608 Matching Annotations
  1. Aug 2022
    1. Thus, our research contributes to aburgeoning literature on cross-group friendships by showingthat the positive effects of friendship can extend beyond inter-group attitudes per se to institutional attitudes, and by directlytesting causal links from cross-race friendships to positiveintergroup outcomes (cf. Pettigrew, 1998).
    2. To the extent that a friend’s perceivedmembership in the university in-group is salient, cross-groupfriendship may increase the likelihood that minority-groupstudents will eventually incorporate a university identity as partof themselves.
    3. Therefore, friendships with majority-group peers may be key in the development of dual identityamong minority-group students, and may provide a route towardrelational diversity within institutions of higher education.
    4. Our research underscoresthe importance of the interpersonal climate for addressing issuesof access and diversity within such institutions, and shows thatthe development of affiliative ties across group boundariesprovides an important vehicle for achieving relational diversity.
    5. Together, thefindings of these studies suggest that efforts to increase cross-group friendship are not incompatible with institutional effortsto clearly communicate acceptance of the minority group bysupporting organizations or activities centered on the ethnic orracial background of that group.
    6. This analysisrevealed the predicted three-way interaction, b 5 0.80, F(1, 126)5 6.10, p < .02.

      Replicated the finding that minority group individuals with high race-based rejection anxiety having friendships with majority-group peers increased university satisfaction. Additionally, minority group individuals with high race-based rejection anxiety are overall less satisfied with the university than minority group individuals with low race-based rejection anxiety.

    7. .

      Participants attended three friendship-intervention sessions. For the first two sessions, they asked and answered increasingly personal questions about one another for 45 minutes. For the third session, they played a game of Jenga together and then filled out a questionnaire assessing university satisfaction.

    8. .

      Participants were informed about the nature of the study, filled out RS-race and RS-personal questionnaires, and then gave their informed consent.

    9. Within 2 weeks of theinformation session, participants were randomly assigned to asame- or cross-group partner, with the restriction that partnersneeded to have compatible schedules.

      Participants were randomly assigned to an experimental (cross-group) or control (same-group) condition. The independent variable being manipulated is race of person interacted with (different or the same), and the dependent variable being measured was is university satisfaction.

    10. Over the course of data collec-tion, the ethnic composition of the undergraduate population atthe university was, on average, 34.4% White and 12.0% Latino.Our sample consisted of 76 White participants and 59 Latinoparticipants.
    11. The model for university satisfaction revealed a significant in-teraction between number of majority-group friends and RS-race, b 5 0.46, F(1, 34) 5 7.19, p 5 .01.

      Minority group individuals with high race-based rejection anxiety having friendships with majority-group peers decreased dissatisfaction at their university.

    12. The analysis for belonging revealed a significant main effect ofRS-race, b 5 0.25, F(1, 34) 5 6.17, p < .02.

      minority group individuals with high race-based rejection anxiety having friendships with majority-group peers decreased lack of belonging.

    13. We specifically addressed thequestion of whether friendships developed with majority-grouppeers over the 1st year of college predicted feelings of belongingin the university 1 to 2 years later, as well as change in satis-faction with the university over this time period.
    14. Study 1 was a 3-year longitudinal study of two cohorts of AfricanAmerican college students at a university where African Amer-icans represented less than 10%, and Whites represented morethan 50%, of the student body over the course of data collection(see Mendoza-Denton et al., 2002).
    15. Given these converging lines of research, we tested whetherfriendships with majority-group peers would buffer minoritystudents who are high in RS-race from feelings of alienation anddiscomfort in historically White university settings.
    16. In a longitudinalstudy of African American students (Study 1), cross-groupfriendships with majority-group peers buffered studentshigh in RS-race from lack of belonging and dissatisfactionat their university. An experimental intervention (Study 2)that induced cross-group friendship replicated the findingsand established their specificity for minority-group stu-dents.
    17. Givenresearch documenting the benefits of cross-group friend-ship for intergroup attitudes, we tested whether friend-ships with majority-group peers would attenuate theeffects of RS-race within these contexts
  2. www.researchgate.net www.researchgate.net
    1. More specifically, we ex-pected that the development of a cross-group friendship wouldlead to more initiation of intergroup interactions during the diaryperiod, particularly among those who were originally predisposedto anxiety in such interactions. We further hypothesized that par-ticipants higher in RS-race would report more anxious mood overthe diary period but that this anxiety would be attenuated throughthe development of cross-group friendship.
    2. Bringing together the above literatures, we hypothesized thatonly participants who are likely to experience anxiety in intergroupcontexts (either because of RS-race or implicit prejudice) shouldshow signs of hormonal stress responses when they first meet across-group partner, but that cross-group friendship should atten-uate such stress responses over the course of friendship develop-ment. As a corollary, participants who scored lower on measuresof RS-race or implicit prejudice were not expected to show suchattenuation in the cross-group condition because they should havebeen less likely to exhibit hormonal stress responses in the firstplace.
    3. On the one hand, we hypothesized that cortisol reactivityshould be the least pronounced among participants who werepredisposed to anxiety in intergroup contexts but also paired witha cross-group partner with prior intergroup contact. On the otherhand, a series of recent findings have led to an alternate hypothesisthat participants with prior intergroup contact may engendergreater threat among outgroup partners who are vigilant for cues ofrejection in intergroup encounters.
    4. Generally, we propose that cross-group friendship improvesintergroup interactions through systematic disconfirmations ofnegative expectations about intergroup experiences (Mendoza-Denton, Page-Gould, & Pietrzak, 2006).
    5. Zande, 1999), we report a study in which friendship was inducedbetween same- and cross-group dyads of Latinos/as and Whites.
    6. Building on the experimental paradigm used by Wright andcolleagues (Wright et al., 1998, 2002, 2005; Wright & van der
    7. Wright and his colleagues(see Wright, Aron, & Tropp, 2002; Wright, Brody, & Aron, 2005;Wright, Ropp, & Tropp, 1998; Wright & van der Zande, 1999)described research that provided initial evidence for the causaleffects of cross-group friendship on self-reported anxiety.
    8. Even though interactions between members of different socialgroups are sometimes characterized by anxiety and threat (Blas-covich, Mendes, Hunter, Lickel, & Kowai-Bell, 2001; Mendes,Blascovich, Lickel, & Hunter, 2002; Stephan & Stephan, 1985,2000), a growing body of research suggests that cross-groupfriendship can attenuate such anxiety.
    9. These findings provide experimental evidence that cross-group friendship is beneficial forpeople who are likely to experience anxiety in intergroup contexts.
    10. Cross-group friendship led to decreases incortisol reactivity (a hormonal correlate of stress; W. R. Lovallo & T. L. Thomas, 2000) over 3 friendshipmeetings among participants high in race-based rejection sensitivity (R. Mendoza-Denton, G. Downey,V. J. Purdie, A. Davis, & J. Pietrzak, 2002) and participants high in implicit prejudice (A. G. Greenwald,B. A. Nosek, & M. R. Banaji, 2003). Cross-group partners’ prior intergroup contact moderated therelationship between race-based rejection sensitivity and cortisol reactivity. Following the manipulation,participants kept daily diaries of their experiences in an ethnically diverse setting. Implicitly prejudicedparticipants initiated more intergroup interactions during the diary period after making a cross-groupfriend. Participants who had made a cross-group friend reported lower anxious mood during the diaryperiod, which compensated for greater anxious mood among participants high in race-based rejectionsensitivity.
    11. The authors induced cross-group friendship between Latinos/as and Whites to test the effects ofcross-group friendship on anxiety in intergroup contexts.
  3. Jun 2022
    1. To address these limitations, in Experiment 1, we requiredparticipants to rate their confidence after each individual response(rather than after each pair of responses).
    2. In Experiment 4, we manipulated participants’ confidence priorto administration of the MRT. All participants first completed aline judgment task that was intentionally difficult, so that par-ticipants would be unable to gauge their performance.
    3. Upon completion of the line judgment task, participants wererandomly informed that their performance on the line judgmenttask was either above average (‘‘high confidence’’condition) orbelow average (‘‘low confidence’’ condition).
    4. Ifconfidence mediates mental rotation performance, then partic-ipants in the high confidence condition should outperform theircounterparts in the low confidence condition.
    5. Cooke-Simpson and Voyer (2007) provided tentative evi-dence that confidence predicted MRT performance, but thatstudy had several critical limitations.
    6. correlation was comparable to that observed in prior studies(r = ?.69; Cooke-Simpson & Voyer, 2007).
    7. As illustrated in Fig. 2, confidence predicted accuracy acrossboth sexes, r(67) = ?.56, p\.001, and the strength of this
    8. Fig. 6

      Accuracy percentage as a function of confidence within male and female participants in experiment 3.

      Accuracy and confidence were significantly positively correlated for both males and females.

    9. Fig. 5

      Accuracy percentage as a function of confidence between male and female participants in Experiment 3.

      Accuracy and confidence were significantly positively correlated for both males and females, and females increased their accuracy as a function of confidence more so than males did.

    10. Thisresult replicates the general pattern observed in Experiment 1(Fig. 3), and indicates that both males’ and females’ confi-dence was indeed calibrated to their performance.
    11. Experiment3 therefore supported the hypothesis that mental rotation ismediated by confidence.
    12. Experiment 3 provided a further test of whether the sex differencein performance is better explained by confidence or by omissions.
    13. Results are summarized in Table 1. The sex difference in accu-racy was replicated in the omission condition but not in the com-mission condition.
    14. Thus, the sex difference in mental rotationwas attributable to confidence rather than omissions.
    15. mediates mental rotation. If confidence was unrelated to mentalrotation, then the sex difference should be equivalent acrossgroups (i.e., no interaction should occur).
    16. Critically, an inter-action in either direction would suggest that confidence
    17. .

      It could also be the case that confidence would have more of an effect on the commission group than the omission group, which would exacerbate the sex difference in MRT performance.

    18. .

      There were two conditions in this experiment, one in which participants could omit trials at their discretion (omission) and one in which they had to respond to every trial (commission). The idea here was that confidence would have less of an effect on the commission group than the omission group, which would eliminate the sex difference in MRT performance.

    19. In Experiment 2, we sought to attenuate the sex difference inmental rotation performance by rendering confidence irrelevantto the task.
    20. Fig. 4

      Shows the mediating relationships between sex, confidence, and mental rotation, via Baron and Kenny's regression method for simple mediation.

      Sex negatively predicted both confidence rating and mental rotation score, whereas confidence positively predicted mental rotation score. Confidence seems to mediate the sex difference in MRT performance.

    21. In conclusion, Experiment 1 corroborated the finding thatconfidence predicted mental rotation performance both acrossand within sexes (Cooke-Simpson & Voyer, 2007). Experiment1 further demonstrated, for the first time, that confidence pre-dicted mental rotation performance within individuals: Partic-ipants were more accurate on trials for which they were moreconfident. These results thus provide the most precise evidenceto date of the relation between confidence and mental rotation.Finally, Experiment 1 also provided the first evidence of thedirection of this relationship: Mediation analyses revealed thatconfidence mediated the sex difference in mental rotation per-formance whereas mental rotation performance did not mediatethe sex difference in confidence.
    22. If confidence mediates mental rotation performance,then confidence ought to predict accuracy on the MRT acrosssexes, within each sex, and possibly even within individuals.
    23. In Experiment 1, we tested whether confidence predicted men-tal rotation performance between sexes, within each sex, andwithin individuals.
    24. Thus, mental rotation performance did notmediate the sex difference in confidence, but rather confidencestrongly mediated the sex difference in mental rotation per-formance.
    25. Fig. 3

      Accuracy percentage as a function of confidence rating within male and female participants in experiment 1.

    26. Fig. 2

      Accuracy percentage as a function of confidence rating between male and female participants in experiment 1.

    27. So, together, Figs. 2 and 3 reveal thatmales and females were similarly successful at calibrating theirconfidence to their accuracy (Fig. 3), though males tendedtoward the upper part of the distribution on both confidence andaccuracy (Fig. 2).
    28. Table 1

      Mean accuracy percentage (along with sample size and standard deviation) for males and females and effect size of sex difference in accuracy percentage for all conditions of each experiment.

      Effect size of sex difference in accuracy percentage was significant in the first and second experiments (p < .05; p < .01), the confidence condition of the third experiment (p < .001), the high confidence condition of the fourth experiment (p < .01), and the low confidence condition of the fourth experiment (p < .06).

    29. As expected, an independent samples t-test revealed that males(M = 5.61, SD = 1.02) were more confident than females (M =4.62, SD = 1.41), d = .74, t(65) = 3.26, p\.01.
    30. Specifically, if confidence medi-ates mental rotation, then (1) confidence should predict mentalrotation scores not only between sexes, but also within sexes, (2)rendering confidence irrelevant to the task should attenuate thesex difference, and (3) manipulating participants’ confidenceshould affect their mental rotation performance.
    31. .

      Women provide fewer responses to the MRT than men, which means that they deliberately abstain from responding to some MRT questions. This could indicate that women are less confident of their MRT responses than men are.

    32. Because confidence appears tobe an important component of the MRT, it stands as a plausiblemediator of performance.
    33. The most common measure of mental rotation performanceis the Mental Rotations Test (MRT; Vandenberg & Kuse, 1978),which is based on the 3-dimensional block figures introduced byShepard and Metzler (1971) and updated by Peters et al. (1995).
    34. .

      Evidence from previous research indicates that confidence mediates performance on cognitive tasks, but the relationship between confidence and spatial ability is under studied and not well understood.

    35. Preliminary evidence suggests that confidence might indeedunderlie the sex difference in mental rotation.
    36. .

      Multiple studies have found that stereotype threat (especially for sex stereotypes) can affect performance on cognitive tasks by influencing confidence.

    37. We therefore tested whether confidence mediatedmental rotation performance.
    38. basic cognitive skills, such as attention, memory, and judgment(e.g., Schmader et al., 2008), which ultimately would affectperformance.
    39. The beliefthat one (or one’s social group) is skilled or poor at a given taskmay well affect one’s confidence when approaching that taskand this effect on confidence may have cascading effects on
    40. Much of the research on gender role and sex stereotype effectsassumes confidence as a potential cognitive mechanism bywhich those social factors exert their effect.
    41. So, in summary, beliefs about and aware-ness of sex stereotypes are both related to the sex difference inmental rotation performance. But how exactly might sex ste-reotypes affect performance?
    42. Mental rotation performance may also be affected by mereawareness of, rather than belief in, the stereotype that men aresuperior to women on spatial tasks.
    43. .

      Research has suggested that women who hold the stereotypical belief that men are better than women at spatial tasks might perform worse on these spatial tasks as a result of this belief.

    44. Performance on spatialtasks thus is clearly related to gender role beliefs and traits.
    45. Gender role beliefs and traits may partially explain the sex dif-ference in mental rotation performance.
    46. Here, we examined whether one suchsociocognitive factor, namely participants’ confidence, contrib-uted to this sex difference in mental rotation performance.Although this presumed relation between confidence and men-tal rotation performance has received little empirical attention,related research on gender roles, sex stereotypes, and stereotypethreat provides a rich source of supportive evidence.
    47. .

      Research has reliably demonstrated that men perform better at the MRT than women do. It is unlikely that there is a purely biological explanation for this sex difference, and sociocognitive factors probably play a role in the sex difference.

    48. Given the complexity of the taskand the magnitude of the sex difference, it likely has multi-ple causes or mediators.
    49. Of all cognitive sex differences, the mental rotation of abstractfigures in 3-dimensional space is the most robust (Halpern, 2000;Hines, 2004; Linn & Petersen, 1985; Maccoby & Jacklin, 1974).
    50. Thus, confidence medi-ates the sex difference in mental rotation performance and hencethe sex difference appears to be a difference of performancerather than ability. Results are discussed in relation to otherpotential mediators and mechanisms, such as gender roles,sex stereotypes, spatial experience, rotation strategies, work-ing memory, and spatial attention.
    51. On tasks that require the mental rotation of 3-dimensional figures, males typically exhibit higher accuracythan females. Using the most common measure of mental rota-tion (i.e., the Mental Rotations Test), we investigated whetherindividual variability in confidence mediates this sex differ-ence in mental rotation performance.
    1. Table 1

      Average percentage of problems attempted for males and females in sets 1 and 2 for the 3 minute and 6 minute time conditions in study 2. Significant overall effects are shown for time (3 min or 6 min), test half (set 1 or 2), sex (male or female), interaction between time and sex, and interaction between time and test half.

      Percentage of problems attempted was generally higher in the 6 min condition than in the 3 min condition (time difference), in the 2nd set than in the 1st set (practice effect), and for males than for females (sex difference). The interaction effect between time and sex indicates that males attempt more problems than females throughout, but that the difference decreases as more time is given. The interaction effect between time and test half indicates that participants attempt more problems in the second half of the test than in the first half, but that the difference decreases as more time is given.

    2. Table 2

      Mean amount of problems solved (with SD) for males and females in the 3 minute and 6 minute time conditions of study 2. Significant main effects are shown for sex (male or female) and time (3 min or 6 min).

      Main effect of sex indicates that on average, males solved more problems than females. Main effect of time indicates that on average, participants solved more problems in the 6 min condition than in the 3 min condition.

    3. Fig. 3.

      Magnitude of effect size in sex differences as a function of problem position.

      Shows that the magnitude of sex difference effect size increased the further subjects got into the set. I.e., the further subjects got into the set, the greater the sex difference in performance was (males outperformed females).

    4. Fig. 1.

      The figures that are shown in the Vandenburg and Kuse MRT. The target stimulus is the leftmost stimulus shown here. Two of the stimuli to the left of the target figure are rotated versions of the target figure, and two of them are distractor figures. Participants had to identify which figures were rotated versions of the target figure.

    5. Fig. 2.

      Percentage of problems attempted as a function of problem position for males and females in the first and second sets of study 1.

      Both males and females would attempt less problems the further they got in the set, but this effect was greater for females than for males. Also, both males and females attempted more problems the further they got in the second set than in the first set, revealing a practice effect.

    6. .

      Varying the amount of stimuli presented to the participants in the MRT could reveal sex differences as a result of women spending more time making sure that their MRT answers are correct than men do.

    7. Thus, the third approach to the issue of Sex differencesand the time factor in the MRT examined the sex differ-ence in a RT paradigm where only two mental rotationfigures were used.
    8. .

      A past study found evidence to support the assertion that women take more time than men do on the MRT because women spend extra time making sure that their answer is correct while men do not do this. This is an alternative explanation to women taking more time than men do on the MRT because they simply cannot solve spatial problems as fast as men can.

    9. For this reason, Study 2, which manipulatestime directly, compares sex differences under the stan-dard condition with sex differences which are observedwhen time is increased, but within limits.
    10. Here, we administered the MRT under identical con-ditions, but allowing two durations.
    11. .

      Large sample size allows for the examination of sex differences on the MRT as it progresses. This condition documents the effect of time constraints on MRT performance between sexes (baseline condition).

    12. If time pressure is a significant factor in per-formance, we expect to see two indicators in the data.First, we expect to see that females attempt significantlyfewer problems than males and, second, we expect to seethat the magnitude of the sex difference increases as theproblem position increases.
    13. In the present study, three different approaches to theproblem of time constraints are taken, each examining adifferent aspect of how time might affect the sex differ-ences on the MRT.
    14. Thus, our understanding of the role of time con-straints in MRT sex differences remains inconclusive.
    15. Several studies find that when enough time is pro-vided for the V&K MRT, sex differences on the MRTdisappear (Goldstein, Haldane, & Mitchell, 1990;Voyer, 1997), leading to the conclusion that sex differ-ences arise because the sexes differ in the amount of timetaken to perform the mental rotation.
    16. The idea that the activational effects of hormones mightlead to relatively faster mental rotations touches uponthe time factor in MRT sex differences.
    17. That males and females differ in performance on Van-denberg and Kuse (1978) mental rotation task is wellknown (Voyer, Voyer, & Bryden, 1995). The causes areless well understood.
    18. .

      Sex differences may arise because of qualitative differences in how the sexes solve spatial problems, or because of spatial problem solving ability being modulated by hormonal differences between the sexes.

    19. We conclude that performancefactors may play a role in sex difference on mental rotation tasks, but do not account for all of the differences.
    20. In accounting for the well-established sex differences on mental rotation tasks that involve cube stimuli of the Shepard and Met-zler (Shepard & Metzler, 1971) kind, performance factors are frequently invoked.
    1. .

      Lots of research has investigated the cause of sex differences in MRT performance. One proposed explanation is that men and women emphasize different instructional aspects under timed conditions. For this reason, men are able to complete more items than women do when taking the MRT.

    2. Gender differences in favor of men in spatial abilities are one of the most commonly replicated finding inpsychology research (Hedges & Nowell, 1995; Linn & Petersen, 1985; Voyer, Voyer, & Bryden, 1995). Among thetasks that produce such differences, the Mental Rotations Test (MRT), developed by Vandenberg and Kuse (1978),produces the largest effects (Linn & Petersen, 1985; Peters, 2005).
    3. Eighty undergraduate students (40 males, 40 females) completed the MRT while ratingtheir confidence in the accuracy of their answers for each item. As expected, gender differences in favor of men were obtained.Results also indicated a positive correlation between confidence ratings and scores on the MRT, as well as negative correlationsbetween confidence ratings and MRT outcomes presumed to reflect propensity to guess. More elaborate analyses using a measureof accuracy of predictions (the Brier score) indicated that men have a more accurate perception of their performance on the MRTthan women do.



    1. Further support for a constant anduniform biological explanation comes from studies that findsimilar effect sizes for gender across cultures (Silverman,Phillips, & Silverman, 1996).
    2. The distinct hormonal environments of men and womenmay play a role.
    3. .

      Hormones having an activational effect on cognitive functioning in adulthood could also cause sex differences in the MRT. Research has suggested that greater exposure to androgens/estrogens in adulthood can affect cognitive functioning.

    4. .

      Organizational effects during prenatal development could also play a role in MRT sex differences, since these effects could cause the brain to develop differently based on androgen or estradiol exposure. They could also occur as a result of hemispheric specialization. Right hemisphere specialization favors spatial tasks, and left hemisphere specialization favors verbal tasks, and male brains are more lateralized to the right while female brains are more bilateral. For this reason, men may have an advantage in spatial processing that causes them to be better at the MRT. .

    5. When brain imaging studies are done on subjects per-forming mental rotation tasks, there is some evidence ofactivation in motor areas of the brain.
    6. .

      Neuroimaging studies have shown that mental rotation involves parietal region activation. Specifically, males activate the left inferior parietal region and the right head of the caudate nucleus while completing MRTs and females activate the parietal lobe in general while completing MRTs.

    7. Biological factors have also been discussed as potentialcauses for this difference. Biological theories stress theimportance of genetics, hormonal influence, brain organi-zation, and maturational factors.
    8. There is,however, no consensus as to the role of task complexity ongender differences in mental rotation.
    9. Task difficulty is one potential performance factor thathas been explored (Bryden, George, & Inch, 1990; Collins& Kimura, 1997).
    10. .

      Differences in performance on the mental rotation task could be due to cultural norms subconsciously influencing performance or due to task variables that inflate male performance advantage (such as task difficulty, previous task exposure, time limits, and weighted scoring systems).

    11. .

      The mental rotations test (MRT) was created by Vandenberg and Kuse (1978). It uses line drawing of block stimuli and consists of two 10-item sections in which the subject is required to match two of four choices to a target figure.

    12. A number of explanations have been advanced for theexistence of the gender difference in mental rotation.
    13. Voyer, Voyer, & Bryden’s (1995) meta-analysis of sexdifferences in spatial abilities, found that the average differ-ence (using Cohen’s d = (M1 − M2)/σ) between men andwomen on the (MRT; Vandenberg & Kuse, 1978), was 0.94(this represents a very large effect), indicating that men per-form nearly one standard deviation above the average per-formance of women.
    14. The ability to mentally rotate an object has been foundto produce one of the largest sex differences in the cogni-tive literature (Linn & Petersen, 1985).
    15. The visuospatial ability referred to as mental rotation has been shown to produce one of the largest and most consistent sex differences,in favor of males, in the cognitive literature.
    16. Sex differences were also seen in the patterns of correlations between rotation tasks and other neuropsycho-logical measures. Current results suggest men may rely more on left hemisphere processing than women when engaged in rotational tasks.© 2003 Elsevier Ltd. All rights reserved.



    1. .

      If men have a visuospatial advantage and global bias, and if women have a local advantage, then men should be better at classifying line orientation than women, and men should exhibit a global advantage and women should exhibit a local advantage regardless of relevant classification property. It could also be that men would be faster than women in global classification and women would be faster than men in local classification regardless of relevant classification property, Likewise, if gender differences in global-local processing are only related to visuospatial performance, then global bias for men and local bias for women would only be observed for line orientation classification but not for closure classification.

    2. .

      Global-local processing is typically tested using hierarchical stimuli (larger figures are composed of smaller figures). Generally speaking, people usually display a global advantage (faster responses to larger figures than smaller figures) in processing the hierarchical stimuli. Also, they typically display global-to-local interference (conflicting information between the larger figures and the smaller figures interferes with responses to the smaller figures but not the larger ones). Global advantage can be confounded by factors like visual angle, exposure duration, retinal position, density of elements, number and relative size of elements, and the nature of the stimuli at the global and local level.

    3. .

      Men and women were shown hierarchical stimuli that differed in closure (open/closed shape) and line orientation (oblique/horizontal or vertical line) at the global or local level. They had to classify the stimuli on the basis global variation or local variation.

    4. Pre-vious results with similar stimuli and mixed groups of participants(Han, Humphreys, & Chen, 1999; Kimchi, 1994) showed the globaladvantage that is typically observed with hierarchical stimuli (e.g.Kimchi, 1992; Navon, 1977), as well as faster classification by clo-sure than by line orientation. In addition, the global advantage wasmore pronounced when local classification was based on line ori-entation than on closure (Han et al., 1999; Kimchi, 1994).
    5. Thus, in this study, we examined gender differences in global–local processing with a visuospatial judgment task (line orienta-tion) and a shape judgment task (open vs. closed shape).
    6. .

      The stimuli used in this study were specifically designed to evoke the typical global advantage. The study also included both spatial and non-spatial task contexts to see if sex differences in global-local perception only happened in spatial task contexts.

    7. The investigation of gender differences in global–localprocessing in the context of a spatial and a non-spatial task will al-low us to examine whether these differences, if they exist, charac-terize visual perception of women and men in general, or whetherthey are related only to visuospatial performance.
    8. The main purpose of the present study was to systematicallyexamine gender differences in global–local processing.
    9. The inconsistencies in the results of these studies are mostprobably due to the differences in stimulus and task variables,which are known to affect global–local performance, as discussedearlier.
    10. Direct examinations of gender differences in global–local pro-cessing are sparse, and the results are equivocal.
    11. .

      Gender differences in global-local bias could be the result of hemispheric specialization differences in men and women. Women are better at cognitive tasks that use the left hemisphere, which is more implicated in local processing, and men are better at cognitive tasks that use the right hemisphere, which is more implicated in global processing.

    12. .

      Research suggests that men use more "global" reference points (such as the position of the sun in the sky) when navigating, while women use more "local" reference points (such as landmarks) when navigating.

    13. The gender differences in navigation and way-finding also ap-pear to suggest a global bias for men versus a local bias for women.
    14. .

      Gender differences in mental rotation tasks could be accounted for by men and women using different strategies to mentally rotate an object. Research suggests that men use a more holistic or "global" approach when mentally rotating objects, while women use a more segmented or "local" approach when when mentally rotating objects.

    15. .

      Men outperform women on a variety of spatial tasks, from mental rotation tasks to maze navigation tasks.

    16. Gender differences in spatial abilities have been reported in anumber of studies over the years (see Voyer, Voyer, & Bryden,1995, for a review).
    17. Direct examinations of gender differences in global–local processing are sparse, and the results are incon-sistent. We examined this issue with a visuospatial judgment task and with a shape judgment task.Women and men were presented with hierarchical stimuli that varied in closure (open or closed shape)or in line orientation (oblique or horizontal/vertical) at the global or local level. The task was to classifythe stimuli on the basis of the variation at the global level (global classification) or at the local level (localclassification).
    18. This finding suggests that women aremore distracted than men by misleading global oriented context when performing local orientation judg-ments, perhaps because women and men differ in their ability to use cognitive schemes to compensatefor the distracting effects of the global context. Our findings further suggest that whether or not genderdifferences arise depends not only on the nature of the visual task but also on the visual context.



    1. .

      This study found that there are significant sex differences in number line estimation task performance and in confidence judgments about performance. Women/girls are significantly less precise on their number line estimation task performance than men are, and are significantly less confident about performance than men are.

    2. Nevertheless, we conducted linear mixedmodels to statistically test for developmental effects to assess whether the magnitude of genderdifferences in confidence increases (or decreases) with grade and found no evidence ofdevelopmental effects in gender differences in confidence (Appendix 4).
    3. Finally, our analyses included data from participants ranging from early childhood toadulthood, but we chose not to focus on developmental trends in our outcomes of interest.
    4. .

      This study couldn't evaluate sex differences in degree of overconfidence on number-line estimates because judgments must be made on the same scale as performance, and this was not the case for many of the experiments talked about here.

    5. .

      Measures of number line estimation performance were not detailed enough to be able to calculate absolute accuracy (degree of overconfidence) on them, so sex differences in absolute accuracy were not able to be determined.

    6. Across allparticipants, we found the mean values of gamma were positive for both genders (boys/men: n =309, M = .18, SD = .31; girls/women: n = 400, M = .20, SD = .28), which suggests that participantshave some ability to monitor the accuracy of their estimates. However, no reliable gender differencewas observed for relative accuracy (forest plot and analyses are displayed in Appendix 3). Takentogether, the present outcomes suggest that although girls/women (as compared to boys/men) areless confident in their task performance, they are equally able to discriminate between estimates thatare more versus less precise.
    7. Although we cannot calculate measures of absolute metacognitive accuracy, we were able toassess whether gender differences exist for relative accuracy, or the degree to which participants candiscriminate between number-line estimates that are more (vs. less) precise.
    8. Because of these issues, assessing the degree of over- or under-confidence for the number-line estimation task will require future advances in measurement ofjudgments and performance (so they can be made on comparable scales) for this task.
    9. In the current study, we found gender differences in confidence, even when controlling forperformance. We also found that the magnitude of the gender differences observed forconfidence were smaller than those for performance (g = .30 versus .52).
    10. .

      The experiments detailed in this meta-analysis could have their methods slightly adjusted in order to test whether girls/women having lower confidence scores on number line estimation tasks than boys/men is because of lower self-efficacy on math tasks or because of lower perceived familiarity with the numbers.

    11. .

      Boys/men may be higher in their trial-by-trial confidence judgments than girls/women because girls/women tend to have lower self-efficacy when performing math tasks than men. It could also be that girls/women being less familiar with specific numbers than boys/men lowers their number-line estimation confidence.

    12. That is, as compared toboys/men, girls/women may generally have less task-specific efficacy for number-line esti-mation (either because of its math component, spatial component, or both) and may also haveless perceived familiarity with the numbers. In fact, lower self-efficacy may push some girls/women away from engaging in math tasks, which in turn could reduce their actual familiaritywith those numbers.
    13. As mentioned in the Introduction, confidence judgments can be influ-enced by multiple theory- and experience-based factors (e.g., Undorf et al. 2018). The observedgender gap in confidence could be explained by differences in judgment cue use by girls/womenand boys/men.
    14. Although considerable debate exists over the causes of such gender differences when they areobserved (e.g., Hyde 2014), we imagine that psychological (e.g., differences in math attitudes;Sidney et al. 2019), social (e.g., differences in early spatial experiences, such as exposure to spatiallanguage, media, and toys; Caldera et al. 1989; Doyle et al. 2012; Pruden and Levine 2017; orgendered stereotypes about math and spatial ability, McGlone and Aronson 2006; Moè andPazzaglia 2006), and possibly even biological factors (e.g., sexual dimorphism in the parietalcortex; Goldstein et al. 2001) could contribute to the gender differences observed in the number-line estimation task (as is the case for performance; e.g., Tosto et al. 2018).
    1. In summary, the current study indicates that sex differences inglobal self-assessments of performance do not always coincide with sexdifferences in moment-to-moment spatial performance monitoring.Even though female students were in most cases less confident thanmale students in their general spatial ability, their trial-by-trial meta-cognitive monitoring accuracy was not impaired in either an absoluteor relative sense. Thus, female students appear to have relatively ac-curate perceptions of their spatial performance for spatial orientationand spatial visualization tasks.
    2. .

      Research suggests that even women with high spatial reasoning ability are underconfident about that ability. This study's results showed that women generally had lower absolute accuracy than men when making global performance postdictions on spatial orientation tasks than men did.

    3. Regardless, the current results show that even female studentspursuing STEM degrees are less confident than male students in theirability to reason spatially about some STEM related content. It is un-clear if these differences reflect true underconfidence or if they are dueto actual differences in academic spatial ability.
    4. .

      This study's results differing from the findings of previous studies could also be because students in this sample are less susceptible to having their metacognitive monitoring accuracy affected by negative stereotypes than students in more typical samples. The negative stereotype of women being worse at spatial tasks would normally make it so that women have worse metacognitive monitoring accuracy, but in this case they do not because they strongly identify with spatially oriented tasks (being STEM majors).

    5. .

      This study's results differing from the findings of previous studies could be because of the sample. This sample was taken from a STEM university, so both the men and the women in the sample had above average spatial reasoning ability and spatial experience. In a sample from a less STEM-oriented university, this would not be the case. Hence, this sampling difference may have biased the results of the study so that sex differences were not as prominent as they would be in a more typical setting.

    6. Given that female students have lower visual-spatial working memoryspans than male students (Voyer et al., 2017), they may be more sus-ceptible to monitoring errors in dynamic spatial domains that they arenot susceptible to in static spatial tasks.
    7. Although we observed limited sex-related differences in monitoringaccuracy in the current study, more substantial sex-related differencescould be present in qualitatively different tasks that require dynamicspatial processing.
    8. The cues people attend to can also vary when monitoring is pro-spective vs. retrospective (Nelson, 1990).
    9. However, the sex differences in relativeaccuracy we observed for the PSTV:R, suggest that the quality of thecues students used to monitor their performance may have differed formen and women. It is unclear whether these differences are task-spe-cific or reflect sex differences in cue utilization during prospectivemonitoring that are less prevalent in retrospective monitoring tasks.
    10. .

      Women might be worse at the mental rotation task than men because they pay attention to less informative cues than men do when completing the task. In other words, women might have a less effective strategy of solving the mental rotation task than men, and that's why they perform worse on it.

    11. Table 6

      No idea how to interpret this. Seems to be statistical figures for questions on the perceived spatial ability test.



    1. C

      Alpha lateralization index scores for correct and incorrect responses to valid and invalid trials in the 75% condition. Alpha lateralization index score showed no statistical trends (p = 0.532).

    2. (B)

      Differences in alpha lateralization index between high and low RT trials by cue reliability percentages. Differences in alpha lateralization index between high and low RT trials significantly decreased with cue reliability percentages (p < 0.01).

    3. (A

      Differences in alpha lateralization index between correct and incorrect trials by cue reliability percentages. Differences in alpha lateralization index between correct and incorrect trials showed a statistical trend of decreasing with cue reliability percentages (p = 0.081).

    4. This reveals an interesting pattern:for the left ROI, both ipsi- and contralateral attention conditionslead to a decrease in alpha power compared with pre-cue values.However, in the right ROI, we observed a contralateral decreasebut a slight ipsilateral increase.
    5. For the left ROI, the observed decreases were signifi-cant for both conditions (Fig. 6, p  0.05; significant time sam-ples indicated). For the right ROI, only the contralateral attentioncondition lead to a trend ( p  0.064).
    6. A lin-ear regression analysis showed that with decreasing cue reliabil-ity, there was a strong trend toward decreasing differences in thealpha-lateralization index between correct and incorrect trials(R 2  0.043, p  0.081). This effect was significant for reactiontimes: with decreasing cue reliability, the difference in the alpha-lateralization index between low- and high-RT trials becomessmaller and eventually flips from positive to negative values(R 2  0.135, p  0.01)
    7. As expected, in the 50% condition alpha-lateralization index val-ues were rather low and did not correlate with performance (data notshown): both correct and incorrect, and low- and high-RT trialsshowed similarly low alpha-lateralization index values (in the rangeof 0.01– 0.02; correct vs incorrect: t(17)  0.436, p  0.669; low vshigh RT: t(17)  0.375, p  0.712).
    8. D

      Alpha lateralization index scores for high and low RT responses to valid and invalid trials in the 75% condition. Alpha lateralization index score showed a near statistically significant trend of having a higher ratio of low RT to high RT trials for the invalid trials than for the valid trials (p = 0.056).

    9. For discrimination rate this effect was not sig-nificant (t(17)  0.638, p  0.532), for RT a near significant trend wasobserved (t(17)  2.048, p  0.056).
    10. This was further substantiated by analysis of the invalid cue trialsfrom the 75% condition, on which an opposite pattern was ob-served: high alpha lateralization was detrimental for performance oninvalid cue trials (Fig. 4C,D).
    11. B

      Alpha lateralization index scores for low RT and high RT trials in the 100% reliability condition. Alpha lateralization index score is significantly higher for low RT than high RT trials (p < 0.05).

    12. A

      Alpha lateralization index scores for correct and incorrect trials in the 100% reliability condition. Alpha lateralization index score is significantly higher for correct than incorrect trials (p < 0.05).

    13. This analysis showsthat a higher alpha-lateralization index precedes better perfor-mance: both correct trials and fast RTs are related to high alphalateralization values whereas incorrect trials and slow RTs showless alpha lateralization. Paired-sample t tests confirmed thatthese differences were significant (correct vs incorrect: t(17) 2.187, p  0.05; low vs high RT: t(17)  2.556, p  0.05).
    14. A

      Topographical plots showing pre-stimulus alpha power in sensors as a contrast between attention left and attention right (-0.06 to 0.06) in the 100%, 75%, and 50% conditions. Pre-stimulus alpha power in sensors over left and right somatosensory regions showed significant lateralization in the 100% and 75% conditions. Pre-stimulus alpha power in the sensor over the left somatosensory region showed significant lateralization in the 50% condition, and the effect was much weaker than in the other conditions.

    15. B

      Bar graph showing alpha lateralization index (0 to 0.06) for the 100%, 75%, and 50% cue reliability conditions. Alpha lateralization index significantly decreased with cue reliability percentage.

    16. alpha-lateralization index showed no significant effect (R 2 0.000, p  0.964).
    17. This decrease could not be explained by a difference in overallipsilateral plus contralateral alpha power between the conditions(data not shown), as a similar test on the denominator (normal-ized per subject using the average power over conditions) in the
    18. There was a significant parametric decrease of thealpha-lateralization index with decreasing cue reliability (Fig.3B) as assessed by linear regression (R 2  0.150, p  0.01).
    19. The alpha lateralization was significanton sensor level both in the 100% (see before) and 75% condi-tion ( p  0.01 for two clusters above left and right sensori-motor regions). For the 50% condition the effect was muchweaker, however, a significant cluster was found in sensorsover left sensorimotor regions ( p  0.05).
    20. C

      Standardized brain volume showing pre-stimulus alpha power sources as a contrast between t-values (-5 to 5) for attention to left hand and attention to right hand in the 100% condition. Pre-stimulus alpha power in sources from the right and left sensorimotor cortices showed significant lateralization such that t-scores were higher in the right somatosensory region during left hand attention and higher in the left somatosensory region during right hand attention.

    21. B

      Average frequency versus time for alpha power in sensors over right and left somatosensory regions in the 100% condition. Alpha power showed a sustained decrease during the prestimulus interval (t = -1 to 0 s). Left hemispheric sensors were mirrored to combine them with right-hemispheric sensors, which is why only attention left alpha power is shown in this plot.

    22. A

      Topographical plot showing pre-stimulus alpha power in sensors as a contrast between attention left and attention right (-0.06 to 0.06) in the 100% condition. Pre-stimulus alpha power in sensors over left and right somatosensory regions showed significant lateralization such that alpha power was higher in the right somatosensory region during the left trials and higher in the left somatosensory region during the right trials.

    23. A cluster-based randomization test overthe 3D source space showed that the lateralized difference inalpha activity between attention-left and attention-right was sig-nificant for the right somatosensory source ( p  0.01) andshowed a trend for the left source ( p  0.062). Note that data ofonly 17 subjects was used for the source analysis.
    24. A time-frequency analysis of the lower frequencies (5–35Hz) showed that alpha lateralization was sustained through-out the 1 s before stimulus onset (Fig. 2 B) and that none of theother lower frequencies between 5 and 35 Hz showed a sub-stantial modulation.
    25. Spectral analysis revealed a lateralized pattern of alpha power. Acluster-based randomization test over the sensors further showedthat the alpha lateralization had two significant clusters of sensorsabove left and right sensorimotor regions ( p  0.05 for bothclusters) (Fig. 2 A).
    26. B

      Discrimination rate (% correct) for valid and invalid cue trials in the 50%, 75%, and 100% cue reliability conditions, and reaction time (in ms) for valid and invalid cue trials in the 50%, 75%, and 100% cue reliability conditions.

    27. To summarize, the behavioral results confirmed the expectedoutcome: performance on invalid trials was significantly worsethan on valid trials, both in terms of discrimination rate and RT.Invalid cues had a more detrimental effect on RT for the 75%condition than for the 50% condition. Subjects were faster on the100% condition than on the 75% or 50% conditions.
    28. In terms of discrimination rate there were no differencesbetween the reliability conditions (100% vs 75%, t(17)  0.687,p  0.502; 100% vs 50%, t(17)  1.208, p  0.244), but subjectswere faster on the 100% condition compared with the other twoconditions (100% vs 75%, t(17)  2.445, p  0.05; 100% vs50%, t(17)  2.960, p  0.01).
    29. Therewas neither a significant effect of reliability on discrimination rate(F(1,17)  0.847, p  0.370), nor on RT (F(1,17)  1.479, p 0.241). There was a significant effect of validity both on discrim-ination rate (F(1,17)  6.534, p  0.05) and on RT (F(1,17) 23.239, p  0.001), with higher discrimination rates and lowerRTs for validly cued trials. Furthermore, the interaction effectbetween reliability and validity was not significant for discrimi-nation rate (F(1,17)  0.458, p  0.508), but showed a highlysignificant effect for RT (F(1,17)  11.715, p  0.01).
    30. A

      Experimental procedure. Subjects were cued on which hand they should attend to using an arrow (0.2 s), presented with a pre-stimulus interval fixation cross (1.0-1.8 s), presented with an electrical target stimulus to the cued hand and an electrical distractor stimulus to the non-cued hand (0.24 s), presented with a fixation cross during which they performed the discrimination task (max 1.5 s), and then presented with a fixation cross that indicated whether or not they successfully performed the task (0.2 s).

    31. .

      This study asked whether somatosensory alpha activity, which occurs in anticipation of information processing, reflects how attentional resources are allocated. It also asked about the extent to which somatosensory alpha activity is top-down modulated by how much anticipation there is.

    32. .

      The brain is constantly receiving sensory information, and it needs to filter this information according to behavioral relevance in order to process it effectively. Hence, the brain might process sensory information more or less thoroughly depending on how relevant it anticipates that information to be. Oscillatory alpha band activity may modulate how thoroughly sensory areas process sensory information based on how demanding a related task is.

    33. Wehypothesized that prestimulus somatosensory alpha powerwould modulate with respect to attention and that thestrength of this modulation would increase parametricallywith cue reliability. Since we posit that alpha activity plays adirect role in modulating neuronal processing, we further hy-pothesized that prestimulus alpha would be predictive of so-matosensory discrimination performance.
    34. cue reliability

      The degree to which inferences based on a cue are consistent with inferences based on other cues in the environment.

    35. These results indicate that the somatosensory alpha rhythmserves the same functional role as posterior alpha.

      In visual spatial attention tasks, alpha activity decreases on the side of the brain opposite to the area being attended to and increases on the side of the brain opposite to the area being ignored, which suppresses distracting inputs and increases visual detection performance. In a somatosensory WM task, somatosensory alpha activity increased on the same side of the brain as the tactile stimulus and increased somatosensory WM performance.

    36. In support of such an alpha mechanism, visual attention isknown to modulate alpha activity over parieto-occipital cortex asmeasured with electroencephalography (Foxe et al., 1998).
    37. ipsilateral

      Belonging to or occurring on the same side of the body.

    38. contralateral

      Relating to or denoting the side of the body opposite to that on which a particular structure or condition occurs.

    39. .

      Researchers used to think the alpha oscillations reflect cortical idling, but they now think it reflects the state of the underlying neural network when processing information. In this way, alpha oscillations are involved in cognitive processing.