Estimation Strategy Utilization Is Modulated by Implicit Emotion Regulation: Evidence from Behavioral and Event-Related Potentials Studies

Abstract

A large number of studies have studied the influence of emotional experience on an individual’s estimation performance, but the influence of implicit emotion regulation is still unknown. Participants were asked to complete the following tasks in order: idiom matching task, multiplication computational estimation task (MCE task), gender judgment task (GJ task), and emotional experience intensity assessment task. The words matching task was adopted to achieve the purpose of implicit emotion regulation (implicit reappraisal and implicit suppression). Behavioral results showed that implicit reappraisal and implicit suppression equally contributed to improving an individual’s estimation speed (but not ACC (accuracy)). The MCE task related ERP (event-related potential) results showed that the influence of implicit emotion regulation on estimation consisted of two phases. In the first phase (encoding phase), implicit reappraisal both enhanced (larger P1 amplitudes) and weakened (smaller N170 amplitudes) an individual’s encoding sensitivity, while implicit suppression enhanced an individual’s encoding sensitivity (larger P1 amplitudes). In the second phase (estimation strategies retrieval phase), implicit reappraisal (but not implicit suppression) cost more attention resources (larger LPC2 and LPC3 amplitudes). The present study suggested that both implicit reappraisal and implicit suppression contributed to improving an individual’s estimation performance, and the regulation effect of implicit suppression (vs. implicit reappraisal) was better.

Mathematical problem solving is modulated by word priming

“…One hundred and eight TDME problems (e.g., 32 × 67) were used in the present study, with 54 of them suitable for the UD strategy and the other 54 suitable for the DU strategy. The principles for choosing the TDME problems were consistent with previous studies (Zhu et al, 2023 ; Zhu, Jiang, Wang, et al, 2021 ; Zhu, Li, et al, 2022 ). To be more specific: (1) no operand had its closest decade equal to 10 (e.g., 13 × 47), or 100 (e.g., 24 × 98); (2) no operands had 0 (e.g., 41 × 70) or 5 (e.g., 35 × 58) as unit digits; (3) operands were not repeated in the decade (e.g., 42 × 49) or unit (e.g., 47 × 57); (4) no digits were repeated within operands (e.g., 44 × 67); (5) no tie problems were used (e.g., 36 × 36); and (6) the first operand was smaller than the second (e.g., 32 × 57), owing to Chinese preferred operand‐order‐specific representation; that is, they performed better while the first operand was smaller (vs. larger) than the second.…”

“…One hundred and eight TDME problems (e.g., 32 Â 67) were used in the present study, with 54 of them suitable for the UD strategy and the other 54 suitable for the DU strategy. The principles for choosing the TDME problems were consistent with previous studies (Zhu et al, 2023;Zhu, Li, et al, 2022). To be more specific: (1) no operand had its closest decade equal to 10 (e.g., 13 Â 47), or 100 (e.g., 24 Â 98); (2) no operands had 0 (e.g., 41 Â 70) or 5 (e.g., 35 Â 58) as unit digits; (3) operands were not repeated in the decade (e.g., 42 Â 49) or unit (e.g., 47 Â 57); (4) no digits were repeated within operands (e.g., 44 Â 67);…”