Cerebral Lateralization - University of Toronto

Quantitative Assessment of Handedness andCerebral Lateralization

J O U R N A L O F N A T U R A L S C I E N C E S | 0 6

BEAUTY DAS¹, JED A. MELTZER² 𝄒³ 𝄒⁴, AND BLAIR C. ARMSTRONG¹ 𝄒⁵¹ Department of Psychology, University of Toronto Scarborough, Canada² Rotman Research Institute, Baycrest Health Sciences, Canada³ Department of Psychology, University of Toronto, Canada⁴ Department of Speech-Language Pathology, University of Toronto, Canada⁵ Department of Language Studies, University of Toronto Scarborough, Canada

Handedness, defined as a preference for one hand over the other, is the most studied humanasymmetry due to its connection to various lateralized behaviours, and hence many studieshave focused on developing a valid assessment. A prominent questionnaire is the EdinburghHandedness Inventory (EHI); however, its psychometric properties have been questioned, anddue to its complex response format, various modified versions of EHI are in use. One of them isa 7-item questionnaire produced by Dragovic and Milenkovic (2013) that seeks to reduce apotentially continuous variable to a categorical one by clearly classifying people as right- orleft-handed. The purpose of this study was to develop a questionnaire to more accuratelyquantify mixed-handedness as a continuous variable and to investigate the correlationbetween Dragovic’s modified 7-item EHI, new items created for this study, and threeperformance measures (grooved pegboard, finger tapping, and grip strength/dynamometer). Atotal of 113 self-reported right-, left- and mixed-handed participants were randomly recruitedto complete the questionnaire and behavioural measures. The questionnaire data wassubmitted to exploratory factor analysis and resulting factor scores were examined forcorrelations with behavioural tests. Compared to the modified EHI, the questionnaire showedfurther continuous grading of handedness. Moreover, the degree of handedness on thequestionnaire showed a stronger correlation with all the performance measures than theperformance measures had amongst themselves. These findings show that the novelquestionnaire with modern-day items can provide an accurate estimate of the degree of mixed-handedness in both right- and left-handed individuals. Future studies should examine thesemeasures on a larger sample of left-handed and ambidextrous people, who tend to be morevariable than right-handers in their usage. The questionnaire can also be suitable for studyingthe relationship between variable handedness and other aspects of brain lateralization.

Keywords: handedness, preference/performance measures, factor analysis

Abstract

PRIMARY RESEARCH ARTICLE


Handedness is the most pronouncedlateralized behaviour observed in humans.It is described as a preference for onehand over the other when performingunimanual tasks (Cavill & Bryden, 2003).Handedness can be further divided intotwo components: direction and degree.Direction defines whether the person isright- or left-handed, whereas degreedefines the strength of that direction, thatis, how strongly they prefer that hand(Steenhuis & Bryden, 1989). Severalstudies have shown that about 90% of thepopulation prefers the right hand (Annett,1985), with only 10% showing left-handpreference. In order to eliminate potentialconfounding effects, hand preference isoften measured to subtype individuals intogroups when studying other lateralizedbehaviours due to its relatedness tovarious asymmetric cerebral functions(Corballis, 2003). For example, 90% ofright-handers were found to be left-cerebrally dominant for language, whereasthis is the case for only 70% of left-handers (Corballis, 2003). A study byKnecht et al. (2000) also found left-cerebral activation during word generationto be linearly related to the degree ofright-handedness. Other than language,researchers discovered handedness to beassociated with other functionaldifferences, including intelligence(Johnston et al., 2009; Nicholls et al.,2010, 2012) and schizotypy (a continuumof personality characteristics andexperiences, ranging from normal to

psychosis) (Bryson et al., 2009; Chapmanet al., 2011; Nicholls et al., 2005). Thisunique association between variouslateralized cerebral functionalities andhandedness makes handedness a crucialentity to measure accurately.

Scientists have developed severalinstruments to measure handedness.Because handedness is determined basedon personal preference, self-reportquestionnaires with certain unimanualtasks are commonly used to measure bothdirection and degree (Cavill & Bryden,2003). Although hand preference measuresare convenient, the subjectivity of tasksand the inability to administer them topeople of all ages and demographics (e.g.,children and older adults) limit theirusefulness (Bryden et al., 1996, 2000a,2000b) and cause some researchers to leantowards hand performance measures (anindirect method). Hand performancemeasures use physical measurements ofmanual activities to objectively quantifyskill differences between the hands(Ocklenburg & Güntürkün, 2018).

The most widely used hand preferencemeasure to date is the EdinburghHandedness Inventory (EHI) (Edlin et al.,2015), a questionnaire reflecting 10unimanual tasks with adjacent right andleft choice columns, where participantsreport their performance with a + formoderate and ++ for strong preference(Oldfield, 1971). Despite its widespreaduse, a few studies have questioned its


Introduction


psychometric properties and suggested asimplified form of EHI as a betterassessment of primary handedness factor(Bryden 1977; Dragovic, 2004; Veale,2014). Although it was explicitly statedthat the accuracy of the questionnairedepends on following the exact formatwhen administered (Oldfield, 1971), thecomplicated response format makes itunfeasible for many potential participants(Mcmeekan & Lishman, 1975; Fazio et al.,2012). As a result, various researchers tendto use diverse modified forms of the EHI interms of items, response format, andcriteria used to distinguish left- and right-handers (Edlin et al., 2015). A modifiedversion of the EHI produced by Dragovicand Milenkovic (2013) has achieved fairlywidespread use, with 35 CrossRef citationsnoted as of July 22, 2020, on the Taylorand Francis Online website; however, itfocuses more on direction rather than thedegree of handedness, as it is specificallyoptimized to classify participantsunambiguously as right- or left-handed,whereas many people could otherwise beconsidered to exhibit mixed-handedness.Moreover, most of the availablequestionnaires contain items that areoutdated due to technological changes; forexample, nowadays people use a vacuumcleaner more than a broom and also usedevices such as phones and computersextensively, compared to the prevailingconditions in 1971 when the EHI wasoriginally published.

The present work aimed to address the

issues outlined above. Namely, it intendedto develop a questionnaire by adding newitems to the modified EHI of Dragovic andMilenkovic (2013) to quantify both degreeand direction of handedness, aiming at afine-grained estimate of mixedhandedness. The study also sought tocharacterize the correlations between saidquestionnaire, the modified EHI, and threedifferent commonly used performancemeasures, in order to identify thesuitability of the new items compared tothe extensively studied EHI items forquantifying handedness, to reduce relianceon subjectively measured hand preferencequestionnaires alone, and to benefit fromthe increased reliability and validityavailable from combining preference andperformance measures (Corey et al., 2001;Brown et al., 2006). A questionnaire wasdeveloped using the seven items fromDragovic and Milenkovic (2013) (will bereferred to as Dragovic’s items/7items)along with 25 additional items (will bereferred to as new items) that we selectedfrom other questionnaires (Cohen, 2008;Strien, 2002) or created ourselves. Threeperformance measures were also collectedin a randomized order, namely the GroovedPegboard Test (GPT), the Finger Tapping(FT) test, and the Grip Strength test (usingDynamometer). The three performancetests are widely used to measure manualasymmetries due to their ability to assessdistinct aspects of hand performance,including visuomotor control, motor speed,and raw strength, respectively. Followingdata collection, exploratory factor analysis



and comparisons of correlations wereperformed on the Laterality Quotient (LQ)obtained from the preference andperformance measures. LQ is an operationprevalent in handedness literature that isused to establish the degree and directionof handedness, typically using the formula(R - L/R + L) x 100, where a score of +100represents strong right-handedness and-100 represents strong left-handedness.Successful completion of this study wouldprovide the scientific community with anupdated questionnaire that potentiallytaps into the cerebral asymmetriesunderlying lateralized motor function andcategorizes the degree of individuallateralization more sensitively than thetraditional dichotomy of right- and left-handedness groups.


Methods

Participants

This study involved 113 participants, themajority of whom were students inIntroductory Psychology courses at theUniversity of Toronto Scarborough, whosigned up through SONA, the researchrecruitment software of the Department ofPsychology, and obtained a course creditfor participation. Of the 113 participants,98 self-reported as right-handed (65F,33M, mean age 19.2, SD 3.22), 12 as left-handed (9F, 3M, mean age 19.2, SD 3.26),and 3 as ambidextrous (3F, mean age 20.8,SD 5.16). Written informed consent andbackground information were obtainedfrom all participants before starting the

experiment. This project was approved byDelegated Ethics Review Committees(DERCs) at the University of TorontoScarborough.

Apparatus and Procedure

All participants were required to completethe questionnaire along with the threeperformance tasks (grooved pegboard test,finger tapping test, and grip strength test).The order in which the questionnaire andthe performance tests were administeredwas randomized.

Handedness Questionnaire. The questionnaireconsists of 32 items: 7 items fromMilenkovic and Dragovic (2013) and 25new items obtained from other researchquestionnaires or created based oneveryday situations (Appendix A). TheDragovic items were added as acomparison due to the immense evidenceof their ability to capture handedness. Theparticipants were asked to report theirhand preference for each item on a five-point Likert scale, options being “alwaysleft,” “usually left,” “no preference,”“usually right,” and “always right.” Eachresponse was scored from 1 to 5,respectively. Three different lateralityquotients (LQ) were calculated: the firstfrom Dragovic’s seven items, the secondfrom the 25 new items, and the third fromthe combination of both (32 items). TheLQs calculated using:

(R-performance — L-performance) / (R-performance + L-performance) x 100



ranged from -100, indicating strong left-hand preference, to 100, indicating strongright-hand preference.

Grooved Pegboard (GP). The groovedpegboard contains 25 grooved-peg holesarranged in a 5 x 5 array on a 10 cm x 10cm metal plate (Lafayette InstrumentCompany). The standard instructions onthe Lafayette Instrument Model 32025 GPUser’s Manual (Lafayette Instrument, 2014)were followed when administering thetask. The pegboard was placed mid-linewith the subjects, and they were asked tostart with their dominant hand. For theright hand, pegs are to be insertedsequentially from left to right for each rowand in the opposite direction for the lefthand. Participants were also instructed topick up only one peg at a time, not to pickup a peg if dropped (but rather pick a newpeg from the well containing them) and tobe as fast as they could. The timing beganwhen the participant picked up the firstpeg and ended when they inserted the lastpeg. Each hand was tested once, and thetotal time (in seconds) taken to insert allthe pegs was recorded for each hand.

Finger Tapping (FT). The Finger Tappingtask was programmed in Psychopy(Standalone v3.2.4) (Pierce et al., 2019),and the responses were collected using afour-key button box (202mm x 137mm x35mm LWH; The Black Box Toolkit). Thetask consists of three trials per hand, witheach trial containing 15 s of tappingfollowed by 6 s of break. The participants

were asked to use their index finger totap, and, before each trial, they werenotified of the hand to be used, followedby a countdown. The order of the trialswas randomized. At the end of the task,the total number of taps for each trial wasrecorded and averaged for each hand.

Grip Strength (GS). GS was measured usinga Jamar Plus Digital Hand Dynamometer.The built-in “standard test left and righthand” feature on the dynamometer wasused, with the number of trials set to 3.The participants were asked to hold thedynamometer while sitting with a straightarm and provide maximum GS efforts. Thetrials were performed in an alternatingmanner. At the end of all six tests,performance average and standarddeviation were recorded for each hand.The units of measurement were kilograms.

Data Coding

Performance on the questionnaire, GPT,FT, and Dynamometer were expressed as alaterality quotient (LQ) using the formula:

(R-performance — L-performance) / (R-performance + L-performance) x 100

(Milenkovic & Dragovic, 2013; Davidson &Tremblay, 2013). For all measures exceptGPT, a positive LQ obtained using thisformula indicates right-hand superiority,whereas negative LQ indicates left-handsuperiority. For consistency, GPT LQsresulting from the equation above were

multiplied by -1, so that all positive LQsmean right-hand superiority. All data werethen subjected to factor analysis andcorrelation matrix using RStudio software.

Data Clean-Up

From a total of 113 participants that wererandomly recruited (98 self-reported right-and 12 self-reported left-handed, 3ambidextrous), some failed to completethe FT task successfully as the datasuggested that they were not pressing thebutton as fast as they actually could.Therefore, five participants were excludedaltogether (all right-handed), and for anadditional 10 participants, the first trial(out of three trials) was eliminated due tohigher tapping rates on the second andthird trials, which means that the first trialdidn’t represent their optimalperformance. Altogether, FT data from atotal of 108 participants was used in theanalysis; the rest remained unchanged (i.e.113).

Statistical Analysis

All graphs and statistical analyses wereproduced using RStudio (v 1.2.5033).



Results

performance + L-performance) x 100(average was used in case of multipletrials; Dragovic & Milenkovic, 2013;Davidson & Tremblay, 2013). Positive LQswere considered as right-hand dominanceand negative LQs as left-hand dominance.LQs obtained from Dragovic’s 7-items werefound to be heavily bimodal, showingextreme right or left values (as intended),whereas LQs obtained from the new itemsshowed more variability (Figure 1). FromFigure 1A, it is also visible that althoughthe 7-item questionnaire categorizedright-handed individuals more consistentlymixed-handed people also got categorizedinto strongly right- or left-handed groups.Conversely, the new items categorizedmixed-handed people as less stronglyright-handed (Figure 1B). Together, thequestionnaire seemed to categorize allthree groups quite precisely (Figure 1C).LQs obtained from the Dynamometerappeared to be more strongly left-skewedcompared to those of the GPT and FT, withDynamometer and FT categorizing mixed-handed people as more left-handed (Figure2). Figure 2 also shows that data fromright-handed individuals were slightlymore accurately depicted by FT comparedto GPT and the Dynamometer test, with themajority of LQs being above 0. However,data from left-handed individuals weremore spread out in GPT compared to theother two and were more accuratelydepicted by Dynamometer.

Factor Analysis Produced a Two-factorSolution

New Items Showed Better Effectiveness atCategorizing Mixed-handed People

The Laterality Quotient (LQ) wasestablished using the formula: (R-performance — L-performance) / (R-



Figure 1. Laterality Quotient (LQ) obtained from Dragovic’s 7-item questionnaire. (A) Additional new items. (B) All items combined. (C) Based on participant’s self-reported handedness (n = 113:98 right, 12 left, and 3 ambidextrous).

Responses on the questionnaire (32 items)were subjected to parallel analysis andexploratory factor analysis using the Rpackage psych. A parallel analysis wasperformed on the questionnaire (32 items)to determine the number of factorspresent, involving a comparison ofeigenvalues between the data andrandomized data with the same structure(Horn, 1965). This procedure suggested thepresence of two distinct factors, as therewere two points above the dashed linedepicting an eigenvalue of 1 (Kaisercriterion; Figure 3A), and the softwarereported a 2-factor solution as most likely.A two-factor solution was then producedusing exploratory factor analysis (EFA)

with the rotation “varimax” and factorextraction method “minimum residual”(Williams et al., 2012), with factor 1explaining 48% and factor 2 explaining 5%of the total variance. Pearson’s correlationreported the correlation between the twofactors to be 0.11 (Figure 3B). The analysisshowed that the 7-item questionnaireloaded heavily onto factor 1 (heavycriteria: a difference >.1 between theloadings), with items 3 and 4 loading onlyonto factor 1 (in blue) using a cut-offvalue of 0.30 (Table 1). Most of the newitems loaded more or less onto bothfactors, with 7 of them (items 10, 13, 16,18, 23, 26, 30) loading heavily onto factor1 (in yellow) and 6 of them (items 8, 12,



Figure 2. The Laterality Quotient (LQ) obtained from Grooved Pegboard test (GPT). (A) Dynamometer. (B) Finger Tapping (FT) test. (C) Based on participant’s self-reported handedness (n = 113: 98right, 12 left, and 3 ambidextrous).

14, 17, 29, 31) loading heavily onto factor2 (in brown; Table 1). Additionally, amongthe new items, item 22 loaded only ontofactor 1 and items 15, 19, 20, 25, 28, and32 loaded only onto factor 2. Table 1 alsoshows that “combing hair” almost equallyloaded onto both factors, and, from all theitems, “eating an apple” appeared to havethe lowest loading onto both factors.

Performance Measures Correlated Betterwith the New Items Than with Each Other

Next, the Pearson’s correlation matrix wascomputed between items of Dragovic’squestionnaire (7), new items (25), the totalquestionnaire (7+25) and the three

performance measures, along with thetwo-factor scores obtained from factoranalysis. The degree of correlationbetween GPT, FT and the Dynamometertest with the seven items from Dragovic,new items, and the questionnairecombining all the items appeared to bestronger compared to the degree ofcorrelation among themselves (Figure 3B).To test the significance of thiscorrelational difference in within-performance measures and between-performance measures and new items,Fisher’s Z statistical tests were performedto compare correlation coefficients(criteria used: dependent, overlappinggroups; one-sided). Statistical analysis



Table 1. Factor loadings obtained from the two-factor exploratory factor analysis solution using varimaxrotation (n = 113). The first 7-items are from Dragovic’s questionnaire (in blue) and the rest are the additionalnew items. A cut of value of 0.3 was used when interpreting the loadings (in red) and items were considered toload heavily if the difference between the two loadings were >.1. Yellow and brown show items that loadheavily onto factor 1 and 2 respectively; black for low loadings and green for similar loadings.

showed Dynamometer-new itemscorrelation to be higher thanDynamometer-GPT correlation (p = .004)and Dynamometer-FT correlation (p =.002), as well as FT-new items correlationto be higher than FT-Dynamometercorrelation (p = .004) and FT-GPTcorrelation (p = .02), the rest being non-significant (p ≥ .2). The correlation plotalso showed that although Dragovic’s

questionnaire correlated better with theGPT, the 32-item questionnaire showed astronger correlation with FT andDynamometer compared to that ofDragovic’s (Figure 3B). Next, since amongthe three performance measuresDynamometer showed the highestcorrelation with the new items (Figure 3B),pairwise t-tests were performed in order toassess whether Dynamometer LQ



handedness factor 1 scores (HF1) than 2(HF2; Figure 3B).

significantly differs from that of FT andGPT. Pairwise t-tests (Bonferronicorrection) on performance LQs showedthe mean difference between GPT and FT(p = .023, paired) and Dynamometer and FT(p = .078, paired) were significant, with anon-significant difference betweenDynamometer and GPT (p = 1.00, paired).All preference and performance measuresdisplayed a stronger correlation with

Figure 3. (A) Presence of two factors on the scree plot obtained from parallel analysis performed on thequestionnaire. (B) Correlation plot between items: LQs from Dragovic’s questionnaire (7), new items (25),questionnaire combining all the items (32), Grooved Pegboard Test (GPT), Finger Tapping (FT) test,Dynamometer, handedness factor 1 scores (HF1) and handedness factor 2 scores (HF2). n = 108 for FT and 113for the rest.

Discussion

The purpose of this project was to build aquestionnaire to quantify mixed-handedness along with right- and left-handedness accurately and examine thecorrelation between the new items,


Because of this variability, using any ofthese performance measures alone will notcategorize individuals with precision;however, combining them might proveuseful as suggested by Corey et al. (2001).The two-factor solution generated fromEFA shows Dragovic’s seven items loadingmostly onto factor 1, with the new itemsdistributed between factors 1 and 2.Although the underlying construct that allthese items measure is handedness, someitems contribute more to one factor thanthe other. Dragovic’s items loading heavilyonto factor 1 might indicate that the firstfactor is taking explicit handedness intoaccount, whereas the second factor mayaccount for more subtle variability relatedto the extent to which the preferred handis favoured exclusively. For example, itemssuch as “using a computer mouse”,“holding a pencil eraser..”, “using arazor..”, which loaded onto factor 1,require a stronger degree of manual skillcompared to items such as “using a tvremote”, “holding a cellphone to ear...”,“petting an animal”, “opening a drawer”,which loaded onto factor 2 (Table 1). Thisnotion is supported by the finding thathand preference for “skilled” activities(requiring use/manipulation of tools/objects) is strongly lateralized and hencemay load onto one factor, whereas “lessskilled” activities (picking up objects) loadonto another (Steenhuis & Bryden, 1989).In future studies, it could be interesting tosee how each of these constructs (“skilled& less skilled”) correlate with a range oftasks known to impact lateralization (e.g.,

Dragovic’s 7 items, and the three differentperformance measures (GPT, FT, andDynamometer).

The new items and the questionnaire bothseem to categorize mixed-handedindividuals more sensitively thanDragovic’s questionnaire, putting them inthe less strongly right-handed group(Figure 1). The LQs from Dragovic’squestionnaire appear to “force” individualsto the one extreme of handedness (left orright). This makes sense as the purpose ofthe modified EHI was to “preventunjustifiable categorization of individualswith otherwise clear right-handpreferences into the mixed category”(Dragovic & Milenkovic, 2013), hence thelimited variability. However, the purposehere is not to classify people intocategories, but rather to generate a fine-grained quantitative estimate of thedegree of handedness. Combining theDragovic items with the new itemsprovides a middle ground by generating abroad spectrum of possible degrees ofhandedness that correlate withperformance measures. Although ourresults are promising, a sample containinga higher number of ambidextrous/left-handed people and people of differentdemographics is needed to establish thatwith more certainty.

While the performance measures quantifyright-handed people quite accurately, theyfail to do so in most cases for the left- andmixed-handed population (Figure 2).



language, intelligence).

Traditionally, a factor loading of 0.3 isused as a cut-off, as a factorability of 0.3indicates that approximately 10% of therelationships within the data areaccounted for by that factor (Williams etal., 2012). Following this, all the itemsexceed the cut-off generously in at leastone of the two factors. The only item witha poor loading on both factors is “eatingan apple.” Therefore, the elimination ofthis item would likely increase theconstruct validity of the questionnaire(Table 1, in black). The correlational plotportrays an interesting finding: thecorrelation between the new items and theperformance measures is stronger than theintercorrelation between theseperformance measures, which wasstatistically found to be true only in thecase of Dynamometer and FT. This findingaligns with another study by Brown et al.,(2006) which suggested that the reason forthis could be that all three performancemeasures tap into different motor abilitieswhich possibly don’t correlate well (e.g.motor speed VS visuomotor control).Hence, it is important to combinepreference measures with the performanceones to tap into different constructs oflaterality. The higher correlation ofdynamometer LQ out of all threeperformance measures with new items LQpresented the question of whetherperformance LQs significantly differ fromeach other. According to the analysis, asignificant difference is present between

Dynamometer and FT and GPT and FT, butnot Dynamometer and GPT. This could bedue to the dichotomous nature of handperformance measures and a sampleconsisting mostly of right-handedindividuals.

The broad range provided by the additionof new items to the modified EHIdeveloped by Milenkovic and Dragovic(2013) allows the appropriatecharacterization of mixed-handedness andopens new possibilities in the field ofhandedness research. In future studies, thecorrelation between various componentsthat are measured by the handperformance tasks and the questionnaireitems should be studied. Additionally, thecorrelation of these performance andpreference measures with other measuresof lateralized abilities could be examined,including language processing (lefthemisphere advantage) and spatialattention (right hemisphere advantage), tosee whether certain items/performancetests correlate more with these tests andexplore why that might be the case.


I would like to express my sinceregratitude to my supervisor, Dr. Jed Meltzerand co-supervisor, Dr. Blair Armstrong fortheir invaluable support and guidancethroughout this research project. Thecompletion of this study could not havebeen possible without their expertise.

Acknowledgements



AppendixAppendix A. Handedness Questionnaire Used in this Study. A combination of 7 Dragovic’s items+ new 25 items), as per Gonzales and Nelson (2019), Cohen (2008), Strien(2002) and Milenkovic and Dragovic (2013).



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