In the field of patient safety, the World Health Organization (WHO) has identified misdiagnosis as one of the primary contributors to unsafe healthcare delivery (World Health Organization & World Alliance for Patient Safety Research Priority Setting Working Group, 2008). To make a diagnostic judgment, health professionals must rely on clinical evidence and, in addition, possess an adequate level of confidence in that judgment. The discrepancy between actual diagnostic accuracy and the perceived confidence in that accuracy represents a critical threat to patient safety (Meyer et al., 2013). When a professional feels unsure about a clinical diagnosis, they tend to seek additional information to confirm or rule out their hypothesis, thereby helping minimize diagnostic error. In this regard, the self-assessment that professionals make about their decisions largely determines the course of action they adopt in clinical practice, modulated by their level of confidence.

Therefore, the quality of care is closely related to healthcare professionals’ self-assessment capacity and to self-regulated learning (Murdoch-Eaton & Whittle, 2012; Sandars & Cleary, 2011). However, several studies have shown a weak correlation between self-assessments and standardized external assessments among health professionals (Davis et al., 2006; Eva et al., 2004), with coefficients ranging from 0.02 to 0.65 in the case of students (Gordon, 1991). This gap highlights the need to strengthen metacognition, understood as the ability to monitor, regulate, and evaluate one's own performance, in the training of future healthcare professionals (Honeycutt et al., 2021; Naug et al., 2011; Prokop, 2019; Tan et al., 2010).

One of the key components of clinical practice is the interpretation of medical images, which constitutes a fundamental basis for accurate diagnostic judgments. This skill requires rigorous and early training (Weimer et al., 2024), since the interpretation of these images entails reasoning with complex spatial information. In this context, spatial ability plays a central role, as these tasks rely on spatial mental representations and on processes such as mental rotation and visualization (Hegarty et al., 2007). Spatial ability is defined as the capacity to generate, retain, retrieve, and transform well-structured visual images (Lohman, 1996). Health science students are frequently required to reason about spatial concepts such as shape, relative position, and the connections between anatomical structures (Hegarty et al., 2007), which involves learning to mentally represent internal body structures that are not directly observable.

Consequently, given the relevance of spatial ability in the interpretation of medical images and in clinical decision-making, it is essential to consider it a central component of metacognition in health education. Analyzing how students judge their own spatial performance allows researchers to understand and optimize these processes from the early stages of university studies. Although numerous studies analyze confidence in responses, confidence does not equate to competence, and further research is needed to examine the degree of discrepancy between confidence judgments and actual performance in health sciences students.

Metacognition is defined as reflection on one's own thinking. In other words, it is the ability to plan, monitor, and evaluate learning and performance by regulating one's own cognitive processes (Flavell, 1979; Lai, 2011). In the clinical context, metacognition has been associated with greater patient safety, enhanced decision-making, and a lower incidence of diagnostic errors in clinical professionals (Kuiper & Pesut, 2004; Royce et al., 2019; Siqueira et al., 2020). Conversely, insufficient metacognitive skills can lead to overestimation of abilities or reduced self-monitoring and, consequently, to medical errors (Medina et al., 2017; Royce et al., 2019). For example, Pusic et al. (2015) reported that medical students rated their diagnostic judgments on an X-ray image as "definitely correct," although this was accurate in only 69% of cases.

Metacognitive knowledge and metacognitive regulation are the two components of metacognition, the latter involving monitoring and control processes (Nelson & Narens, 1994; Schraw & Dennison, 1994). Metacognitive monitoring refers to activities that involve reviewing and evaluating the quality of cognition, whereas metacognitive control refers to decisions made based on information from monitoring operations (for a review, see Fiedler et al., 2019; Nelson & Narens, 1990). Control processes determine the confidence threshold at which an action is initiated. For example, in the clinical context, a physician may proceed with treatment only when they have more than 90% confidence in a diagnosis (e.g., Djulbegovic et al., 2014; Pauker & Kassirer, 1980). Inaccurate monitoring deteriorates decision quality; thus, the effectiveness of control depends directly on monitoring accuracy (Ackerman & Thompson, 2017). In this context, confidence judgments (hereinafter, CJ) are among the most widely used measures of metacognitive monitoring (Fleming & Lau, 2014). CJs reflect an individual's belief in the accuracy of their decisions following a cognitive task. According to Nelson and Narens (1990), “a system that monitors itself (even if imperfectly) can use its own introspections as information to alter the behavior of the system” (p. 128). Thus, confidence levels help determine whether individuals feel sufficiently competent in a particular domain or whether further learning is required (Dautriche et al., 2021), guiding control decisions such as seeking help or checking for errors (Dunlosky & Hertzog, 1998; Dunlosky & Metcalfe, 2009; Efklides, 2011; Metcalfe, 2002, 2009; Nelson & Narens, 1990).

Empirically, monitoring accuracy is assessed by comparing subjective performance evaluations (e.g., CJs) with objective task performance, measuring the correspondence between the two. A recent meta-analysis (Jin et al., 2022) across 16 countries showed significant correlations between performance and mean confidence, suggesting that individuals who feel more confident tend to perform better. Judgment accuracy, defined as the relationship between objective performance and CJs, is considered a central indicator of monitoring quality (Kleitman & Moscrop, 2010). Monitoring accuracy can be assessed through absolute accuracy, relative accuracy, or the bias index (Burson et al., 2006; Nelson, 1996). Absolute accuracy is calculated as the difference between estimated performance scores (confidence judgments) and objective performance; values closer to zero indicate better calibration. The bias index is another form of absolute accuracy that measures the degree to which an individual has an excess or a deficiency of confidence when making a prediction. The bias index reflects the direction and degree of discrepancy between the judgment and performance (Schraw, 2009), with positive values indicating overconfidence and negative values indicating underconfidence. Thus, overconfident students show higher confidence judgments, while underconfident students show lower confidence judgments relative to objective performance on the task.

In educational contexts, students who consistently underestimate their performance (with low confidence) may lose motivation to learn, whereas those who overestimate their performance may be at a disadvantage in the long term, as this can hinder their motivation to learn new techniques (since they feel confident that they know everything). Numerous studies have shown that students tend to be inaccurate in judging their performance (Fitzsimmons & Thompson, 2024), tending to overestimate, that is, they assess their performance as higher than their actual performance on tests (Händel & Dresel, 2018; Kruger & Dunning, 1999). This phenomenon, known as overconfidence bias, varies depending on contextual and individual factors. Due to the nature of test administration, confidence ratings are sensitive to item difficulty. Thus, difficulty directly impacts the CJs assigned, so that with difficult items, participants tend to show overconfidence, and with easy items, a lack of confidence. This effect is called the easy-difficult effect (Juslin et al., 2000; Lichtenstein & Fischhoff, 1977). It is worth noting that item difficulty is closely related to participants' ability: lower-ability participants tend to be overconfident with moderately difficult items, whereas higher-ability participants tend to be well calibrated or even slightly underconfident (Morphew, 2021; Stankov et al., 2012). Other studies, however, indicate that low-performing participants were more accurate, overestimating less than those considered high-performing in a test on pedagogical knowledge (Florín & Grecu, 2019). Despite these discrepancies, the literature converges in pointing out that lower-performing students generally tend to overestimate their performance to a greater extent than those of average or high performance, a pattern also observed in disciplines related to health sciences and allied sciences (Bunce et al., 2023; Cale, 2023; Morphew, 2021).

Although these individual differences exist, confidence tends to manifest as a relatively stable characteristic across ability levels; thus, overconfidence is not limited to low performers. Stankov and Lee (2014) noted that this trend persists even among high-ability groups, suggesting that overconfidence is a widespread phenomenon. In the healthcare field, Cleary et al. (2019) found that medical students—in a sample of 157 participants—overestimated their performance on two tasks (medical history and physical examination) in 98% and 95% of cases, respectively, and their judgment accuracy remained moderately stable across tasks. Similar findings have been reported in other studies that show poor calibration in medicine (Benjamin et al., 2022; Berner & Graber, 2008; Meyer et al., 2013) as well as in education (Callender et al., 2016; Foster et al., 2017; Huff & Nietfeld, 2009), where a low correspondence between confidence and performance can lead to systematic diagnostic errors (Berner & Graber, 2008).

In addition to ability and difficulty, gender emerges as a relevant factor in metacognitive calibration. Several studies have shown that, in general knowledge tasks or university contexts, women tend to show lower confidence and less overestimation than men (de Bruin et al., 2017; Buratti et al., 2013), resulting in better calibration, reflected in lower bias scores (Pallier et al., 2002). In a numerical series task with a large sample of 6,544 participants (58% women), men showed higher confidence, with no differences in performance, and exhibited greater overconfidence biases, although with a small effect size. The meta-analysis by Rivers et al. (2020) supported these findings partially by analyzing six studies with 758 participants, where men were more confident and, in some cases, also more accurate. Even when controlling for performance, women reported approximately 7% less confidence than men. However, gender differences appear to be task-dependent: in mathematical problems, women have shown superior calibration (Bench et al., 2015). McMurran (2020) found that men were more confident but less calibrated, particularly on low-difficulty problems; as task complexity increased, women’s calibration improved and gender differences in confidence decreased. Overall, although men tend to appear more confident (Lundeberg & Mohan, 2009; Stankov & Lee, 2008), the evidence indicates that overconfidence decreases in both genders with practice and time (Hadwin & Webster, 2013).

This study had two primary objectives. The first was to analyze calibration and bias in a sectioning task, considering gender, spatial ability, and item difficulty. This task was selected because it requires spatial reasoning about object sections, an essential component of anatomical learning. Spatial ability was estimated using MR and visualization tests, which provide complementary measures of individuals’ capacity to mentally manipulate spatial information. We hypothesized that students with greater spatial ability would exhibit better calibration (scores closer to zero) and less overconfidence bias. Furthermore, calibration was expected to decrease and overconfidence to increase as item difficulty rose. Men were also expected to exhibit poorer calibration than women, with a greater overconfidence bias.

The second objective was to analyze performance across three spatial tests (sections, visualization, and mental rotation) as a function of confidence, task difficulty, and the proportion of items attempted. We expected performance to be higher among students with greater spatial ability and higher confidence, whereas increased difficulty would reduce performance. In line with previous research, men were expected to outperform women on the MR task. Additionally, we hypothesized that students who answered a greater number of items under time pressure would perform worse, reflecting a speed-accuracy trade-off. However, this negative effect would be attenuated in participants with greater spatial ability, compensating for the impact of responding quickly on performance.

Data was collected in a single, collective session at the beginning of the first academic year in a classroom specifically prepared for this purpose. The task order was counterbalanced across academic degrees, and the session lasted approximately two hours.

The task measures participants’ ability to mentally rotate representations of three-dimensional block figures. In each trial, a comparison figure was presented along with four response alternatives. Of these four figures, only two were rotated versions of the comparison figure, while the other two figures were distractors that do not match the comparison figure. Thus, each trial has two correct options and two incorrect options. Participants were instructed to select the two correct rotated versions. In total, the test consists of two parts of 12 items each and a completion time of 3 minutes for each part, with a 4-minute break between both parts. Three practice trials are included prior to the experimental trials. A trial was scored as correct when both correct options were selected. The maximum score was 24. Internal consistency was Cronbach's ⍺ = 0.833.

After each trial, participants were instructed to provide a confidence judgment (MR conf), rating their confidence in the accuracy of their responses on a scale from 0 (I am not at all sure) to 100 (I am very sure).

Each item presented a cube with faces labeled by letters and a flat net of the cube showing one labeled face and another marked with a question mark. Participants were required to choose from the 9 options the one that represents the face marked with a question mark. Item difficulty depended on the number of rotations needed to reach the solution, so that for the simplest difficulty, two turns had to be applied and for the most complex, five turns. Each difficulty consisted of 5 items. The test included 2 practice trials and 20 experimental trials with 9 answer choices, only one of which was correct. The time limit was 30 minutes. One point was awarded per correct answer, for a maximum score of 20. Internal consistency was Cronbach's ⍺ = 0.865.

As with the MRT, participants assigned their confidence level (VZ conf) after selecting each trial on a scale of 0 to 100.

The first objective of this study was to analyze monitoring measures in a spatial task that assesses the ability to infer two-dimensional sections of three-dimensional objects, a competency required for success in anatomical sciences in first-year health sciences students. While this type of metamemory task is not included in classic factor analyses of spatial ability (Carroll, 1993; Lohman, 1988), it is considered a key component of effective spatial thinking in STEM disciplines. Cross-sections in health sciences often involve visualizing the internal structure of three-dimensional objects, such as anatomical structures of the human body, which highlights the relevance and novelty of the present study for educational practice and for improving diagnostic skills in students in training. Given the relevance of spatial abilities in interpreting medical images and their role in clinical judgment, the second objective was to examine performance across different spatial tests considering individual and performance-related factors.

Regarding the first objective, the models provided several notable findings. First, and consistent with the hypotheses, students with greater spatial ability (as measured by the VZ test) were more likely to show perfect calibration (a calibration index of 0). However, when considering the full calibration continuum in the conditional model (scores from 0 to 1), calibration loss increased for students with lower MR scores as item difficulty increased. In other words, the loss of calibration associated with the increase in difficulty is modulated by MR ability. This finding suggests that participants with stronger MR ability exhibit less calibration decline as the task becomes more demanding, whereas those with lower MR ability show more substantial calibration loss under high difficulty. These results align with prior work showing a positive association between spatial ability and calibration (Clem et al., 2013; Garg et al., 2001), indicating that greater spatial performance supports not only higher objective performance, but also more accurate self-assessment of one's performance. More broadly, the findings agree with studies showing that students with greater ability demonstrate better calibration (Morphew, 2021; Stankov et al., 2012). To our knowledge, this study provides the first evidence of a differentiated contribution of spatial skills depending on the type of calibration required: the VZ emerged as a predictor of perfect calibration, whereas MR ability, in interaction with difficulty, plays a key role in miscalibration. Other studies have reported similar findings where different spatial skills contribute differently depending on task demands (Fernández-Méndez et al., 2020; Fernández-Méndez et al., 2024b; Frick, 2019). Regarding gender differences, the zero-inflation model showed that women were less likely than men to be perfectly calibrated, indicating that men more frequently fell into the “exact calibration” category. However, within the continuous component (among participants who were not perfectly calibrated), women showed lower miscalibration scores, meaning that their deviations from perfect calibration were smaller. Thus, men were more often perfectly calibrated, but when miscalibrated, women tended to be closer to accuracy. These findings partially overlap with studies reporting better calibration among women in mathematical tasks (Bench et al., 2015; McMurran, 2020). However, the relationship between gender and the presence of perfect calibration or the variation in miscalibration reported in this study constitutes a novel contribution. The results suggest that, while women tend to make calibration errors, these are less pronounced than those observed in men, while the total absence of error is more frequently associated with the male gender. Interestingly, dispersion effects suggested that women and participants with higher SEC performance were more consistent in their calibration, pointing to potentially greater stability in metacognitive monitoring—an avenue worth exploring in future work.

Turning to the bias index, the hurdle model’s zero component indicated a higher probability of zero bias among men, as well as among participants with high scores on the SEC task itself and on items of lower difficulty. In other words, women were about 50% less likely than men to display zero bias. Item difficulty exerted a strong influence, with each unit increase in difficulty reducing the likelihood of zero bias by 39%. Better SEC performance increased the odds, with each 10% performance increase raising the odds of having zero bias by 63%.

The continuous model, which considers trend scores from − 1 (absolute overconfidence) to + 1 (absolute underconfidence), excluding values of 0, confirmed a general pattern of overconfidence, as indicated by the negative intercept. This aligns with evidence showing that overconfidence is a widespread phenomenon across cognitive and judgment tasks (Stankov et al., 2014).

Regarding the observed effects, the interaction between the score on the SEC task and the difficulty of the item stands out. Performance on the task modulates the impact of difficulty on bias: higher performance leads to a lower tendency towards overconfidence as the difficulty of the item increases. Thus, although high-performing students showed overall milder overconfidence bias, this bias still increased as items became more challenging, but to a lesser extent than in low-performing students, suggesting a better ability to adjust their confidence judgments as task demands increase. These results confirm the hypotheses put forward and align with the so-called easy-hard effect (Juslin et al., 2000; Lichtenstein & Fischhoff, 1977), in addition to showing less bias in high-achieving students (Bunce et al., 2023; Cale, 2023; Morphew, 2021).

Regarding gender, the pattern resembled that observed for calibration. In the zero-bias component, being male was associated with a higher probability of showing no bias, while in the continuous model, being female was related to a lower degree of overconfidence. This indicates that, when bias occurs, women tend to exhibit it to a lesser degree than men. Overall, these findings confirm the initial hypothesis and align with previous literature documenting greater male overconfidence in spatial tasks (Ariel et al., 2018; Pallier et al., 2002) as well as lower confidence levels among women in university contexts (de Bruin et al., 2017; Buratti et al., 2013).

Regarding the second objective of the study, the results show that confidence is a predictor of performance in the three tests (MR, VZ, and SEC). Thus, students who reported higher confidence were also those who achieved the best results, confirming the initial hypothesis and providing evidence of the importance of this monitoring measure as a predictor of performance (Stankov, 2013). Furthermore, spatial ability, measured through the VZ test, emerged as a predictor of expected performance in both MR and SEC. However, its relevance was especially pronounced in tasks involving the visualization of sections and slices in different planes of the figures in the SEC task. This suggests that visualization ability may support students, more than the other abilities evaluated here when dealing with the spatial demands of anatomical tasks. This is consistent with the results of the study by Koh et al. (2023), highlighting that students with high spatial ability performed better in anatomy tests that required memorization and drawing of an anatomical structure of the heart. Since spatial skills are known to be trainable (Uttal et al., 2013), the present results support the idea that training prior to coursework may improve students’ ability to interpret anatomical images, thereby enhancing future clinical diagnostic performance. Conversely, students with low spatial abilities may struggle to infer and reason about two-dimensional planes, negatively impacting course performance and slowing the acquisition of skills necessary for ¡interpreting spatial images, as previous research on the importance of spatial ability for promoting learning in clinical tasks has already indicated (Cleam et al., 2013, Garg et al., 2001; Hoyek et al., 2009).

The analysis of difficulty in the tasks where it was operationalized (section task and visualization task) shows the expected results, where higher difficulty was associated with a lower probability of answering correctly. However, the interactions found indicate that difficulty interacts with both confidence and spatial ability to predict success in the SEC task. The first interaction showed that students with higher confidence experienced a sharper decline in accuracy as task difficulty increased. That is, confidence predicts accuracy on easier items, but becomes less informative as tasks become more demanding. The second relevant interaction revealed that high spatial visualization ability attenuates the negative effect of difficulty, where the probability of success decreases to a greater extent when the items become more complicated as spatial ability decreases. Thus, spatial ability appears to be a stronger predictor of success on more challenging items. These findings suggest that increasing confidence alone does not guarantee better results, particularly in complex and more challenging contexts. Educational interventions should therefore aim not only to enhance confidence, but to ensure that confidence aligns with actual performance, that is, to improve students’ calibration.

Regarding the percentage of unanswered items, the results indicate that a higher percentage of answered items predicts a lower accuracy in the MR task. The effect is modulated by confidence and the level of spatial ability: confidence becomes less predictive as the number of answered items increases. In other words, answering more items affects the outcome differently depending on the level of confidence. Students with high confidence experience a larger drop in accuracy when answering more items, whereas low-confidence students show a more modest decline, as their performance was already low even with few items answered. Thus, confidence is a strong predictor overall, although the feeling of confidence decreases and ceases to reflect actual accuracy as the number of items answered increases. These results support the idea that the time spent per item is a critical factor in time-constrained tasks, such as the MRT. In this type of test, a faster response time usually involves a trade-off between speed and accuracy, particularly in people with lower visualization ability. For instance, Liesefeld et al. (2015) showed that participants who modulated their speed based on item complexity committed fewer errors, while those who maintained high speed on complex items showed a substantial increase in errors in an MR task. Thus, slowing down and taking more time to respond to complex items or objects appears to be a more effective strategy for improving accuracy. The strategy of going faster or slower is also modulated by contextual factors, so that, with an appropriate incentive, speed can be reduced and performance improved, while, with an increase in time pressure, execution speed can be increased, compromising performance (Liesefeld et al., 2015). Likewise, strategies for solving MR items are not fixed; they can be modified through instructions (Fernández-Méndez et al., 2024b) or through manipulation of time pressure. Voyer and Saunders (2004) identified two response styles in MR tasks and found that responding to a greater number of items (risky style) was associated with better overall performance, while a tendency to not take risks (cautious style), with a greater number of unanswered items, was linked to lower scores. However, crucial distinction is that Voyer and Saunders analyzed total test performance, whereas the present study analyzes the probability of answering a given item correctly. These two perspectives are not mutually exclusive, but complementary, since the probability of answering more items will be associated with a greater probability of having more correct answers, even if the probability of getting each item right is reduced by spending less time solving it. Our results support the idea that speed in answering, with a greater number of items answered, entails a loss of precision per item, considering that the magnitude of this cost depends on individual spatial ability. This has important implications for healthcare training, where the main objective would be to increase accuracy in solving a particular diagnostic problem, regardless of the time cost.

Regarding gender, the tested models showed no differences in MR or SEC performance, suggesting that when other variables are held constant, gender differences only emerge in the visualization task in interaction with difficulty. Specifically, men were more likely to answer correctly on easy items, but the gender difference diminished as item difficulty increased. The non-replicability of the gender effect favoring men in the MR task is highlighted (Fernández-Méndez et al., 2024b; Zell et al., 2015). This finding may be due to the higher proportion of women in health science careers. It is possible that gender differences depend on domain-specific experience and may be influenced by self-efficacy and self-concept, which act as internal informational cues (Jansen et al., 2014; Pallier, 2003; Retelsdorf et al., 2015). Notably, the meta-analysis by Xue et al. (2024) found no gender differences in confidence, sensitivity, or metacognitive efficiency in perceptual decision-making tasks.

In conclusion, research on these factors is crucial because reliable monitoring is essential for making appropriate control decisions (Ackerman & Thompson, 2017), such as seeking help or verifying errors (Efklides, 2011; Metcalfe, 2002, 2009). Individuals with lower confidence, a high threshold that determines when to stop seeking further evidence, and strong cognitive abilities should achieve better results (Katyal & Fleming, 2024). This is particularly important in diagnostic judgments, where patient safety may be compromised. The present findings highlight the importance of considering both spatial ability and task difficulty when examining monitoring and accuracy in spatial tasks. For example, in visual diagnostic tasks, such as the interpretation of radiographs, monitoring is especially difficult, since there are few external cues to support self-monitoring, people have difficulty recalling where they looked (Kok et al., 2017; Võ et al., 2016), and radiologists often report using visualization strategies inconsistent with their actual behavior (Aizenman et al., 2017). This study also clarifies gender differences in metacognitive tasks and does not support the idea of greater accuracy among men in spatial reasoning tasks, finding greater accuracy in men in visualization, modulated by task difficulty among health science students.

Further research should focus not only on examining monitoring in different tasks linked to clinical practice, but also on analyzing how actions are initiated within error detection frameworks (Jackson et al., 2016). Monitoring accuracy is crucial, but the translation of awareness into corrective action is equally important. It is therefore essential to evaluate in detail the metacognitive processes of professionals and to analyze possible changes in these processes during a particular clinical activity (Cleary et al., 2015; Eva & Regehr, 2011; Pieschl, 2009; Sargeant et al., 2010). For example, Garbayo et al. (2023) developed a self-assessment metacognitive calibration instrument to help students learn to maintain a deliberate awareness of their reasoning process in simulated clinical environments. Students with high accuracy benefited most from unrestricted control over their learning process, while students with low accuracy benefited more from restricted control, which protected them from poor self-regulated decisions. Taking into account different levels of task calibration may allow educators to design interventions that improve monitoring while also acknowledging that students do not benefit equally from self-regulated learning strategies. Furthermore, it will allow researchers to evaluate how these metacognitive judgments impact control processes, such as study time or effort invested (Mihalca et al., 2017). The disparity in self-control behaviors among students (Thiede et al., 2010) highlights the need to strengthen monitoring accuracy early in health sciences training (de Bruin et al., 2017).

It is important to note that this study was conducted in the first weeks of the first year of different health science degrees, when students had no prior exposure to spatially intensive coursework or clinical practice. This ensured that spatial or metacognitive performance was not biased by previous training. It is likely that the students' monitoring capacity will vary as experience is acquired, and, although confidence measures may provide information about metacognitive processing (Kleitman & Stankov, 2007; Stankov et al., 2012), it is necessary to evaluate how accurate this measure is with respect to actual performance and how it evolves from the early years of training to professional clinical practice, since there is no evidence of when students improve their metacognition during professional health training (Siqueira et al., 2020). Further studies are needed to analyze the progression of monitoring longitudinally and in other real clinical scenarios where spatial ability may be relevant for image interpretation and clinical diagnosis.

The study presented has certain limitations that should be mentioned for its proper interpretation. The unequal proportion of women in health science degrees has resulted in a sample with a much larger number in the women's group than in the men's group. In addition, it would have been desirable to have a difficulty index associated with the MRT, to analyze this variable in a similar way to the SEC and VZ tests. Moreover, the MRT lacked item-specific difficulty indices, making it impossible to analyze difficulty in the same way as in SEC and VZ. Although difficulty in MR tasks is typically operationalized via angular disparity, the MRT contains four response options with different angular disparities, complicating the computation of item-level difficulty. Finally, it should be noted that there are other monitoring measures that have not been studied and that would add value to the present results, such as indices related to relative accuracy.