High lexical construal levels decrease negativity bias in online reading: evidence from recognition and eye-tracking experiments

Negativity bias in online reading widely occurs and leads to harmful results at the individual and social levels, but few studies have examined how to reduce this bias from the perspective of lexical processing. This study examined whether and how high lexical construal levels decrease negativity bias through a recognition memory experiment (Study 1) and an eye-tracking experiment (Study 2). In Study 1 (N = 70), results showed a negativity bias in recognition memory, which was attenuated under high construal levels, primarily through a reduction in the discrimination sensitivity (A’) advantage for negative words.

In Study 2 (N = 53), eye-tracking revealed that at low construal levels, negative words were processed more quickly than positive words, indicating automated processing. Critically, at high construal levels, this pattern reversed, with positive words being processed more efficiently. These findings demonstrate that high lexical construal levels reduce negativity bias not by equalizing processing, but by attenuating the automatic advantage for negative words and shifting efficiency toward positive information.

We would like to acknowledge the reviewers for their helpful comments on this paper. This work was supported by the National Natural Science Foundation of China [Grant Nos. 72574253, 72574254 to MZ]; the Humanities and Social Sciences Research Project of the Ministry of Education of the People’s Republic of China [Grant No. 24YJAZH223 to MZ]; the Major Program of the National Social Science Foundation of China [Grant No. 24&ZD158 to MZ]; and the Humanities and Social Science Fund of the Ministry of Education of China [Grant No. 18YJA190003 to SH]. Beijing Normal University, Beijing, China Central University of Finance and Economics, Beijing, China The authors declare no competing interests.

The research protocol was approved by the Institutional Review Board of Central University of Finance and Economics (Beijing, China; IRB#202004010035) on November 22, 2021. All participants were thoroughly informed about the purpose of the study, the voluntary nature of participation, the right to withdraw at any time without consequences, the use of data for academic research and publication purposes, and the anonymity of reporting results. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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