Relationship between feedback frequency and task performance: evidence on the mediating role of heart rate

This study looks into the impact of feedback frequency, a source of cognitive load, on heart rate (HR) and heart rate variability (HRV). The experiment was undertaken using a sample of 96 university students, wherein the feedback frequency was manipulated during an arithmetic task performance. During the task, participants were instructed to wear the E4 Empatica device, which is used to assess HR and HRV. The metrics used to measure HRV were the Standard Deviation of NN Intervals (SDNN) and Root Mean Square of Successive Differences (RMSSD). The study revealed notable disparities in the average HR values across the three feedback conditions. Optimal performance was achieved when HR was elevated (indicating the highest level of cognitive load) and feedback frequency was moderate. Further, HR mediated the association between feedback frequency and task performance. However, no significant impact of feedback frequency on HRV was found.

1 Introduction

Feedback is crucial in personal, professional, educational, and organizational development. Bakker (2015) suggested performance feedback as a job resource that assists individuals in maximizing the alignment between their needs, abilities, and job requirements. Feedback enhances the learning process and improves individual performance. This has been echoed by Park et al. (2019), Lam et al. (2011), and Saenz et al. (2019). Sparr and Sonnentag (2008) emphasized the importance of the feedback environment in promoting well-being in the workplace. For a review of the significance of supervisory feedback and its impact on the workplace, we refer to Dello Russo et al. (2022).

Feedback is commonly obtained from external sources but can also be obtained through self-evaluation, a less commonly studied approach (Mabe and West, 1982). To experience a sense of competence, an individual needs to possess the ability to assess their performance (Ilgen et al., 1979). However, placing exclusive reliance on self-evaluation may not consistently yield accurate information. Therefore, one must possess external manifestations to assess one’s ability (Ilgen et al., 1979). Feedback can be analyzed from different dimensions, including valence (Zhou, 1998), source (Brett and Atwater, 2001; Vancouver and Morrison, 1995), type (Sheppard, 1992), specificity (Earley et al., 1990; Goodman et al., 2004), and frequency (Chhokar and Wallin, 1984; Lam et al., 2011), the latter of which remains understudied.

One of the critical aspects of feedback is frequency, which plays a vital role in shaping performance outcomes (Lam et al., 2011). According to Ilgen et al. (1979), maintaining a balanced feedback frequency is crucial for enhancing the recipient’s sense of competence. Lam et al. (2011) recommend that a moderate frequency is preferable to optimize performance. However, some studies propose that enhancing feedback can enhance performance outcomes. For instance, the studies conducted by Bilodeau (1966) and Cook (1968) demonstrated that increased feedback on simulated tasks facilitated individuals to enhance their performance progressively. According to Anderson et al. (1971), people with the correct response knowledge demonstrated much more learning than those not provided with the same information. This highlights the need to identify the optimal feedback frequency for optimal performance outcomes.

Though prior literature has explored the relationship between feedback frequency and task performance, the underlying mechanisms driving this relationship remain unclear. To address this gap, we draw on cognitive load theory (CLT, Sweller, 2011), which suggests that feedback serves as a source of information that causes intrinsic and extraneous cognitive load. In support of this, Wang et al. (2019) found that detailed feedback resulted in lower extraneous cognitive load. Since the learners’ cognitive resources are limited, including feedback as a stimulus throughout the performance stage may significantly impact the individual’s cognitive load (Lam et al., 2011). According to Higgins et al. (1986) and Kaegi et al. (1999), self-evaluation can place a more significant burden on working memory due to the lack of feedback, leading to mental stress and a range of emotional responses. Thus, identifying the optimal feedback frequency is essential for managing cognitive load effectively and enhancing performance outcomes.

This study aims to answer the following research questions:

RQ1: What is the relationship between feedback frequency and cognitive load?

RQ2: What is the optimal feedback frequency to achieve optimal performance outcomes?

RQ3: How does cognitive load shape the relationship between feedback frequency and task performance?

The cognitive load causes changes in physiological factors such as heart rate (HR, Cranford et al., 2014; Mulder, 1992; Vanneste et al., 2021) and heart rate variability (HRV, Ayres et al., 2021; Solhjoo et al., 2019; Vanneste et al., 2021), and physiological arousal (Horvers et al., 2024). To objectively measure cognitive load resulting from feedback frequency, we employ HR and HRV as physiological indicators. These measures have been widely recognized as reliable markers of cognitive load (Ayres et al., 2021; Blitz et al., 1970; Finsen et al., 2001; Thayer et al., 2012).

2 Hypothesizing the links

HR and HRV have long been established as key psychophysiological indicators of cognitive load (refer review Ayres et al., 2021; Thayer et al., 2012). The relationship between cognitive load and HR is positive (Ayres et al., 2021), whereas it is inverse with HRV (Ayres et al., 2021; Solhjoo et al., 2019). Cognitive load can be induced in an individual from various sources. Feedback is one such source (Lam et al., 2011). Several studies have analyzed the relationship between various feedback dimensions and cognitive load. For example, feedback type (Zheng et al., 2021), feedback valence (Redifer et al., 2021), and haptic feedback (Costa et al., 2019). However, the impact of frequent feedback on cognitive load using physiological measures is yet to be explored. Since feedback is considered to be a significant source of cognitive load, we propose the following hypothesis:

Hypothesis 1: There exists a significant mean difference in HR across the three conditions of feedback frequency.

Hypothesis 2: A significant mean difference in HRV exists across the three conditions of feedback frequency.

Feedback frequency has long been recognized as a key factor in influencing task performance (Chhokar and Wallin, 1984; Horvers et al., 2024; Ilgen et al., 1979; Lam et al., 2011). Traditionally, in performance management, it has been believed that frequent feedback results in better performance. For example, Mertens et al. (2021) conducted a field investigation that found evidence supporting the notion that increased feedback produces superior outcomes. Park et al. (2019) found that providing feedback more frequently had a more substantial impact on performance. Moreover, Kang et al. (2004) discovered that participants who received feedback after each session exhibited improved performance outcomes in a simulated job task. However, Lam et al. (2011) challenged the “more is better” concept, suggesting that optimal performance is achieved with moderate feedback frequency. Despite these findings, the evidence on how feedback frequency affects task performance remains inconclusive. Hence, we propose the following hypothesis:

Hypothesis 3: A significant mean difference in task performance exists across the three conditions of feedback frequency.

Hypothesis 4: Optimal performance in task is achieved when frequent feedback is provided.

Further, several studies have presented empirical evidence that performance is negatively related to HR (Chen et al., 2023; Solhjoo et al., 2019) and positively related to HRV (Forte et al., 2022; Luque-Casado et al., 2013; Maier and Hare, 2017; Schaich et al., 2020). Muthukrishnan et al. (2017) conducted a study that suggested a possible correlation between reduced HRV and impaired cognitive functions. However, it remains a question whether HR and HRV play a role in shaping the relationship between feedback frequency and task performance. Thus, we propose the following hypothesis:

Hypothesis 5: HR mediates the relationship between feedback frequency and task performance.

Hypothesis 6: HRV mediates the relationship between feedback frequency and task performance.

3 Methods

3.1 Participants

The study was approved by the Institute’s ethics committee. A theoretical evaluation was conducted through an experimental study employing a sample of 96 master’s students from an Indian university (N = 96). An email detailing the study requirements was sent to the eligible students, and 102 students initially agreed to participate. However, six students were excluded from the study due to incomplete/bad recordings. All participants possessed basic knowledge of arithmetic computations. A between-subjects approach was utilized, wherein participants were assigned randomly to one of three feedback frequency conditions. To establish symmetry, the distribution of individuals in each condition was balanced. A monetary incentive for the best performer of INR 5,000 (approximately $60) was introduced to encourage individuals to remain engaged and motivated throughout the tasks.

3.2 Experimental task

Participants were given an arithmetic test with a time limit that included addition, subtraction, and multiplication. An arithmetic test was used as a measure of task performance since it provides an objective way to obtain performance scores and requires focused attention and working memory (Musso et al., 2019). Additionally, arithmetic tasks are highly influenced by feedback (Peters et al., 2017), making it a reliable measure of task performance. The test was designed to be completed within a time limit of around 6 min using LabVanced (Finger et al., 2017). In this experiment, a query was displayed on the monitor for 3 s, after which there was a five-second gap for the participant to respond.

3.3 Procedures

The experiment was conducted in a controlled laboratory setting with a serene ambiance and stable temperature to enhance the precision of the findings. Before accessing the laboratory, every participant gave informed consent to participate in the study. Subsequently, participants were instructed to provide their demographic details. Further, the participants were directed to wear an E4 Empatica wristband. A three-minute relaxation interval was designated for assessing the baseline levels of HR and HRV. Data recording began at that point in time. Following the initial baseline period, each participant was equipped with a computer system and detailed instructions to aid them in effortlessly completing a mental arithmetic exam.

3.4 Device

The E4 wristband (Schuurmans et al., 2020) was used to measure HR and HRV. We used the Standard Deviation of NN Intervals (SDNN, in milliseconds) and Root Mean Square of Successive Differences (RMSSD, in milliseconds) based on the inter-beat interval (IBI) data extracted from the device as a quantifiable marker for HRV. The IBI files were artifacted by the device.

HR is a typically reported measurement and is measured as the number of beats in a period of time, most often reported per minute (Charles and Nixon, 2019). SDNN is the standard deviation of all normal RR (NN) intervals (Kleiger et al., 2005). RMSSD is the square root of the squares of the successive differences between NN intervals, essentially the average change in the interval between beats (von Neumann et al., 1941).

3.5 Feedback frequency

Participants were randomly assigned to one of the three feedback frequency conditions using a computer-generated randomization process. The randomization was implemented before participants began the study to prevent bias. The three conditions of feedback frequency are: Condition 1, where they received immediate feedback after each question, i.e., 20 times (high feedback frequency); Condition 2, where they received feedback after every set of five questions (moderate feedback frequency, given 4 times during the test, with each interval lasting 40 s); and Condition 3, where they received no feedback throughout the test (self-evaluation). The feedback information included the scores for correct answers out of the total questions.

3.6 Task performance

The overall performance score obtained from the arithmetic exam was used to evaluate each participant’s performance on specific tasks. Each correctly answered question earned one point, while erroneous responses received zero points. Each condition required the presentation of 20 questions. Thus, the maximum achievable score was 20, whereas the minimum achievable score was 0. The final scores were then converted into percentage scores and used for the analysis.

3.7 Control variables

We controlled for gender (1 = male; 0 = female) because prior research suggests that gender can influence how people process the feedback information and respond to it (Geddes and Konrad, 2003). Given that skin temperature is associated with cognitive and emotional processing (Ayres et al., 2021), we also controlled skin temperature to ensure the robustness and validity of our findings.

4 Results

An a priori power analysis was conducted using G*Power version 3.1 to determine the minimum sample size required to respond to our research questions (Faul et al., 2009). Results indicated the required minimum sample size to achieve 80% power for detecting a medium effect, at a significance criterion of α = 0.05, was n = 55 for Linear Multiple Regression: Fixed Model, R² increase test.

The mean age of the participants was 25 years (SD = 3.44). Out of the total participants, 53 were males, and the remaining were females. As shown in Table 1, gender had a significant positive relationship with task performance indicating males performed better than females (r = 0.29, p < 0.01). Further, there exists a statistically significant negative association between HR and feedback frequency (r = −0.23, p < 0.05) and a positive association between HR and task performance (r = 0.25, p < 0.05). Further, HR had a statistically significant negative relation with both SDNN (r = −0.42, p < 0.001) and RMSSD (r = −0.44, p < 0.001). Finally, SDNN and RMSSD had a statistically significant positive association (r = 0.66, p < 0.001).

Table 1

Table 1. Descriptives and correlation coefficients.

Hypothesis 1 proposed that feedback frequency significantly affects HR across the three feedback scenarios. Using a one-way analysis of variance (ANOVA), we found that there was a significant impact of feedback frequency on the mean values of HR [F_(2,93) = 3.24, p < 0.05, η² = 0.07]. Thus, our hypothesis 1 is supported. Further, using the Bonferroni post-hoc test, we identified that the mean HR was highest under moderate feedback frequency and lowest when feedback was provided frequently. The same has been depicted in Figure 1.

Figure 1

Figure 1. The mediating effect of heart rate on the relationship between feedback frequency and task performance.

Second, we proposed that the mean values of HRV will be impacted by the three conditions of feedback (hypothesis 2). We used one-way ANOVA to assess this hypothesis. We found that neither SDNN [F_(2,93) = 1.87, p = 0.16, η² = 0.04], nor RMSSD [F_(2,93) = 2.23, p = 0.11, η² = 0.05] was significantly impacted by the feedback frequency. Hence, we reject our second hypothesis.

In hypothesis 3, we proposed whether feedback frequency had a significant impact on the task performance of the participants. Using one-way ANOVA, we found no significant difference in the mean performance scores across the three feedback conditions [F_(2,93) = 2.419, p = 0.09, η² = 0.05]. Thus, we reject our hypothesis 3. Further, to test our hypothesis 4, indicating that optimal performance is achieved when feedback is provided frequently, we used the Bonferroni post-hoc test. We found that the optimal performance was achieved under moderate feedback conditions. However, the mean difference was not significant. Thus, we reject our hypothesis 4. In Figure 1, we can see that the relationship between feedback frequency and task performance is inverted-U.

In hypothesis 5, we posited that HR would mediate the association between feedback frequency and task performance. To test the mediation, we used regression analysis wherein, at first, we introduced HR as a dependent variable. Given the categorical nature of feedback frequency, dummy coding was utilized (N−1). The results revealed that moderate feedback frequency significantly caused HR to elevate (Table 2, Model 1, β = 4.32, p < 0.05). Also, no feedback caused HR to elevate; however, there was no significant impact (Table 2, Model 1, β = 3.61, p = 0.06). Subsequently, we introduced task performance as a dependent variable and incorporated HR into the model as a predictor variable, along with feedback frequency and control variables. We found HR to influence task performance positively (Table 2, Model 3, β = 0.01, p < 0.05), supporting the mediation effect (hypothesis 5). Refer to Figure 1 for the mediation effects of HR.

Table 2

Table 2. Results of regression analysis for HR mediating the relationship between feedback frequency and task performance.

Further, hypothesis 6 proposed that the relationship between feedback frequency and task performance will also be mediated by HRV. The regression method used to test hypothesis 5 was employed to test this hypothesis. However, as shown in Table 3, neither SDNN nor RMSSD were significantly influenced by feedback frequency. Thus, no mediation effect of HRV was found, leading to the rejection of hypothesis 6.

Table 3

Table 3. Results of regression analysis for HRV (SDNN and RMSSD) mediating the relationship between feedback frequency and task performance.

5 Discussion

Prior research has shown that the presence of information or an increase in cognitive load elevates HR (Alshanskaia et al., 2024; Solhjoo et al., 2019). Consistent with this, our study found significant changes in HR due to changes in feedback frequency. Notably, the highest level of HR was observed under moderate feedback frequency, followed by the no feedback condition. Interestingly, optimal performance was also achieved under the moderate feedback frequency condition, aligning with the findings of Lam et al. (2011).

Furthermore, our study identified that feedback frequency causes fluctuation in HR, which, in turn, influences performance-highlighting HR’s mediating role in this relationship. However, our findings regarding the relationship between HR and performance differ from the previous literature. While prior studies suggest a negative association between HR and performance (Chen et al., 2023; Solhjoo et al., 2019), our results indicate a positive relationship. This positive association can be attributed to introducing feedback as a stimulus in this study.

Additionally, Kaegi et al. (1999) and Vrijkotte et al. (2000) suggested that the absence of feedback would lead to uncertainty and self-reflection, which can cause changes in emotional responses. Our results align with these studies, where heightened HR was observed in the case of self-evaluation (no feedback), indicating an increased cognitive load. When individuals do not receive feedback, they are ambiguous and uncertain about their performance, as indicated by Higgins et al. (1986), which leads to feelings of stress and tension, as described by Kim et al. (2018), further causing elevated HR (Solhjoo et al., 2019).

5.1 Theoretical contribution

This study contributes to performance management and psychophysiology by enhancing our understanding of the cognitive load caused by feedback frequency during task performance. Identifying the optimal feedback frequency as moderate, this study aligns with Lam et al. (2011). Additionally, it extends the literature on feedback frequency and performance by revealing that feedback frequency influences HR (an objective metric for cognitive load), which in turn significantly impacts performance.

Supporting CLT (Sweller, 2011), this study finds that cognitive load varies with feedback frequency, with the lowest observed under high feedback frequency. By employing an objective measure of cognitive load, this study overcomes the limitations of the previous studies (Mertens et al., 2021; Park et al., 2019; Lam et al., 2011) on feedback and cognitive load.

Though HRV is recognized as an objective measure of cognitive load (Ayres et al., 2021; Thayer et al., 2009), this study found no significant association between HRV and feedback frequency.

5.2 Practical implications

The findings of this study have several important implications for instructors or managers in educational and workplace settings, respectively. Given that moderate feedback frequency optimizes both HR and task performance, instructors or managers should consider implementing a balanced feedback mechanism to enhance individual development while minimizing cognitive load.

Additionally, the observed increase in HR under no feedback condition highlights the increased cognitive load caused by self-evaluation, suggesting that educational institutions and organizations should foster a feedback-rich culture to reduce uncertainty and improve individual well-being.

With the advent of AI-driven feedback systems, this study also highlights the need for researchers and designers in human-computer interaction to consider feedback frequency when creating environments that support cognitive efficiency and performance.

5.3 Limitations of the study and future research

Several limitations in the current study are important to note. The study uses an arithmetic test as a measure of task performance, and hence, the results may not be fully generalizable. The impact of information load under various feedback frequencies on HR and HRV might differ in the case of simulations, object/shape manipulations, or any physical activity due to varying cognitive demands on working memory. Although a relaxation period was provided before the experiment, participants were not screened for their psychological history, and its potential effects may not have been completely eliminated. Additionally, while the study aimed to induce cognitive load during task performance through an arithmetic test and feedback processing, the expertise of the problem solver or learner remains an important factor that cannot be overlooked. Moreover, the literature suggests various physiological factors besides HR and HRV work as an indicator of cognitive load (refer to Ayres et al., 2021). The same can be studied further by manipulating several other dimensions of feedback.

6 Conclusion

In summary, the research findings demonstrate that feedback frequency influences HR, with moderate feedback causing HR to elevate significantly, indicating an increased cognitive load. Moreover, the results demonstrated a positive relationship between HR and performance, with HR mediating the relationship between feedback frequency and task performance. Finally, HRV did not show a significant impact on task performance.

Data availability statement

The datasets presented in this article are not readily available because the data contain information that could compromise the privacy of research participants. Requests to access the datasets should be directed to cDIybXMwMDZAaWl0ai5hYy5pbg==.

Ethics statement

The studies involving humans were approved by the Ethics Committee of the Indian Institute of Technology, Jodhpur. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

MS: Conceptualization, Data curation, Formal analysis, Writing – original draft, Writing – review & editing. VG: Conceptualization, Funding acquisition, Methodology, Writing – original draft, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by the Indian Institute of Technology, Jodhpur, under the Seed Grant: I/SEED/GVRR/20200081.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

Alshanskaia, E. I., Zhozhikashvili, N. A., Polikanova, I. S., and Martynova, O. V. (2024). Heart rate response to cognitive load as a marker of depression and increased anxiety. Front. Psych. 15:1355846. doi: 10.3389/fpsyt.2024.1355846

PubMed Abstract | Crossref Full Text | Google Scholar

Anderson, R. C., Kulhavy, R. W., and Andre, T. (1971). Feedback procedures in programmed instruction. J. Educ. Psychol. 62, 148–156. doi: 10.1037/h0030766

Crossref Full Text | Google Scholar

Ayres, P., Lee, J. Y., Paas, F., and van Merriënboer, J. J. G. (2021). The validity of physiological measures to identify differences in intrinsic cognitive load. Front. Psychol. 12:538. doi: 10.3389/fpsyg.2021.702538

PubMed Abstract | Crossref Full Text | Google Scholar

Bakker, A. B. (2015). Towards a multilevel approach of employee well-being. Eur. J. Work Organ. Psy. 24, 839–843. doi: 10.1080/1359432X.2015.1071423

Crossref Full Text | Google Scholar

Bilodeau, I. M. (1966). “Information feedback” in Acquisition of skill. ed. E. A. Bilodeau (New York: Academic Press).

Google Scholar

Blitz, P. S., Hoogstraten, J., and Mulder, G. (1970). Mental load, heart rate and heart rate variability. Psychol. Forsch. 33, 277–288. doi: 10.1007/BF00424555

PubMed Abstract | Crossref Full Text | Google Scholar

Brett, J. F., and Atwater, L. E. (2001). 360° feedback: accuracy, reactions, and perceptions of usefulness. J. Appl. Psychol. 86, 930–942. doi: 10.1037/0021-9010.86.5.930

PubMed Abstract | Crossref Full Text | Google Scholar

Charles, R. L., and Nixon, J. (2019). Measuring mental workload using physiological measures: a systematic review. Appl. Ergon. 74, 221–232. doi: 10.1016/j.apergo.2018.08.028

PubMed Abstract | Crossref Full Text | Google Scholar

Chen, Y., Wang, Z., Tian, X., and Liu, W. (2023). Evaluation of cognitive performance in high temperature with heart rate: a pilot study. Build. Environ. 228:109801. doi: 10.1016/j.buildenv.2022.109801

Crossref Full Text | Google Scholar

Chhokar, J. S., and Wallin, J. A. (1984). A field study of the effect of feedback frequency on performance. J. Appl. Psychol. 69, 524–530. doi: 10.1037/0021-9010.69.3.524

Crossref Full Text | Google Scholar

Cook, D. M. (1968). The impact on managers of frequency feedback. Acad. Manag. J. 11, 263–277. doi: 10.2307/254752

Crossref Full Text | Google Scholar

Costa, J., Guimbretière, F., Jung, M. F., and Choudhury, T. (2019). Boostmeup: improving cognitive performance in the moment by unobtrusively regulating emotions with a smartwatch. Proc. ACM Interact. Mobile Wear Ubiquit. Technol. 3, 1–23. doi: 10.1145/3328911

Crossref Full Text | Google Scholar

Cranford, K. N., Tiettmeyer, J. M., Chuprinko, B. C., Jordan, S., and Grove, N. P. (2014). Measuring load on working memory: the use of heart rate as a means of measuring chemistry students’ cognitive load. J. Chem. Educ. 91, 641–647. doi: 10.1021/ed400576n

Crossref Full Text | Google Scholar

Dello Russo, S., Mirfakhar, A. S., and Miraglia, M. (2022). What is the narrative for practice? A review of recommendations on feedback and a guide to writing impactful practical implications. Appl. Psychol. 72, 1624–1652. doi: 10.1111/apps.12450

PubMed Abstract | Crossref Full Text | Google Scholar

Earley, P. C., Northcraft, G. B., Lee, C., and Lituchy, T. R. (1990). Impact of process and outcome feedback on the relation of goal setting to task performance. Acad. Manag. J. 33, 87–105. doi: 10.5465/256353

Crossref Full Text | Google Scholar

Faul, F., Erdfelder, E., Buchner, A., and Lang, A.-G. (2009). Statistical power analyses using G*power 3.1: tests for correlation and regression analyses. Behav. Res. Methods 41, 1149–1160. doi: 10.3758/BRM.41.4.1149

PubMed Abstract | Crossref Full Text | Google Scholar

Finger, H., Goeke, C., Diekamp, D., Standvoß, K., and König, P. (2017). LabVanced: a unified JavaScript framework for online studies. In: International conference on computational social science (Cologne).

Google Scholar

Finsen, L., Søgaard, K., Jensen, C., Borg, V., and Christensen, H. (2001). Muscle activity and cardiovascular response during computer-mouse work with and without memory demands. Ergonomics 44, 1312–1329. doi: 10.1080/00140130110099065

PubMed Abstract | Crossref Full Text | Google Scholar

Forte, G., Morelli, M., Grässler, B., and Casagrande, M. (2022). Decision making and heart rate variability: a systematic review. Appl. Cogn. Psychol. 36, 100–110. doi: 10.1002/acp.3901

Crossref Full Text | Google Scholar

Geddes, D., and Konrad, A. M. (2003). Demographic differences and reactions to performance feedback. Hum. Relat. 56, 1485–1513. doi: 10.1177/00187267035612003

Crossref Full Text | Google Scholar

Goodman, J. S., Wood, R. E., and Hendrickx, M. (2004). Feedback specificity, exploration, and learning. J. Appl. Psychol. 89, 248–262. doi: 10.1037/0021-9010.89.2.248

PubMed Abstract | Crossref Full Text | Google Scholar

Higgins, E. T., Strauman, T., and Klein, R. (1986). Standards and the process of self-evaluation. Handb. Motivat. Cogn. 1, 23–63.

Google Scholar

Horvers, A., Molenaar, I., Van Der West, H., Bosse, T., and Lazonder, A. W. (2024). Multimodal measurements enhance insights into emotional responses to immediate feedback. Front. Psychol. 14:386. doi: 10.3389/fpsyg.2023.1294386

PubMed Abstract | Crossref Full Text | Google Scholar

Ilgen, D. R., Fisher, C. D., and Taylor, M. S. (1979). Consequences of individual feedback on behavior in organizations. J. Appl. Psychol. 64, 349–371. doi: 10.1037/0021-9010.64.4.349

Crossref Full Text | Google Scholar

Kaegi, D. M., Halamek, L. P., Van Hare, G. F., Howard, S. K., and Dubin, A. M. (1999). Effect of mental stress on heart rate variability: validation of simulated operating and delivery room training modules. Pediatr. Res. 45:77A. doi: 10.1203/00006450-199904020-00463

Crossref Full Text | Google Scholar

Kang, M. G., Koh, S. B., Cha, B. S., Park, J. K., Woo, J. M., and Chang, S. J. (2004). Association between job stress on heart rate variability and metabolic syndrome in shipyard male workers. Yonsei Med. J. 45, 838–846. doi: 10.3349/ymj.2004.45.5.838

PubMed Abstract | Crossref Full Text | Google Scholar

Kim, H. G., Cheon, E. J., Bai, D. S., Lee, Y. H., and Koo, B. H. (2018). Stress and heart rate variability: a meta-analysis and review of the literature. Psychiatry Investig. 15, 235–245. doi: 10.30773/pi.2017.08.17

PubMed Abstract | Crossref Full Text | Google Scholar

Kleiger, R. E., Stein, P. K., and Bigger, J. T. Jr. (2005). Heart rate variability: measurement and clinical utility. Ann. Noninvasive Electrocardiol. 10, 88–101. doi: 10.1111/j.1542-474X.2005.10101.x

PubMed Abstract | Crossref Full Text | Google Scholar

Lam, C. F., DeRue, D. S., Karam, E. P., and Hollenbeck, J. R. (2011). The impact of feedback frequency on learning and task performance: challenging the “more is better” assumption. Organ. Behav. Hum. Decis. Process. 116, 217–228. doi: 10.1016/j.obhdp.2011.05.002

Crossref Full Text | Google Scholar

Luque-Casado, A., Zabala, M., Morales, E., Mateo-March, M., and Sanabria, D. (2013). Cognitive performance and heart rate variability: the influence of fitness level. PLoS One 8:e56935. doi: 10.1371/journal.pone.0056935

PubMed Abstract | Crossref Full Text | Google Scholar

Mabe, P. A., and West, S. G. (1982). Validity of self-evaluation of ability: a review and meta-analysis. J. Appl. Psychol. 67, 280–296. doi: 10.1037/0021-9010.67.3.280

Crossref Full Text | Google Scholar

Maier, S. U., and Hare, T. A. (2017). Higher heart-rate variability is associated with ventromedial prefrontal cortex activity and increased resistance to temptation in dietary self-control challenges. J. Neurosci. 37, 446–455. doi: 10.1523/JNEUROSCI.2815-16.2016

PubMed Abstract | Crossref Full Text | Google Scholar

Mertens, S., Schollaert, E., and Anseel, F. (2021). How much feedback do employees need? A field study of absolute feedback frequency reports and performance. Int. J. Sel. Assess. 29, 326–335. doi: 10.1111/ijsa.12352

Crossref Full Text | Google Scholar

Mulder, L. J. (1992). Measurement and analysis methods of heart rate and respiration for use in applied environments. Biol. Psychol. 34, 205–236. doi: 10.1016/0301-0511(92)90016-N

PubMed Abstract | Crossref Full Text | Google Scholar

Musso, M. F., Boekaerts, M., Segers, M., and Cascallar, E. C. (2019). Individual differences in basic cognitive processes and self-regulated learning: their interaction effects on math performance. Learn. Individ. Differ. 71, 58–70. doi: 10.1016/j.lindif.2019.03.003

Crossref Full Text | Google Scholar

Muthukrishnan, S. P., Gurja, J. P., and Sharma, R. (2017). Does heart rate variability predict human cognitive performance at higher memory loads. Indian J. Physiol. Pharmacol. 61, 14–22.

Google Scholar

Park, J. A., Johnson, D. A., Moon, K., and Lee, J. (2019). The interaction effects of frequency and specificity of feedback on work performance. J. Organ. Behav. Manag. 39, 164–178. doi: 10.1080/01608061.2019.1632242

PubMed Abstract | Crossref Full Text | Google Scholar

Peters, S., Van der Meulen, M., Zanolie, K., and Crone, E. A. (2017). Predicting reading and mathematics from neural activity for feedback learning. Dev. Psychol. 53, 149–159. doi: 10.1037/dev0000234

PubMed Abstract | Crossref Full Text | Google Scholar

Redifer, J. L., Bae, C. L., and Zhao, Q. (2021). Self-efficacy and performance feedback: impacts on cognitive load during creative thinking. Learn. Instr. 71:101395. doi: 10.1016/j.learninstruc.2020.101395

Crossref Full Text | Google Scholar

Saenz, G. D., Geraci, L., and Tirso, R. (2019). Improving metacognition: a comparison of interventions. Appl. Cogn. Psychol. 33, 918–929. doi: 10.1002/acp.3556

Crossref Full Text | Google Scholar

Schaich, C. L., Malaver, D., Chen, H., Shaltout, H. A., Al Hazzouri, Z., Herrington, D. M., et al. (2020). Association of heart rate variability with cognitive performance: the multi-ethnic study of atherosclerosis. J. Am. Heart Assoc. 9:e013827. doi: 10.1161/JAHA.119.013827

PubMed Abstract | Crossref Full Text | Google Scholar

Schuurmans, A. A., De Looff, P., Nijhof, K. S., Rosada, C., Scholte, R. H., Popma, A., et al. (2020). Validity of the Empatica E4 wristband to measure heart rate variability (HRV) parameters: a comparison to electrocardiography (ECG). J. Med. Syst. 44, 1–11. doi: 10.1007/s10916-020-01648-w

Crossref Full Text | Google Scholar

Sheppard, K. (1992). Two feedback types: do they make a difference? RELC J. 23, 103–110. doi: 10.1177/003368829202300107

Crossref Full Text | Google Scholar

Solhjoo, S., Haigney, M. C., McBee, E., van Merrienboer, J. J. G., Schuwirth, L., Artino, A. R., et al. (2019). Heart rate and heart rate variability correlate with clinical reasoning performance and self-reported measures of cognitive load. Sci. Rep. 9:14668. doi: 10.1038/s41598-019-50280-3

PubMed Abstract | Crossref Full Text | Google Scholar

Sparr, J. L., and Sonnentag, S. (2008). Feedback environment and well-being at work: the mediating role of personal control and feelings of helplessness. Eur. J. Work Organ. Psy. 17, 388–412. doi: 10.1080/13594320802077146

Crossref Full Text | Google Scholar

Sweller, J. (2011). “Cognitive load theory” in The psychology of learning and motivation: Cognition in education. eds. J. P. Mestre and B. H. Ross (Elsevier Academic Press), 37–76. doi: 10.1016/B978-0-12-387691-1.00002-8

Crossref Full Text | Google Scholar

Thayer, J. F., Åhs, F., Fredrikson, M., Sollers, J. J., and Wager, T. D. (2012). A meta-analysis of heart rate variability and neuroimaging studies: implications for heart rate variability as a marker of stress and health. Neurosci. Biobehav. Rev. 36, 747–756. doi: 10.1016/j.neubiorev.2011.11.009

PubMed Abstract | Crossref Full Text | Google Scholar

Thayer, J. F., Hansen, A. L., Saus-Rose, E., and Johnsen, B. H. (2009). Heart rate variability, prefrontal neural function, and cognitive performance: the Neurovisceral integration perspective on self-regulation, adaptation, and health. Ann. Behav. Med. 37, 141–153. doi: 10.1007/s12160-009-9101-z

PubMed Abstract | Crossref Full Text | Google Scholar

Vancouver, J. B., and Morrison, E. W. (1995). Feedback inquiry: the effect of source attributes and individual differences. Organ. Behav. Hum. Decis. Process. 62, 276–285. doi: 10.1006/obhd.1995.1050

Crossref Full Text | Google Scholar

Vanneste, P., Raes, A., Morton, J., Bombeke, K., Van Acker, B. B., Larmuseau, C., et al. (2021). Towards measuring cognitive load through multimodal physiological data. Cogn. Tech. Work 23, 567–585. doi: 10.1007/s10111-020-00641-0

Crossref Full Text | Google Scholar

von Neumann, J., Kent, R. H., Bellinson, H. R., and Hart, B. I. (1941). The mean square successive difference. Ann. Math. Stat. 12, 153–162. doi: 10.1214/aoms/1177731746

Crossref Full Text | Google Scholar

Vrijkotte, T. G. M., van Doornen, L. J. P., and de Geus, E. J. C. (2000). Effects of work stress on ambulatory blood pressure, heart rate, and heart rate variability. Hypertension 35, 880–886. doi: 10.1161/01.HYP.35.4.880

PubMed Abstract | Crossref Full Text | Google Scholar

Wang, Z., Gong, S. Y., Xu, S., and Hu, X. E. (2019). Elaborated feedback and learning: examining cognitive and motivational influences. Comput. Educ. 136, 130–140. doi: 10.1016/j.compedu.2019.04.003

Crossref Full Text | Google Scholar

Zheng, L., Zhong, L., and Niu, J. (2021). Effects of personalised feedback approach on knowledge building, emotions, co-regulated behavioural patterns and cognitive load in online collaborative learning. Assess. Eval. High. Educ. 47, 109–125. doi: 10.1080/02602938.2021.1883549

PubMed Abstract | Crossref Full Text | Google Scholar

Zhou, J. (1998). Feedback valence, feedback style, task autonomy, and achievement orientation: interactive effects on creative performance. J. Appl. Psychol. 83, 261–276. doi: 10.1037/0021-9010.83.2.261

Crossref Full Text | Google Scholar