Are There Gender Differences in Executive Functions in Musicians and Non-Musicians?
Abstract
Abstract. Until now, better performance in executive functions (EF) in musicians compared to non-musicians has not been investigated in relation to possible gender differences. For that, it is the main goal of this study to investigate possible gender differences in executive functions. Sixty-three musicians and 64 non-musicians, 63 men and 64 women respectively, completed tests of (a) cognitive processing speed (ZVT), (b) working memory (2-Back Task), (c) inhibition (Flanker Task), and (d) cognitive flexibility (Wisconsin Card Sorting Test, WCST). Results showed a significantly better performance for the target accuracy in the working memory task for musicians compared to non-musicians but not in the other tasks of executive functions. Furthermore, women demonstrated a better performance than men for the target accuracy in the 2-Back Task. However, only cognitive processing speed predicted working memory performance but not the group affiliation or gender. This study revealed that gender differences in executive functions are less likely to appear also in a trained sub-group.
Over the last decades, there is an increasing interest in the effects of music on cognitive functions in children and adults (Schellenberg, 2004; Schellenberg & Weiss, 2013). It has been shown, that musically trained participants scored higher than untrained participants on an Intelligence Quotient (IQ) composite score as well as on the verbal and nonverbal subtest. For the nonverbal subtest and the composite score, the result holds true if gender, socioeconomic factors as well as a first language were held constant (Schellenberg, 2011). There has been speculation that although mental or perceptual processing speed is important, executive functions (EF) seem to be a mediating variable between musical training and general cognitive abilities (Schellenberg & Weiss, 2013). EF are a family of effortful top-down mental processes needed to control, monitor, and regulate cognitive and emotional processes (Diamond, 2013). There is a consensus that three core EF’s can be identified: updating and monitoring of working memory representations, inhibition of dominant or prepotent responses, and shifting between tasks or mental sets (Miyake et al., 2000). Working memory involves holding information in mind and mentally working with it. Inhibition is typically defined as the ability to keep your mind focused and to resist internal and external distractions. Shifting, or also named cognitive flexibility, is the ability to change flexibly between different mental sets, different task instructions, or the perspective spatially (Diamond, 2013).
Executive Functions in Musicians
In quasi-experimental studies, which investigate the effects of a long-lasting musical training in adults (compared to those who did not receive this training), musically trained adults scored higher than untrained adults in specific tests of EF. For example, in a study of Bialystok and DePape (2009) musicians showed a better performance in an auditory Stroop test, which measures inhibition compared to monolinguals and bilinguals on overall response speed. However, the musicians demonstrated only a smaller incongruent cost for conditions based on the pitch but not on the word. The relation of musical ability and EF was investigated in detail by Slevc et al. (2016). In their study, 48 participants had less than 2 years of formal musical training and another 48 participants had at least 5 years of formal training. The three different parts of EF were investigated with an auditory (auditory Stroop task) and a visual version (visual Simon arrow task) inhibition task, with an auditory pitch-back and a visual letter-back task for updating and an auditory and visual switching task for cognitive flexibility. Their results showed that individual differences in musical ability predict performance on working memory (updating) tasks (auditory and visual n-back tasks) but showed little relation to inhibition tasks (auditory and visual Stroop test) and cognitive flexibility (auditory and visual task switching). They did not analyze possible gender differences in their sample. On a neuroscientific basis, it has been shown that practicing music for a longer time increases connectivity among others in the prefrontal cortex, which is a relevant active brain area for EF (Zuk et al., 2014).
Besides those quasi-experimental studies, some experimental designs exist in which, for example, the training of music lessons is compared to other kinds of training and the effects on EF are examined, however, these studies are very rare. For example, if 4- to 6-year-olds were assigned computer-based music or visual art training, only the music group showed better performance on a go/no-go task (Moreno et al., 2011).
One might conclude that most of the studies on EF in musicians and non-musicians are quasi-experimental, none of them had investigated possible gender differences. There was one individual difference approach in musical training and EF, in which it has been shown that for example musical training only predicted the latent variable of working memory updating, but not the one of inhibition and shifting, even if the results were controlled for IQ, socioeconomic status, and handedness (Okada & Slevc, 2018).
Gender Differences in Executive Functions
Today it seems to be widely agreed upon that men and women are much more similar in most cognitive aspects than anticipated (Jäncke, 2018). However, some studies or meta-analyses resume gender differences, whereby the strongest difference in cognitive abilities is reported in the spatial task of mental rotation with 3-dimensional stimuli (Voyer et al., 1995). Again, even this result is questioned in other studies (e.g. Jansen-Osmann & Heil, 2007). Regarding gender differences in EF, the picture is quite diverse: For example, in visual-spatial working memory, there was an advantage of men over women except for memory for location (Voyer et al., 2017). Saylik et al. (2018) showed that the processing of working memory components may differentiate by gender with either men outperforming women or vice versa. Regarding inhibition ability, the possible gender difference might depend on the task used. When applying a flanker task in combination with a go/no-go task, it could be demonstrated that incompatible flankers impaired performance, and this impairment was more pronounced in women than in men (Stoet, 2010). Investigating cognitive flexibility, the Wisconsin Card Sorting Test (WCST) is often used. In this test, participants must sort cards with symbols differing in color, shape, and numbers and alter their approach when unannounced shifts in the sorting principle appear. Gender differences in this task have been reported from the work of Boone et al. (1993) in a group of older adults from 45 years on favoring women. Grissom and Reyes (2019) concluded that overall, there was only little support of significant gender differences in EF, although single studies found some evidence (see above).
Gender Differences in Musicians in Cognitive Tasks
In general, there has been little research concerning gender differences in the area of music and cognition. Those studies, which have investigated gender differences, have focused mainly on perceptual aspects, like, for example, pitch perception. One study examined the influence of background music while solving a cognitive flexibility task, the WCST (Feizpour et al., 2018). Their results showed that music had both adverse and beneficial effects on various behavioral measurements in the WCST, with some of them being different between men and women. Regarding the cognition of music, one might be interested in the investigation of the general pattern of a musical system or of idiosyncratic representations in music. Here, it has been demonstrated that women are better in the recognition of well-known and novel melodies with or without lyrics than men, an effect which holds true for musicians and non-musicians. The authors assume that declarative memory underlies knowledge about music and women have an advantage in declarative memory (Miles et al., 2016).
However, another interesting question is if the gender of musicians plays an important role while solving music independent cognitive tasks. For example, Pietsch and Jansen (2012) demonstrated that the gender difference favoring men in solving a mental rotation task disappears in musicians but not in students of pedagogy and sport science. One reason for this could have been that in this study female musicians showed a faster cognitive processing speed, which is related to mental rotation performance, than male musicians. Another possible explanation is that female musicians have a higher degree of androgynous characteristics and that they show some traits, which are more observable in males (Kemp, 1985).
Goal of the Study
It is the main goal of this study to investigate possible gender differences in EF in male and female musicians with a long-term deliberate practice (Platz et al., 2014) compared to non-musicians. According to Grissom and Reyes (2019), gender differences in EF are not expected in the group of non-musicians. Gender differences in musicians should be investigated, and if they exist it must be examined whether they relate to a different cognitive processing speed like in the study of Pietsch and Jansen (2012).
Methods
Participants
Sixty-three musicians (31 men, Mage = 22.58, SD = 2.95 and 32 women, Mage = 21.59, SD = 2.27) and 64 non-musicians (32 men, Mage = 23.23, SD = 3.44 and 32 women, Mage = 22.16, SD = 1.74) participated in the current study. With a medium effect size f = 0.25, an α-level of p = .05 and a power of 1 − β = .80 a power analysis with G*power (Faul et al., 2007) for the two-factorial analysis of variance (ANOVA) resulted in n = 128 to detect the main effects and possible interaction between both factors, 32 participants in each group. Participants were recruited by personal contact at the university. The musicians played their main instrument for more than 9 years (M = 14.38, SD = 3.10) and practiced more than 6 hr per week (M = 12.40, SD = 7.48). Thirty-one (12 men and 19 women) of them played piano, 16 played wind instruments (6 men and 10 women), 4 students played violins (2 men and 2 women), 2 students played cello (1 man and 1 woman), 6 men played guitars and 4 men drums. The participants of the control group did not play any instrument. All participants gave their written informed consent and data were processed anonymously. The experiment was conducted according to the guidelines of the declaration of Helsinki.
Material
Demographic Questionnaire
Demographic data of the participants concerning gender, age, and time spent practicing their instrument was recorded with a self-generated questionnaire.
Cognitive Processing Speed
The “Zahlenverbindungstest” (ZVT; Oswald, 2016) measures cognitive processing speed. Participants were asked to connect 90 scrambled presented numbers on a sheet of paper in ascending order as fast as possible. In a single test situation, participants had to complete four sheets. The needed time to solve each sheet was measured in seconds and the meantime of the four sheets was calculated. The internal consistency and the 6-month test-retest reliability are about .90–.95. The correlation between processing speed, the number connection test, and the standard IQ test varies between r = .60–.80 (Vernon, 1993). Cronbach’s α in this study was .94.
Executive Functions
The three different subcomponents of EFs, namely updating, inhibition, and cognitive flexibility, were investigated and assessed using the 2-Back Task, the Flanker Task, and the WCST. All three tasks, based on the experimental control program Presentation (version 19.0; Neurobehavioral Systems), were run on a 15″ laptop located approximately 40 cm in front of the participants.
2-Back Task
A 2-Back Task is a specific form of the n-Back Task for the measurement of working memory and working memory capacity (Kirchner, 1958), also known as updating. Participants were presented a sequence of letters. They had to indicate with a right mouse click when the current letter matches the letter two letters before. If there were no matches, no reaction was required. The letter in each task is presented for 500 ms. Regardless of the reaction time, the next letter appears after 2,500 ms. In total, there was one practice block with 10 trials and three blocks of 50 trials (10 targets and 40 distractors each). The test lasted around 10 min. Reaction time and accuracy for the target items were measured.
Flanker Task
The Flanker Task (Eriksen & Eriksen, 1974) measures inhibition, which is a core component of EF. The test aims to analyze how well participants are able not to react to irrelevant stimuli. Participants were shown pictures of letters on a 15″ laptop monitor. The letter in the middle was flanked by three other letters on each side. Participants had to press the left mouse key if the letter in the middle was an H or a K, and the right mouse button if the letter was an S or a C. The flanking letters were H or K (congruent if the middle letter was H or K, incongruent if the middle letter was S or C), S or C (congruent if the middle letter was S or C, incongruent if the middle letter was H or K), and A or P (neutral condition). According to this combination, there were 24 different tasks, eight in each condition. One task was shown and presented until the response. After 500 ms of the response the next task was presented. There were 10 practice trials with feedback. In total, there were three blocks of 32 trials (96 trials in total). Within each block the conditions were randomized. There were two short self-paced pauses between the three blocks. The test lasted around 10 min and participants could make short breaks between the blocks. Reaction times and accuracy in the three different conditions were measured; the maximum accuracy rate was 32 for each condition.
Wisconsin Card Sorting Test
According to Feizpour et al. (2018), the WCST measures cognitive flexibility and shifting (Dehaene & Changeux, 1991). Within this test, several cards with symbols of different colors and shapes were presented to the participants and they were asked to match the cards according to a specific rule, which they had to figure out using the computer’s feedback (e.g., matching by color or shape). There was one block with five practice trials and 64 experimental trials. Stimuli were presented until the participant reacted, then the feedback was given and after 1,500 ms the next trial appeared. The participants were told whether a particular match was right or wrong. The test lasted around 15 min. The number of corrects sorts, as well as the perseverative errors, were measured.
Procedure
The tests were conducted in a laboratory of the University of Regensburg. Participants had to complete the tests in a single session in the following order: Demographic questionnaire, ZVT, 2-Back Task, Flanker Task, and WCST. Each test session lasted around 45 min.
Statistical Analysis
For the statistical analysis, SPSS 26 was used.
First, the effects of Group and Gender on cognitive processing speed and EF were investigated: An ANOVA with Group (musicians vs. non-musicians) and Gender (men vs. women) as independent between-subject factors was calculated for each outcome measure of the tests. The dependent variables thus were: the completion time for the ZVT, reaction time and accuracy for the 2-Back-Task, reaction time and accuracy for the Flanker Task, and correct sorts and perseverative errors for the WCST. Additionally for the Flanker Task, the within-subject factor Condition (congruent, incongruent, and neutral) was included as an independent variable.
Second, the relevance of cognitive processing speed on EF was investigated in more detail: For each task and measurement, a regression (Method: Enter) with the predictor’s Group, Gender, and cognitive processing speed as well as the interaction between Group × Cognitive Processing Speed and Gender × Cognitive Processing Speed was calculated.
Results
The Effects of Group and Gender on Executive Functions
Cognitive Processing Speed: ZVT
Regarding the dependent variable ZVT, which measured cognitive processing speed, there was one significant main effect for the factor Gender, F(1, 123) = 14.60, p < .001, ηp2 = .106 but not for the factor Group, F(1, 123) = 1.18, p = .279, ηp2 = .010 nor a significant interaction between both factors, F(1, 123) = 0.102, p = .750, ηp2 = .001. Women had lower values (M = 50.73, SD = 8.44) than men (M = 57.13, SD = 10.23), which means that they were faster in completing the task.
Working Memory: 2-Back Task
Concerning the target accuracy, the effects of Group, F(1, 123) = 4.84, p = .030, ηp2 = .038 as well as the one of Gender, F(1, 123) = 3.93, p = .050, ηp2 = .031 but not the interaction between both factors, F(1, 123) = 0.18, p = .676, ηp2 = .001 were significant. Musicians (M = 89.74%, SD = 8.21) had a higher accuracy than non-musicians (M = 85.52%, SD = 12.82), women (M = 89.49%, SD = 8.49) had a better performance than men (M = 85.70%, SD = 12.77) (Figure 1).
Regarding the target reaction time, there were neither main effects of Group, F(1, 123) = 0.138, p = .711, ηp2 = .001 nor of Gender, F(1, 123) = 0.48, p = .827, ηp2 < .001 and no interaction between both factors, F(1, 123) = 0.023, p = .880, ηp2 < .001.
Regarding a possible speed-accuracy trade off, there was no correlation between the reaction time and accuracy, r(127) = −.031, p = .732 and neither for the musicians, r(63) = .028, p = .828 and non-musicians, r(64) = −.080, p = .531.
Inhibition: Flanker Task
Regarding the accuracy, there was only a main effect of Condition, F(2, 246) = 7.53, p = .001, ηp2 = .058, whereas all other main effects and interactions were not significant (all ps > .222). The accuracy was lower in the incongruent condition (M = 30.70, SD = 1.39) compared to the neutral condition (M = 31.13, SD = 1.13), t(126) = −3.554, p = .001, and the congruent condition (M = 31.17, SD = 1.09), t(126) = 3.287, p = .001. There was no difference between the congruent and neutral condition, t(126) = −0.284, p = .777.
Concerning the reaction time, there was only a main effect of Condition, F(2, 246) = 17.90, p < .001, ηp2 = .127, whereas all other main effects and interactions were not significant (all ps > .326). The reaction time was higher in the incongruent condition (M = 558.10, SD = 106.65) compared to the congruent condition (M = 530.44, SD = 96.20), t(126) = 5.972, p < .001. There was no difference between the incongruent and neutral condition (M = 553.10, SD = 105.30), t(126) = 1.013, p = .313, but between the congruent and neutral condition, t(126) = 4.365, p < .001).
Cognitive Flexibility: Wisconsin Card Sorting Test
Regarding the dependent variable correct sorts, there were neither significant main effects for the factor Group, F(1, 122) = 0.307, p = .581, ηp2 = .002 and for the factor Gender, F(1, 123) = 2.291 p = .133, ηp2 = .018 nor a significant interaction between both factors, F(1, 123) = 2.148, p = .145, ηp2 = .017.
For the dependent variable perseverative errors neither significant main effects for the factor Group, F(1, 123) = 0.120, p = .730, ηp2 = .001 and for the factor Gender, F(1, 123) = 0.784, p = .378, ηp2 = .006 nor a significant interaction between both factors, F(1, 123) = 0.563, p = .454, ηp2 = .005 could be detected.
Table 1 shows an overview of the results (p is given, p < .05) for the main effects of Group and Gender, and possible interactions. Only for the 2-Back Task, the main effects of Group and Gender were significant.
The Relevance of Cognitive Processing Speed on Executive Functions
Working Memory: 2-Back-Task
The regression showed that 18.7% of the target accuracy was predicted by cognitive processing speed, F(5, 121) = 5.583, p < .001 (Table 2). Only the cognitive processing speed predicted target accuracy. If the participants needed more time to complete the ZVT, the target accuracy was lower (r = −.311, p < .001).
The regression showed that 15.4% of the reaction time was predicted by cognitive processing speed, F(5, 121) = 4.408, p < .001 (Table 3). Only the cognitive processing speed predicted reaction time. If the participants needed more time to complete the ZVT, the reaction time was higher (r = .366, p < .001).
Inhibition: Flanker Task
For the Flanker Task the flanker effect (incongruent-congruent) was calculated for accuracy and reaction time. The regression showed that the flanker effect on accuracy could not be predicted by the factors included, F(5, 121) = 0.923, p = .469. Furthermore, the analysis for the flanker effect on reaction time could not be predicted by the factors included, F(5, 121) = 0.784, p = .563.
Cognitive Flexibility: Wisconsin Card Sorting Test
The two regression conducted for the correct sorts as well as the perseverative errors showed, that neither the correct sorts, F(5, 121) = 1.135, p = .346 nor the perseverative errors, F(5, 121) = 0.529, p = .754 could be predicted by the factors included.
Discussion
Our results are straightforward: There was a significantly better performance for the target accuracy in the working memory task for musicians compared to non-musicians and for women compared to men. However, when accounting for cognitive processing speed this target accuracy was only significantly predicted by the cognitive processing speed and neither by gender nor group anymore. Men and women, and musicians and non-musicians did not differ in their performance in the inhibition task and the cognitive flexibility task.
Executive Functions in Musicians and Non-Musicians
A significant difference in the cognitive performance of musicians and non-musicians was only visible in the working memory measurement. In this study, the working memory capacity was measured with the 2-back task, demonstrating better performance at least in the accurate measurement for musicians compared to non-musicians. This is in line with a study of Gagnon and Nicoladis (2020) who demonstrate a better memory capacity measured in visual and motor memory. However, they did not find any differences between musicians and non-musicians in verbal memory tasks (forward and backward digit span test and two other tests of motor working memory). The result is also in line with a meta-analysis of Talamini et al. (2017) who showed among others a moderate effect size of the difference between musicians and non-musicians in working memory tasks. Thereby the type of stimuli presented explained only a small part of the variability across studies. The musicians’ advantage could be shown with verbal stimuli (also letters which were read as in this study). However, they did not investigate gender as a possible moderator of the results.
There were no differences in the inhibition and shifting performance between musicians and non-musicians, which is in contrast, for example, to the study of Meyer et al. (2020), who measured executive function with the dimensional change card sort test, which is like the WCST used here. They found better performance in the flanker test as well as in the processing speed (measured with the NIH TB pattern comparison process speed) whereas we could not find any difference between musicians and non-musicians in processing speed measured with the trail-making test. The flanker tests in the study of Meyer et al. (2020) used arrows, while we used letters in our study. Even though our results contradict the results of Meyer et al. (2020) they are in line with the study of Slevc et al. (2016). In their study, individual differences in musical ability predicted performance on working memory (updating) tasks (auditory and visual n-back tasks) but not in inhibition and cognitive flexibility. However, in their study, they also investigated musical ability by the musical ear test, which correlates to the years of musical training. To conclude, the “better” performance of musicians compared to non-musicians must be taken with caution. Studies differ due to the measurement of EF, the years of practice, the instrument played by the musicians and the possible control variables such as intelligence or socioeconomic status. Also, our study demonstrated, that not the group affiliation but the cognitive processing speed predicted working memory performance.
Gender Difference in Executive Functions
Women, musicians as well as non-musicians, showed better working memory capacity compared to men. However, as mentioned before, only cognitive processing speed predicted the working memory performance but not the gender. There were no gender differences in the inhibition and cognitive flexibility task, which is in line with the assumption of Grissom and Reyes (2019). The result also confirms the assumption of Jäncke (2018) that women and men are more similar in cognitive aspects than may be assumed.
Gender differences that only appear in musicians but not in non-musicians could not be shown in EF. Whereas for mental rotation performance the facilitating effect for female musicians was demonstrated (Pietsch & Jansen, 2012), this was not evident for EF. One reason for this might be that a possible gender difference in EF is much less explicit than in a cognitive domain like mental rotation where gender differences favoring men are quite common.
Limitations and Conclusion
The study is limited by the quasi-experimental design, allowing no causal conclusions and the fact that results could be influenced by other factors such as socioeconomic status or begin of musical training in young childhood. However, age, cognitive processing speed, and gender distribution were similar, SES was not obtained since the study of Slevc et al. (2016) excluded the SES as a relevant factor. The measurements were chosen due to other studies, however, working memory can be assessed regarding different subcomponents in a more differentiated way. Also, the WCST is used to measure cognitive flexibility, which is in line with recent other studies in the research on music cognition (e.g., Feizpour et al., 2018), however, also other shifting paradigms could be used in further studies.
To conclude, this study has demonstrated that (a) gender differences in EF are less likely to appear also in a trained sub-group, and (b) musicians might only have better working memory but not per se better EF. However, better working memory is related to cognitive processing speed. For this, the enhancing effect of music training on EF must be investigated more precisely as it is quite recently suggested for the possible benefit of sports activity on EF (Diamond & Ling, 2019).
References
(2009). Musical Expertise, bilingualism, and executive functioning. Journal of Experimental Psychology: Human Perception and Performance, 35(2), 565–574. https://doi.org/10.1037/a0012735
(1993). Wisconsin Card Sorting Test performance in healthy, older adults: Relationship to age, sex, education, and IQ. Journal of Clinical Psychology, 49(1), 54–60. https://doi.org/10.1002/1097-4679
(1991). The Wisconsin card sorting test: Theoretical analysis and modeling in a neuronal network. Cerebral Cortex, 1(1), 62–79. https://doi.org/10.1093/cercor/1.1.62
(2013). Executive functions. Annual Reviews, 64, 135–168. https://doi.org/10.1146/annurev-psych-113011-143750
(2019). Aerobic-exercise and resistance-training interventions have been among the least effective ways to improve executive functions of any method tried thus far. Developmental Cognitive Neuroscience, 37, Article 100572. https://doi.org/10.1016/j.dcn.2018.05.001
(1974). Effects of noise letters upon the identification of a target letter in a nonsearch task. Perception & Psychophysics, 16, 143–149. https://doi.org/10.3758/BF03203267
(2007). G*power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146
(2018). Cognitive sex differences in effects of music in Wisconsin Card Sorting Test. Psychology of Music, 48(2), 252–265. https://doi.org/10.1177/0305735618795030
(2020). Musicians show greater cross-modal integration, intermodal integration and specialization in working memory than non-musicians. Psychology of Music. Advance online publication. https://doi.org/10.1177/0305735619896088
(2019). Let’s call the whole thing off: Evaluating gender and sex differences in executive function. Neuropsychopharmacology, 44, 1–11. https://doi.org/10.1038/s41386-018-0179-5
(2018). Sex/gender differences in cognition, neurophysiology, and neuroanatomy. F1000Research, 7, Article 805. https://doi.org/10.12688/f1000research.13917.1
(2007). Suitable stimuli to obtain (no) gender differences in the speed of cognitive processes involved in mental rotation. Brain and Cognition, 64(3), 217–277. https://doi.org/10.1016/j.bandc.2007.03.002
(1985). Psychological androgyny in musicians. Bulletin of the Council for Research in Music Education, 85, 102–108. http://www.jstor.org/stable/40317946
(1958). Age differences in short-term retention of rapidly changing information. Journal of Experimental Psychology, 55(4), 352–358. https://doi.org/10.1037/h0043688
(2020). Superior fluid cognition in trained musicians. Psychology of Music, 48(3), 434–447. https://doi.org/10.1177/0305735618808089
(2016). Sex differences in music: A female advantage at recognizing familiar melodies. Frontiers in Psychology, 7, Article 278. https://doi.org/10.3389/fpsyg.2016.00278
(2000). The unity and diversity of executive functions and their contributions to complex ‘‘frontal lobe’’ tasks: A latent variable analysis. Cognitive Psychology, 41(1), 49–100. https://doi.org/10.1006/cogp.1999.0734
(2011). Short-term music training enhances verbal intelligence and executive function. Psychological Science, 22, 1425–1433. https://doi.org/10.1177/0956797611416999
(2018). Individual differences in musical training and executive functions: A latent variable approach. Memory & Cognition, 46, 1076–1092. https://doi.org/10.3758/s13421-018-0822-8
(2016). Zahlen-Verbindungs-Test ZVT
[Number-Connection-Test] (3rd ed.). Hogrefe.(2012). Different mental rotation performance in students of music, sport and education. Learning and Individual Differences, 22(1), 159–163. https://doi.org/10.1016/j.lindif.2011.11.012
(2014). The influence of deliberate practice on musical achievement: A meta-analysis. Frontiers in Psychology, 5, Article 646. https://doi.org/10.3389/fpsyg.2014.00646
(2018). Sex differences in emotion recognition and working memory tasks. Frontiers in Psychology, 9, Article 1072. https://doi.org/10.3389/fpsyg.2018.01072
(2004). Music lessons enhance IQ. Psychological Science, 15(8), 511–514. https://doi.org/10.1111/j.0956-7976.2004.00711.x
(2011). Music lessons, emotional intelligence, and IQ. Music Perception, 29(2), 185–194. https://doi.org/10.1525/mp.2011.29.2.185
(2013).
Music and cognitive abilities . In D. DeutschEd., The Psychology of Music (3rd. ed., pp. 499–550). Academic Press. https://doi.org/10.1016/B978-0-12-381460-9.00012-2(2016). Tuning the mind: Exploring the connections between musical ability and executive functions. Cognition, 152, 199–211. https://doi.org/10.1016/j.cognition.2016.03.017
(2010). Sex differences in the processing of flankers. The Quarterly Journal of Experimental Psychology, 63(4), 633–638. https://doi.org/10.1080/17470210903464253
(2017). Musicians have better memory than non-musicians: A meta-analysis. PLoS One, 12, Article e0186773. https://doi.org/10.1317/journal.pone.186773
(1993). Der Zahlen-Verbindungstest and other trail-making correlates of general intelligence. Personality and Individual Differences, 14(1), 35–40. https://doi.org/10.1016/0191-8869(93)90172-Y
(1995). Magnitude of sex differences in spatial abilities: A meta-analysis and consideration of critical variables. Psychological Bulletin, 117(2), 250–270. https://doi.org/10.1037/0033-2909.117.2.250
(2017). Sex differences in visual-spatial working memory: A meta-analysis. Psychonomic Bulletin & Review, 24, 307–334. https://doi.org/10.3758/s13423-016-1085-7
(2014). Behavioral and neural correlates of executive functioning in musicians and non-musicians. PLoS One, 9, Article e99868. https://doi.org/10.1371/journal.pone.0099868
