Citation: Zhao, M.; Ji, Z.; Zhang, J.;
Zhu, Y.; Ye, C.; Wang, G.; Yin, Z.
Assessing Distinct Cognitive
Workload Levels Associated with
Unambiguous and Ambiguous
Pronoun Resolutions in
Human–Machine Interactions. Brain
Sci. 2022, 12, 369. https://doi.org/
10.3390/brainsci12030369
Academic Editors: Enrico Vezzetti,
Andrea Luigi Guerra, Gabriele
Baronio, Domenico Speranza and
Luca Ulrich
Received: 18 February 2022
Accepted: 8 March 2022
Published: 11 March 2022
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Article
Assessing Distinct Cognitive Workload Levels Associated with
Unambiguous and Ambiguous Pronoun Resolutions in
Human–Machine Interactions
Mengyuan Zhao
1
, Zhangyifan Ji
2
, Jing Zhang
2
, Yiwen Zhu
2
, Chunhua Ye
2
, Guangying Wang
2
and Zhong Yin
2,3,
*
1
College of Foreign Languages, University of Shanghai for Science and Technology, Shanghai 200093, China;
mengyuan.zhao@usst.edu.cn
2
Shanghai Key Laboratory of Modern Optical System, Engineering Research Center of Optical Instrument and
System, Ministry of Education, University of Shanghai for Science and Technology, Shanghai 200093, China;
202440428@st.usst.edu.cn (Z.J.); 202440470@st.usst.edu.cn (J.Z.); 202440468@st.usst.edu.cn (Y.Z.);
192550424@st.usst.edu.cn (C.Y.); 192550434@st.usst.edu.cn (G.W.)
3
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology,
Shanghai 200093, China
* Correspondence: yinzhong@usst.edu.cn; Tel.: +86-21-55271064
Abstract:
Pronoun resolution plays an important role in language comprehension. However, little is
known about its recruited cognitive mechanisms. Our investigation aims to explore the cognitive
mechanisms underlying various types of pronoun resolution in Chinese using an electroencephalo-
graph (EEG). We used three convolutional neural networks (CNNs)—LeNeT-5, GoogleNet, and
EffifcientNet—to discover high-level feature abstractions of the EEG spatial topologies. The output
of the three models was then fused using different scales by principal component analysis (PCA) to
achieve cognitive workload classification. Overall, the workload classification rate by fusing three
deep networks can be achieved at 55–63% in a participant-specific manner. We provide evidence
that both the behavioral indicator of reaction time and the neural indicator of cognitive workload
collected during pronoun resolution vary depending on the type of the pronoun. We observed an
increase in reaction time accompanied by a decrease of the theta power while participants were
processing ambiguous pronoun resolution compared to unambiguous controls. We propose that am-
biguous pronoun resolution involves a more time-consuming yet more flexible cognitive mechanism,
consistent with the predictions of the decision-making framework from an influential pragmatic
tradition. Our results extend previous research that the cognitive states of resolving ambiguous
and unambiguous pronouns are differentiated, indicating that cognitive workload evaluated using
the method of machine learning for analysis of EEG signals acts as a complementary indicator for
studying pronoun resolution and serves as an important inspiration for human–machine interaction.
Keywords:
electroencephalograph; pronoun resolution; cognitive workload; machine learning;
principal component analysis
1. Introduction
In natural language, a referential pronoun is often used to denote an previously
mentioned individual [
1
]. Pragmatically, a significant role of pronouns is to connect new
information to what has already been presented in the context [
2
]. Pronoun resolution is
thus a fundamental process in language comprehension [
3
]. Although it is argued that the
readers determine pronoun referents mainly based on the gender of the pronoun [
4
], this
task is complicated by the fact that pronouns may present referential ambiguity, where the
gender information is insufficient for referent identification. The resolution of ambiguous
pronouns, which pose a challenge in language comprehension, has aroused wide interest
in the transdisciplinary research field of linguistics, psychology, neuroscience, and machine
Brain Sci. 2022, 12, 369. https://doi.org/10.3390/brainsci12030369 https://www.mdpi.com/journal/brainsci
