Behavioral and brain synchronization differences between expert and novice teachers when collaborating with students

Brain and Cognition 139 (2020) 105513

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Behavioral and brain synchronization differences between expert and novice teachers when collaborating with students ⁎

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Binghai Sun, Weilong Xiao, Xiaodan Feng, Yuting Shao, Wenhai Zhang , Weijian Li School of Teacher Education, Zhejiang Normal University, Jinhua, Zhejiang, China Research Center of Tin Ka Ping Moral Education, Zhejiang Normal University, Jinhua, Zhejiang, China

A R T I C LE I N FO

A B S T R A C T

Keywords: Expert teacher Novice teacher Cooperative interaction Interpersonal brain synchronization fNIRS-based hyperscanning

Differences in behavior and neural mechanisms between expert and novice teachers when collaborating with students are poorly understood. This study investigated whether expert teachers do better in collaborating with students than novice teachers and explored the neural basis of such differences. Novice teacher and student (NTS) dyads and expert teacher and student (ET-S) dyads were recruited to complete an interactive task consisting of a cooperation and an independent condition. During the experiment, neural activity in the prefrontal cortex of the participants was recorded with functional near-infrared spectroscopy. The results show higher accuracy for the ET-S dyads than the NT-S dyads in the cooperation condition; however, no difference was found in the independent condition. Increased interpersonal brain synchronization (IBS) was detected in the left dorsolateral prefrontal cortex of participants in ET-S dyads, but not in NT-S dyads in the cooperation condition. Moreover, an interaction effect of dyad type and conditions on IBS was observed, revealing IBS was stronger in ET-S dyads than in NT-S dyads. In ET-S dyads, IBS was positively correlated with the teachers’ perspective-taking ability and accuracy. These findings suggest that expert teachers collaborate better with students than novice teachers, and IBS might be the neural marker for this difference.

1. Introduction In the past several decades, evidence has shown superior performance of expert teachers when it comes to representative behavior involved in teaching, such as planning before the lesson (Shaw, 2017; Wolff, Van, Jarodzka, & Boshuizen, 2015), reflection after the lesson (Farrell, 2013; Hall & Smith, 2006), and classroom management (Wang, Lu, Duan, & Zhou, 2013; Wolff et al., 2015; Wolff, Jarodzka, Niek, & Boshuizen, 2016). For example, the results of research on the ability to simultaneously perceive and interpret classroom situations for effective classroom management showed that the perception of expert teachers appears to be more knowledge-driven, whereas novice teachers appear more image-driven (Wolff et al., 2016). In another study, expert teachers monitored more areas than novice teachers, while novice teachers skipped certain areas (Wang et al., 2013). However, previous studies mainly focused on differences in the teachers’ teaching abilities (e.g., classroom management, teaching strategy) between expert and novice teachers. Insufficient attention, however, has been paid to the teachers’ abilities to cooperate with students. Teaching is a complex system of interacting elements, which include the teachers, the students, and the content being taught (Lampert,

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2003). Effective teaching requires the teachers to keep a balance between the students and the content, and then cooperate with the students to produce the desired outcomes. Unlike in other domains (e.g., chess, music, or sport), in the teaching domain, teachers cannot achieve the desired goals without the collaboration of their students (Cohen, 2011; Stigler & Miller, 2018). According to previous behavioral studies (Auerbach, Higgins, Brickman, & Andrews, 2018; Farrell, 2013; Hall & Smith, 2006; Shaw, 2017), expert teachers perform better than novice teachers in the teaching domain, regarding, for example, the planning and reflection that go on before and after the lesson. However, whether expert teachers also do better in cooperating with students than novice teachers is an open question. In recent years, substantial work has gone into exploring the neural substrates of teachers’ teaching abilities (Lee, Byeon, & Kwon, 2009; Lee, Lee, & Kwon, 2008; Takeuchi, Mori, Suzukamo, & Izumi, 2019), such as metacognition abilities during teaching sessions or the generation and application of common knowledge on biological phenomena. However, one of the limitations of these studies is that they have mainly focused on the neural substrates of the teachers’ cognition. Still, very little is known about the neural substrates of mutual interaction (e.g., cooperation) between teacher and student. In recent years,

Corresponding authors at: School of Teacher Education, Zhejiang Normal University, 688 Yingbin Road, Jinhua 321004, Zhejiang, China (W. Li). E-mail addresses: [email protected] (W. Zhang), [email protected] (W. Li).

https://doi.org/10.1016/j.bandc.2019.105513 Received 21 August 2019; Received in revised form 16 December 2019; Accepted 18 December 2019 0278-2626/ © 2019 Elsevier Inc. All rights reserved.

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Lu, Au, Jiang, Xie, & Yam, 2013; Rumble, Van Lange, & Parks, 2009; Xu, Kou, & Zhong, 2012). We also assessed the participants’ emotional states (Xue, Lu, & Hao, 2018) and arithmetic abilities. These measures allowed us to verify whether any effects of teacher type (i.e., novice or expert) on cooperative performance were independent from these other factors.

a few studies investigated interactions between teachers and students (Pan, Novembre, Song, Li, & Hu, 2018; Zheng, Chen, Liu, Long, & Lu, 2018), but they did not compare different types of teachers (e.g., novices vs. experts). Hence, to clarify this issue, this study aimed to investigate whether expert teachers do better in cooperating with students than novice teachers and to elucidate the neural origins of such differences. To achieve these goals, we compared the teacher-student interaction performance of expert and novice teachers in an “independent condition” (interaction with a computer) and a “cooperation condition” (interacting with a student), using a “two-person neuroscience” technique (Koike, Tanabe, & Sadato, 2014), which has also been named “hyperscanning” (Babiloni & Astolfi, 2014; Montague et al., 2002). In contrast to previous neuroimaging studies that investigated the neural correlates underlying social cognition by measuring the brain activity of one individual at a time (Hari & Kujala, 2009), hyperscanning focuses on dyads or groups rather than individuals (Bevilacqua et al., 2019). Hyperscanning is thus suitable for investigating the dynamic interactions among multiple brains and thereby exploring the neural basis of social interactions (Babiloni & Astolfi, 2014; Mu, Cerritos, & Khan, 2018). Notably, functional near-infrared spectroscopy (fNIRS)-based hyperscanning has the advantage of a higher tolerance to body movement, and therefore allows to conduct studies in more ecologically valid environments (Scholkmann, Holper, Wolf, & Wolf, 2013). Using fNIRSbased hyperscanning, a number of studies have demonstrated that interpersonal brain synchronization (IBS) can be a neural marker of diverse social interactions (Gvirts & Perlmutter, 2019), such as, action coordination (Cui, Bryant, & Reiss, 2012; Vanessa, Christian, Wolfgang, & Kerstin, 2018), social communication (Jiang, Chen, Dai, Shi, & Lu, 2015; Stolk et al., 2014), and teaching interaction (Holper et al., 2013; Liu, Zhang, et al., 2019). In previous fNIRS-based hyperscanning studies, increased task-related IBS was uniformly found in brain regions involved in social interactive activities, especially in the prefrontal cortex (PFC) (Baker et al., 2016; Cui et al., 2012; Lu & Hao, 2019; Naoyuki et al., 2015; Tang et al., 2016). The PFC has been shown to be related to basic cognitive abilities such as attention (Adolphs, 2014) and memory (Vartanian et al., 2014), and to some important social cognitive abilities, which include cognitive control (Miller & Cohen, 2001) as well as perspectivetaking and goal maintenance (Knoch, Schneider, Schunk, Hohmann, & Fehr, 2009). A number of studies using fNIRS-based hyperscanning confirmed that interpersonal cooperative activities are accompanied by significant IBS in the PFC (Cui et al., 2012; Lu & Hao, 2019; Osaka, Minamoto, Yaoi, Azuma, & Osaka, 2014). Based on these recent studies, the PFC was thus chosen as the region of interest in this study. In this study, we aimed to provide behavioral and neural evidence for differences between experts and novice teachers when they cooperate with students and to reveal the underlying neural mechanisms, using a cooperative arithmetic task in combination with fNIRS-based hyperscanning. We manipulated the teacher type in the teacher-student pairs (expert teacher (ET) vs. novice teacher (NT)) and the interaction condition in the task (independent condition vs. cooperation condition), and recorded the neural activities in the PFC of the individuals in a pair simultaneously during the task. Based on evidence from social interaction experiments, especially from studies focusing on cooperation (Cui et al., 2012; Osaka et al., 2014), we hypothesized that the expert teacher and student (ET-S) dyads would show higher accuracy and faster response times than the novice teacher and student (NT-S) dyads in the cooperation condition. Moreover, we also hypothesized that such increased behavioral performance should be associated with stronger IBS in the PFC. Furthermore, we measured the participants’ perspective-taking abilities, personality traits related to cooperation and competition, and dispositional empathy, because these variables have been found to affect cooperative behavior (Galinsky, Ku, & Wang, 2005; Haesevoets, Reinders Folmer, Bostyn, & Van Hiel, 2018; Jin, Li, He, & Shen, 2017;

2. Material and methods 2.1. Participants We recruited 68 healthy participants including 16 novice teachers (three male, 25.81 ± 4.69 years old, length of teaching experience 25.00 ± 25.95 months, range 3–72 months), 18 expert teachers (four male, 38.00 ± 4.30 years old, length of teaching experience 193.67 ± 60.83 months, range 120–236 months) (Carter, Cushing, Sabers, Stein, & Berliner, 1988; Sabers, Cushing, & Berliner, 1991), and 34 students (seven male, 20.15 ± 1.67 years old). All participants were right-handed, as assessed via the Edinburgh Handedness Inventory (Oldfield, 1971), and had normal or corrected-to-normal vision. A teacher and a student of the same sex were randomly matched into a dyad in order to avoid cross-sex effects (Balliet, Li, Macfarlan, & Van Vugt, 2011). The members of each dyad were unacquainted with each other. Before the experiment, written informed consent was obtained from all participants, and participants received 40 Chinese yuan or a gift of the same value for participation at the end of the study. The Ethics Committee of the Zhejiang Normal University approved the study protocol. 2.1.1. Expert teachers In line with previous studies (Wang et al., 2013; Wang, Shen, Tian, & Zhou, 2010), expert teachers were considered if they met the following two criteria: First, they had more than 10 years of teaching experience (Meyer, 2004), and held a job title as first-level teacher approved by the local government. Second, we invited the person in charge of teaching at their school to recommend the expert teacher based on criterion one. Expert teachers were selected from middle schools in different cities of Zhejiang province of China. 2.1.2. Novice teachers Senior students and new teachers were selected as novice teachers, following earlier work (Lian, Meng, & Liao, 2003; Shi, Li, & Li, 2014). Senior students were individuals who were studying at a university with a focus on education. The senior students had undergone teacher training and participated in school-based field practice for more than 3 months were selected. New teachers were individuals who had started their teaching career less than 5 years before they participated in this study (Lian et al., 2003). Novice teachers were selected from middle schools in different cities of Zhejiang province. Senior students were selected from Zhejiang Normal University. 2.2. Subjective measurements All participants were asked to report their emotional state with the Chinese version of the Positive Affect and Negative Affect Scale (PANAS, Huang, Yang, & Li, 2003) before and after the task. On this scale, a Cronbach’s α of 0.85 for positive affect and an α of 0.83 for negative affect have been reported. After the experiment, the subjects had to complete the Chinese version of the Interpersonal Reactivity Index (IRI-C; Cronbach’s α of the IRI-C ranges from 0.61 to 0.85; Huang, Li, Sun, Chen, & Davis, 2012) to evaluate their dispositional empathy, the Chinese version of the Cooperative and Competitive Personality Scale (CACP; Cronbach’s α for the cooperation scale is 0.85, and Cronbach’s α for the competition scale is 0.71; Xie, Yu, Chen, & Chen, 2006) to assess their personality traits related to cooperation and competition, and the arithmetic subscale of the Chinese version of the 2

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Fig. 1. The experimental settings and the flow of a trial. (A) The experimental setup. The participants were sat face-to-face in front of the computer with a wallboard. (B) The Flow of a Trial. In each trial, the participants have to undergo the following stage: the fixation period (500 ms), the decision period (5000 ms), the feedback period (3000 ms), and the blank interval (2500 ms). (C) The 3 * 5 optode probe set. The probe patch was placed on the prefrontal cortex of each participant. The red dots mean the detector, the blue dots mean the emitter, and the yellow dot means the Fpz in the 10–20 system. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

results. Finally, a blank interval was presented for 1500 ms (Fig. 1B). It worth to note that the members of each dyad have to select the number within 5 s rather than selecting after their partner have finished. Each number could only be chosen once in each block. In order to make sure that the members of each dyad easily understood the feedback, in the cooperation condition, the teacher was denoted as Participant #1 and the student was denoted as participant #2. During the task session, verbal or physical communication was not allowed.

Wechsler Intelligence Scale (WIS-A-C, Yao, Chen, Jiang, & Tam, 2007) to make sure that differences in arithmetic abilities were not the cause of performance differences between the two groups. During the completion of the questionnaires for subjective measurements, no discussion between participants was allowed. 2.3. Experimental procedure and tasks The members of each dyad were asked to evaluate their emotion state upon arrival, and then instructed to sit in front of a computer. A wallboard was placed between computers to prevent the participants from seeing each other (Fig. 1A). The experimental procedure consisted of two sub-sessions. Firstly, participants were given instructions and a comprehensive explanation about the task. Secondly, the members of each dyad were asked to complete the cooperation and independent conditions of the task. Each condition contained resting-state period and task period. During the resting-state period, the participants were asked to close their eyes and relax their minds (Lu et al., 2010). The brain activity recorded during the resting-state period was defined as the baseline (Cheng, Li, & Hu, 2015; Cui et al., 2012). During the task period, the members of each dyad performed the task, together or alone, depending on the respective condition. Task conditions were counterbalanced across dyads. The brain activity recorded during the task periods was defined as task-related activity (Cui et al., 2012; Lu & Hao, 2019). The task periods contained six blocks and each block included nine trials. In each trial, a fixation cross was presented in the center of the screen for 500 ms, followed by an equation (e.g., “() + () = multiple of 3”) in the center and the numbers 1–9 displayed below the equation. The members of each pair were both asked to make a decision, select the correct number (without talking to each other), and complete the equation, all within 5000 ms. Feedback was presented for 3000 ms, with the result of the calculation in the center (“correct” or ”wrong”) and the numbers selected by the members of the pair above the correct

2.4. fNIRS data acquisition An optical topography system with the sampling rate set to 10 Hz (ETG-4000, Hitachi Medical Corporation, Japan) was employed to record measurement data (Baker et al., 2016; Cui et al., 2012), and 3 × 5 probe patches were used to collect signals from the participants separately (Cheng et al., 2015; Cui et al., 2012). The modified Beer-Lambert law (Wyatt, Delpy, Cope, Wray, & Reynolds, 1986) was then applied to calculate concentration changes in oxy- and deoxy-hemoglobin, based on the optical density changes observed in the measurement data recorded during the task and resting-state sessions. Each patch contained 22 measurement channels (CH). These channels were consisted by eight emitters and seven detectors (the distance between each emitter and detector was set at 30 mm). A 3 × 5 probe patch, attached to a flexible swimming cap, was placed over the prefrontal region of each participant. According to the international 10–20 system, the lowest probes of the patch were placed along the Fp1–Fp2 line, and the yellow emitter of the lowest probe was placed precisely on the frontal pole midline point (Fpz) (Cheng et al., 2015; Cui et al., 2012; Sai, Zhou, Ding, Fu, & Sang, 2014) (Fig. 1C). To obtain correspondence between the measured channels and the positions on the PFC, a virtual registration method was applied (Singh, Okamoto, Dan, Jurcak, & Dan, 2005; Tsuzuki et al., 2007).

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2.5.3. Evaluation of IBS The WTC toolbox, which is based on MATLAB (The MathWorks, Inc., Natick, MA, USA) was used to evaluate the brain synchronization between the members of a pair with the HbO time-series signal as the index (Grinsted, Moore, & Jevrejeva, 2004). WTC can identify locally phase-locked behavior between two time-series by measuring crosscorrelations as a function of frequency and time (Chang & Glover, 2010; Cui et al., 2012; Grinsted et al., 2004; Torrence & Compo, 1998). First of all, we assessed the synchronization (coherence) of the 22 paired channels in the resting-state and the task-state period. Then, based on previous studies, we calculated the task-related coherence (Cheng et al., 2015; Cui et al., 2012; Xue et al., 2018), separately for the 16 NT-S dyads and the 18 ET-S dyads, defined as the difference value between the average coherence of the task period and the average coherence of the resting period across the 22 paired channels. Finally, before further analysis, we converted the task-related coherence values into Fisher’s z-statistics (Chang & Glover, 2010; Cheng et al., 2015; Cui et al., 2012).

2.5. fNIRS data analysis During the fNIRS data analysis, no filtering or detrending procedures were applied, in accordance with previous studies (Cheng et al., 2015; Cui et al., 2012; Dai et al., 2018; Zheng et al., 2018). Besides, we did not perform any artifact correction, as wavelet transform coherence (WTC) normalizes the amplitude of the signal according to each time window, and the amplitude is thus not vulnerable to the transient spikes induced by movements (Dai et al., 2018; Liu, Branigan, et al., 2019; Nozawa, Sasaki, Sakaki, Yokoyama, & Kawashima, 2016; Zheng et al., 2018). The data from individual channels were nevertheless visually inspected for excessive noise, and channels with excessive noise were excluded from subsequent analyses. 2.5.1. fNIRS data signal Oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) signals can be used to record changes in cerebral blood flow when a participant is engaging in cognitive or athletic tasks. However, previous studies indicated that HbO signals are more sensitive to these changes (Hoshi, 2007), and they are therefore widely used in fNIRSbased hyperscanning studies (Baker et al., 2016; Cui et al., 2012; Nozawa et al., 2016). Thus, in this study, we only focused on HbO signals when calculating interpersonal brain synchronization (IBS).

2.6. Data analysis 2.6.1. Behavioral data Analyses of behavioral performance, subjective measurements, and fNIRS hyperscanning data were carried out using SPSS 25.0 (IBM, New York, NY, USA), with the alpha level set at 0.05 (two-tailed). Effect sizes for t-tests (Cohen’s d) and analyses of variance (ANOVA) (ηp2) were calculated, and Bonferroni correction was applied for multiple comparisons; simple effect analyses were performed when an interaction effect was observed. The behavioral performance index included accuracy rates and response times. The accuracy rate was defined as the proportion of trials that were correct divided by the total number of trials, and the response time was defined as the mean response time across all trials in the decision stage. For the accuracy rate, a two-way ANOVA with dyad type (NT-S dyads vs. ET-S dyads) and task condition (cooperation vs. independent) was conducted. Then, independent-sample t-tests were applied with dyad type (NT-S dyads vs. ET-S dyads) as the independent variable and the accuracy rate as the dependent variable to examine differences between the two dyad types. For the learning curves based on accuracy

2.5.2. Frequency band of IBS Based on the duration of one trial (the time from the beginning to the end of one trial was 10 s) and visual inspection of the graphs regarding WTC (Fig. 2), we found that the period band between 6.4 and 25.6 s (frequency band between 0.04 Hz and 0.16 Hz) was the most sensitive to our task. Moreover, evidence from previous studies indicated that low-frequency oscillations are a reliable index of IBS (Achard, Salvador, Whitcher, Suckling, & Bullmore, 2006; Zuo et al., 2010), and a number of studies has detected IBS in lower-frequency bands in social interactions (Cheng et al., 2015; Lu & Hao, 2019). Besides, using the frequency band between 0.04 Hz and 0.16 Hz can avoid the impact of high-frequency noise, such as cardiac pulsation (about 0.7–4 Hz), and reduce the influence of low-frequency noise, such as respiration (about 0.2–0.3 Hz) (Xue et al., 2018). Thus, in the present study, this frequency band (between 0.04 Hz and 0.16 Hz) was chosen as the target frequency band.

Fig. 2. The frequency band of interested. This WTC graph based on raw HbO signal from CH10 in a representative ET-S dyad. The black borders represent the frequency band of interest (6.4–25.6 period). The color bars denote the value of WTC (1 = highest coherence, 0 = lowest coherence). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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rates, we conducted a three-way ANOVA with dyad type as the between groups factor (NT-S dyads vs. ET-S dyads), and task condition (cooperation vs. independent) and time as within factors (block 1 vs. block 2 vs. block 3 vs. block 4 vs. block 5 vs. block 6) to test changes in accuracy rates across blocks. For the reaction times, a two-way ANOVA with dyad type (NT-S dyads vs. ET-S dyads) and task condition (cooperation vs. independent) was carried out to test the effect of the dyads and task conditions on the response times of the members of the dyads. For the subjective measurements of the participants, a reliability analysis was conducted to test the quality of the questionnaires used in this study. A number of independent-samples t-tests were then conducted to compare between the two types of dyads to make sure that the teachers and students did not show considerable differences. Finally, paired t-tests were performed to verify that emotional states did not affect the performance of the participants.

Table 1 Descriptive statistics for psychological measurements.

CACP-Coop of teacher CACP-Comp of teacher CACP-Coop of student CACP-Comp of student IRI of teacher IRI of student Perspective taking of teacher Perspective taking of student WIS-A-C of teacher WIS-A-C of student

Whole Sample

NT-S dyads

ET-S dyads

M

SD

M

SD

M

SD

4.14 2.74 3.76 3.21 3.58 3.57 3.63 3.76 13.56 13.35

0.60 0.67 0.52 0.56 0.45 0.29 0.51 0.51 2.51 2.24

4.03 2.76 3.78 3.10 3.72 3.48 3.46 3.74 13.81 13.19

0.66 0.59 0.44 0.63 0.42 0.29 0.40 0.56 2.50 2.20

4.23 2.72 3.74 3.31 3.46 3.64 3.78 3.79 13.33 13.50

0.55 0.74 0.59 0.48 0.56 0.28 0.56 0.47 2.57 2.33

Note: IRI means the scale of Interpersonal Reactivity Index; Perspective taking means the subscale of the Interpersonal Reactivity Index; WIS-A-C means the arithmetic subscale of the Wexler Intelligence Scale, which represents the calculate ability; CACP-Coop means cooperative personality traits; CACP-Comp means competitive personality traits. NT-S: novice teacher and student dyad; ET-S: expert teacher and student dyad. M means the mean value; SD indicate standard deviation.

2.6.2. fNIRS data For the fNIRS data, one-sample t-tests were conducted on the “coherence increase” in the 22 paired channels, to confirm synchronicity between channels, with false discovery rate (FDR) correction. Then, a two-way ANOVA with dyad type (between-subjects factor) and task condition (within-subject factor) was conducted to test the effect of these two variables on the synchronous channels. Furthermore, a threeway ANOVA with dyad type as the between groups factor (NT-S dyads vs. ET-S dyads) and task condition (cooperation vs. independent) and time as within factors (block 1 vs. block 2 vs. block 3 vs. block 4 vs. block 5 vs. block 6) was conducted to assess changes in task-related IBS across blocks. Finally, a bivariate Pearson correlation analysis was performed to assess the relationship between the brain and behavioral indices.

higher in the cooperation condition (M = 60.15, SD = 23.63) than in the independent condition (M = 19.93, SD = 0.03), and a significant main effect of dyad type, F (1, 32) = 4.63, p = 0.039, ηp2 = 0.13, showing that accuracy rates in the ET-S dyads (M = 45.12, SD = 33.37) were higher than those in the NT-S dyads (M = 35.50, SD = 28.69). There was also a significant main effect of time, F (1, 32) = 13.11, p < 0.001, ηp2 = 0.29, and an interaction effect of task condition and time on accuracy rates, F (5, 160) = 13.15, p < 0.001, ηp2 = 0.29. The simple effect test showed that, in the cooperation condition, accuracy rates in block 3 to 6 were significantly higher than those in block 1 and 2. In contrast, no difference was found between block 1 and block 2 (Fig. 3B). As for the teachers’ response times, the two-way ANOVA yielded a significant main effect of task condition on response times, F (1, 32) = 7.80, p = 0.009, ηp2 = 0.20, indicating that response times were faster in the cooperation condition (M = 1137.29, SE = 71.02) than in the independent condition (M = 1355.99, SE = 70.24). No main effect of dyad type (p = 0.693) and no interaction effect (p = 0.977) on the teachers’ response times were found (Fig. 3C). For the students’ response time, the ANOVA revealed a significant main effect of task condition on response times, F (1, 32) = 9.32, p = 0.005, ηp2 = 0.23, showing that response times were slower in the cooperation condition (M = 1323.26, SE = 93.22) than in the independent condition (M = 1065.20, SE = 63.58). However, no main effect of dyad type (p = 0.131) and no interaction effect (p = 0.068) on the students’ response times were found (Fig. 3D).

3. Results 3.1. Subjective measurements The subjective measurements showed that participants of different types of dyads did not differ in cooperative and competitive personality traits, dispositional empathy, perspective-taking, or calculate ability (all p teacher > 0.065; all p student > 0.114). These results indicate that the participants in the NT-S and ET-S pairs were matched well. Also, the paired t-tests on the emotional state of the participants showed no differences before and after the task in the teacher (all p > 0.167) or the student group (all p > 0.071). The subjective measurement results are presented in Table 1. 3.2. Behavioral performance The two-way ANOVA yielded a significant main effect of task condition on accuracy rates, F (1, 32) = 111.12, p < 0.001, ηp2 = 0.78, showing that accuracy rates were significantly higher in the cooperation condition (M = 60.15, SD = 23.63) than in the independent condition (M = 19.93, SD = 0.03). The ANOVA also revealed a significant main effect of dyad type on accuracy rates, F (1, 32) = 4.63, p = 0.039, ηp2 = 0.13, indicating that accuracy rates in the ET-S dyads (M = 45.12, SD = 33.37) were higher than those in the NT-S dyads (M = 35.50, SD = 28.69). However, no interaction effect between dyad type and task condition was observed (p = 0.055) (Fig. 3A). The independent-sample t-tests showed that accuracy rates in the ET-S dyads (M = 67.78, SD = 19.82) were higher than those in the NT-S dyads (M = 51.56, SD = 25.21) in the cooperation condition, t (32) = -2.10, p = 0.044, Cohen’s d = 0.72; in the independent condition, however, we did not observe a significant difference, t (32) = −1.01, p = 0.319. Regarding the learning curves, the three-way ANOVA revealed a significant main effect of task condition, F (1, 32) = 111.12, p < 0.001, ηp2 = 0.78, showing that accuracy rates were significantly

3.3. Effects of dyad and task condition on IBS To confirm synchronicity between channels, we performed a series of one-sample t-tests on the task-related IBS for the 22 paired channels across the four conditions with FDR correction (NT-S dyad in the cooperation condition, NT-S dyad in the independent condition, ET-S dyad in the cooperation condition, ET-S dyad in the independent condition). The results showed that, for the ET-S dyads in the cooperation condition, Channel 6 (CH 6, t (17) = 3.48, p = 0.003, Cohen d = 0.84) and Channel 10 (CH 10, t (17) = 3.63, p = 0.002, Cohen d = 0.88) showed synchronicity between the teachers and students. However, only Channel 10 survived after the FDR correction (q = 0.05). In contrast, in the other three conditions, no synchronous channels were observed. A visualization of the task-related IBS across the four conditions and the synchronous channels is presented in Fig. 4. A two-way ANOVA was then performed on the task-related IBS at 5

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Fig. 3. Behavioral results. (A) The mean accuracy rate of the NT-S and ET-S dyads in the cooperation and independent condition. (B) The learning curve of the NT-S and ET-S dyads in the cooperation and independent condition. (C) The mean response time of the cooperation and independent condition of the teachers in different dyads. (D) The mean response time of the cooperation and independent condition of the students in different dyads. NT-S Dyad: novice teacher and student dyad; ETS Dyad: expert teacher and student dyad. Error bars indicate standard errors. *p < 0.05; **p < 0.01; ***p < 0.001.

Channel 10. A significant main effect of task condition on IBS at Channel 10 was found, F (1, 32) = 5.28, p = 0.028, ηp2 = 0.14, with higher IBS in the cooperation condition (M = 0.02, SD = 0.08) than in the independent condition (M = −0.03, SD = 0.09). No main effect of dyad type was detected (p = 0.094). Moreover, a significant interaction effect of dyad type and task condition on IBS at Channel 10 was observed, F (1, 32) = 4.35, p = 0.045, ηp2 = 0.12 (Fig. 5). The simple effect test showed that, in the cooperation condition, the task-related IBS of the ET-S dyads (M = 0.06, SE = 0.02) was significantly higher than that of the NT-S dyads (M = −0.02, SE = 0.02), p = 0.007.

average accuracy rate of the task was positively correlated with the task-related IBS at Channel 10 (r = 0.65, p = 0.003). Furthermore, the perspective-taking ability of the expert teacher was positively correlated with the task-related IBS at Channel 10 (r = 0.58, p = 0.012). Moreover, the result of the Pearson correlation analysis also showed that perspective-taking ability was positively associated with the average accuracy rate of the task (r = 0.68, p = 0.002). These results suggest a positive relationship between the increased IBS and cooperative behavior. The results of Pearson correlation analyses are presented in Table 2.

3.4. IBS changes at Channel 10 across different blocks

4. Discussion

The three-way ANOVA yielded a significant main effect of task condition, F (1, 32) = 5.28, p = 0.028, ηp2 = 0.14 and a significant interaction effect of task condition and dyad type on the IBS at Channel 10, F (1, 32) = 4.35, p = 0.045, ηp2 = 0.12 (Fig. 6). The simple effect test showed that, in the cooperation condition, the task-related IBS was significantly higher in the ET-S dyads (M = 0.06, SE = 0.02) than in the NT-S dyads (M = −0.02, SE = 0.02), p = 0.007. No other main effect or interaction effect was observed (all p ≥ 0.105).

In this study, by combining a real-time social interaction task and the fNIRS-based hyperscanning technique, we explored whether expert teachers do better in collaborating with students than novice teachers, and investigated the neural origins of such differences. The behavioral results demonstrate higher accuracy rates for ET-S dyads than NT-S dyads. Besides, significant task-related IBS was only found in the ET-S dyads at Channel 10 (located in the left dorsolateral PFC (DLPFC)) in the cooperation condition. The correlation analysis on the expert teachers showed that task-related IBS was positively correlated with the perspective-taking ability of the expert teacher as well as with the accuracy rate of the ET-S dyads. These findings suggest that expert teachers do better in cooperating with students. This study extends our knowledge on differences between expert and novice teachers in the teacher-student interaction field and sheds light on possible neural origins of such differences.

3.5. Relationship between IBS and behavior We conducted a series of Pearson correlation analyses to evaluate the relation between the task-related IBS at Channel 10 and behavior performance (e.g., accuracy rate) or subjective measurement (e.g., perspective taking) in the ET-S pairs. The results showed that the 6

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Fig. 4. Interpersonal brain synchronization (IBS) in different conditions after FDR correction. (A) One-sample t-test map of IBS in the prefrontal cortex (PFC) in the independent condition in ETS dyads. (B) One-sample t-test map of IBS in the prefrontal cortex (PFC) in the independent condition in NT-S dyads. (C) One-sample t-test map of IBS in the prefrontal cortex (PFC) in the cooperation condition in ET-S dyads. (D) Onesample t-test map of IBS in the prefrontal cortex (PFC) in the cooperation condition in NT-S dyads. NT-S: novice teacher and student dyad; ET-S: expert teacher and student dyad. The color bars denote the t values. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 5. The interpersonal brain synchronization (IBS) of ET-S dyads and NT-S dyads at channel 10 in different task condition. The interaction effect of task condition and dyad type on IBS at channel 10 indicated that expert teachers excel at social interaction. NT-S Dyad: novice teacher and student dyad; ET-S Dyad: expert teacher and student dyad. Error bars indicate standard errors. *p < 0.05; **p < 0.01***; p < 0.001.

Fig. 6. The changes of interpersonal brain synchronization (IBS) at channel 10 of NT-S dyad and ET-S dyad in different task condition across different blocks. NT-S Dyad: novice teacher and student dyad; ET-S Dyad: expert teacher and student dyad. Error bars indicate standard errors. *p < 0.05; **p < 0.01***; p < 0.001.

teaching interaction and the task employed in this study, we propose that the teacher’s capability of adopting another person’s perspective to infer a partner’s intentions and anticipate their actions may explain their better performance. The results of previous studies indicated that in the teaching domain, the perspective-taking ability of the teacher is essential for the effectiveness of the teaching (O'Keefe & Johnston, 1989), including inferring the children’s thought processes in the class (Miller, 2001) and the fluency of the discussion between the teacher and the student (Chadwick & Ralston, 2010). Hence, it is reasonable to assume that

4.1. Differences in collaboration task induced by higher perspective-taking The behavioral results show that the expert teachers cooperated better with their students than the novice teachers. This is in line with previous studies that compared teaching abilities (e.g., planning, reflection, classroom management) between expert and novice teachers (Farrell, 2013; Hall & Smith, 2006; Okas, van der Schaaf, & Krull, 2014; Shaw, 2017; Wolff et al., 2016), and also suggested that expert teachers do better when interacting with their students. Why do expert teachers cooperate better with students? Taking into account the specific 7

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the left DLPFC positively correlated with accuracy rates. The perspective-taking scores of the expert teachers were also positively correlated with task-related IBS. This may indicate that when expert teachers cooperate with their students, they automatically make more efforts to adopt the students’ perspective to infer their intentions, use more mental resources to anticipate the students’ actions, and pay more attention to the actual responses of the students. However, the neural and psychological mechanisms underlying these correlations need to be further explored in the future. We did not observe significant task-related IBS in the independent condition. This result is in line with the Cooperative Interaction Hypothesis (Lu & Hao, 2019), which suggests that IBS can be detected when individuals are engaged in a mutually cooperative interaction. It should be noted that we also did not observe significant task-related IBS in the NT-S dyads in the cooperation condition. This may have been caused by the low-level efficiency of the cooperation between the novice teacher and student. In the cooperation condition, the novice teachers, similar to the experts, might try to take their students’ perspective. They lack, however, the experience of teaching and interacting with students and, as a consequence, we did not detect IBS between the novice teachers and their paired students.

Table 2 Summary of Pearson’s correlations, means, and standard deviations for scores on the accuracy rate, IBS of Channel10, and perspective taking. Measure

1

2

3

M

SD

1 Accuracy Rate 2 IBS of CH 10 3 Perspective Taking M SD

– 0.65** 0.68** 67.68 19.81

– – 0.58* 0.06 0.07

– – – 3.78 0.56

– – – – –

– – – – –

Note. Pearson’s correlations for the ET-S dyads (n = 18 dyads) are presented in the table. Means (M) and standard deviations (SD) for the synchronization group are presented. IBS means the Interpersonal Brain Synchronization. * p < 0.05, ** p < 0.01. *** p < 0.001.

expert teachers possess higher perspective-taking abilities due to longer years of teaching experience. Moreover, Pearson correlation analyses showed that the ability of perspective-taking of the expert teachers was positively correlated with the accuracy rates of the ET-S dyads. Besides, the psychological measurements showed a marginally significant effect indicating that the expert teachers had higher perspective-taking abilities than the novice teachers. These results indicate that adopting another person’s perspective to infer a partner’s intentions and anticipate a partner’s actions may improve performance. Furthermore, a two-way ANOVA with dyad type as between groups factor and task condition as within factor showed higher accuracy rates from block 1 to block 6 for the ET-S dyads in the cooperation condition indicates that short-term practice cannot explain the better social interaction performance of the expert teachers in our experiment. There were also no significant differences between the different types of teachers and students regarding calculation abilities. Taking together the evidence from previous studies and the results of this study, we conclude that expert teachers show better performance in cooperating with students, and that their perspective-taking abilities may be the cause of their better behavioral performance.

4.3. Ecological task validity needs to be improved Previous studies demonstrated that face-to-face interaction promotes IBS (Gvirts & Perlmutter, 2019; Jiang et al., 2012; Tang et al., 2016). However, in this study, participants were asked to sit in front of a computer and a board was placed between computers to prevent the participants from seeing each other. This may have reduced the IBS between the partners. However, the reason we separated them with a wallboard was to confirm that IBS was induced by the cooperation instead of by sitting face-to-face or eye contact. In addition, in this study, verbal or physical communication was not allowed between the participants, which may have reduced the interaction between teachers and students. However, during the feedback phase of the task, the computer provided the participants with two types of information: the selection of their partner, which they can use to infer the partner’s next selection, and the task performance on the current trial (correct or not correct), which indicates whether they inferred their partner’s intention correctly. To some degree, the task in this study is indeed interactive. However, the setting (e.g., no verbal or physical communication and a board between the participants) reduced the ecological validity of the task. In the future, we need to keep a good balance between experiment control and ecological validity.

4.2. Left prefrontal IBS as neural marker of behavior performance The fNIRS results showed increased task-related IBS in the ET-S dyads in the cooperation condition, in accordance with previous studies reporting that increased task-related IBS is generally associated with mutual interaction between individuals, and that it can serve as a neural marker for different social interactions such as cooperation (Cheng et al., 2015; Cui et al., 2012; Miller et al., 2019), communication (Jiang et al., 2015; Xue et al., 2018), and competition (Liu, Saito, Lin, & Saito, 2017). In this study, since the participants in each dyad were unknown to each other prior to the study, the possibility of taskrelated IBS being influenced by prior familiarity can be ruled out. Moreover, based on the self-reported emotional states of the participants (before and after the task; all p > 0.071), the possibility of taskrelated IBS being influenced by emotional factors can also be ruled out. The increase in task-related IBS thus indicates that the ET-S dyads in the cooperation condition experienced effective social interaction. The increased task-related IBS in the ET-S dyads in the cooperation condition could be roughly mapped to the left PFC, more exactly to the DLPFC. The left DLPFC is known to be associated with integrating information about oneself and others (Raposo, Vicens, Clithero, Dobbins, & Huettel, 2010; Takeuchi, Mori, Suzukamo, & Izumi, 2017), perspective-taking and inferring the intentions of other people (Hillebrandt, Dumontheil, Blakemore, & Roiser, 2013), and theory of mind (Dixon et al., 2018; Nguyen, Fan, George, & Perreault, 2018). Previous studies also showed that the DLPFC plays important roles in cognitive control (Fregni et al., 2005; MacDonald, Cohen, Stenger, & Carter, 2000; Miller & Cohen, 2001) and executive functions (Decety, Jackson, Sommerville, Chaminade, & Meltzoff, 2004; Richeson et al., 2003). Here, we found that the increase in task-related IBS at Channel 10 in

4.4. Limitations Several additional limitations of this study should be noted. First, previous studies revealed that the temporal-parietal junction (TPJ) and the inferior parietal cortex are essential brain areas for social cognitive neuroscience and involved in mentalization, theory of mind, shared self-other representations, and the mutual social attention system (Decety & Sommerville, 2003; Gvirts & Perlmutter, 2019; Reddish, Fischer, & Bulbulia, 2013; Semin & Smith, 2008). The roles of these brain regions (e.g., PFC and TPJ) could be further examined with different cooperative tasks. Second, Baker et al. (2016) have reported the effect of sex composition on interpersonal neural correlates underlying collaborative interpersonal interaction. Although three-quarters of participants in the current study were female, there were a few male participants in different dyads. Hence, whether the results were affected by the sex composition of the sample needs to clarified in future studies. Third, the data were collected in a laboratory setting with a short cooperative interaction period (about 10 min), and it remains unclear whether they can be generalized to real contexts. Future studies need to explore the dynamic nature of teacher-student cooperation in more practical settings. 8

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In the present study, we found behavioral and neural differences between expert and novice teachers when they collaborate with students. Using a combined behavior and fNIRS-based hyperscanning approach, we found that in the cooperation condition, the expert teachers interacted with students better than the novice teachers, and the brain activity in the PFC was more strongly synchronized between the partners in ET-S dyads than between the partners in NT-S dyads. Besides, the increased task-related IBS was positively correlated with the perspective-taking abilities of the expert teachers and the behavior performance of the ET-S dyads. These results thus show that expert teachers show better performance in cooperating with students than novices, and that increased task-related INS in the PFC may be the neural substrates underlying cooperative performance.

situation. Nature Communications, 9(1), 2405. Decety, J., & Sommerville, J. A. (2003). Shared representations between self and other: A social cognitive neuroscience view. Trends in Cognitive Sciences, 7(12), 527–533. Decety, J., Jackson, P. L., Sommerville, J. A., Chaminade, T., & Meltzoff, A. N. (2004). The neural bases of cooperation and competition: An fMRI investigation. NeuroImage, 23(2), 744–751. Dixon, M. L., De La Vega, A., Mills, C., Andrews-Hanna, J., Spreng, R. N., Cole, M. W., & Christoff, K. (2018). Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks. Proceedings of the National Academy of Sciences, 115(7), E1598–E1607. Farrell, T. S. (2013). Reflecting on ESL teacher expertise: A case study. System, 41(4), 1070–1082. Fregni, F., Boggio, P. S., Nitsche, M., Bermpohl, F., Antal, A., Feredoes, E., ... PascualLeone, A. (2005). Anodal transcranial direct current stimulation of prefrontal cortex enhances working memory. Experimental Brain Research, 166(1), 23–30. Galinsky, A. D., Ku, G., & Wang, C. S. (2005). Perspective-taking and self-other overlap: Fostering social bonds and facilitating social coordination. Group Processes & Intergroup Relations, 8(2), 109–124. Grinsted, A., Moore, J. C., & Jevrejeva, S. (2004). Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes in Geophysics, 11(56), 561–566. Gvirts, H. Z., & Perlmutter, R. (2019). What guides us to neurally and behaviorally align with anyone specific? A neurobiological model based on fNIRS hyperscanning studies. The Neuroscientist, 1073858419861912. https://doi.org/10.1177/ 1073858419861912. Haesevoets, T., Reinders Folmer, C., Bostyn, D. H., & Van Hiel, A. (2018). Behavioural consistency within the prisoner's dilemma game: The role of personality and situation. European Journal of Personality, 32(4), 405–426. Hall, T. J., & Smith, M. A. (2006). Teacher Planning, Instruction and Reflection: What we know about teacher cognitive processes. Quest, 58(4), 424–442. Hari, R., & Kujala, M. V. (2009). Brain basis of human social interaction: From concepts to brain imaging. Physiological Reviews, 89(2), 453–479. Hillebrandt, H., Dumontheil, I., Blakemore, S. J., & Roiser, J. P. (2013). Dynamic causal modelling of effective connectivity during perspective taking in a communicative task. NeuroImage, 76, 116–124. Holper, L., Goldin, A. P., Shalóm, D. E., Battro, A. M., Wolf, M., & Sigman, M. (2013). The teaching and the learning brain: A cortical hemodynamic marker of teacher–student interactions in the socratic dialog. International Journal of Educational Research, 59, 1–10. Hoshi, Y. (2007). Functional near-infrared spectroscopy: Current status and future prospects. Journal of Biomedical Optics, 12(6), 062106. Huang, L., Yang, T., & Li, Z. (2003). Applicability of the positive and negative affect scale in Chinese. Chinese Mental Health Journal, 01, 54–56. Huang, X., Li, W., Sun, B., Chen, H., & Davis, M. H. (2012). The validation of the Interpersonal Reactivity Index for Chinese teachers from primary and middle schools. Journal of Psychoeducational Assessment, 30(2), 194–204. Jiang, J., Chen, C., Dai, B., Shi, G., & Lu, C. (2015). Leader emergence through interpersonal neural synchronization. Proceedings of the National Academy of Sciences, 112(14), 4274–4279. https://doi.org/10.1073/pnas.1422930112. Jiang, J., Dai, B., Peng, D., Zhu, C., Liu, L., & Lu, C. (2012). Neural synchronization during face-to-face communication. Journal of Neuroscience, 32(45), 16064–16069. Jin, X., Li, P., He, J., & Shen, M. (2017). Cooperation, but not competition, improves 4year-old children’s reasoning about others’ diverse desires. Journal of Experimental Child Psychology, 157, 81–94. Knoch, D., Schneider, F., Schunk, D., Hohmann, M., & Fehr, E. (2009). Disrupting the prefrontal cortex diminishes the human ability to build a good reputation. Proceedings of the National Academy of Sciences, 106(49), 20895–20899. Koike, T., Tanabe, H. C., & Sadato, N. (2014). Hyperscanning neuroimaging technique to reveal the “two-in-one” system in social interactions. Neuroscience Research, 90, 25–32. Lampert, M. (2003). Teaching problems and the problems of teaching. Yale University Press. Lee, H. J., Lee, J. K., & Kwon, Y. J. (2008). Pre-service biology teachers' brain activation patterns during the generation and application of commonness knowledge on biological phenomena: An fMRI study. The Korean Journal of Biological Education, 36(3), 251–265. Lee, J. K., Byeon, J. H., & Kwon, Y. J. (2009). Science teachers' brain activation and functional connectivity during scientific observation on the biological phenomena. Journal of The Korean Association For Science Education, 29(6), 730–740. Lian, R., Meng, Y., & Liao, M. (2003). Research on the relationship of the teaching strategies with achievement goals and personality characteristics of expert, experienced and novice teachers. Journal of Psychological Science, 26(1), 28–31. Liu, J., Zhang, R., Geng, B., Zhang, T., Yuan, D., Otani, S., & Li, X. (2019). Interplay between prior knowledge and communication mode on teaching effectiveness: Interpersonal neural synchronization as a neural marker. NeuroImage, 193, 93–102. Liu, T., Saito, G., Lin, C., & Saito, H. (2017). Inter-brain network underlying turn-based cooperation and competition: A hyperscanning study using near-infrared spectroscopy. Scientific reports, 7(1), 8684. Liu, W., Branigan, H. P., Zheng, L., Long, Y., Bai, X., Li, K., ... Lu, C. (2019). Shared neural representations of syntax during online dyadic communication. NeuroImage, 198, 63–72. Lu, C. M., Zhang, Y. J., Biswal, B. B., Zang, Y. F., Peng, D. L., & Zhu, C. Z. (2010). Use of fNIRS to assess resting state functional connectivity. Journal of Neuroscience Methods, 186(2), 242–249. Lu, K., & Hao, N. (2019). When do we fall in neural synchrony with others? Social Cognitive and Affective Neuroscience, 14(3), 253–261. Lu, S., Au, W. T., Jiang, F., Xie, X., & Yam, P. (2013). Cooperativeness and

Declaration of Competing Interest The authors declare no competing interests. Acknowledgments Thanks to the teachers and students who participated in this experiment. Also, thanks to Professor Chuasheng Chen for his suggestion about the detail of the experiment and the data analysis. Besides, thanks to Hui Zhou for her encouragement. Funding This work was supported by the National Natural Science Foundation of China (31871124). Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.bandc.2019.105513. References Achard, S., Salvador, R., Whitcher, B., Suckling, J., & Bullmore, E. D. (2006). A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. Journal of Neuroscience, 26(1), 63–72. Adolphs, R. (2014). Social attention and the ventromedial prefrontal cortex. Brain, 137(6), 1572–1574. Auerbach, A. J., Higgins, M., Brickman, P., & Andrews, T. C. (2018). Teacher knowledge for active-learning instruction: Expert–novice comparison reveals differences. CBE—Life Sciences Education, 17(1), ar12. Babiloni, F., & Astolfi, L. (2014). Social neuroscience and hyperscanning techniques: Past, present and future. Neuroscience & Biobehavioral Reviews, 44, 76–93. Baker, J. M., Liu, N., Cui, X., Vrticka, P., Saggar, M., Hosseini, S. H., & Reiss, A. L. (2016). Sex differences in neural and behavioral signatures of cooperation revealed by fNIRS hyperscanning. Scientific Reports, 6, 26492. Balliet, D., Li, N. P., Macfarlan, S. J., & Van Vugt, M. (2011). Sex differences in cooperation: A meta-analytic review of social dilemmas. Psychological Bulletin, 137(6), 881–909. Bevilacqua, D., Davidesco, I., Wan, L., Chaloner, K., Rowland, J., Ding, M., & Dikker, S. (2019). Brain-to-brain synchrony and learning outcomes vary by student–teacher dynamics: Evidence from a real-world classroom electroencephalography study. Journal of Cognitive Neuroscience, 31(3), 401–411. Carter, K., Cushing, K., Sabers, D., Stein, P., & Berliner, D. (1988). Expert-novice differences in perceiving and processing visual classroom information. Journal of Teacher Education, 39(3), 25–31. Chadwick, S., & Ralston, E. (2010). Perspective-taking in structured and unstructured online discussions. International Journal of Teaching and Learning in Higher Education, 22(1), 1–11. Chang, C., & Glover, G. H. (2010). Time–frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage, 50(1), 81–98. Cheng, X., Li, X., & Hu, Y. (2015). Synchronous brain activity during cooperative exchange depends on gender of partner: A fNIRS-based hyperscanning study. Human Brain Mapping, 36(6), 2039–2048. Cohen, D. K. (2011). Teaching and its predicaments. Harvard University Press. Cui, X., Bryant, D. M., & Reiss, A. L. (2012). Nirs-based hyperscanning reveals increased interpersonal coherence in superior frontal cortex during cooperation. NeuroImage, 59(3), 2430–2437. Dai, B., Chen, C., Long, Y., Zheng, L., Zhao, H., Bai, X., ... Ding, G. (2018). Neural mechanisms for selectively tuning in to the target speaker in a naturalistic noisy

9

Brain and Cognition 139 (2020) 105513

B. Sun, et al.

and explicit learning. Journal of Psychological Science, 37(4), 912–919. Singh, A. K., Okamoto, M., Dan, H., Jurcak, V., & Dan, I. (2005). Spatial registration of multichannel multi-subject fNIRS data to MNI space without MRI. NeuroImage, 27(4), 842–851. Stigler, J. W., & Miller, K. F. (2018a). Expertise and expert performance in teaching. In K. A. Ericsson, R. R. Hoffman, A. Kozbelt, & A. M. Williams (Eds.). The Cambridge handbook of expertise and expert performance (pp. 1169–1170). Cambridge: Cambridge University Press. Stigler, J. W., & Miller, K. F. (2018b). The differential influence of experience, practice, and deliberate practice on the development of superior individual performance of experts. In K. A. Ericsson, R. R. Hoffman, A. Kozbelt, & A. M. Williams (Eds.). The Cambridge handbook of expertise and expert performance (pp. 1980–2045). Cambridge: Cambridge University Press. Stolk, A., Noordzij, M. L., Verhagen, L., Volman, I., Schoffelen, J. M., Oostenveld, R., ... Toni, I. (2014). Cerebral coherence between communicators marks the emergence of meaning. Proceedings of the National Academy of Sciences, 111(51), 18183–18188. Takeuchi, N., Mori, T., Suzukamo, Y., & Izumi, S. I. (2017). Integration of teaching processes and learning assessment in the prefrontal cortex during a video game teaching–learning task. Frontiers in Psychology, 7, 2052. Takeuchi, N., Mori, T., Suzukamo, Y., & Izumi, S. I. (2019). Activity of prefrontal cortex in teachers and students during teaching of an insight problem. Mind, Brain, and Education, 13(3), 167–175. Tang, H., Mai, X., Wang, S., Zhu, C., Krueger, F., & Liu, C. (2016). Interpersonal brain synchronization in the right temporo-parietal junction during face-to-face economic exchange. Social Cognitive & Affective Neuroscience, 11(1), 23–32. Torrence, C., & Compo, G. P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79(1), 61–78. Tsuzuki, D., Jurcak, V., Singh, A. K., Okamoto, M., Watanabe, E., & Dan, I. (2007). Virtual spatial registration of stand-alone fNIRS data to MNI space. NeuroImage, 34(4), 1506–1518. Vanessa, R., Christian, G., Wolfgang, S., & Kerstin, K. (2018). Brain-to-brain synchrony in parent-child dyads and the relationship with emotion regulation revealed by fnirsbased hyperscanning. NeuroImage, 178, 493–502. Vartanian, O., Bouak, F., Caldwell, J. L., Cheung, B., Cupchik, G., Jobidon, M. E., ... Silvia, P. J. (2014). The effects of a single night of sleep deprivation on fluency and prefrontal cortex function during divergent thinking. Frontiers in Human Neuroscience, 8, 214. Wang, F. X., Shen, J. L., Tian, H. J., & Zhou, Z. K. (2010). The difference between expert and novice teachers in perceiving static classroom teaching scenes: An eye tracking study. Psychological Development and Education, 26(6), 612–617. Wang, F., Lu, Y., Duan, Z., & Zhou, Z. (2013). Teachers’ perception of students’ classroom behaviors: An eye movements study. Psychological Development and Education, 29(4), 391–399. Wolff, C. E., Jarodzka, H., Niek, V. D. B., & Boshuizen, H. P. A. (2016). Teacher vision: Expert and novice teachers’ perception of problematic classroom management scenes. Instructional Science, 44(3), 243–265. Wolff, C. E., Van, D. B. N., Jarodzka, H., & Boshuizen, H. P. A. (2015). Keeping an eye on learning: Differences between expert and novice teachers’ representations of classroom management events. Journal of Teacher Education, 66(1), 68–85. Wyatt, J., Delpy, D., Cope, M., Wray, S., & Reynolds, E. (1986). Quantification of cerebral oxygenation and haemodynamics in sick newborn infants by near infrared spectrophotometry. The Lancet, 328(8515), 1063–1066. Xie, X., Yu, Y., Chen, X., & Chen, X. P. (2006). The measurement of cooperative and competitive personality. Acta Psychologica Sinica, 38, 116–125. Xu, H., Kou, Y., & Zhong, N. (2012). The effect of empathy on cooperation, forgiveness, and “returning good for evil” in the prisoner's dilemma. Public Personnel Management, 41(5), 105–115. Xue, H., Lu, K., & Hao, N. (2018). Cooperation makes two less-creative individuals turn into a highly-creative pair. NeuroImage, 172, 527–537. Yao, S., Chen, H., Jiang, L., & Tam, W. C. C. (2007). Replication of factor structure of Wechsler Adult Intelligence Scale-III Chinese version in Chinese mainland non-clinical and schizophrenia samples. Psychiatry and Clinical Neurosciences, 61(4), 379–384. Zheng, L., Chen, C., Liu, W., Long, Y., & Lu, C. (2018). Enhancement of teaching outcome through neural prediction of the students' knowledge state. Human Brain Mapping, 39(2), 3046–3057. Zuo, X. N., Di Martino, A., Kelly, C., Shehzad, Z. E., Gee, D. G., Klein, D. F., ... Milham, M. P. (2010). The oscillating brain: Complex and reliable. NeuroImage, 49(2), 1432–1445.

competitiveness as two distinct constructs: Validating the cooperative and competitive personality scale in a social dilemma context. International Journal of Psychology, 48(6), 1135–1147. MacDonald, A. W., Cohen, J. D., Stenger, V. A., & Carter, C. S. (2000). Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science, 288(5472), 1835–1838. Meyer, H. (2004). Novice and expert teachers' conceptions of learners’ prior knowledge. Science Education, 88(6), 970–983. Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24(1), 167–202. Miller, J. G., Vrtička, P., Cui, X., Shrestha, S., Hosseini, S. H., Baker, J. M., & Reiss, A. L. (2019). Inter-brain synchrony in mother-child dyads during cooperation: An fNIRS hyperscanning study. Neuropsychologia, 124, 117–124. Miller, K. K. (2001). Teacher perspective-taking: Developmental and individual differences. Educational Research Quarterly, 25(2), 22–33. Montague, P. R., Berns, G. S., Cohen, J. D., McClure, S. M., Pagnoni, G., Dhamala, M., ... Fisher, R. E. (2002). Hyperscanning: Simultaneous fMRI during linked social interactions. NeuroImage, 16(4), 1159–1164. Mu, Y., Cerritos, C., & Khan, F. (2018). Neural mechanisms underlying interpersonal coordination: A review of hyperscanning research. Social and Personality Psychology Compass, 12(11), e12421. Naoyuki, O., Takehiro, M., Ken, Y., Miyuki, A., Minamoto, S. Y., & Mariko, O. (2015). How two brains make one synchronized mind in the inferior frontal cortex: Fnirsbased hyperscanning during cooperative singing. Frontiers in Psychology, 6, 18111. https://doi.org/10.3389/fpsyg.2015.01811. Nguyen, T., Fan, T., George, S. R., & Perreault, M. L. (2018). Disparate effects of Lithium and a GSK-3 inhibitor on neuronal oscillatory activity in prefrontal cortex and hippocampus. Frontiers in Aging Neuroscience, 9, 434. Nozawa, T., Sasaki, Y., Sakaki, K., Yokoyama, R., & Kawashima, R. (2016). Interpersonal frontopolar neural synchronization in group communication: An exploration toward fNIRS hyperscanning of natural interactions. NeuroImage, 133, 484–497. Okas, A., van der Schaaf, M., & Krull, E. (2014). Novice and experienced teachers’ views on professionalism. Trames, 18(4), 327–344. O'Keefe, P., & Johnston, M. (1989). Perspective taking and teacher effectiveness: A connecting thread through three developmental literatures. Journal of Teacher Education, 40(3), 20–26. Oldfield, R. C. (1971). The assessment and analysis of handedness: The Edinburgh inventory. Neuropsychologia, 9(1), 97–113. Osaka, N., Minamoto, T., Yaoi, K., Azuma, M., & Osaka, M. (2014). Neural synchronization during cooperated humming: A hyperscanning study using fNIRS. ProcediaSocial and Behavioral Sciences, 126, 241–243. Pan, Y., Novembre, G., Song, B., Li, X., & Hu, Y. (2018). Interpersonal synchronization of inferior frontal cortices tracks social interactive learning of a song. NeuroImage, 183, 280–290. Raposo, A., Vicens, L., Clithero, J. A., Dobbins, I. G., & Huettel, S. A. (2010). Contributions of frontopolar cortex to judgments about self, others and relations. Social Cognitive and Affective Neuroscience, 6(3), 260–269. Reddish, P., Fischer, R., & Bulbulia, J. (2013). Let’s dance together: Synchrony, shared intentionality and cooperation. PloS One, 8(8), e71182. Richeson, J. A., Baird, A. A., Gordon, H. L., Heatherton, T. F., Wyland, C. L., Trawalter, S., & Shelton, J. N. (2003). An fMRI investigation of the impact of interracial contact on executive function. Nature Neuroscience, 6(12), 1323–1328. Rumble, A. C., Van Lange, P. A., & Parks, C. D. (2009). The benefits of empathy: When empathy may sustain cooperation in social dilemmas. European Journal of Social Psychology, 40(5), 856–866. Sabers, D. S., Cushing, K. S., & Berliner, D. C. (1991). Differences among teachers in a task characterized by simultaneity, multidimensional, and immediacy. American Educational Research Journal, 28(1), 63–88. Sai, L., Zhou, X., Ding, X. P., Fu, G., & Sang, B. (2014). Detecting concealed information using functional near-infrared spectroscopy. Brain Topography, 27(5), 652–662. Scholkmann, F., Holper, L., Wolf, U., & Wolf, M. (2013). A new methodical approach in neuroscience: Assessing inter-personal brain coupling using functional near-infrared imaging (fniri) hyperscanning. Frontiers in Human Neuroscience, 7, 813. Semin, G. R., & Smith, E. R. (2008). Embodied grounding: Social, cognitive, affective, and neuroscientific approaches. Cambridge University Press. Shaw, R. D. (2017). I can hardly wait to see what I am going to do today: Lesson planning perspectives of experienced band teachers. Contributions to Music Education, 42, 129–152. Shi, W., Li, Y., & Li, X. (2014). Differences between expert and novice teachers in implicit

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