- Jun 2022
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link.springer.com link.springer.com
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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.
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Second, given that gender differencesdo occur in number-line estimation performance, any gender differences in confidence couldarise because people’s confidence tracks their performance.
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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).
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To foreshadow, such gender differences in number-line estimation performance also occurred in the present research, which motivated our focuson confidence.
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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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There are gender differences in the performance of various cognitive tasks: women tend to be better at verbal tasks and men tend to be better at spatial tasks.
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Because number-line estimation is a spatial task, it is reasonable to assume that men would perform better on it than women.
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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.
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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.
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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).
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Does a gender gap occur in which girls are less confident than boys when they are engaged inmath tasks such as number-line estimation?
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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.
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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?
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onlinelibrary.wiley.com onlinelibrary.wiley.com
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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
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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).
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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.
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engage human visual and cognitive systems (including thedorsal visual stream), with fairly disparate tasks showingvarying degrees of sex differences in performance (Millerand Halpern, 2014).
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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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There may or may not be sex differences in the splenium of the corpus callosum. Some studies have reported the splenium ;to be larger and more bulbous in females. If there are sex differences in the splenium, then they might be related to sex differences in intrahemispheric vs. interhemispheric neural processing, word recognition, and reading.
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It has been reported that males have a greater degree of cerebral laterality than females, which may result in sex differences in the development of reading ability or the functional organization of the brain for language more generally.
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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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The amygdala, which has a role in visual processing, differs by biological sex in terms of size and other functions. The female amygdala responds more strongly to negative emotional valence stimuli, while the male amygdala responds more strongly to positive emotional valence stimuli. There are also sex differences in amygdala activity while viewing sexual stimuli.
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There are sex differences in face perception and the neural basis of face processing, implying sex differences in the brain areas underlying these functions. There are also sex differences in the brain area that perceives human bodies compared to non-body objects. In particular, this area is more active in males than in females when the person is viewing a threatening male.
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The LOC is a ventral stream area that has shown strong fMRI responses to object vs. nonobject stimuli, implicating it in object perception. Sex differences in the LOC have not been investigated, but they should be since the LOC is involved in object size perception and there are sex differences in object size perception. Other sex differences in object recognition may be due to sex differences in cortical thickness in the ventral visual cortex.
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This section of the article focuses on sex differences in the ventral visual stream, which supports conscious visual perception.
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Some fMRI studies have shown sex differences in BOLD signals at the visual cortex, which may be related to sex differences in visual acuity and color perception.
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Some EEG studies have shown that VEP waveform (which may be related to contrast sensitivity performance) differs by biological sex. It is yet unknown if these differences are due to underlying anatomical differences, gonadal hormone release differences, or differences in the visual cortex/retina.
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components
Needs definition.
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Visual evoked potentials (VEPs
Electrical potentials recorded from scalp overlying visual cortex that have been extracted from the electroencephalogram by signal averaging.
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Sex differences in motion perception have not been well-studied. One study suggested sex differences in the known motion processing areas of the human visual cortex, and another showed sex differences in biological motion perception.
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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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Several studies have demonstrated sex differences in color perception.
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This review article summarizes sex differences in basic visual processing, reviews sex differences in object recognition, and discusses sex differences in visuospatial processing (not at length).
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Sex differences in color sensitivity may be the result of X-linked genes that control spectral sensitivity of retinal photoreceptors.
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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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Most studies have found that men have greater visual acuity than women, but some studies have suggested that women have greater visual acuity than men in specific lighting conditions (especially in the dark).
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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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Brabyn and McGuinness (1979) compared contrast sensitivity in men and women, and found that womens' sensitivity to lower spatial frequencies was higher and mens' sensitivity to higher spatial frequencies was lower,. Abramov et al. (2012) did the same and found that mens' contrast sensitivity was higher at all spatial frequencies. Both studies suggested sex differences in contrast sensitivity.
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Male and female individuals may significantly differ in their abilities to perceive contrast differences (contrast sensitivity).
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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.
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additional sex differences in visual perception and itsbasis in the human visual system and in the visual cortexin particular.
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In short, sex differences in both bodysize and brain size predict sex differences in visual percep-tion. This Mini-Review summarizes and discusses many
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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.
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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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www.nature.com www.nature.com
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Small effect sizes, low power, and varying methodology may explain why literature about sex differences is mixed. There is probably a complex explanation as to what causes sex differences in visual perception, and those doing research on the subject should keep that in mind.
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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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Methodologically, this study suggests that between-subjects designs are most effective at controlling for confounds in studies about sex differences. Mechanistically, this study shows that sex differences are of complex origin and cannot be understood through simplistic explanations. Conceptually, this study suggests that sex differences in cognition could be what causes sex differences in vision.
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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.
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Our results stand in contrast to many previous studies of sex differences in visual perception.
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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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The study controlled for the effect of age on sex, since sex differences can depend on age.
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This study had very high statistical power, so the results were probably accurate for the most part. This study's most significant finding is the diversity of sex differences in visual processing.
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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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Significant sex differences appeared in numerous visual processing paradigms, but followed no discernible pattern. In short, findings were complex and varied markedly from paradigm to paradigm.
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This was the first major study of sex differences in visual perception.
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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.
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Figure 5.
Mean % of error in interpreting the Ebbinghaus, Muller-Lyer, Ponzo, Ponzo-Hallway, and Tilt illusions in men and women. Women were significantly more susceptible to the Ponzo illusion than males were (p<0.001).
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Table 3.
Number of participants, independent t-test results, significance value (p), and effect size (Cohen's d) for the Ebbinghaus, Muller-Lyer, Ponzo, Ponzo-Hallway, and Tilt illusions. Females were significantly more susceptible to the Ponzo illusion than males were.
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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%).
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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).
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(k)
Slope for visual search reaction time (measured in ms) in women (white) and men (black). No significant differences.
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(j)
Visual search reaction time (measured in ms) in women (white) and men (black). No significant differences.
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(i)
Threshold for which participants achieved 75% correct responses in identifying the orientation of a Gabor patch in women (white) and men (black). No significant differences.
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(h)
% of correct interpretation for upright 800ms biological motion in females (white) and males (black). No significant differences.
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(g)
% of correct interpretation for upright 200ms biological motion in females (white) and males (black). No significant differences.
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(f)
% of correct interpretation for inverted 800ms biological motion in females (white) and males (black). Men had a significantly higher percentage of correctness than women at the p<0.05 level.
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(e)
% of correct interpretation for inverted 200ms biological motion in females (white) and males (black). No significant differences.
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(d)
% of coherent dots needed to detect motion direction for females (white) and males (black). Females needed significantly more dots to detect motion direction than males did at the p<0.05 level.
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(c)
Contrast detection threshold for females (white) and males (black) measured in cd/m^2. No significant differences.
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(b)
Reaction time on the Simon task for females (white) and males (black) measured in ms. No significant differences.
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(a)
Reaction time on a simple reaction time task for females (white) and males (black) measured in ms. Females had significantly slower reaction time than males at the p<0.001 level.
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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).
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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).
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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).
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(c)
Stimulus onset asynchrony time (measured in ms) to show 75% accuracy rate for the Vernier discrimination task with the 25 element mask in women (white) and men (black). Women needed a significantly longer SOA time than men to show a 75% accuracy rate (p<0.05).
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(d)
Stimulus onset asynchrony time (measured in ms) to show 75% accuracy rate for the Vernier discrimination task with the 5 element mask in women (white) and men (black). Women needed a significantly longer SOA time than men to show a 75% accuracy rate (p<0.05).
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stimulus-onset-asynchrony
Amount of time between the start of one stimulus and the start of another stimulus.
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(b)
Performance of females (white) and males (black) on the Vernier discrimination task (measured in ms). No significant difference.
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(a)
Performance of females (white) and males (black) on the Freiberg visual acuity task (measured in decimals). Males performed significantly better on this task than females at the p<0.001 level.
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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).
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Table 2.
Tests taken by participants, number of participants for each test, independent samples t-test results for each test, p value for each test, and Cohen's d for each test. The visual acuity, visual backwards masking (25 and 5 gratings), simple reaction time, motion direction, and biological motion (inverted 200%) tests showed significant differences between male and female participants.
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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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Figure 2
The illusions that participants were tested on, including the Ebbinghaus illusion (EB), the Muller-Lyer illusion (ML), the Ponzo illusion (PZ), the Ponzo-hallway illusion (PZh), and the tilt illusion (TT). For each illusion, the participants were presented with two versions of the illusions that were different sizes (EB, PZh), lengths (ML, PZ), or orientations (TT), and were asked to alter one of the illusions to match the size, length, or orientation of the other illusion.
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(h)
Simon task, which measured participants' difference in accuracy or reaction time between trials in which stimulus and response are congruent and trials in which they are incongruent.
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response conflict
In choice reaction tasks, the interference of an irrelevant stimulus or stimulus feature such that choice reaction time to produce the correct response is slowed (APA Dictionary of Psychology).
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(g)
Visual search task, which measured participants' ability to select a specific image within an array of similar images.
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(e)
Contrast detection threshold task, which tests the participants' contrast detection threshold. Participants were presented with a red circle and then a green circle over time, and were told to indicate in which circle an image appeared.
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(d)
Orientation discrimination task, which tests the participants' orientation discrimination ability.
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(f)
Biological motion direction discrimination task for upright and inverted point-light walkers, which tests the participants' biological motion direction discrimination ability.
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staircase method
A variation of the method of limits in which stimuli are presented in ascending and descending order. When the observer's response changes, the direction of the stimulus sequence is reversed. This method is efficient because it does not present stimuli that are well above or below threshold (APA Dictionary of Psychology).
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Orientation discrimination
The ability to perceive the orientation of an object (clockwise or counterclockwise, in this case).
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www.researchgate.net www.researchgate.net
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This study provides more evidence that women rely more on PC visual processing than men do, but does not determine whether this is because women have an advantage in chromatic or spatial aspects of visual processing. It is also unknown whether this advantage is impacted by hormones cycling during the menstrual cycle.
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It is then possible that cyclingestrogen and progesterone or their interaction enhance PC-processing in women.
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In addition to estrogen, progesterone is implicated in visualprocessing
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Some studies have found that E fluctuations during the menstrual cycle may modulate which color wavelengths the visual field is most sensitive to, providing further evidence that E may play a role in vision.
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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].
Needs clarification.
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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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E might also influence vision through an intermediate mechanism like GABA, which mediates cortical inhibition. Cortical inhibition is important in determining visual responses, so E might indirectly improve visual processing by increasing GABA release, since GABA release controls cortical inhibition.
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The results of this study indicated that women were more sensitive to contrast changes in the red-green stimulus than men were. This might be because some of the female participants had a sex-linked genetic abnormality that allows them to be more sensitive to contrast differences in red-green stimuli, but that is unlikely. It could also be that estrogen receptors (ERs), which are exclusively found in the retinas of premenopausal women, give premenopausal women an advantage in detecting contrast differences in red-green stimuli.
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hemizygous
Describes an individual who has only one member of a chromosome pair or chromosome segment rather than the usual two
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The interactioneffect of stimulus type and gender on mean reaction times wassignificant, but there were no significant gender differences inmean reaction times for either the MC- or PC-biased stimulus.
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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.
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Men had lower contrast thresholdsthan women to the large, achromatic, drifting stimulus, but thedifference was not statistically significant for this target.
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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.
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Figure 2
Mean values of reaction times (in milliseconds) for MC-biased and PC-biased stimuli in men and women. Reaction times for the MC-biased stimuli were significantly lower than contrast thresholds for the PC-biased stimuli in both men and women
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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.
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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).
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Figure 1
Mean values of contrast thresholds for MC-biased and PC-biased stimuli in men and women. Contrast thresholds for the MC-biased stimuli were significantly lower than contrast thresholds for the PC-biased stimuli in both men and women.
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Table 1
Table depicting main effects (F and p values) of stimulus type, gender, and an interaction between stimulus and gender on contrast thresholds and mean reaction times, as well as the gender effects (t and p values; independent t-test of [values for men - values for women]) of MC-biased stimuli and PC-biased stimuli on contrast thresholds and mean reaction times. Stimulus type, and an interaction between stimulus type and gender, had significant main effects on contrast threshold, and PC-biased stimuli had a significant gendered effect on contrast thresholds. Stimulus type and an interaction between stimulus type and gender had significant main effects on mean reaction times. There was no significant gendered effect of MC-biased or PC-biased stimuli on mean reaction time.
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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).
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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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The current study engaged male and female participants in tasks that activated the PC pathway more and tasks that activated the MC pathway more. It was predicted that male participants would activate the MC pathway more than the PC pathway, and that female participants would activate the PC pathway more than the MC pathway.
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Theresults of these studies suggest that men may rely more on MCprocessing, while women may rely more on PC processing.
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Although previous studies of gender effects on visualprocessing are heterogeneous, as a group they suggest thepossibility of sexual dimorphism in parallel visual processing[5].
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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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There are two pathways for processing visual information: the parvocellular pathway and the magnocellular pathway.
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parvocellualr
Visual processing necessary for perceiving movement, depth, and small differences in brightness.
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magnocellular (MC)
visual processing necessary for perceiving color and form (fine details).
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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.
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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.
Tags
Annotators
URL
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Local file Local file
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two-tailed t-test
Statistical significance test evaluating whether a sample is greater than or less than a specific value range. Critical distribution area is two-sided.
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Significance levels
The probability of rejecting the null hypothesis when it is true.
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between-subject
Variability for individuals themselves in the sample.
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within
Variability of specific scores for individuals in the sample.
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voxel
A value on a regular grid in three-dimensional space. In this case, composes the 3-dimensional brain image.
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T2*-sensitive functional imag-ing
MR imaging frequency that displays CSF as the brightest contrast, white matter as the second brightest contrast, and gray matter as the third brightest contrast. Stronger than T2 MR imaging frequency.
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3-T Siemens Trio MRI scan-ner
3 Tesla-powered scanner model. In our replication, we will be using a different scanner.
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fixation cross
A cross presented to research participants in a perception task with the intent directing the participants' attention to wherever the investigator wants them to look.
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inter stimulus intervals
The amount of time between the end of one stimulus being presented and the start of another stimulus being presented.
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T1-weighted images
MR imaging frequency that displays gray matter as the brightest contrast, white matter as the second brightest contrast, and CSF matter as the third brightest contrast.
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cerebellum
Structure at the lower back of the brain, associated with motor control.
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cortex
Outermost brain layer, associated with higher order cognitive abilities.
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- May 2022
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Local file Local file
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deflection
Phenomenon in which voltage measured by EEG changes from positive to negative or negative to positive very quickly.
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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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The current study found that distinct brain areas are activated in identifying the correct tool for a specific context versus identifying the incorrect tool for a specific context, and provides additional evidence that the ventral visual stream processes contextual information related to tool use before the parietofrontal tool-use network processes sensorimotor information related to tool use.
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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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Conceptual apraxia is the result of disrupted ventral stream information processing, as the ventral stream would usually send information to parietal areas that would in turn process what an object is and how it should be used, but apparently cannot do this in conceptual apraxia. The current study found evidence that different brain areas activate for correct and incorrect tool use, suggesting the existence of separate networks for the two. Conceptual apraxia might happen because the incorrect tool use network, which involves parietofrontal areas (the ventral stream), is damaged. Specifically, these areas cannot generate error signals in response to the perception of incorrect tool use, causing incorrect tool use to be possible.
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Apraxia is a deficit characterized by being unable to select the correct tools for a specific task. Patients with conceptual apraxia can know a tool is correct for a specific task, but will perform that same task with an incorrect tool. The ability to carry out a specific task with the correct tool (as opposed to simply knowing said tool is correct for that task) may be controlled by the temporal cortex and insula.
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Table 5
EEG activations for contextually incorrect tool use over contextually correct tool use from 300ms to 400ms by specific brain lobe and region. "Lobe" refers to the specific lobe within the brain, "region" refers to the specific region within that lobe, and "XYZ(TAL)" are the 3-dimensional Talairach coordinates for the voxel encompassing that region. Z-value and k value need definition.
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Table 3
EEG activations for contextually incorrect tool use over contextually correct tool use from 0ms to 100ms by specific brain lobe and region. "Lobe" refers to the specific lobe within the brain, "region" refers to the specific region within that lobe, and "XYZ(TAL)" are the 3-dimensional Talairach coordinates for the voxel encompassing that region. Z-value and k value need definition.
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Table 4
EEG activations for contextually incorrect tool use over contextually correct tool use from 100ms to 200ms by specific brain lobe and region. "Lobe" refers to the specific lobe within the brain, "region" refers to the specific region within that lobe, and "XYZ(TAL)" are the 3-dimensional Talairach coordinates for the voxel encompassing that region. Z-value and k value need definition.
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FIGuRE 6
Comparison of fMRI and EEG imaging of activations from 0ms to 100ms and 100ms to 200ms using identical Talairach Z planes. Activation was very similar between the two (EEG confirms fMRI data).
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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 relationships deemed contextually correct do not cause an error signal in the insula/temporal cortex, leading the parietofrontal network to process the sensorimotor aspects of using that tool in that context. Tool-object relationships deemed contextually incorrect do cause an error signal in the insula/temporal cortex, leading the parietofrontal network to process the incorrectness of using that tool in that context.
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tool–object interactions.
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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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FIGuRE 5
Visual depiction of significant brain activity recorded by EEG 0ms to 100ms following image presentation, 100ms to 200ms following image presentation, and 300ms to 400ms following image presentation. Activation for correct over incorrect tool use are shown in red, and activation for incorrect over correct tool use are shown in green.
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Incorrect tool use was associated with early activations (image onset through 100ms) of the bilateral insula, temporal areas, anterior cingulate, and posterior cingulate, and later activations (100ms to 200ms) of the cuneus, insula, and posterior cingulate. Correct tool use was associated with even later activations (300ms to 400ms) of the occipital and temporal areas. These results suggest ventral activation precedes dorsal activation for contextual tool use decisions and actions.
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Precuneus
Brain region with complex functions such as memory, information integration relating to environmental perception, cue reactivity, mental imagery strategies, episodic memory retrieval, and affective pain responses.
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the PCC and precuneus have also been reported to serve functions relating to tool use. The PCC functions in relation to viewing familiar stimuli, visually guided grasping, viewing graspable objects, and viewing tool-related objects. The precuneus functions in relation to various types of memory-related visual information recall.
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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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The STC and STG have many functions that are related to tool use (especially in terms of high-level visual processing), and in particular show impairment of tool function understanding when damaged. The current study demonstrated that the STC and STG are activated during judgement of tool use in an incorrect context.
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The insula was activated by incorrect tool use. It serves many different functions, including contextual understanding of visual and somatosensory stimuli, as well as deriving "body ownership" of movement and deciding whether to act or not. The current study suggests the insula play a role in decision making through deriving an understanding of incorrect contextual action.
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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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Different areas were activated for incorrect over correct contextual tool use than for correct over incorrect tool use. The researchers expanded their model of matching and mismatching tool relationships using this information.
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The current study focused on identifying the contextual aspects of action error using fMRI, in contrast to previous studies which have focused on identifying other various aspects of action error using fMRI. The researchers propose that the contextual aspects of action error activate ventral stream areas like the temporal cortex and insula.
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Table 2
fMRI activations for contextually incorrect tool use compared to contextually correct tool use by specific brain lobe and region. "Lobe" refers to the specific lobe within the brain, "region" refers to the specific region within that lobe, and "XYZ(TAL)" are the 3-dimensional Talairach coordinates for the voxel encompassing that region. Z-value and k value need definition.
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Table 1
fMRI activations for contextually correct tool use compared to contextually incorrect tool use by specific brain lobe and region. "Lobe" refers to the specific lobe within the brain, "region" refers to the specific region within that lobe, and "XYZ(TAL)" are the 3-dimensional Talairach coordinates for the voxel encompassing that region. Z-value and k value need definition.
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canonical regions
Needs definition.
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Parietal areas, lateral frontal areas, and cortical movement areas contribute to the visual perception of tools, and were activated by contextually correct tool use. PCC, parietal cortices, cuneus, and precuneus contribute to understanding and production of complex tool-related movements, and were also activated by contextually correct tool use.
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The temporal cortex, which is related to to tool-related processing, was activated by correct tool use contexts.
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Different brain regions are activated for contextually correct and contextually incorrect tool use, and at different times. Subjects performed equally well in identifying correct and incorrect tool use, so results were probably accurate.
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conceptual apraxia
A neurological condition in which the afflicted individual makes content and tool selection errors.
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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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The purpose of this study was to evaluate the neural correlates of correct and incorrect contextual tool use by using fMRI to understand the spatial aspect of brain activation during related tasks and using EEG to understand the temporal aspect of brain activation during related tasks. fMRI showed activity in different brain regions for tool use in correct and incorrect contexts, and EEG showed early activity (immediately following image presentation) in specific brain areas for incorrect over correct tool use and later activity (300ms to 400ms following image presentation) in specific brain areas for correct over incorrect tool use. The current study expands the researchers' previous work on the same topic and may shed light on a potential mechanism for conceptual apraxia.
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(C,D)
EEG recordings of the left and right parietal regions of the brain showing ERPs (magnitude over time) when presented with images of contextually correct tool use, contextually incorrect tool use, and tools only. The first line represents when participants were presented with the cue, and the second line represents when participants were presented with the image. For both the right and left parietal regions, magnitude immediately following image presentation was significantly higher for incorrect tool use than for correct tool use and tools only, and magnitude at about 300ms to 400ms following image presentation was significantly lower for correct tool use than for incorrect tool use or tools only.
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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.
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(A,B)
EEG recordings of the left and right temporal regions of the brain showing ERPs (magnitude over time) when presented with images of contextually correct tool use, contextually incorrect tool use, and tools only. The first line represents when participants were presented with the cue, and the second line represents when participants were presented with the image.
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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.
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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.
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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.
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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.
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FIGuRE 3
fMRI images of brain areas significantly activated by comprehension of incorrect tool use (green) vs correct tool use (red) from different orientations (anterior, posterior, lateral (left), lateral (right), dorsal, and ventral).
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FIGuRE 2
fMRI images of significant differences in brain activation for identifying correct tool use when compared to identifying tools alone (above), and identifying incorrect tool use when compared to identifying tools alone (below).
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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).
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Overall subjects were 95% accurate in their assessment of correctversus incorrect contextual tool–object interaction.
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In other words, subjects were notmore or less accurate for either image category.
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(B)
Participants were recorded using EEG for 2 15 m blocks with a 3m resting period between each block. During each block, participants were presented with twenty-five images of incorrect tool use (2s), twenty-five images of correct tool use (2s), and twenty-five images of tools alone (2s; control), with fixation crosses (4s to 6s) and a cue (500 ms) being presented before each image and fixation crosses (4s to 6s) being presented after each image.
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(A)
Participants were recorded using fMRI for six 5m trials with 1m rest periods between each trial. During each trial, participants were presented with eight images of correct tool use (2s), eight images of incorrect tool use (2s), and eight images of tools alone (2s; control), with fixation crosses presented between each image (6s to 8s).
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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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The current study was designed to determine the neural correlates of conceptually understanding tool-object interactions. It involved the use of specific visual stimuli to allow subjects to identify the contextual nature of tool use as well as the use of fMRI and EEG to record subjects' neural activity while they assessed the correctness versus incorrectness of tool use in given contexts.
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It was predicted that the parietofrontal network would activate for identification of correct tool use, while temporal areas, insula, cingulate, and cuneus/precuneus would activate for identification of incorrect tool use, and that ventral areas would activate earlier for incorrect over correct tool use, while dorsal areas would activate earlier for correct over incorrect tool use.
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Not very much is known about the neural correlates of determining the conceptual "correctness" of tool-object interactions, so the current study focused on the neural activations associated with understanding contextually correct and incorrect tool-object interactions.
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www.nature.com www.nature.com
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(c)
Freiburg visual acuity task, which tests the participants' visual acuity.
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(b)
Visual backwards masking task, which tests the participants' visual backwards masking ability. First stimulus is shown, then an inter-stimulus interval (ISI; blank screen) is shown, then one of two second stimuli are shown.
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(a)
Vernier duration task, which tests the participants' ability to detect a misalignment between visual stimuli.
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Investigators analyzed data from participants in previous visual perception studies to determine sex differences. Males outperformed females on less than half of multiple visual perception measures, and females never outperformed males.
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Table 1.
Sampling information for participants that engaged in various visual perception tests. Information for each test includes the amount of participants that took the test, their mean ages (plus standard deviation), and their age range, in addition to the p-value for mean ages (plus standard deviation).
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axis I disorders
Non-personality disorder mental health conditions (DSM-IV).
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vernier duration
Needs definition.
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Knowing if there are visual perception sex differences could help us to determine whether sex differences in similar areas are actually due to visual perception sex differences.
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Simon task
Measures the difference in accuracy or reaction time between trials in which stimulus and response are on the same side (congruent) and trials in which they are on opposite sides (incongruent), with responses being generally slower and less accurate in the incongruent condition.
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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 ).
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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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spatial frequencies
Measure of how often any repeating structure across position in space repeats per distance unit.
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slant estimation
Needs definition. Probably another visual task of some sort.
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contrastsensitivity
The ability to perceive subtle differences in shading and patterns.
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Research has found that there are sex differences in visual, auditory, and somatosensory abilities.
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visual backward masking
Presentation of a visual stimulus ("mask") immediately following presentation of a different visual stimulus ("target") resulting in a failure to consciously perceive the first stimulus (my guess would be that they measure the participants' ability to see the first stimulus after seeing the second stimulus).
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visual acuity
A measure of the eye's ability to distinguish shapes and object details at a given distance.
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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.
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