiment which allows for a sense of togetherness [1]. Research suggests that children who read with the learning-companion robot consider their reading companion to support their reading comprehension and that it motivates a deepening social connection [2]. Mann et al [3] find that people are more responsive to robots than computer-based healthcare systems. Keshmiri et al [4] identify that tele-communicating through a Manuscript received January 15, 2019; accepted June 19, 2019. Date of publication June 28, 2019; date of current version July 15, 2019. This letter was recommended for publication by Associate Editor T.
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