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.

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).

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.

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).

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).

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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.

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

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).

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In particular,alpha activity might serve to direct the flow of information throughthe brain and allocate resources to relevant regions (Jensen andMazaheri, 2010). This is consistent with previous work suggestingthat sensory alpha activity is involved in directing focal attention(Pfurtscheller and Lopes da Silva, 1999; Suffczynski et al., 2001).

Here, we investigated whether somatosensory alpha activity istop-down modulated according to the anticipation of sensoryinput. Further, we asked whether the alpha modulation has con-sequences for somatosensory discrimination performance.

Thisstudy demonstrates that prestimulus alpha lateralization in the somatosensory system behaves similarly to posterior alpha activityobserved in visual attention tasks. Our findings extend the notion that alpha band activity is involved in shaping the functional architec-ture of the working brain by determining both the engagement and disengagement of specific regions: the degree of anticipation modu-lates the alpha activity in sensory regions in a graded manner. Thus, the alpha activity is under top-down control and seems to play animportant role for setting the state of sensory regions to optimize processing.

The brain receives a rich flow of information which must be processed according to behavioral relevance. How is the state of the sensorysystem adjusted to up- or downregulate processing according to anticipation? We used magnetoencephalography to investigate whetherprestimulus alpha band activity (8 –14 Hz) reflects allocation of attentional resources in the human somatosensory system.

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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.

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.

The cues people attend to can also vary when monitoring is pro-spective vs. retrospective (Nelson, 1990).

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.

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Furthermore, female STEMmajors had lower self-evaluations than their male counterparts of vi-suospatial abilities needed for scientific reasoning.

Across multiple spatial measures, female students displayedlower confidence in their item-level monitoring and global assessmentsof performance than did male students, even when no actual differencesin spatial performance were present (e.g., Paper Folding Test andSpatial Relations Test).

3.6. Self-perceptions of everyday and academic spatial ability

bsoluteaccuracy measures for each task were significantly positively correlatedfor male students. The same pattern was present for female studentswith the exception that their absolute accuracy for the Paper Foldingtest and PSVT:R were not significantly correlated. However, Fisher r-to-z tests indicated that there were no significant differences between themagnitudes of the absolute accuracy correlations for male or femalestudents. Relative accuracy measures for the Spatial Relations test andthe Paper Folding test were also significantly positively correlated. Noother correlations between relative accuracy measures were significantand no sex differences were present for these correlations. Correlationsbetween the absolute and relative accuracy measures were not sig-nificant with the exception that female student's relative and absoluteaccuracy for the PSVT:R was significantly negatively correlated. AFisher r-to-z test indicated that this negative correlation for femalestudents was significantly different from the correlation for male stu-dents, Z = 2.31, p < .05. T

Table 3 shows that both maleand female students were underconfident in their global predictionsand postdictions for their performance on the Spatial Relations Test andPSVT:R but their estimates were well calibrated for the Paper FoldingTest. There were no sex differences in the degree of underconfidencestudents displayed for either predictions or postdictions on the PSVT:Ror for predictions of performance on the Spatial Relations Test. Abso-lute accuracy for global postdictions of performance for the SpatialRelations Test were significantly lower for females than male studentswhich indicates that female students were more underconfident in theirperformance than male students after completing the Spatial RelationsTest. There were no sex differences for either global predictions orpostdictions for the Paper Folding test.

no reliable effects.

It shows that both female and malestudent performed consistently across each spatial measure, with po-sitive moderate to high correlations between most tests. Fisher r-to-ztests testing sex differences in the magnitude of each correlation found

Fisher r-to-z tests indicated that there were no sexdifferences in the relative accuracy for global prediction of performancefor the Spatial Relations Test (Male: r = 0.22, p = .01; Female:r = 0.37, p = .001), Z = 1.2, p = .23, Paper Folding Test (Male:r = 0.40, p = .001; Female: r = 0.29, p = .01), Z = 0.91, p = .36, orPSVT:R (Male: r = 0.48, p = .001; Female: r = 0.56, p = .001),Z = 0.90, p = .37. There were also no sex differences in relative accu-racy for global postdictions of performance on the Spatial Relations Test(Male: r = 0.44, p = .001; Female: r = 0.60, p = .001), Z = 1.61,p = .11, Paper Folding Test (Male: r = 0.71, p = .001; Female:r = 0.68, p = .001), Z = 0.42, p = .68, or PSVT:R (Male: r = 0.67,p = .001; Female: r = 0.59, p = .001), Z = 0.97, p = .32.

. Table 2 shows that male andfemale students were equally accurate in terms of both their absoluteand relative accuracy for all spatial measures except for the relativeaccuracy of performance on the PSVT:R. Surprisingly, female studentsdisplayed higher relative accuracy on the PSVT:R than did male stu-dents. Apparently they were better able to discriminate correct fromincorrect responses, even though males performed better on the test.

There were no significant sex differences in performance onthe Paper Folding test or the Spatial Relations test, but male studentsdid outperform female students on the PSVT:R. Although females per-formed as well as male students on several spatial tasks, female studentsconsistently predicted lower performance than male students on allspatial tasks. They generated lower mean CJs, global predictions, andglobal postdictions for each test, producing significant sex differences inevery task and for each prediction type except for global predictions forthe Paper Folding test.

Consistent with previous findings, male students per-formed significantly better on the symmetry span task than femalestudents. There were no sex differences in performance on the Raven'sProgressive Matrices task.

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Absolute accuracy (also referred to as calibration) refers towhether the average magnitude of an individual's judgments corre-sponds to their overall level of performance. Relative accuracy refers toone's ability to discriminate between correct and incorrect spatial taskdecisions (i.e., manifest higher confidence for correct than for incorrectitem responses). In the current experiment, we compared sex differ-ences for both absolute and relative accuracy on measures of spatialorientation and spatial visualization.

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Sex differences in spatial strategy use could also cause sex differ-ences in item-level monitoring accuracy. Metacognitive monitoring isan inferential process that involves evaluating cues (e.g. item char-acteristics, processing fluency, etc.) that are present at the time of amonitoring judgment and applying beliefs or heuristics to infer thequality of these processes (Dunlosky & Tauber, 2014; Koriat, 1997;Schwartz, Benjamin, & Bjork, 1997).

These differences instrategy preference may be due to differences in the accuracy of mon-itoring strategy effectiveness.

They also adopt differentstrategies than male students to solve spatial problems (Allen &Hogeland, 1978; Goldstein, Haldane, & Mitchell, 1990; Kail, Carter, &Pellegrino, 1979; Lohman, 1986; Miller & Santoni, 1986; Peña,Contreras, Shih, & Santacreu, 2008; Prinzel & Freeman, 1995; Raabe,Höger, & Delius, 2006; Tapley & Bryden, 1977).

The limited research examining sex differences in monitoring spa-tial cognition is especially surprising because sex differences in spatialcognitive performance have been indirectly linked to metacognitivevariables

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Only a few studies have explored whether there are sex differencesin metacognitive monitoring accuracy in non-spatial domains (Hertzog,Dixon, & Hultsch, 1990; Lichtenstein & Fischhoff, 1981; Lundeberg,Fox, & Punćcohaŕ, 1994).

Taken to-gether, the limited available evidence suggests that sex differences maybe present in some domains (memory for categorical lists, narrative textrecall) and not others (general knowledge), and there does not appearto be clear evidence for a general male or female advantage in mon-itoring ability.

Despite this large body of research examining sex differences inspatial cognitive performance, few experiments have focused on po-tential sex differences in metacognitive monitoring accuracy in spatialdomains (e.g., Cooke-Simpson & Voyer, 2007; Estes & Felker, 2012).

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Substantial sex differences in performance favoring males overfemales are present for many measures of spatial processing (Halpern &Collaer, 2005).

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The current study evaluated sex differences in (1) self-perceptions of everyday and academic spatial ability, and(2) metacognitive monitoring accuracy for measures of spatial visualization and spatial orientation.

Acrossmultiple spatial measures, female students displayed lower confidence in their item-level monitoring and globalassessments of performance than did male students, even when no actual differences in spatial performanceoccurred. Women were also less confident in their self-assessments of their visual-spatial ability for scientificdomains than were men. However, the absolute and relative accuracy of CJs did not differ as a function of sexsuggesting that women can monitor their spatial performance as well as men.

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However, gender remained astatistically significant predictor of confidence: Even when controlling for trial-level estimationprecision, girls/women were .038 points less confident in their estimates than were boys/men.

Thus, it appears that the average girl/woman was about 7%less confident than the average boy/man (.048 / .688 = .0697; Model A).

Using the fixed-effects estimates, girl’s/women’s confidence wasestimated to be .048 points lower than boy’s/men’s (p = .001; Model A).

Using linearmixed-effects models to predict confidence, we again found that girls/women were slightlyless confident than boys/men (replicating the findings from the Hedges’ g meta-analysis).We also found that these gender differences remained when estimation precision wasaccounted for.

To summarize the main outcomes, we again found thatgirls/women were less precise in their number-line estimates than were boys/men (replicatingthe findings from the Hedges’ g meta-analysis).

The fixed-effects model resulted in a similar overall mean effect size,g = .26, 95% CI [.10, .41], p = .002.

A gender difference occurred in confidence favoringboys/men (Fig. 2). The overall weighted effect size was g = .30, 95% CI [.12, .47], p = .002.No significant heterogeneity was observed among the effect sizes, Q(17) = 19.03, p = .33.

This indexrevealed a small, non-significant amount of heterogeneity among the effect sizes; I2 = 10.69%.

Our analyses revealed medium gender differences innumber-line estimation performance favoring boys/men (g = .52; Appendix 2).

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Thus, any gender differences in confidence couldarise from (somewhat) accurate monitoring of performance or from gender biases in makingconfidence judgments. Thus, if gender differences occur in confidence, will they remain whengender differences in performance is statistically controlled?

Thus,whether gender differences will occur in confidence for this task remains an open question. Asimportant, we also investigated whether gender differences occur in confidence when taskperformance (i.e., estimation precision) is controlled. This analysis is critical because whengender differences occur in performance and in confidence, then the former differences inperformance could (appropriately) be producing the gender differences in confidence.

To date, few investigations are available about gender differences in trial-by-trial confidencejudgments for math tasks, and that evidence is mixed.

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As with other metacognitive judgments (e.g.,judgments of learning), confidence judgments are not based on direct access to how preciselynumbers are represented in memory. Rather, theories of metacognition distinguish between twotypes of information that can be used as a basis for judgments (e.g., Koriat 1997; Koriat andAckerman 2010; Koriat and Levy-Sadot 1999). Theory-based judgments are informed bypeople’s naive beliefs about learning or their perceptions about their own abilities. In contrast,experience-based judgments are informed by on-line monitoring during task performance. In thenumber-line estimation task, both of these factors could influence confidence judgments aboutestimation performance.

Second, given that gender differencesdo occur in number-line estimation performance, any gender differences in confidence couldarise because people’s confidence tracks their performance.

Given such gender differences in number-line estimation performance, will gender differencesalso occur in confidence? Answering this question is important for a couple reasons. First, ifgirls/women are less confident in their estimation performance (as compared to boys/men),differences in confidence could partly be contributing to the differences in performance (for anexample in the context of the mental rotation task, see Estes and Felker 2012).

To foreshadow, such gender differences in number-line estimation performance also occurred in the present research, which motivated our focuson confidence.

Consistent with the above rationale, gender differences have been observed in number-lineestimation performance across development and for various numerical scales, with mediumeffect sizes on average (Bull et al. 2013; Gunderson et al. 2012; Hutchinson et al. 2019;LeFevre et al. 2010; Reinert et al. 2017; Thompson and Opfer 2008).

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Thus, to motivate our interest in confidence, we begin by first considering why (and whether)gender differences occur in number-line estimation performance.

To the extent that space and number are intertwined in the number-line estimation task – the focaltask in our analyses – one might anticipate gender differences due to the inherent spatial character-istics of the task.

The present research evaluates the extent to which gender differences arise in confidence onnumber-line estimation, a task which taps the fundamental ability to estimate numerical magnitude(and is predictive of future math achievement; e.g., Bailey et al. 2014; Booth and Siegler 2006, 2008;Fazio et al. 2014; Fuchs et al. 2010; Geary 2011; Schneider et al. 2018; Siegler 2016; Siegler et al.2011, 2012; Siegler and Thompson 2014; Tosto et al. 2018).

Does a gender gap occur in which girls are less confident than boys when they are engaged inmath tasks such as number-line estimation?

Boys/men were more precise (g = .52) andmore confident (g = .30) in their estimates than were girls/women. Linear mixed modelanalyses of the trial-level data revealed that girls’/women’s estimates had about 31%more error than did boys’/men’s estimates, and even when controlling for precision, girls/women were about 7% less confident in their estimates than were boys/men.

Prior research has found gender differences in spatial tasks in which men perform better,and are more confident, than women. Do gender differences also occur in people’sconfidence as they perform number-line estimation, a common spatial-numeric taskpredictive of math achievement?

This Mini-Review presents considerable evidence in sup-port of the thesis that females and males see the world dif-ferently and that this reflects corresponding sexdifferences in the human visual system

In short, sex differ-ences in the human visual system, although controversial,are undeniable. Additional investigation of sex differencesin the human visual system would contribute to analready considerable amount of evidence in support of sexdifferences in the nervous system generally and stronglycounter the traditional assumption in many fields of neu-roscience research that sex differences are negligible ornonexistent (Cahill, 2006; Cahill and Aswad, 2015).

Although some of these tasks (e.g., mental rotation) aresometimes associated with visual processing in the dorsalstream (Podzebenko et al., 2002), it is possible that sexdifferences observed in various measures of visuospatialability reflect differences in cognition rather than invision, which again highlights the requirement for addi-tional studies of sex differences in human perception andcognition in general.

engage human visual and cognitive systems (including thedorsal visual stream), with fairly disparate tasks showingvarying degrees of sex differences in performance (Millerand Halpern, 2014).

Over the past several decades, many studies have reportedsex differences in visuospatial ability, in particular, superi-or performance in males ( Maccoby and Jacklin, 1974;Linn and Petersen, 1985; Voyer et al., 1995). Unfortu-nately, visuospatial performance has been measured byextremely diverse stimuli and tasks that differentially

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In anexhaustive review of experiments on sex differences inlaterality, Hiscock et al. (1995) concluded that most if notall findings of vision-related sex differences in lateralitywere genuine.

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In short, although sex differencesin color vision may be related to both retinal and corticalfactors, additional studies are required to validate and elu-cidate such differences.

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Although this finding has also been observed inother mammals (Seymoure and Juraska, 1997), some havespeculated that sex differences in visual acuity in humansare related to the roles that men and women played inearly human hunter–gatherer societies, in which malesmay have been required to be able to identify prey orthreats at greater distances (Silverman and Eals, 1992;Sanders et al., 2007; Stancey and Turner, 2010; Abramovet al., 2012a).

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Theseauthors speculated that this sex difference reflects differ-ences in visual pattern analysis mode in which femalesemphasize use of low spatial frequencies that carryinformation about overall object form, whereas malesuse a more “segregative” mode that emphasizes individ-ual objects and fine detail inherent in high spatial fre-quency visual input.

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This section summa-rizes sex differences observed in standard psychophysicalstudies of visual perception and also presents related find-ings from neurophysiological and neuroimaging studies.

additional sex differences in visual perception and itsbasis in the human visual system and in the visual cortexin particular.

In short, sex differences in both bodysize and brain size predict sex differences in visual percep-tion. This Mini-Review summarizes and discusses many

In contrast to reproductive capacity, sex differencesin human brain function are largely a matter of degree.This Mini-Review of sex differences in the human visualsystem presents a large body of evidence indicating thatsex differences in visual perception and its neural basis arereal and lends support to the folk belief that males andfemales really do see the world differently, even if only toa degree.

This Mini-Review summarizes a wide range of sex differ-ences in the human visual system, with a primary focuson sex differences in visual perception and its neuralbasis. We highlight sex differences in both basic andhigh-level visual processing, with evidence from behavior-al, neurophysiological, and neuroimaging studies. Weargue that sex differences in human visual processing, nomatter how small or subtle, support the view that femalesand males truly see the world differently.

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We found that, for about a third of these tests, females performed significantly worse thanmales. In no paradigm did females outperform males.

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It is unclear why our results differ from previous studies, but it is possible that the small methodologicaldifferences we describe may have a large effect, and further studies should explore these effects in more detail.

Our results stand in contrast to many previous studies of sex differences in visual perception.

It is important to emphasize that visual tasks also rely on non-visual processes. It is therefore possible thatsome of the differences we report may be non-visual in nature.

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Our results are in line with studies demonstrating no correlations between similar paradigms in visual per-ception 22,39,53–55 .

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Using fifteen differentvisual tasks and more than 870 participants, we found that males significantly outperformed females in simple RT,visual acuity, visual backward masking, motion direction detection, biological motion, and the Ponzo illusion. Wedid not find significant sex differences for contrast detection threshold, visual search, orientation discrimination,the Simon effect, and four of five visual illusions.

Figure 5.

Table 3.

For Sample C (Table 1), we found a significant sex difference for the Ponzo illusion with amedium effect size (Table 3, Fig. 5; t(170) = −3.15, p = 0.002, d = 0.24). Females were 3.5% more susceptible tothe illusion than males (−11.8 vs −8.3%).

Results from three tests differed between males and females, i.e. RT, biological motion (inverted condition at800 ms) and motion direction. In all cases, males performed better than females (Fig. 4).

Using the 25 elements grating, females needed an SOA of 47.78 ms to reach the criterion level of 75%correct answers, whereas males needed an SOA of 39.9 ms (t(624) = 2.09, p = 0.03, d = 0.17) to reach the criterionlevel (see Table 2). When using the 5 elements grating, both males and females showed longer SOAs than with the25 elements grating; females again needed longer SOA than males (113.1 vs. 99.93 ms, respectively; t(624) = 2.57,p = 0.01, d = 0.20).

Females (22.66) as compared to males (21.19) did not differ in their vernier duration(t(624) = 1.21, p = 0.22; Table 2).

Males had a higher visual acuity compared to females (1.61 vs 1.46; t(623) = −4.37, p < 0.001).The effect size was medium (d = 0.35).

In detail, we found significant differences in Sample A, with 626 participants, on visual acuity and visual back-ward masking with both masks, but not for the unmasked vernier (see Fig. 3 and Table 2).

Out of the 10 perceptual tests (3 tests for 626 participants and 7 additional tests for 200participants), males performed significantly better than females in 5 tests: visual acuity, visual backward maskingwith 25 and 5 gratings, RT, biological motion, and motion direction.

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It is then possible that cyclingestrogen and progesterone or their interaction enhance PC-processing in women.

In addition to estrogen, progesterone is implicated in visualprocessing

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However, if some of our female subjects areindeed heterozygous carriers for red-green deficiency, evidenceindicates that the advantage in red-green contrast sensitivitymight belong to men due to deficient red-green discriminationfound in heterozygous carriers [24-26].

A review byParlee [33] highlighted evidence for cyclical effects on visualprocessing, and a later review [34] of this research suggests thereis an increased cortical capacity for visual information processingin women during peak estradiol levels of the menstrual cycle.

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While neither stimulus isabsolutely processed by one parallel pathway or the other, it isreasonable to assume that PC processes underlie sensitivity tothe small, red-green target. Likewise, processing for the large,drifting stimulus is certainly biased toward the MC pathways.

Men had lower contrast thresholdsthan women to the large, achromatic, drifting stimulus, but thedifference was not statistically significant for this target.

In this experiment, we found that women were moresensitive than men to the contrast changes in the small, red-green, stationary stimulus, which is more likely to be processedstrongly by the PC pathway.

Figure 2

There was no main effect of gender (F = 0.50,p = 0.48), but there was a significant interaction of gender andstimulus type on reaction times (F = 4.13, p = 0.04). Unlike theresults for contrast thresholds, there was no gender difference inreaction times for either the MC or PC-biased stimulus.

Both men andwomen had significantly lower mean reaction times for the MC-biased stimulus than for the PC-biased stimulus (F = 93.0, p <0.001).

The main effect of genderwas not significant (F = 2.43, p = 0.12), but there was a significantinteraction of gender and stimulus type (PC-biased vs. MC-biased) on contrast thresholds (F = 4.80, p = 0.03). As shown inFigure 1, women were more sensitive than men to the PC-biasedstimulus (t = 1.94, p = 0.05), but men and women were equallysensitive to the MC-biased stimulus (t = -1.22, p = 0.23).

As shown in Table 1, contrast thresholds for the MC-biased stimulus were significantly lower than for the PC-biasedstimulus (F = 246, p < 0.001).

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Theresults of these studies suggest that men may rely more on MCprocessing, while women may rely more on PC processing.

Although previous studies of gender effects on visualprocessing are heterogeneous, as a group they suggest thepossibility of sexual dimorphism in parallel visual processing[5].

Neurons in the MC pathwayare more sensitive to object location, movement, low spatialfrequency and global analysis of visual scenes. Neurons in the PCpathway are thought to be more involved with object and patternrecognition as well as color (in particular, red-green) opponency[3,4].

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The results of this experiment add to the body of evidence that women may relymore on parvocellular visual processes than men.

We present a limited review of the literature on gender differences in visualprocessing. We then add evidence to that body of literature, reporting the resultsof an examination of gender differences in response to stimulus conditions favoringmagnocellular (MC) and parvocellualr (PC) processing.

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It is surprising that similar studies in vision research are few and often under-powered 16–19 (with the notableexception of the well-established male preponderance of red-green color blindness 20,21 or sex differences in eyemovements 22 ).

Taken together, these studies revealmixed and complex effects of sex on visual perception. Moreover, it is clear that a comprehensive study on sexdifferences is missing from the literature.

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We report the results of fifteenperceptual measures (such as visual acuity, visual backward masking, contrast detection threshold ormotion detection) for a cohort of over 800 participants. On six of the fifteen tests, males significantlyoutperformed females. On no test did females significantly outperform males. Given this heterogeneityof the sex effects, it is unlikely that the sex differences are due to any single mechanism.

Further, we argue that theloss of function in insula/temporal areas may be directly related totool-use deficits seen in conceptual apraxia.

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fMRI showed that primary activations for identifying incorrect tooluse were found at temporal cortex and insula, while activationsfor correct tool use were seen along the canonical parietofrontaltool use network. Source localization analysis of EEG waveformsprovided additional information about the temporal evolution ofthese activations; insula, temporal cortex, and cuneus were exclu-sively active to incorrect tool use 0–200 ms following image onset,while occipitotemporal areas were exclusively active to correct tooluse 300–400 ms after image onset.

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If the tool–object relationship is determined to be contextuallyappropriate, no (tool-use specific) error signal arises from insula/superior temporal cortex. In this case, the parietofrontal networkwould then derive the adequate (task relevant) sensorimotor repre-sentation and motor plan for that tool–action goal pair. Alternatively,if the tool–object relationship is determined to be contextuallyinappropriate, perhaps the insula/superior temporal areas serve togenerate an error signal allowing for appropriate perception of tooluse error.

tool–object interactions.

Although currently speculative, our temporal and spatial resultsallow us to suggest that insula and superior/middle temporal cortexmay serve as a “gatekeeper,” evaluating the contextual correctness of

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Precuneus

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This relates to thecurrent study in superior temporal/insula activations seen in thejudgment of too use in an incorrect context, and further supportshigh-level visual functions in superior temporal areas cortex.

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Unlike the findings of correct over incorrect context, incorrectover correct contextual tool use activated novel areas that lie ventralto the parietofrontal regions, as well as on the mesial brain sur-face, particularly the insula, superior and middle temporal cortex,posterior cingulate, and cuneus/precuneus.

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Event-related fMRI analysis showed distinct activationsin bilateral insula, superior temporal cortex, anterior cingulate, andposterior cingulate for tool use in incorrect contexts (Figure 3).Bilateral activations for tool use in correct contexts tool use wereseen in posterior temporal areas and occipital cortex extendingalong the temporal–parietal–occipital junction, superior parietalcortex, premotor areas, lateral prefrontal areas, and anterior cin-gulate (Figure 3). EEG results largely confirm the fMRI data, whilefurther elaborating the temporal activation features. With analysisof EEG data focused on time bins identified through our previouswork (Mizelle and Wheaton, 2010b), we observed early activations(e.g., during the first 200 ms following image onset) exclusively forincorrect over correct tool use in temporal cortex, insula, cuneus, andposterior cingulate (Figure 5). Later time windows (300–400 ms)showed occipital and temporal activity (Figure 5) for identifica-tion of correct over incorrect tool use exclusively.

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A briefdeflection was seen following onset of the cue, and large, sustaineddeflections were present following onset of the image. As comparedto tool-only images, these responses were larger for correct andincorrect tool use at temporal and parietal areas. Waveforms for cor-rect and incorrect tool use diverged at two times following onset ofthe image (0–200 and 300–400 ms following image onset; Figure 4).This was most noticeable at bilateral temporal and parietal regions,where activation for incorrect use was greater immediately fol-lowing image onset (0–200 ms) and later at occipital, parietal, andtemporal regions (300–400 ms), where activation was greater forcorrect over incorrect tool use.

However, at 300–400 msafter image presentation (Figure 5; Table 5), activation differencesexclusive for identifying correct over incorrect tool use were seen atoccipitotemporal areas and cuneus.

From 100–200 ms post image presentation (Figure 5; Table 4),these activation differences shifted posteriorly to cuneus, lingualgyrus, insula, superior temporal cortex, and were still exclusive toincorrect over correct tool use.

When thesewaveforms were subjected to analysis (Figure 5; Table 3), sLO-RETA showed early activation differences (0–100 ms post imagepresentation) exclusively for identifying incorrect over correct tooluse predominantly at insula, superior temporal cortex, and anteriorand posterior cingulate.

For both [cor-rect > tool] and [incorrect > tool] comparisons, primary acti-vations were generally seen at premotor areas, inferior frontalgyrus, SPL, IPL, posterior temporal cortex, middle and inferioroccipital gyri, cuneus, lingual gyrus, insula, fusiform gyrus, andcingulate gyrus.

This analysisshowed that bilateral premotor and parieto-occipital areas wereactive in comprehension of correct tool use (Figure 3; Table1), while bilateral regions along the insula, superior tempo-ral cortex, mesial prefrontal cortex, and posterior cingulatewere active in comprehension of incorrect contextual tool use(Figure 3; Table 2).

Overall subjects were 95% accurate in their assessment of correctversus incorrect contextual tool–object interaction.

In other words, subjects were notmore or less accurate for either image category.

A role is suggested for theventral stream in providing semantic/contextual information toparietofrontal areas prior to interaction with a tool or object (Creemand Proffitt, 2001b; Valyear and Culham, 2010). In our previouswork, a distinct temporal–insula–precuneus–cingulate network wasengaged in differentiating matching from mismatching tool–objectpairings (Mizelle and Wheaton, 2010b).

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Others using ERPanalyses have identified the N400 effect in response to identi-fication of anomalous tool use (Sitnikova et al., 2003, 2008).Similarly, this response has been seen in extracting movement-related semantic information, such as identifying the incorrectconclusion of an action sequence (Reid and Striano, 2008) andin determining uncooperative hand–hand interactions (Shibataet al., 2009).

Others have evaluated the understanding of toolsimilarity based on action relatedness (comparing tools used inthe same way) or functional relatedness (comparing tools usedin the same context; Canessa et al., 2008), and highlighted theimportance of retrosplenial and inferotemporal cortex in under-standing functional properties of tools.

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We identified distinct regional and temporalactivations for identifying incorrect versus correct tool use. The posterior cingulate, insula, andsuperior temporal gyrus preferentially differentiated incorrect tool–object usage, while occipital,parietal, and frontal areas were active in identifying correct tool use. Source localized EEGanalysis confirmed the fMRI data and showed phases of activation, where incorrect tool-useactivation (0–200 ms) preceded occipitotemporal activation for correct tool use (300–400 ms).