Extensive occupational finger use delays age effects in tactileperception—an ERP study

Eva-Maria Reuter & Claudia Voelcker-Rehage &

Solveig Vieluf & Axel H. Winneke & Ben Godde

Published online: 7 March 2014# Psychonomic Society, Inc. 2014

Abstract Tactile expertise, resulting from extensive use ofhands, has previously been shown to improve tactile percep-tion in blind people and musicians and to be associated withchanges in the central processing of tactile information. Thisstudy investigated whether expertise, due to precise and de-liberate use of the fingers at work, relates to improved tactileperception and whether this expertise interacts with age. Atactile pattern and a frequency discrimination task were con-ducted while ERPs were measured in experts and nonexpertsof two age groups within middle adulthood. Independently ofage, accuracy was better in experts than in nonexperts in bothtasks. Somatosensory N70 amplitudes were larger with in-creasing age and for experts than for nonexperts. P100 ampli-tudes were smaller in experts than in nonexperts in the fre-quency discrimination task. In the pattern discrimination task,P300 difference wave amplitude was reduced in experts andlate middle-aged adults. In the frequency discrimination task,P300 was more equally distributed in late middle-aged adults.We conclude that extensive, dexterous manual work leads toacquisition of tactile expertise and that this expertise mightdelay, but not counteract, age effects on tactile perception.Comparable neurophysiological changes induced by age andexpertise presumably have different underlying mechanisms.

Enlarged somatosensory N70 amplitudes might result fromreduced inhibition in older adults but from enhanced, specificexcitability of the somatosensory cortex in experts. RegardingP300, smaller amplitudes might indicate fewer available re-sources in older adults and, by contrast, a reduced need toengage as much cognitive effort to the task in experts.

Keywords Touch perception . Somatosensory perception .

Aging . Expertise . Plasticity

Blind Braille readers have superior tactile abilities, as com-pared with people with normal vision (Frings, Amendt, &Spence, 2011; Goldreich & Kanics, 2003, 2006; Pascual-Leone & Torres, 1993; Van Boven, Hamilton, Kauffman,Keenan, & Pascual-Leone, 2000). Musicians, including stringinstrumentalists and pianists, have been shown to have differ-ent cortical representations of tactile stimuli (Elbert, Pantev,Wienbruch, Rockstroh, & Taub, 1995) and to perform betterin tactile tasks than other individuals (Ragert, Schmidt,Altenmüller, & Dinse, 2004; Wong, Gnanakumaran, &Goldreich, 2011).This superior performance in tactile percep-tion is likely due to the extensive use-dependent stimulation ofthe fingers (Ragert et al., 2004; Wong et al., 2011).

Whether other work-related tactile expertise acquired dur-ing many years of on-the-job training of manual dexterity isbeneficial for touch perception is not known. Existing findingsare inconsistent and even include reports of reduced tactileperception as a consequence of extensive hand use (Hilsenrat& Reiner, 2010; Shahbazian, Bertrand, Abarca, & Jacobs,2009; Tremblay, Mireault, Létourneau, Pierrat, & Bourrassa,2002). In our own previous study on age- and expertise-related differences in touch perception, we compared expertsin finger dexterity (e.g., precession mechanics) with nonex-perts (e.g., service employees) with regard to tactile and hapticperformance (Reuter, Voelcker-Rehage, Vieluf, & Godde,

Electronic supplementary material The online version of this article(doi:10.3758/s13414-014-0634-2) contains supplementary material,which is available to authorized users.

E.<M. Reuter :C. Voelcker-Rehage : S. Vieluf :A. H. Winneke :B. Godde (*)Jacobs Center on Lifelong Learning and Institutional Development,Jacobs Universtiy Bremen, Campus Ring 1, 28759 Bremen,Germanye-mail: b.godde@jacobs-university.de

C. Voelcker-Rehage :A. H. Winneke : B. GoddeAGEACT Research Center, Jacobs Universtiy Bremen,Bremen, Germany

Atten Percept Psychophys (2014) 76:1160–1175DOI 10.3758/s13414-014-0634-2

2012). We did not find significant support for the assumptionthat frequent use of the hands in the workplace improves touchperception in the right hand (Reuter et al., 2012). However, inright-handed people, the right hand is extensively used ineveryday manual tasks, too. Thus, beneficial effects ofwork-related expertise might have been masked.

Expertise and aging

Age-related changes, such as increased tactile thresholds(Bowden & McNulty, 2013; Deshpande, Metter, Ling,Conwit, & Ferrucci, 2008; Dinse, 2006; Reuter et al., 2012;Tremblay, Wong, Sanderson, & Cote, 2003) and reducedperformance in tactile discrimination tasks (Manning &Tremblay, 2006; Master, Larue, & Tremblay, 2010; Reuteret al., 2012), become visible already in late middle-adulthoodand increase with older age (Kaneko, Asai, & Kanda, 2005;Reuter et al., 2012; Wickremaratchi & Llewelyn, 2005). Re-sults from studies in the cognitive (e.g., Kennedy, Taylor,Reade, & Yesavage, 2010) and motor (e.g., Vieluf,Mahmoodi, Godde, Reuter, & Voelcker-Rehage, 2012) do-mains suggest that expertise can postpone age-related func-tional decline (Horton, Baker, & Schorer, 2008; Krampe &Charness, 2006). In the tactile domain, evidence for stabilityof tactile acuity on the fingertip in older blind Braille readershas been reported (Legge, Madison, Vaughn, Cheong, &Miller, 2008). Others report decline also in blind people, butwith superior tactile acuity as compared with sighted age-matched individuals across the lifespan (Goldreich &Kanics, 2003, 2006; Stevens, Foulke, & Patterson, 1996).

Event-related potentials evoked by tactile stimuli

Mechanically applied tactile stimuli evoke event-related po-tentials (ERPs) to investigate early, stimulus-driven, somato-sensory processing markers, as well as later, endogenouslydriven, cognitive processes (Bolton & Staines, 2012). Theearly somatosensory ERP components P50 and N70 are gen-erated in the contralateral primary somatosensory cortex(Allison, McCarthy, Wood, & Jones, 1991; Schubert et al.,2008) and represent processing of the stimuli’s physical prop-erties (Schubert, Blankenburg, Lemm, Villringer, & Curio,2006). The N70 is followed by the P100, which is sensitiveto attention and is thought to represent bilateral secondarysomatosensory cortical processing (Bolton & Staines, 2012;Hämäläinen, Kekoni, Sams, Reinikainen, & Näätänen, 1990;Tanaka et al., 2008).

The P300 is a modality-independent cognitive ERP com-ponent that is most prominent at parietal electrode sites (Kida,Kaneda, & Nishihira, 2012). Its amplitude is related to atten-tional resource allocation to a given task when memory orcontext updating is involved (Daffner et al., 2011; Kida et al.,

2012; Kok, 2001; Polich, 2007). The P300 is larger at asmaller deviant-to-standard ratio and for easier, relative tomore difficult, tasks (Polich, 1987, 2007). P300 latenciesreflect the timing of stimulus evaluation processes (Kutas,McCarthy, & Donchin, 1977; McCarthy & Donchin, 1981).

Influence of expertise and age on somatosensory corticalprocessing

Enhanced perceptual abilities in musicians have been arguedto result from larger cortical representations of the stimulatedfingers and increased cortical excitability in the somatosenso-ry cortex (Ragert et al., 2004). Increased excitability of thesomatosensory system, as a result of extensive tactile stimu-lation, was shown to lead to larger amplitudes of early so-matosensory ERPs (Giriyappa, Subrahmanyam, Rangashetty,& Sharma, 2009; Höffken et al., 2007; Ragert, Franzkowiak,Schwenkreis, Tegenthoff, & Dinse, 2008). Using fMRI, in-creased activity in the somatosensory cortex has been shownin violinists while processing tactile stimuli (Elbert et al.,1995). Similar results are available for early ERPs of othermodalities (auditory, Baumann, Meyer, & Jäncke, 2008;visual, Curran, Gibson, Horne, Young, & Bozell, 2009).

The amplitudes of early somatosensory ERPs also increasewith age (Adler & Nacimiento, 1988; Desmedt & Cheron,1980; Drechsler, 1978; Stephen et al., 2010; Stephen et al.,2006), likely due to general disinhibition and more unspecificactivation associated with aging (Drechsler, 1978; Lenz et al.,2012; Pellicciari, Miniussi, Rossini, & De Gennaro, 2009).Until now, interaction effects of age and expertise on somato-sensory ERPs have not been investigated together in a singlestudy.

Influence of expertise and age effects on the P300

Data on expertise-related modulation of cognitive processes inthe somatosensory domain are rare, especially for tactile per-ception. Using an active oddball task, Iwadate, Mori,Ashizuka, Takayose, and Ozawa (2005) found soccer playersto have larger P300 amplitudes than had nonsoccer players formedian nerve stimuli applied to a lower limb. This might pointto facilitated cognitive processing of somatosensory stimuliapplied to the foot, as a result of the frequent use and, hence,stimulation of the foot in soccer players. Similarly, studies onauditory expertise revealed that musicians had larger P300amplitudes than nonmusicians in a pitch discrimination task(Tervaniemi, Just, Koelsch, Widmann, & Schröger, 2005) andin a deviant cadences detection task (James, Michel, Britz,Vuilleumier, & Hauert, 2011). However, Radlo, Janelle,Barba, and Frehlich (2001) found that expert baseball playershad reduced P300 amplitudes, as compared with intermediatebaseball players, during a visual discrimination task. Giventhat experts outperformed nonexperts behaviorally, the results

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were interpreted as indicative of more efficient perceptualdecision making in experts.

Age-related changes in the P300 are well described(Friedman, 2012; Polich, 1996; Pontifex, Hillman, & Polich,2009). Prolongation of P300 latency in older adults indicatesslower information processing (Cona, Arcara, Amodio,Schiff, & Bisiacchi, 2013; Gaál, Csuhaj, & Molnár, 2007;Riis et al., 2009). Age-related decrease of P300 amplitude ismost prominent at parietal electrodes (Fjell &Walhovd, 2001;Polich, 1996; Walhovd, Rosquist, & Fjell, 2008). It mightreflect reduced availability of attentional resources to be allo-cated to the task and to suppress irrelevant neuronal operations(Pontifex et al., 2009; Walhovd et al., 2008). With increasingage, the P300 scalp topography becomes more uniform, incontrast to a pronounced frontal to parietal amplitude increasein young adults (Friedman, Kazmerski, & Fabiani, 1997;Friedman, Simpson, & Hamberger, 1993; Polich, 2012).Age-related differences in tactile discrimination performancehave been shown to be related to differences in the topograph-ical distribution of the P300 (Reuter, Voelcker-Rehage, Vieluf,Winneke, & Godde, 2013).

Overall study aims and hypotheses

The present study had two main research aims. First, weinvestigated the effect of work-related expertise on tactileperception. We asked whether extensive stimulation of thefingers in experts is related to improved tactile discrimination,as well as to neurophysiological changes on two levels: thesomatosensory processing level and a higher order cognitiveprocessing level. We assessed tactile discrimination perfor-mance and electrophysiological data in early and late middle-aged experts and nonexperts by use of a tactile pattern and atactile frequency discrimination task.

On the basis of the assumption that frequent stimulation ofthe finger tips induces perceptual learning (Ragert et al., 2004;Wong et al., 2011), we hypothesized that experts shouldperform better than nonexperts, as has been shown for blindpeople (Goldreich & Kanics, 2006; Legge et al., 2008) andmusicians (Ragert et al., 2004). We further expected largeramplitudes for the early ERP components P50 and N70 inexperts, indicative of greater excitably of the somatosensorysystem (Giriyappa et al., 2009; Höffken et al., 2007). Weassumed reduced P100 amplitudes in experts as a possibleindicator of reduced attentional effort (Bolton & Staines,2012). Moreover, we hypothesized larger P300 amplitudesin experts, as compared with nonexperts, indicating moreefficient use of preattentively encoded neural information forstimulus categorization (James et al., 2011; Tervaniemi et al.,2005).

Second, we investigated the interaction of age and exper-tise to reveal whether extensive use of hands is able to coun-teract age-related decline in tactile perception. On the

behavioral level, expertise might influence age trajectories intwo different ways. On the one hand, it might reduce the slopeof age-related decline in experts (i.e., age × expertise interac-tion). On the other hand, expertise might result in betterperformance at any age, but the slope of age-related declineshould be similar in experts and nonexperts (i.e., main effectsof age and expertise but no interaction). With respect to thesomatosensory processing level, we expected larger somato-sensory ERP amplitudes for both older adults and experts(Lenz et al., 2012). Consequently, if both age and expertisehave the same facilitating effect on somatosensory ERP com-ponents, regardless of underlying mechanisms, highest ampli-tudes should be found in older experts. Regarding cognitiveERP components, we assumed that expertise might influenceage-related differences in P300 distribution (Reuter et al.,2013). Amore “youth-like” P300 topography in older experts,as compared with older nonexperts, was expected to parallelthe benefit in behavioral performance.

Finally, we expected longer peak latencies for somatosen-sory ERPs and the P300 in older adults, indicating generalslowing of processing with age (Bolton & Staines, 2012;Peters, 2002; Salthouse, 1996). Since comparable data forexpertise is missing, we did not have firm hypotheses regard-ing expertise effects or expertise × age interaction effects onpeak latency, but we postulated that age-related slowingwould be less pronounced in older experts than in oldernonexperts.

Method

Participants

Forty-seven healthy, right-handed participants took part in theexperiment in the framework of the Bremen-Hand-Study@Jacobs, Bremen, Germany. Data for the two groupsof nonexperts on the pattern discrimination task have beenpublished previously in an article comparing tactile perfor-mance in middle-aged and young nonexperts (Reuter et al.,2013).

Participants were recruited through information flyers andarticles in local newspapers. All of them took part voluntarilyand gave their informed consent to the procedure, which wasapproved by the ethics commission of the German Psycho-logical Society. They received 8 Euros per hour as monetarycompensation.

Participants were assigned to four subsamples dependingon age and level of expertise: “early middle-aged nonexperts”(EMN), 36–47 years of age; “early middle-aged experts”(EME), 37–48 years of age; “late middle-aged nonexperts”(LMN), 56–66 years of age; and “late middle-aged experts”(LME), 55–66 years of age.

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To be considered as expert, at least 10 years of workexperience in an occupation with high demands on fine motorcontrol were required (Ericsson & Smith, 1991). We consid-ered precision mechanics (opticians, dentists, goldsmiths,watchmakers, dental technicians, and hearing care profes-sionals) as experts, since they frequently manipulate smallobjects and, thus, stimulate their fingertips. By contrast, non-experts did not perform any tasks with a high demand of finemotor control in their job (e.g,, consultants, insurance agents,office clerks) (Reuter et al., 2012; Vieluf, Godde, Reuter, &Voelcker-Rehage, 2013; Vieluf et al., 2012).

The inclusion criteria of at least 10 years of work experi-ence and being part of the active workforce determined ourage range. While younger participants would not have had thechance to acquire expertise yet, older participants would havebeen likely to be retired and, thus, to differentially use theirhands in everyday life. All participants did not engage inhobbies involving extensive finger dexterity.

Participants were screened for demographic informationthat included their educational background, hand usage duringwork and leisure time, hand dominance, and health by use of aquestionnaire. Additionally, the Purdue Pegboard test (PurduePegboard model 32020, Lafayette Instruments, Lafayette, IN)was used to measure clinical dexterity. The test confirmedthat, independently of the group, participants placed morepegs with the right than with the left hand (Vieluf et al.,2012). Screenings further confirmed that the two expertgroups used their hands more frequently at work than didthe two nonexpert groups. Table 1 shows screening resultsfor each group. The four groups did not differ in their levels ofeducation, weekly working hours, weekly hours of

handwriting, hand dominance, or weekly hours of typing butdid with respect to their health status.

Experimental tasks and behavioral data analysis

Participants performed two tactile, two-choice discriminationtasks with unequal probabilities of the two stimulus types,similar to active oddball tasks. Spatial and temporal discrim-ination tasks were conducted in order to assess both domainsof tactile perception. In both tasks, participants had to activelydiscriminate tactile stimuli produced by a piezoelectric wafer(piezo: TeleSensory, MountainView, Ca; casing and control-ler: metec AG, Stuttgart, Germany) to the tip of participants’left index finger. We tested the left hand because we assumedthat expertise-related differences might be more prominent inthe left than in the right hand, since experts use their left handmore often than do nonexperts (Jäncke, Schlaug, & Steinmetz,1997).

Figure 1 illustrates the wafer and the stimulation patternsand frequencies. The wafer consisted of eight plastic pins in a2 × 4 orientation that could be individually controlled. In thespatial task, participants had to discriminate between twotactile patterns that were presented in a stable, nonoscillatingmanner, with a maximal amplitude of 1.8 mm. Either astraight line or a zigzag line, both formed by four pins, waspresented. This task will be referred to as pattern discrimina-tion task (PDT) henceforth. In the temporal task, the stimuliconsisted of all eight pins and were presented with differentfrequencies. A frequency of 120 Hz and a frequency of180 Hz had to be distinguished. The rationale for choosingfrequencies of 120 and 180 Hz, which are much higher as

Table 1 Demographic information per age group and results of ANOVAwith factor group

Variable Group Means and Standard Errors F Statistic

EMN(N =11, 7 females)

EME(N = 10, 5 females)

LMN(N = 12, 6 females)

LME(N = 14, 8 females)

F df p ηp2

Age 42.90 (1.03) 42.40 (1.24) 59.70 (0.84) 59.43 (0.80) 102.685 3,44 .001a,b,c,d .875

Education 15.50 (0.86) 15.15 (0.937) 14.82 (1.01) 16.04 (0.78) 0.369 3,42 .776 .026

Weekly working hours 34.75 (0.17) 35.38 (5.71) 34.08 (4.20) 34.43 (3.40) 0.160 3,40 .997 .001

Subj. hand usage 15.45 (1.96) 33.00 (1.66) 14.33 (1.78) 32. 21 (1.56) 34.469 3,43 .001b,c,e,f .706

Health −0.30 (0.15) −0.18 (0.10) 0.09 (0.13) 0.29 (0.16) 2.992 3,44 .041b .169

Handedness 11.68 (0.27) 11.60 (0.27) 11.92 (0.77) 11.35 (0.32) 1.085 3,44 .365 .069

Typing 9.93 (2.92) 6.10 (1.86) 16.31 (3.29) 9.12 (1.87) 2.730 3,44 .055 .157

Handwriting 3.03 (0.97) 4.73 (2.36) 4.71 (1.77) 3.38 (1.03) 0.318 3,44 .812 .021

Note. Bold numbers indicate significant effects. EMN = early-middle-aged nonexperts; EME = early middle-aged experts; LMN = late middle-agednonexperts; LME = late middle-aged experts; Age, age at test session; Education, years of education; Weekly working hours; Subj. hand usage, self-reported hand use (sum score of nine items, 5-point scale); Health, mean z-score of subj. (mean of two items, 5-point scale) and obj. health (sum of recentillnesses), a lower score indicates better health; Handedness, number of activities executed with the right hand (modified Edinburgh HandednessInventory [Oldfield, 1971], 12 items); Typing, weekly hours of typing; Handwriting, weekly hours of handwriting. Superscripts a–f denote significant (p< .05) difference in Bonferroni-corrected post hoc comparisons between: EMN and LMN (a), EMN and LME (b), EME and LMN (c), EME and LME(d), EMN and EME (e), and LMN and LME (f)

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compared with previous studies (e.g., Hodzic, Veit, Karim,Erb, & Godde, 2004; Reuter et al., 2012; Voelcker-Rehage &Godde, 2010), was to avoid artifacts in the EEG signal ofinterest (~0.1–30 Hz) caused by the on- and offset of the pinsin each frequency cycle. The stimuli in this task were matchedfor total power (squared product of amplitude and frequency)(Harris, Arabzadeh, Fairhall, Benito, & Diamond, 2006;Voelcker-Rehage & Godde, 2010). In the following, this taskis referred to as frequency discrimination task (FDT). Acustom-made amplifier (QueroSys, Schotten, Germany) drovethe stimulation and was controlled by the software Presenta-tion (Neurobehavioral Systems, Albany, CA).

Following the oddball design (Polich, 1996), in both tasks,for each participant, one of the two stimuli was randomlyselected as standard stimulus and was presented on approxi-mately 80 % of the trials (number of trials in the PDT, M =315. 43, SE= 3.85; in the FDT,M = 322.93, SE = 1.18). On theremaining approximately 20 % of the trials (PDT,M = 80.19,SE = 1.34 trials; FDT, M = 77.51, SE = 1.01 trials), the other,deviant stimulus was presented.

Stimuli were presented for 600 ms each. The interstimulusinterval was varied randomly between 800 and 1,200 ms, witha mean of 1,000 ms. A total of 400 trials, separated into eightblocks, were conducted for each tasks. A pause interval of 10 swas given between the blocks. Participants were asked tofixate a cross on a screen and to respond to each stimulus asquickly and as accurately as possible by pressing the left orright button of a custom-made two-button response box withtheir right index and right middle fingers, respectively. Thefixation cross was used to avoid eye or head movements thatmight disturb the EEG and to ensure that participants kepttheir eyes open.

For each task, the definition of the two stimuli as eitherstandard or deviant and the assignment of the buttons wasrandomized and counterbalanced for each participant. Theparticipants wore both ear plugs and headphones, to preventusing the sound of the stimulation to identify the stimuli. Allindividuals first performed the PDT, followed by the FDT.This order was chosen to avoid potential effects of tactilehigh-frequency stimulation on the performance in the PDT

(Voelcker-Rehage & Godde, 2010). Before each task, partic-ipants were given test trials with feedback to make sure thatthe tasks were well understood.

Performance was measured by means of d-prime (d′) as anindicator of accuracy. We calculated d′ as the difference be-tween the standardized probability of correct responses andfalse alarms for the deviant stimuli. This method is an unbi-ased measure of stimulus discriminability that takes bothsensitivity and response tendency into account (Stanislaw &Todorov, 1999).

To account for perfect performance, we followed a proce-dure described by Stanislaw and Todorov (1999) and added0.5 to both the number of hits and the number of false alarmsand added 1 to both the number of signal trials and the numberof noise trials, before calculating the hit rates and false alarmrates.

EEG data recording and ERP analysis

EEG data were recorded using a 32-channel active electrodesystem (ActiveTwo, BioSemi, Amsterdam, Netherlands).Electrodes were placed according to the 10–20 system(Jasper, 1958). Vertical and horizontal eye movements, as wellas mastoid potentials, were recorded with six facial electrodesdesigned for body-surface applications.

The signal was digitized with a sampling rate of 2048 Hzand online band-pass filtered between 0.16 and 100 Hz. TheEEG was offline analyzed and processed with Brain VisionAnalyzer Software 2.0 (Brain Products, Munich, Germany).For ERP analyses, the signal was offline down-sampled to512 Hz, and linked mastoids were used as references. A low-pass filter of 30 Hz and a notch filter of 50 Hz, were applied,and direct current (DC) shifts were corrected by DC detrendcorrection. Eye movements were identified and corrected byuse of individual independent component analysis implement-ed in the analysis software. EEG activity with a gradientsteeper than 5 μV/ms or voltages exceeding −75 or 75 μVwere automatically detected and rejected as artifacts. Trialswith artifacts were excluded channel-wise from further anal-ysis. Only correct trials were analyzed. It was ensured that the

Fig. 1 Experimental stimuli. a Piezoelectric wafer with finger held closeto the pins. b Tactile stimuli with straight line (left) and zigzag line (right).c Schematic drawing of fast (180 Hz) and slow (120 Hz) frequencies;

deviant and standard stimuli were chosen in a randomized fashion acrossparticipants. Stimuli were delivered to left index fingers, and the responsebuttons were pressed with the right index and middle fingers

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participants included in the analysis had a minimum of 26accepted trials in both conditions (M = 53.31, SE = 2.91,deviant condition of PDT; M = 272.88, SE = 9.76, standardcondition of PDT;M = 71.55, SE = 1.49, deviant condition ofFDT;M = 321.13, SE = 3.29, standard condition of FDT; andsee Supplementary Table 1 for mean number of trials pergroup) (Cohen & Polich, 1997). As a result of this criterion,some participants had to be excluded from the ERP analysisdue to insufficient numbers of correct trials. ERP data wereobtained by averaged segments of −100 until 900 ms from thestimulus onset. Peaks were automatically identified in giventime windows (see below). These peaks were visuallyinspected and adjusted, if necessary. Amplitudes were mea-sured as baseline-to-peak values. Latencies refer to the timefrom stimulus onset until the peak amplitude was reached.Early somatosensory components, P50 (40–60 ms) and N70(60–100ms), known to arise largely from the primary somato-sensory cortex (Allison et al., 1991; Hämäläinen et al., 1990),were analyzed at the electrode position C4, situated above thesomatosensory cortex contralateral to the stimulated left hand(Reuter et al., 2013) and surrounding electrodes: Cz, Cp2,Cp6, Fc2, Fc6.

We focused on the standard condition only, since morereliable peaks were revealed, due to the higher number ofavailable trials. The most pronounced and consistent peakswere detected at C4; therefore, we restricted the statisticalanalysis to peaks measured at this site. The P100 (90–150 ms) component was analyzed for the standard conditiononly at electrodes C3 and C4, ipsi- and contralateral to thestimulated hand, respectively, since its source is likely locatedin secondary somatosensory cortices (Bolton & Staines, 2012;Hämäläinen et al., 1990).The P300 (~250–800 ms) is knownto be maximal over the midline electrodes Fz, Cz, and Pz(Fabiani, Gratton, & Coles, 2000) and was analyzed at theseelectrodes. For the P300 analysis, we calculated differencewaves by subtracting the ERP in the standard condition fromthe ERP of the deviant condition. Difference waves reflect therelationship between responses to deviant and to standardstimuli. In the following, the term P300 refers to P300 differ-ence wave. We conducted an automatic P300 peak detectionon the difference wave in the time window 250–700 ms.Again, all peaks were visually inspected and adjusted, ifnecessary.

Statistics

Statistical analyses were done with SPSS for Windows ver-sion 20.0 (IBM Corp., Armonk, NY) and Statistica (StatSoftEurope, Hamburg, Germany). In order to examine differencesamong the age and expertise groups in their tactile discrimi-nation performance, a 2 (age: early middle-aged, late middle-aged) × 2 (expertise: nonexperts, experts) analysis of variance(ANOVA) on d′ to deviant trials were computed separately for

both tasks. Similarly, to analyze differences in early somato-sensory components (i.e., P50 and N70), this ANOVA modelwas applied to peak amplitudes and latencies of the ERPs inthe standard condition. For the analysis of P100 amplitude andlatency, a 2 (age) × 2 (expertise) × 2 (electrode: C3, C4)repeated measures ANOVA was conducted. For the analysisof P300, we calculated a 2 (age) × 2 (expertise) × 3 (electrode:Fz, Cz, Pz) repeated measures ANOVA.

We found group differences in health (see Table 1) andassumed that health might influence performance or neuro-physiological parameters. Therefore, we included health as acovariate in all statistical models. In cases in which we foundat least marginally significant covariate effects, we report theANCOVA model; otherwise, the ANOVA model is reported.For within-subjects factors with more than one degree offreedom (P300 analysis), the Greenhouse–Geissernonsphericity correction was used. Effect sizes are given aspartial eta squares (ηp

2). In order to correct for unequal samplesize per group, we used the complete linear model analysismethod introduced by Overall and Spiegel (1969). Scheffé’stest was used for post hoc comparisons. Correlation analyseswere used to assess the association between d′ and P300amplitudes at electrodes Fz and Pz (Reuter et al., 2013), aswell as between d′ and N70 peak amplitudes. In the following,only the statistical results reaching at least a level of marginalsignificance of p = .10 are reported. An overview of allstatistical outcomes is given in the Supplementary Table 2.

Results

Tactile discrimination performance

Figure 2 illustrates the behavioral performance of all groups inboth tasks. Descriptive results are given in Table 2. For theanalysis of d′ in the PDT the 2 (age) × 2 (expertise) ANOVArevealed a significant main effect for expertise, with expertsperforming better than nonexperts, F(1, 43) = 5.423, p = .025,ηp

2 = .11, and a main effect of age, F(1, 43) = 5.490, p = .024,ηp

2 = .11, with early middle-aged adults performing betterthan late middle-aged adults.

In the FDT, a significant covariate effect for health wasfound, F(1, 42) = 4.773, p = .035, ηp

2 = .10, and thus healthwas included as a covariate. We found a main effect ofexpertise, F(1, 42) = 6.032, p = .018, ηp

2 = .13, again withexperts performing better than nonexperts. The significantcovariate effect indicated that a better health condition posi-tively influenced accuracy in the FDT.

Electrophysiological correlates of tactile discrimination

Figure 3 shows the ERP waveform of the standard stimuli atthe electrode C4 for each group in both tasks. Figures 4 and 5

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depict ERPs for both the deviant and the standard conditionsand the respective difference waves at the midline electrodesfor all groups in both tasks. Descriptive results of ERP pa-rameters are given in Table 3, while a detailed overview on thestatistical results is provided in Supplementary Table 2.

Pattern discrimination task

Amplitudes of somatosensory ERP components

The age × expertise ANOVA did not reveal any significanteffect for P50 amplitudes. For N70 amplitudes, an ANCOVAmodel (2 age × 2 expertise with the covariate health) was used.We found main effects for the factors expertise, F(1, 32) =4.226, p = .048, ηp

2 = .12, and age, F(1, 32) = 4.334, p = .045,

ηp2 = .12, with larger N70 amplitudes for experts, in compar-

ison with nonexperts, and for late middle-aged, as comparedwith early middle-aged, adults (see Fig. 3). The covariateeffect of health was marginally significant, F(1, 32) = 3.002,p = .093, ηp

2 = .09, suggesting that a better health conditionmight be associated with larger N70 peak amplitudes. The age× expertise × electrode did not reveal any significant effectsfor P100 amplitudes.

Latencies of somatosensory ERP components

For P50 and P100 latencies, no significant effects were re-vealed. With respect to N70, we found a significant effect ofage, F(1, 33) = 12.363, p = .001, ηp

2 = .27, with late middle-aged participants having longer latencies than early middle-aged participants.

Amplitude of P300 difference wave

A repeated measures 2 (age) × 2 (expertise) × 3 (electrodes)ANOVA revealed significant between-groups effects of thefactors expertise, F(1, 28) = 5.232, p = .030, ηp

2 = .16, withexperts having smaller P300 peak amplitudes than nonexperts,and age, F(1, 28) = 5.074, p = .032, ηp

2 = .15, with latemiddle-aged adults showing smaller peak amplitudes thanearly middle-aged adults (see Fig. 4). We further found a maineffect of electrode, F(2, 56) = 18.548, p < .001, ηp

2 = .40. Apost hoc analysis indicated significantly larger amplitudes atPz than at Cz (p =.002) and Fz (p < .001). Amplitudes at Fzand Cz did not differ from each other. Moreover, the electrode× age × expertise interaction was significant, F(2, 56) = 4.339,p = .036, ηp

2 = .13. A post hoc analysis revealed that only inEMNwas the P300 amplitude at Pz significantly larger than atFz. In the other groups, activity was rather equally distributed.

Latency of P300 difference wave

The same ANOVA model was applied to P300 peak latenciesfor pattern discrimination. An effect of electrode, F(2, 56) =12.518, p < .001, ηp

2 = .31, with longer latencies at Pz than atCz (p = .009) and Fz (p < .001), and a marginally significantinteraction of electrode and expertise, F(2, 56) = 2.843, p =.068, ηp

2 = .09, were found. Post hoc analysis indicated thatthis interaction was driven by latency differences betweenelectrodes in experts (Pz > Cz with p = .002, and Pz > Fzwith p = .001), while no such difference was revealed innonexperts.

Association between ERP amplitudes and PDT performance

Only for EMN was there a significant correlation betweenP300 amplitude at Fz and d′ (r = .728, p = .041). In the othergroups, this association was not significant, and neither was

Fig. 2 Mean performance levels in tactile pattern (left) and frequency(right) discrimination task for all experimental groups. Error bars indicatestandard errors of the means

Table 2 Behavioral results for tactile discrimination performance, indi-cated by accuracy (d′) in the pattern discrimination task (PDT) and thefrequency discrimination task (FDT) for early middle-aged nonexperts(EMN), early middle-aged experts (EME), late middle-aged nonexperts(LMN), and late middle-aged experts (LME) (with means and standarderrors)

Task Group Means and Standard Errors

EMN EME LMN LME

PDT 1.76 (0.38) 2.46 (0.33) 0.88 (0.35) 1.59 (0.29)

FDT 4.09 (0.37) 4.43 (0.21) 3.67 (0.29) 4.36 (0.18)

1166 Atten Percept Psychophys (2014) 76:1160–1175

the relation between performance and P300 amplitudes at Pz.Also, N70 peak amplitudes were not related to performance.

Frequency discrimination task

Amplitudes of somatosensory components

Conducting a 2 (age) × 2 (expertise) ANOVA, we did not findany effects for P50 amplitudes in the FDT. To analyze N70amplitudes in the FDT, health was included as covariate. We

found significant main effects of expertise, F(1, 31) = 4.309, p= .046, ηp

2 = .12, and age, F(1, 31) = 6.405, p = .017, ηp2 =

.17, with larger amplitudes for experts, as compared withnonexperts, and for late middle-aged adults, as compared withearly middle-aged adults (see Fig. 3). Health revealed a sig-nificant covariate effect, F(1, 31) = 6.738, p = .014, ηp

2 = .18,indicating that, as in the PDT, better health was associatedwith larger N70 amplitudes. For P100 amplitudes, the 2 (age)× 2 (expertise) × 2 (electrode) repeated measures ANCOVArevealed a main effect of expertise, F(1, 31) = 4.559, p = .041,

Fig. 3 Grand-average of somatosensory ERPs for standard stimuli per group at electrode C4 for the pattern (upper row) and frequency discrimination(lower row) tasks. Vertical line at 0 ms indicates the stimulus onset

Fig. 4 Grand-average of P300 in the pattern discrimination task for standard (dotted gray) and deviant (solid gray) stimuli and difference waves (solidblack) per group at electrodes Fz, Cz, and Pz

Atten Percept Psychophys (2014) 76:1160–1175 1167

ηp2 = .13, with smaller amplitudes for experts, as compared

with nonexperts. Age and interaction effects were not signif-icant. The covariate effect of health reached marginal signif-icance, F(1, 31) = 3.853, p = .059, ηp

2 = .11.

Latencies of somatosensory components

The analysis of P50 latency did not reveal any significanteffects. With respect to N70, the 2 (age) × 2 (expertise)ANOVA revealed a significant effect of age, F(1, 32) =6.903, p = .013, ηp

2 = .18, with late middle-aged participantshaving longer latencies than early middle-aged participants. Inthe analysis of P100 latencies, the 2 (age) × 2 (expertise) × 2(electrode) ANOVA revealed only a marginally significantinteraction effect of age and expertise, F(1, 32) = 3.181, p =.084, ηp

2 = .09.

Amplitudes of P300 difference wave

The 2 (age) × 2 (expertise) × 3 (electrodes) ANOVA revealeda main effect of electrode, F(2, 70) = 14.339, p < .001, ηp

2 =.29, with larger peak amplitudes at Pz than at Cz (p = .001) andat Fz (p < .01). The analysis further showed an interactioneffect of electrode and age, F(2, 70) = 3.964, p = .034, ηp

2 =.10 (see Fig. 5). Post hoc analysis revealed that in earlymiddle-aged adults, the P300 amplitude was larger at Pz thanat Fz (p < .001). On the contrary, in late middle-aged partic-ipants, P300 amplitudes were not significantly different

between electrode positions. Unlike for the PDT, no expertiseeffects were found.

Latency of P300 difference wave

The sameANOVAmodel was used for peak latencies analysisin the FDT. As for the PDT, we found a main effect ofelectrode, F(2, 70) = 26.897, p < .001, ηp

2 = .435, withlatencies longer at Pz, as compared with Cz (p < .001) andwith Fz (p < .001).

A main effect of age, F(2, 35) = 7.385, p = .010, ηp2 = .17,

was revealed, with late middle-aged participants showinglonger peak latencies than did early middle-aged participants.Expertise did not influence these effects. However, a trendtoward an age × electrode interaction, F(2, 70) = 2.820,p = .081, ηp

2 = .075, was found, indicating that only thepeak latencies measured at the electrode site Pz werelonger in late middle-aged, in comparison with earlymiddle-aged, adults (p = .033), while there were no differ-ences between age groups at the other electrodes.

Association between ERP amplitudes and FDT performance

A positive correlation between d′ and P300 amplitude at Fzwas found for EMN, r = .725, p = .018, and LMN, r = .734,p = .016. This association was not shown for the groups ofexperts. As for the PDT, we did not find any correlationsbetween N70 amplitudes and performance.

Fig. 5 Grand-average of P300 in the frequency discrimination task for standard (dotted gray) and deviant (solid gray) stimuli and difference waves (solidblack) per group at electrodes Fz, Cz, and Pz

1168 Atten Percept Psychophys (2014) 76:1160–1175

Discussion

We examined the influence of work-related expertise andage on tactile pattern and frequency discrimination perfor-mance. Response accuracy (d′) was positively related toexpertise in both tasks and was negatively related to age inthe PDT. No significant interactions of age and expertisewere found.

In both tasks, N70 amplitudes were larger for latemiddle-aged adults and experts, relative to early middle-aged adults and nonexperts, respectively. Later middle-

aged adults also showed prolonged N70 latencies, where-as no expertise-related differences in latencies werefound. P100 amplitudes were found to be smaller forexperts than for nonexperts in the FDT. Regarding theP300, both older age and expertise were associated withreduced amplitudes in the PDT. In the FDT, only agerevealed this effect. Late middle-aged adults hadprolonged P300 peak latencies in the FDT. Unlike innonexperts (Reuter et al., 2013), experts’ behavioral tac-tile discrimination performance was not shown to beassociated with P300 amplitudes.

Table 3 ERP amplitudes and latencies for P50 and N70 at electrode C4,for P100 at electrodes C3 and C4, and for P300 difference waves atelectrodes Fz, Cz, and Pz in the pattern discrimination task (PDT) and thefrequency discrimination task (FDT) for early middle-aged nonexperts

(EMN), early middle-aged experts (EME), late middle-aged nonexperts(LMN), and late middle-aged experts (LME) (with means and standarderrors)

ERP Electr. DV Task Group Means and Standard Errors

EMN EME LMN LME

P50 C4 Amp PDT 1.22 (0.37) 1.30 (0.17) 1.53 (0.43) 1.49 (0.25)

FDT 1.43 (0.54) 0.85 (0.20) 1.88 (0.50) 1.19 (0.25)

Lat PDT 45.41 (1.84) 46.66 (0.89) 48.10 (2.81) 49.32 (1.40)

FDT 45.14 (3.60) 48.10 (2.27) 44.34 (2.38) 48.18 (2.35)

N70 C4 Amp PDT1,2 −0.77 (0.28) −1.29 (0.34) −1.20 (0.29) −1.91 (0.37)FDT1,2 −0.67 (0.47) −0.90 (0.30) −0.93 (0.32) −1.86 (0.46)

Lat. PDT1 69.09 (2.18) 72.70 (1.02) 79.10 (3.60) 77.47 (1.37)

FDT1 71.18 (3.24) 76.90 (1.84) 79.10 (1.17) 79.86 (1.47)

P100 C3 Amp PDT 1.76 (0.44) 1.24 (0.46) 1.62 (0.27) 1.32 (0.37)

FDT2 1.09 (0.52) 0.97 (0.34) 0.34 (0.19) 0.18 (0.29)

Lat. PDT 111.82 (7.87) 114.99 (5.50) 113.06 (5.07) 107.26 (4.58)

FDT 107.86 (7.84) 94.53 (4.08) 98.14 (5.45) 103.95 (6.84)

C4 Amp PDT 1.31 (0.50) 1.59 (0.62) 1.79 (0.48) 109.54 (1.71)

FDT 0.81 (0.50) 0.86 (0.50) 0.48 (0.35) 0.08 (0.58)

Lat. PDT 110.35 (6.36) 119.63 (6.72) 113.28 (4.78) 1.56 (0.39)

FDT 109.59 (7.35) 100.78 (2.20) 99.61 (3.76) 103.30 (1.32)

P300 Difference Wave Fz Amp PDT1,2,3,5 3.29 (0.71) 2.34 (0.37) 2.64 (0.28) 2.14 (0.54)

FDT3,4 4.86 (0.86) 6.27 (1.14) 5.84 (1.03) 4.96 (0.70)

Lat. PDT3 501.22 (35.69) 463.87 (31.94) 485.84 (47.46) 454.10 (19.30)

FDT1,3 425.20 (12.37) 415.80 (10.93) 435.35 (12.52) 447.09 (15.30)

Cz Amp PDT 4.96 (0.97) 3.15 (0.16) 3.27 (0.52) 1.58 (0.53)

FDT 6.69 (0.92) 6.64 (1.01) 5.36 (1.24) 4.78 (0.81)

Lat. PDT 469.97 (30.00) 489.68 (32.44) 578.61 (35.95) 472.90 (21.72)

FDT 429.88 (18.42) 434.57 (16.47) 445.31 (12.38) 464.84 (21.28)

Pz Amp PDT 6.57 (1.01) 3.76 (0.44) 3.40 (0.78) 3.97 (0.64)

FDT 8.24 (0.66) 9.02 (0.86) 6.41 (0.84) 6.52 (0.54)

Lat. PDT 508.79 (35.12) 567.14 (34.10) 572.51 (33.95) 565.19 (24.15)

FDT 471.88 (11.70) 458.12 (13.24) 525.20 (24.46) 527.52 (18.25)

Note. Electr. = electrode; DV = dependent variable; Lat. = latency in milliseconds; Amp. = amplitude in μV. Bold writing indicates significant maineffects in the respective tasks and measure. Superscripts 1–5 denote significant (p < .05) effects: (1) main effect of age, (2) main effect of expertise, (3)main effect of electrode (4) interaction effect of age and electrode, (5) interaction effect of age, expertise and electrode

Atten Percept Psychophys (2014) 76:1160–1175 1169

Effects of expertise on tactile discriminationand neurophysiological correlates

Experts outperformed nonexperts of the same age in tactilediscrimination tasks

In line with our hypothesis and with findings in blind personsand musicians (Ragert et al., 2004; Wong et al., 2011), expertsperformed better than nonexperts in tactile discriminationtasks with their left hand. In light of previous opposing resultsfor the right hand (Hilsenrat & Reiner, 2010; Reuter et al.,2012), this supports our assumption that expertise effects aremore prominent for the left than for the right hand. We arguethat differences between experts and nonexperts in the righthand are diminished due to the more frequent use of the righthand in regular activities of daily living. Having investigatedonly the left hand here, this, however, has to remain specula-tive, and further investigation involving both hands with dif-ferent tasks is required.

Since this was a cross-sectional study, the causal relationsbetween expertise resulting from the frequent stimulation ofhands in the work place and tactile perception cannot beinferred. Especially with respect to work-related expertise,one might argue that the improved tactile abilities do not resultfrom the frequent use of hands at work but that participantshave chosen their occupation because of their inborn superiormanual dexterity. However, since we found neither expertise-related benefits in tactile perception of the right hand (Reuteret al., 2012) nor improved performance of experts in a clinicaldexterity test that demanded fast manual actions in precisiongrip (Vieluf et al., 2012), it does not seem likely that expertsare naturally more dexterous with their left hand and, there-fore, became precision mechanics. By contrast, we argue thattheir improved tactile perception in fact results from the longterm and extensive use of hands in the work place.

Expertise resulted in increased excitability of the primarysomatosensory cortex

We expected larger amplitudes of early somatosensory ERPcomponents in experts, as compared with nonexperts. Thiswas true for N70 in both tasks, but not for P50. According toHöffken et al. (2007), changes in cortical excitability, whichare likely to lead to enlarged amplitudes, are one key mecha-nism underlying plastic somatosensory cortical reorganiza-tion. This change in excitability has previously been shownafter short-term tactile learning (Godde, Ehrhardt, & Braun,2003; Hodzic et al., 2004; Höffken et al., 2007; Ragert et al.,2008). Our results now indicate that excitability also increaseswith long-term tactile learning—that is, expertise.

With respect to long-term use, enhanced amplitudes of anegative somatosensory ERP were previously shown only for

blind persons (Giriyappa et al., 2009). Furthermore, increasedactivity in the somatosensory cortex of experts has beenshown with fMRI for violinists (Elbert et al., 1995). Thepresent study is the first to show increased ERP amplitudesassociated with occupation-related long-term tactile stimula-tion (expertise).

Latencies of somatosensory ERP components did not differbetween experts and nonexperts, suggesting that expertisedoes not influence the timing of afferent somatosensory infor-mation processing.

Expertise effects on the cognitive processing level were taskdependent

While comparable expertise effects on early somatosensoryERP components were revealed in both tasks, the effects ofexpertise on P300 were different for the PDT and FDT. Onlyin the PDT did experts have a smaller P300 than nonexperts.This finding contradicts our hypothesis that P300 amplitudes,as neural correlate of stimulus categorization, should be en-hanced in experts, as it has been shown for musical expertise(James et al., 2011; Tervaniemi et al., 2005).

Perceptual learning, which has been associated with P300reduction, might explain this finding (Kok, 2001; Sailer,Fischmeister, & Bauer, 2010). Once a task is learned, partic-ipants require fewer attentional resources while performing it.They habituate to the tasks and execute it on a more automatedlevel (Seppänen, Pesonen, & Tervaniemi, 2012). Sailer et al.showed differences in P300 habituation between fast and slowlearners and associated the reduced P300 in fast learners withchanges in the subjective outcome probability and a reductionin attentional effort devoted to the task. We assume that in ourstudy, experts habituated more quickly to the deviant stimuli,most likely already during the practice trials. The behavioralresults show that the task was more difficult for nonexperts,indicating that they, in contrast to the experts, were required toallocate more attentional resources to do the PDT task.

With regard to the FDT, the significantly reduced P100amplitudes in experts also indicate that experts direct lessattention to the stimuli. This supports the notion of moreautomated processing also for the FDT. It remains puzzling,however, why we did not find expertise-related differences inP300 amplitude in the FDT. The PDT and FDT differed interms of difficulty, since d′ values were much higher in theFDT than in the PDT. It is possible that this is a reason why noexpertise effects emerged in the P300 in the FDT. Moreover,with regard to the FDT, although equated for total power, wecannot exclude the possibility that participants used intensitycues, and not purely temporal cues, to discriminate betweenthe two stimuli. Moreover, tactile spatial information andtemporal information are encoded and processed differentlyand constitute two domains of tactile perception (Hollins,

1170 Atten Percept Psychophys (2014) 76:1160–1175

2002; Li Hegner, Lee, Grodd, & Braun, 2010). This mighthave contributed to the different expertise effects in both tasks.

Interactive effects of age and expertise

No interaction effects on the behavioral level

We did not find interaction effects of age and expertise on thebehavioral level, indicating similar age trajectories for expertsand nonexperts. Thus, acquired expertise might result inhigher performance levels but might not prevent age-relateddecline.

In the PDT, early middle-aged participants performed bet-ter than late middle-aged participants. No effect of age wasfound in the FDT. One might assume that age differentiallyaffects the two domains of tactile perception. However, tactileperception has been shown to deteriorate in both domains withincreasing age (Reuter et al., 2012; Wickremaratchi &Llewelyn, 2005). We thus suppose that the FDTwas too easyto reveal age effects, especially since age groups differed onlyby 17 years, on average. Increasing the age difference betweenthe groups and/or task difficulty is likely to lead to strongerage effects.

Increased amplitudes of early somatosensory ERPcomponents with age and expertise might be attributableto different causes

With respect to N70, both age and expertise were associatedwith an amplitude increase in PDT and FDT. Interactioneffects were not revealed, but on a descriptive level, it seemsthat older experts had the largest amplitudes in both tasks. Wecould not find a significant expertise effect for P50 amplitude,although, again on the descriptive level, amplitudes seemed tobe larger in late than in early middle-aged adults. Probably, theage difference between the groups was too small to revealsignificant age effects for P50.

In younger adults, cortical map expansion and increasedexcitability of the somatosensory cortex have previously beenassociated with improved performance after tactile traininginterventions (Hodzic et al., 2004; Höffken et al., 2007; Ragertet al., 2008). By contrast, impaired performance in olderadults has been shown to be associated with increased so-matosensory excitability caused by reduced inhibition (Lenzet al., 2012; Pellicciari et al., 2009). In this context, we suggestthat in experts, increase of somatosensory cortical N70 ampli-tudes reflects stronger but more specific activation, whereas innonexperts, the age-related increase reflects reduced inhibitionand resulting unspecific broader activation, accompanied bylower performance (Drechsler, 1978; Lenz et al., 2012;Pellicciari et al., 2009). In our study, tactile discriminationperformance did not correlate with N70 amplitudes. It remainsspeculative whether these similar effects on early ERP

components have different underlying mechanisms and ef-fects on behavioral outcomes.

In respect to somatosensory processing speed, prolongedN70 latencies in older adults confirmed age-related slowing(Adler & Nacimiento, 1988; Desmedt & Cheron, 1980;Drechsler, 1978; Stephen et al., 2010; Stephen et al., 2006).

Reduced P300 amplitudes with older age and expertise mightrepresent different effects on cognitive processing

Regarding the P300, age and expertise were both associatedwith reduced amplitudes in the PDT. We assume that thisreduction is due to different plastic changes induced by ageand expertise. Reduced P300 amplitudes in older adults, aswere found for both tasks, are a common finding. It mightreflect reduced availability of attentional resources for stimu-lus categorization and suppression of irrelevant neuronal op-erations (Pontifex et al., 2009; Walhovd et al., 2008). In theFDT, an interaction of age and electrode was found, due tomore equal distribution of P300 activity in late than in earlymiddle-aged adults. The latter showed a topographical distri-bution of P300 amplitudes with a clear parietal focus. Thiscomplements the existing literature on a parietal-to-frontalshift with aging (e.g., Adrover-Roig & Barceló, 2010; Reuteret al., 2013; West, Schwarb, & Johnson, 2010). Amplitudereduction in experts, by contrast, is assumed to be the result ofless need to engage as much cognitive effort in the task (Kok,2001; Sailer et al., 2010), as also is indicated by the reducedP100 amplitude in the FDT. This assumption is further sup-ported by the finding that in nonexperts, P300 positivelycorrelated with tactile discrimination performance (Reuteret al., 2013), while this association could not be confirmedfor experts.

P300 latency was found to depend on ag but not expertise

Age-related prolongation of P300 latency was revealed onlyin the FDT. We assume that the expected latency prolongationand electrode × age interaction did not become apparent in thePDT, because mean age difference between groups was rela-tively small. Prolongation with age has previously beenshown for PDT when also including young adults (Reuteret al., 2013). In addition, the fact that the FDT was lessdifficult than the PDTmight have led to the relatively strongerage effect on P300 latencies in the FDT (Gaál et al., 2007).

Summary and future directions

Our results confirmed expertise-related differences in tactilediscrimination accuracy and, thus, complement findings re-garding expertise-related benefits in tactile perception formusicians and blind persons (Ragert et al., 2004; Wong

Atten Percept Psychophys (2014) 76:1160–1175 1171

et al., 2011). Electrophysiological findings revealed that ex-pertise is associated with increased N70 amplitudes, indicat-ing enhanced activation of the somatosensory system. Here-with, we have now confirmed theoretical considerations re-garding the effect of frequent stimulation of the fingers alsofor the frequent use of hands at work. With respect to cogni-tive processing of tactile information, we, for the first time,have shown that extensive use of hands not only affectssomatosensory processing, but also might influence the cog-nitive processing of tactile stimuli. Reduced P300 amplitudesin the PDT and smaller P100 amplitudes in the FDT indicatedless attentional effort in experts, as compared with nonexperts.

As was expected, age-related differences were found formost measures, but neither behavioral performance nor elec-trophysiological data revealed expertise × age interactions.We found larger N70 amplitudes in older adults and expertsbut assume different underlying mechanisms, such as stron-ger, focal, and highly specific excitation with increasing ex-pertise but much broader and unspecific activation as a resultof general disinhibition with increasing age. Together with ourbehavioral findings that experts outperform nonexperts andolder adults perform worse than young adults, this interpreta-tion is well in line with the model described by Lenz et al.(2012). Also, similar age and expertise effects on P300 am-plitudes are thought to be caused by different underlyingprocesses. Smaller amplitudes might indicate fewer availableresources to be allocated to the task in older adults and, bycontrast, a reduced need to engage as much cognitive effort tothe task in experts. In sum, our results indicate that the fre-quent, precise, and deliberate use of the hands is beneficial fortactile perception; however, the slope of the age-related de-cline seems to be unaffected by expertise. Thus, expertisemight buffer age-related functional deterioration and, thereby,contribute to the maintenance of finger dexterity in older age.

This was the first study to investigate the effects of work-related expertise on tactile discrimination using electrophysi-ological data. Future studies are necessary to replicate ourresults. The proposed neural mechanisms involved remainspeculative. Future research using other imaging techniqueswith higher spatial resolution (e.g., fMRI) or methods en-abling the investigation of the balance between excitationand inhibition (e.g.,TMS/tDCS) might complement our find-ings. Ideally, longitudinal data should be obtained to learnmore about the development of tactile expertise and aboutchanges in tactile information processing throughout theworking life span.

Author note This research was supported by the German ResearchFoundation (Deutsche Forschungsgemeinschaft, DFG, VO 1432/7-1 /SPP 1184 and GO 802-7-1). We thank Sandra Fellehner and JanineOhmann for their help with data collection and Samuel Fynes-Clintonand Christopher Adams for carefully proofreading. Solveig Vieluf’scurrent affiliation is the Institute of Sport Science, Saarland University,Saarbrücken, Germany. Axel H. Winneke’s current affiliation is the

Fraunhofer Institute for Digital Media Technology, Project Group Hear-ing, Speech and Audio Technology, Oldenburg, Germany.

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