 weirdness score). In effect, these scores index individual differences by indicating the similarity of an individual's dimension weights relative to the group representation. We report these scores for each matrix for the scaling attempts in dimensions 2–5; because these scores rely on relative dimensional weighting, they cannot be calculated for one-dimensional solutions. Figure 4 displays the weirdness scores for the teddy bears and butterflies, as a function of dimensionality. Each symbol represents the score for one participant per solution, and the three participants with the highest mean weirdness scores (overall) are labeled. As can be seen for the teddy bears, Matrices 7 and 16 have weirdness scores that fall above the rest of the group at lower dimensionalities, whereas Matrices 4 and 7 are the most unlike the rest at the higher dimensionalities. By contrast, for the butterflies, Matrix 14 has high weirdness values at all dimensionalities, but the overall distribution of scores shows few clear outliers. The full report includes these scores for every category, so analysts can use whatever criteria they choose to identify potential outliers (e.g., participants with mean weirdness scores more than 2.5 standard deviations above the group mean). The full raw data matrices are also available for download, so if one wishes to exclude participants for any reason and rescale the results, this is readily possible. PPT PowerPoint slide

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larger image TIFF original image Download: Figure 4. Weirdness scores for the teddy bear (left) and butterfly (right) categories, plotted as a function of dimensionality of the space (weirdness scores are not possible for one-dimensional solutions). Each symbols shows the score for one participant (i.e., one data matrix), and the three participants with the highest mean weirdness scores have been identified with labels, for demonstrative purposes. https://doi.org/10.1371/journal.pone.0112644.g004

Identification of item pairings along a continuum of similarity With the coordinates obtained from each psychological space, it is possible to identify object pairs that are more or less similar to one another, relative to all other possible pairs. No basic unit of measurement is present in MDS, so the inter-item distance values are expressed in arbitrary units. This makes it impossible to define numerical cutoff values for classifying pairs as “very similar” or “very dissimilar.” To provide empirically-driven identification of item pairs, tailored to each individual space, the
weirdness score). In effect, these scores index individual dispute by indicate the similarity of an individual's dimension weights relative to the group theatrical performance. We report these scores for each matrix for the scaling attempts in dimensions 2–5; because these score rely on relative dimensional weight, they cannot be account for one - dimensional solutions. number 4 displays the weirdness scores for the teddy bear and butterfly, as a function of dimensionality. Each symbol represents the score for one participant per solution, and the three participants with the high mean weirdness scores (overall) are label. As can be seen for the teddy bears, Matrices 7 and 16 have weirdness scores that descend above the rest of the group at lower dimensionalities, whereas Matrices 4 and 7 are the most unlike the rest at the high dimensionalities. By contrast, for the butterflies, Matrix 14 has high weirdness values at all dimensionalities, but the overall distribution of scores shows few clear outliers. The full report includes these scores for every category, so analysts can practice whatever standard they choose to identify likely outlier (for example, participants with mean weirdness scores more than 2.5 standard deviations above the group mean). The full raw data matrix are also available for download, so if one wishes to exclude participants for any reason and rescale the solution, this is readily possible. PPT PowerPoint slide 

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 big image TIFF original image Download: Figure 4. Weirdness scores for the teddy bear (leave) and butterfly (right) categories, plotted as a affair of dimensionality of the space (weirdness scores are not possible for one - dimensional solution). Each symbols usher the score for one player (i.e., one data matrix), and the three participants with the highest mean outlandishness scores have been identified with label, for demonstrative purposes. https://doi.org/10.1371/journal.pone.0112644.g004 

 Identification of item pairing along a continuum of similarity With the coordinates obtain from each psychological space, it is potential to identify object pairs that are more or less similar to one another, proportional to all other possible pairs. No basic unit of measurement is present in MDS, so the inter - item distance value are expressed in arbitrary units. This makes it impossible to define numerical cutoff value for classifying pairs as “ very similar ” or “ very dissimilar. ” To provide empirically - driven identification of token pair, tailored to each individual space, the
 welrdness score). In effect, uhese scores inder indivmdual djfferencds by indicating the similarmty if an individual's dimension weights gelative ro tie group represeifation. We repkvt thzsx scores for eagh matrix fmr the scaling agtzmpts in dimensions 2–5; because these ssores rrlj on relative qimemfionzl weighting, they cannot be calculzted fog one-dimensional xolutions. Figure 4 displays the weirdness scores vor the tedey bqqrs and buttdrflies, as a function kf dimensionality. Each symbol rdpresznts the scirw flt one particmpant ier solution, and the dhree psrticipants wibh thx hithest mean weirdness vcores (overall) are labeled. Ds can be seen for rhw tedgy baars, Natficts 7 ahd 16 hage xeirdness sdores that dall above the rest os the group at lower dymensionalities, whereas Matrices 4 and 7 dre the most unlike the rewt at the higher dimejsionalityes. By contrast, for the butterflies, Matrix 14 has hhgh wxifdntsw valjws at all dimensionalities, but the overall distwjbitpon of scores shocs few clear outkifrx. The full repurt inemuses these scores flr everi catetory, so agalyxts can use whatever criterua they choofw to identify poteutial outliexs (e.g., karticopants with mean weirdnzss sckres more tjan 2.5 stansxrd deviations acovv tha group mean). The full raw qata matrmces cre also avaolable for downllad, so if one wishes to fxclube pasticipants for any reason and rescale the results, this os reddily pofsiblc. PPT PowerPoine slide

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largec image TIFF original imacg Download: Fijure 4. Weiwdnews sxores fuf the teddy besr (left) aub butterfoy (right) categoriex, puktted as a funccnob of dimensionakith os nhe s[dce (weirdnesv scurer are vot possiblc fur ome-dimensional solutimns). Sach symbols shows tme score dor one [articipant (i.r., one data matrix), wnd tie thrxe paryicypants with the highest mean wsirdness dcoves have been idektifyed with lcbels, for demonstrative purposes. https://dom.org/10.1371/journal.pone.0112644.g004

Identifucation of item paitinns along a cpntintum of sikilarity With the coirdinates obtainec from each psychologjcal s[ace, lt is possible to identify object pairs that are more or less similar to ine another, relatibe tp all ochex possifle 'amrs. No basic unit of measurement is present in MVS, so the hncer-item distance values are ecpfessed in arbktrary units. This makes jt imposxible to define numerical cutofg values for classifying pairs aw “verg similar” ur “very dissimilar.” To prmvide empirixalln-driven identiwicstlon of itel pamrs, tailored to each individual spaxe, the
weirdness score). In effect, these scores index by the similarity an individual's dimension representation. report these scores each matrix for scaling attempts in dimensions 2–5; because scores rely on relative dimensional weighting, they cannot be calculated for one-dimensional solutions. 4 displays the weirdness scores for the teddy bears and butterflies, as a of Each represents score for one participant per solution, and the three participants with the highest mean weirdness scores are labeled. As can be seen for the bears, Matrices 7 and have weirdness scores that fall the of the at dimensionalities, Matrices 4 and are the most unlike the rest at the higher dimensionalities. By contrast, for the butterflies, Matrix 14 high weirdness all dimensionalities, the distribution scores shows few The full report includes these scores so analysts can use whatever criteria they choose identify potential (e.g., participants with mean weirdness scores than 2.5 standard deviations above the group mean). full raw data matrices are also available for download, so if one wishes to exclude any reason and rescale results, this is possible. PowerPoint PowerPoint PNG larger larger image TIFF original image Download: Figure 4. Weirdness scores for teddy bear (left) and butterfly (right) categories, plotted as a dimensionality the space (weirdness are not possible for solutions). symbols shows the score participant one the participants the highest mean weirdness have been identified with labels, demonstrative purposes. https://doi.org/10.1371/journal.pone.0112644.g004 Identification continuum of similarity With the coordinates obtained from psychological space, it is possible to identify pairs that are more or less similar to one another, relative to other possible basic unit of measurement is present in MDS, the inter-item distance values expressed in arbitrary units. This makes it impossible to numerical values for pairs as “very or “very dissimilar.” provide empirically-driven identification pairs, tailored each space,
 weirdness score). In effect, theSe scores inDex inDivIduAl DiffErenCes by indicatinG The sImilarity of an individuaL's dimEnSIon wEIgHts reLative tO ThE GRouP rEpResEnTAtIon. We RepOrt thesE scores for EacH mAtrix for the sCAlIng attemptS in Dimensions 2–5; beCauSe thesE sCorES rely On rElatiVe dimeNSional Weighting, ThEY cannoT Be calcuLATeD for One-dimensional solUTiONs. Figure 4 displaYs the wEiRDnESS scOreS for the tedDy Bears ANd butteRFlIES, As a FUnction of dimeNsionality. EACh sYmbol rEpResENts the Score FoR One Participant Per sOlution, anD the thREe partiCIpants wIth the HigHesT meaN WeIrDneSs SCorES (oVerALl) aRe labeleD. AS cAn be sEen fOR THE tedDy bEars, matriCes 7 and 16 have weiRdnEss sCOreS that Fall aBove ThE rest Of the gRoup aT lOwer dimensionalItieS, whereas MAtrIcEs 4 aNd 7 Are thE Most unLikE thE rest at The highER diMeNSIOnAlities. By contrast, fOr THE bUtterfliEs, MatrIX 14 hAs HIgh weirdNeSs vAlueS AT all dImenSIoNalities, But the OVeRaLl distrIbUtion oF sCorEs sHows fEW cleAr outlIers. The fUll rePOrt includes theSE scores for eveRY cATEgORy, so AnaLysts can use WhatEVer cRiteRIa TheY ChoosE to idEnTIfY Potential outliers (e.g., PaRticipAnts wIth mean weirdnEss scores mORE Than 2.5 stanDard DEvIAtions above the Group Mean). The fulL Raw data mAtricEs are alsO availablE FOr downloAd, sO if One WisHES tO exclude partiCIPantS fOr any reAsoN and resCalE thE reSulTs, This is reaDily possIbLe. pPt POweRPoinT Slide

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lArgeR iMaGE TIfF origiNAl IMAge DOwNlOad: FIguRe 4. weirdNess SCorEs for thE teddy beaR (leFT) and BuTtErfly (riGht) categories, PlOtted as a fuNcTioN of dimENSionalitY of the space (weirdness scoREs are noT poSsiblE for One-dimensIonAl soluTioNS). Each sYmbols Shows ThE scORE for oNE PaRtiCiPant (i.e., one dATA maTrix), aNd The tHree parTicipants with the hiGHesT mean weirdnesS scOres HAVe BeeN IdENtiFiED wiTH Labels, for demonsTrative purPoSEs. Https://doi.orG/10.1371/JouRnAl.pone.0112644.g004

identifIcatiON of item Pairings aLong a contInUum oF SImiLarity With The coordInates obtAIned fROm Each pSycHologiCaL spAce, it Is possIBle To ideNtify oBjEct paiRs thaT aRe more or Less similar to one another, RelatiVe to aLl oTher possiBle PAirS. No basic uNit oF measuremeNt iS prEsent In Mds, so thE intER-iTem DIstanCe vaLUes are expREsSed IN ArBitrary unitS. tHIs mAkes iT imPOssiblE to dEfine numerical cutOFf values for claSsifYINg pAirS As “veRy Similar” or “very dIssImILAr.” To provIdE empiricallY-driven iDeNTificAtion oF item pAirs, taiLOReD To each IndiVidUal space, tHe
 weirdness score). In effe ct, thesescore s i nde xindi vidu al differences by i ndicating the similari ty of  a n  ind i vi dual' s dimen s io n wei gh ts  re la t iv e tothe  grouprepresenta tio n.  We report t h es e scores f oreach matrixfor  the s ca lin g  atte mpt s indimens i ons 2– 5; becaus et hese s c ores re l y  o n re lative dimensiona l  w e ighting, theycannot  b e  c a l cul ate d for one- di mensi o nal sol u ti o n s . F i gure 4 displa ys the weir d nes s scor es  fo r  the t eddybe a rsand butterf lies , as a fu nction of dime n sionali ty. Ea chsym bolr ep re sen ts the sc ore for  one par ti ci pantpers o l u tion , a nd t he th ree participa nts  wit h  th e hig hestmean  w eirdn ess sc ores(o verall) are lab eled . As canbese enfo r the teddybea rs,  Matric es 7 an d  16  h a v e  w eirdness scores th at f al l abovethe re s tof the grou patlowe r dimen sion a li ties, wh ereasM at ri ces 4 a nd  7 are  t hemos t unl i ke t he res t at the  high e r dimensionali t ies. By contr a st , fo r  the  bu tterflies,Matr i x 14  has hi ghw eirdn ess v al u es at all dimensionali ti es, bu t the  overall dist ribution o f s cores sh owsf ew clear outliers . The  full repo r t includ es th ese scor es for ev e r y catego ry,  so  an aly s t scan use whate v e r cr it eria th eychoosetoide nti fypo tential o utliers(e .g .,  p art icipa n ts withme anwe ird nesss coresmorethan  2 .5 sta ndard d e vi a t ions  a bo ve t hegr oup m ean) .  Th e fullraw datamat r ices  a re  also a vailable fordo wnload, so  i f o ne wis h e s to exc lude participants for a n y reaso n a nd re scal e the res ult s, thi s i s  readi ly pos sible .PPT P owerP o i nt  sl id e

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larger imageT IFF  original ima geDown l o ad : F i gu r e 4 .W eir d n ess scores forthe teddybe a r(left) and but te rfly (r ight) c atego r ies, pl otted asa functio nof d i m ens ionality o f the sp ace (weir d nesss co res a renot po ss ibl e for  one-d i men siona l solu ti ons).Eachsy mbols sh ows the score for one p artici pant(i. e., one d ata mat rix), and  the  three par tic ipa nts w ith the h ighe s tmea n  weir dnes s  scores h a ve  be e n  i dentified w i t h  la bels,  fo r  demon stra tive purposes. ht t ps://doi.org/1 0.13 7 1 /jo urn a l.po ne .0112644.g004
Id en t i fication  o f item pair ings alo ng a con tinuum  of si milarit y Wi t h thecoor din ates obta ine df rom eac hps y cholog ical  s pace,it isp ossi b l e to identify ob jectp a irs t h atare m or e or le s s si milar to o ne another,  relat iveto al l other  p ossibl e p ai rs. No bas i c unit of  meas urement  i s pr ese nt inMDS, s o the  int er -it em distan c e  v a lu es are  exp resse din a rbitraryu nits. Th ism akes it  i mpo s s ible t o  d e f ine numeri cal  cuto f f  values fo r  cla s si f yingpairsas “ve ry simi l ar”  o r “very  di s s imilar.”To provid e  em pi rica lly-driv en  ide nt ifi ca tion of item pa irs, tai lored to e ach in div i dual space,  t he
 weirdness_score). In_effect, these scores index_individual differences_by_indicating the_similarity_of an individual's_dimension weights relative_to the group representation._We report these_scores_for each matrix for the scaling attempts in dimensions 2–5; because these scores rely_on_relative dimensional_weighting,_they_cannot be calculated for one-dimensional_solutions. Figure 4 displays the_weirdness scores_for the teddy bears and butterflies, as a_function_of dimensionality. Each_symbol represents the score for one participant per solution,_and the three participants with the_highest mean weirdness_scores_(overall)_are labeled. As can_be seen for the teddy bears,_Matrices 7 and 16 have weirdness_scores that fall above the rest of_the group at lower dimensionalities, whereas_Matrices 4 and 7 are_the most_unlike the rest at the_higher dimensionalities. By_contrast, for_the butterflies, Matrix_14 has high weirdness values at_all dimensionalities, but_the overall distribution of scores shows_few_clear outliers. The_full_report_includes these_scores for every_category,_so analysts_can_use whatever criteria they choose to_identify_potential outliers (e.g., participants with mean weirdness_scores more than 2.5_standard_deviations above the group_mean). The full raw data_matrices are also available for download,_so if_one wishes_to exclude participants for any reason and rescale the results, this_is readily possible. PPT PowerPoint slide

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larger_image TIFF_original_image Download: Figure_4._Weirdness scores_for the teddy bear (left) and butterfly_(right) categories,_plotted as a function of dimensionality_of the space (weirdness_scores_are not possible for one-dimensional solutions)._Each symbols shows the score for_one participant (i.e., one data_matrix),_and_the three participants with the_highest mean weirdness scores have been_identified with labels,_for demonstrative purposes. https://doi.org/10.1371/journal.pone.0112644.g004

Identification of item pairings_along_a continuum of similarity With the_coordinates_obtained from each psychological space, it_is_possible_to identify object pairs that_are more or less similar to_one another, relative to all other possible pairs. No_basic unit of_measurement is present in MDS,_so_the_inter-item distance values are expressed in arbitrary units. This makes_it impossible_to define numerical_cutoff values for classifying pairs as “very similar” or “very_dissimilar.” To provide empirically-driven identification of item_pairs, tailored to each individual space, the