the interactive effect of achievement motivation and task difficulty on mental effort
TRANSCRIPT
International Journal of Psychophysiology 70 (2008) 144–150
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International Journal of Psychophysiology
j ourna l homepage: www.e lsev ie r.com/ locate / i jpsycho
The interactive effect of achievement motivation and task difficulty on mental effort☆
Rémi L. Capa a,⁎, Michel Audiffren a, Stéphanie Ragot b
a Université de Poitiers, Centre de Recherches sur la Cognition et l'Apprentissage, CNRS, Franceb CIC-INSERM, Centre Hospitalier Universitaire La Milétrie, France
☆ This work was supported by the French MinistrTechnology. We would like to thank Brad Evans and HEnglish.⁎ Corresponding author. CeRCA, Maison des Sciences d
Avenue du Recteur Pineau, 86000 Poitiers, France. Tel49454647.
E-mail address: [email protected] (R.L. Capa
0167-8760/$ – see front matter © 2008 Elsevier B.V. Aldoi:10.1016/j.ijpsycho.2008.06.007
A B S T R A C T
A R T I C L E I N F OArticle history:
The interactive effect of ach Received 9 June 2008Accepted 23 June 2008Available online 27 June 2008Keywords:Heart rateHeart rate variabilityFacial muscle electromyographyAchievement motivationMental effort
ievement motivation and task difficulty on invested mental effort, postulatedby Humphreys and Revelle [Humphreys, M.S., Revelle, W., 1984. Personality, motivation, and performance:a theory of the relationship between individual differences and information processing. Psychol. Rev. 91,153–184], was examined using behavioral, subjective, and effort-related physiological measures. Eighteenapproach-driven participants and 18 avoidance-driven participants were selected based on their motive toachieve success scores and their motive to avoid failure scores. A 2×3 factorial design was used, with threelevels of task difficulty. As expected, approach-driven participants performed better and had a strongerdecrease of midfrequency band of heart rate variability than avoidance-driven participants, especiallyduring the difficult task. These results support the interactive effect of achievement motivation and taskdifficulty on invested mental effort.
© 2008 Elsevier B.V. All rights reserved.
The mobilization of mental effort represents a compensatorystrategy to protect performance in the presence of increased taskdifficulty (Hockey, 1997). The impact of task difficulty on investedmental effort varies between individuals. For example, it is wellknown that the effect of dysphoria or depression tendency on mentaleffort and related physiological reactivity is modulated by taskdifficulty (Brinkmann and Gendolla, 2007, 2008). This effect hasbeen tested in both mental concentration and memory tasks underdifferent clarities of task difficulty levels (i.e., unfixed difficulty level[do your best] and fixed difficulty level). Furthermore, Humphreys andRevelle (1984) postulated that achievement motivation interacts withtask difficulty to influence mental effort mobilization. To our knowl-edge, this prediction has been explicitly tested and validated in onlyone published study using a reaction time task (Capa et al., in press).The purpose of the present study was to further test the interactiveeffect of achievement motivation and task difficulty on effort andrelated physiological reactivity.
The idea that effort mobilization should be a function of theperceived difficulty is not new. It first appeared in the difficulty law ofmotivation, formulated by Ach (1910) and then by Kukla (1972). Kukla(1972), for example, reasoned that a person's intention to try toperform a task would vary with the task's perceived difficulty. Tasks
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that are perceived as easy will result in an intention to try a little, tasksthat are difficult will result in an intention to try hard, and tasks thatare impossible will result in an intention not to try. More recently, themotivational intensity theory states that effort expenditure is directlydependent on perceived difficulty (see Brehm and Self, 1989; Gendollaand Wright, 2005; Wright and Kirby, 2001; for reviews). Specifically,the amount of effort expended in performing a task is predicted toincrease proportionally with the level of perceived difficulty. Thehigher the subjective difficulty level, the more the individual investseffort in the task. Disengagement occurs when the level of difficulty isperceived as impossible. A linear relationship between perceiveddifficulty and invested mental effort as long as success is possible andjustified was confirmed in several studies using physiologicalmeasures. Results from a number of studies indicate that effort-related physiological reactivity is more pronounced and sustainedunder moderately difficult conditions than under easy or impossibleconditions (Aasman et al., 1987; Gendolla and Krüsken, 2001; Lightand Obrist, 1983; Wright and Lockard, 2006; Wright et al., 2003). Theeffect of subjective difficulty on effort-related physiological reactivityis perhaps one of the most robust findings reported in thepsychophysiology literature. Recently, researchers have turned theirattention into the effect of subjective difficulty that mediates therelationship between individual differences and mental effortmobilization.
Subjective difficulty should mediate the relationship betweenachievement motivation and invested mental effort. This dispositionalfactor is determined by the strength of the motive to achieve successrelative to the motive to avoid failure (Atkinson and Raynor, 1974;McClelland et al., 1953). The motive to achieve success reflects arelatively stable personality disposition to strive for success, and to
Table 1Participants' characteristics
Group Age MAS MAF PRF STAI
Approach-driven M 21.87 4.17 1.97 3.50 2.35SD 2.06 .49 .53 .62 .47
Avoidance-driven M 21.78 2.24 3.90 2.51 2.70SD 1.83 .65 .40 .43 .53
Note: M=mean scores; SD=standard deviations; MAS=motive to achieve success;MAF=motive to avoid failure; PRF=personality research form; STAI=state-trait anxietyinventory.
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desire and work toward accomplishing challenging personal andprofessional goals (Atkinson and Raynor, 1974; McClelland et al.,1953). The motive to avoid failure is a relatively stable personalitydisposition to avoid and anticipate negative affects of failure outcomesin terms of shame, embarrassment, humiliation, loss of status andesteem (Atkinson and Raynor, 1974; McClelland et al., 1953). Personshigh in the motive to achieve success and low in the motive to avoidfailure are considered as approach-driven individuals. Conversely,persons low in the motive to achieve success and high in themotive toavoid failure are considered as avoidance-driven individuals.
Based on the description of approach and avoidance-drivenindividuals above, we had two primary expectations. First, weexpected that achievement motivation would interact with subjectivedifficulty to influence invested mental effort. One implicationwas thatapproach-driven individuals are expected to mobilize more mentaleffort than avoidance-driven individuals, especially during difficulttasks. Second, we expected that approach-driven individuals wouldnot differ in perceived difficulty during the experimental task. Oneimplication was that for the same level of perceived difficulty,approach-driven individuals would mobilize more mental effortthan avoidance-driven individuals, especially during difficult tasks.Another assumption was that both approach and avoidance-drivenindividuals would disengage when task difficulty is extremely high.
1. Method
1.1. Selection of participants
1.1.1. Motive measuresA total of 710 (316 male, 394 female) students enrolled in
psychology courses at the University of Poitiers filled out a measureof themotive to achieve success and of themotive to avoid failure. Themean age was 21.06 years (SD=4.27). The motive to achieve successmeasure focused on the “preference for difficult tasks”.1 We referredto the corresponding subscale of the Achievement MotivationInventory (AMI), a multi-faceted measure of achievement motivationdeveloped by Schuler et al. (2004), to construct four items such as “Igenerally prefer difficult tasks more than easy tasks”. In order to assessthe motive to avoid failure, we referred to the work of Hagtvet andBenson (1997) and formulated four items such as “I dislike situationsin which I am not sure of the result”. Ratings were made on 5-pointscales ranging from completely disagree (1) to completely agree (5).
1.1.2. Validity of motive measuresThe Cronbach's alpha for the motive to achieve success and the
motive to avoid failure scales were α= .81 (M=3.10; SD= .76), andα= .69 (M=3.16; SD= .77), respectively. The univariate skewness of themotive to achieve success ranged from − .24 to .06 and its univariatekurtosis ranged from − .72 to − .24. The univariate skewness of themotive to avoid failure ranged from − .70 to .24 and its univariatekurtosis ranged from − .93 to − .14. In the context of analyses, based onindividual items (West et al., 1995), these results are acceptable.
1.1.3. Selection criteria and selected participantsParticipants were classified as approach-driven, if their score (i.e.,
mean of the four items) of the motive to achieve success equaled 3.75or higher (above the 80th percentile), and if their score of the motiveto avoid failure equaled 2.50 or less (below the 20th percentile).Participants were classified as avoidance-driven, if their score of the
1 Fineman (1977) stated that the motive to achieve success is a multidimensionalconcept and that the measurement of the motive should focus on a particular domainrelated to the criterion tasks under study. We chose to focus on the “preference fordifficult tasks” to increase the predictive validity of the motive for engaging. Thismotive disposition is assumed to drive individuals to mobilize effort particularlyduring difficult tasks.
motive to achieve success equaled 2.50 or less (below the 20thpercentile), and if their score of the motive to avoid failure equaled3.75 or higher (above the 80th percentile). Based on the selectioncriteria, 53 approach-driven participants and 79 avoidance-drivenparticipants were selected from the sample of 710 participants.
1.2. Participants
Twenty three of the 53 participants selected as approach-drivenand 27 of the 72 participants selected as avoidance-driven volun-teered to take part in the study. The 50 participants responded againto the measures of the motive to achieve success and of the motive toavoid failure. This procedure was an experimental precaution toascertain of the stability of the participants' characteristics. A self-report motive is built on a participant's self-image. As a result, aparticipant's self-image is determined by social pressures (e.g.,cultural norms and parental values) which probably yielded instabilitybetween the first and second motive measures. Instability of theparticipants' characteristics may also be attributed to the poorpsychometric properties of the motive measures. However, thesemotive measures demonstrated acceptable psychometric propertieson the previous sample of 710 participants. As a result of thisprocedure, five approach-driven participants and nine avoidance-driven participants were excluded from the study because this timethey did not reach the selection criteria. Analyses were conducted onthe results of 18 approach-driven participants (nine male) and 18avoidance-driven participants (nine male).
1.2.1. MeasuresA 16-item achievement motivation subscale from the French
version of the Personality Research Form (PRF; Jackson, 1999) wasadministrated in order to measure the motive to achieve success. Asthere is no validated scale of the motive to avoid failure in French, the20-item from the French version of the State-Trait Anxiety Inventory(STAI; Spielberger and Vagg, 1995) was used as an indicator of themotive to avoid failure.2 Participants indicated their responses on 5-point scales ranging from completely disagree (1) to completely agree(5). Characteristics of the participants are presented in Table 1.Approach-driven participants had a higher PRF score and a lower STAIscore than that of avoidance-driven participants, t(17)=4.85, pb .001,and t(17)=2.38, pb .03, respectively. Correlations among the person-ality scales are presented in Table 2.
In the present study, invested mental effort is measured based on abehavioral, a subjective, and a physiological approach. Performance(i.e., reaction time and reaction time variance) in an informationtransfer task is assumed to be a monotonically increasing function ofthe amount of allocated resources (Humphreys and Revelle, 1984). Weused three common subjective measures: (a) the perceived difficultyscale (e.g., Eccles andWigfield,1995); (b) the task load index (Hart and
2 Elliot and McGregor (1999) specify that the trait anxiety and the motive to avoidfailure are distinct but convergent factors. Consequently, a trait anxiety measure can bea good indicator of the motive to avoid failure level.
Table 2Correlations between scores from MAS, PRF, MAF, and STAI Scales
MAS PRF MAF STAI
MAS –
PRF .77⁎ –
MAF − .82⁎ − .60⁎ –
STAI − .55⁎ − .40⁎ .52⁎ –
Note: MAS=motive to achieve success; PRF=personality research form; MAF=motive toavoid failure; STAI=state-trait anxiety inventory.* pb .05.
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Staveland, 1988); (c) and the rating scale for mental effort (Zijlstra,1993).3 Beside behavioral and subjective measurement methods,cardiovascular reactivity is used to investigate invested mental effort.In the present study, we used heart rate and heart rate variability.Studies showed that an increase in invested mental effort is related toan increase of heart rate (e.g., Gellatly and Meyer, 1992). However,heart rate is influenced by both sympathetic and parasympatheticnervous system and should only respond to effort mobilization whenthe sympathetic impact is stronger (Berntson et al., 1993). Askelrodet al. (1981) introduced power spectral analysis of heart ratefluctuations to quantitatively evaluate beat-to-beat cardiovascularcontrol. The sympathetic nervous system reacts rather slowly andtherefore is reflected mainly in the midfrequency band ranging from.07 to .14 Hz. Fluctuations in this band are associated with short-termregulation of blood pressure which causes a resonance in the veinswith a frequency of about .10 Hz (Mulder et al., 1995). Variability inthis band has been shown to decrease during effortful mentalprocessing in both laboratory (Mulder et al., 1995) and operationalenvironments (e.g., Veltman, 2002). The amplitude of the midfre-quency band is reduced during effortful mental processing becauseheart rate variability is less determined by changes in blood pressure.The midfrequency band is a good index of invested mental effortunder the condition that this band is not influenced by respiratoryactivity (Mulder et al., 1995). Respiratory activity influences heart rateby rhythmically attenuating the vagal influence and thus producing arhythmic increase and decrease in heart rate at the same frequency asrespiration. Variations in the high-frequency band ranging from .15 to.40 Hz mainly reflect respiratory activity (Askelrod et al., 1981). Theamplitude of the high-frequency band is reduced during effortfulmental processing by an increase in respiration activity. The thirdeffort-related physiological index used is facial electromyographic(EMG) activity. Increasing facial EMG activity, especially for thecorrugator supercilii, is an expression of growing compensatory effortnecessary for counteracting decrement in performance efficiencycaused by habituation, boredom, and fatigue (Van Boxtel and Jessurun,1993; Veldhuizen et al., 2003; Waterink and van Boxtel, 1994).4
1.3. Task
The experimental task was a visual memory search task. Therewere three blocks of different levels of difficulty. Each block was
3 The exact amount of mobilized resources is often predicted in literature by asubjective workload or task difficulty instrument (e.g., Ryu and Myung, 2005).However, invested mental effort and task difficulty are distinct factors. Effort refers tomental energy or attentional resources voluntarily allocated to a task. Task difficulty orworkload refers to the quantity of mental energy or attentional resources necessary toperform a task (Kahneman, 1973). In the present study, we used subjective difficultymeasures to ascertain of the task difficulty manipulation.
4 EMG activity of frontalis and orbicularis oris inferior are also used as an expressionof growing compensatory effort (Van Boxtel and Jessurun, 1993; Waterink and vanBoxtel, 1994). Activity of frontalis was not measured because of the practical andtechnical difficulties associated with simultaneously recording EMG signals fromdifferent closely spaced locations on the face of each participant. In previous study,activity of orbicularis oris inferior was contaminated by motion artifacts associatedwith lips movements (the participants have to perform a counting task). We decided,consequently, not to measure orbicularis oris inferior activity.
composed of 64 trials. Participants had to memorize either 1, 2, or 4letter(s) (memory set) and compare with 4 letters in a recognition set.The time course of a trial is presented in Fig. 1. The task was to indicateif any letter in the memory set was in the recognition set by pressingthe “yes” key on the joystick with the right index. If none of thememory letters were in the recognition set, participants pressed the“no” key on the joystick with the right thumb. The probability that aletter in the recognition set appeared or not was equal. At the end ofeach trial, participants received feedback on their reaction time (Fig. 1)which concerned the response speed or the type of error (i.e.,anticipation [reaction timeb150 ms], too slow response [reactiontimeN3000 ms], and decision error). Difficulty was also manipulatedby presenting the recognition set in two different colors (red or green).Participants were asked to count the number of red recognition setswhile carrying out the visual memory search task.5 The three memoryconditions with 1, 2, and 4 consonants are composed of 28, 32, and 44red recognition sets, respectively. Participants were instructed to reactas quickly as possible without making errors and to count the numberof red recognition sets. At the end of each block, participants indicatedthe number of red recognition sets. They received feedback on averagereaction time and number of errors for the visual memory search taskand the correct number of red recognition sets for the counting task.
1.4. Subjective measures
To measure perceived difficulty, we referred to Eccles andWigfield(1995) scale in order to construct four items. One such example is thefollowing: “How hard is this task for you?” Participants responded ona scale of 1 (very easy) to 5 (very difficult) scale.6
The task load index (Hart and Staveland,1988) used six dimensions(i.e., mental demand, physical demand, temporal demand, perfor-mance, effort, and frustration) to assess perceived workload. A scorefrom 0 to 10 is obtained on each dimension. A weighting procedurewas used to combine the six dimension ratings into a global score. Theweighting procedure required the participants to choose whichdimension was more relevant to workload across all pairs of sixdimensions.
The rating scale for mental effort (Zijlstra, 1993) was used to assessthe participants' perceived level of invested mental effort. The ratingscale for mental effort was a univariate scale. This scale ranged from 0to 150 and had nine descriptive indicators along its axis (e.g., noteffortful, awfully effortful).
1.5. Physiological measures
A POLAR Heart Rate Monitor (POLAR Vantage NV, Kempele,Finland) was used to measure heart rate. The heart rate signal wasdigitized at 1000 Hz. The R–R interval sequences were visuallyinspected, and the data considered as artifactual were manuallyreplaced by interpolated or extrapolateddata (meanof the three valuespreceding). The amount of abnormal beats, due to the R–R recordingdevice used,was less than 2%. Then, suitable series of 256 R–R intervalswere chosen for analysis. The heart rate (beats per minute [bpm]) wasobtained from each 256 R–R interval. The fast Fourier transformspectra were also calculated from this 256 R–R interval with HRVanalysis software 1.1 for Windows (Niskanen et al., 2004). The defaultvalues of the HRV analysis software were set for the detrending
5 The dual-task condition was used for a main reason. Aasman et al. (1987), using asimilar task, found no effect of increased difficulty on heart rate variability when thistask was performed under single-task conditions (i.e., visual memory search task).However, the effects on heart rate variability were evident under dual-task conditions(i.e., visual memory search task and counting task).
6 On a sample of 469 participants who performed a mental rotation test, thereliability for the perceived difficulty scale was α= .87 (M=3.48; SD= .83). Theunivariate skewness ranged from -.11 to .19 and the univariate kurtosis ranged from.14 to .48.
Fig. 1. Time course of a trial of the visual memory search task. Example of a relevant target trial with a correct response and a response time feedback of 512ms. A first warning signalwas presented before the memory set. A second warning signal was presented before the recognition set. The recognition set consonants were presented at the corners of a 2.5 cm by2.5 cm square centered on the middle of the computer screen. At the end of each trial, participants received knowledge of result concerning the speed in milliseconds of theirresponse.
Table 3Means and standard deviations of the baseline of physiological measures, thebehavioral data, the subjective data, and the physiological data across stimuli conditions
Baseline Memory load
1 2 4
MRT – 872.94 (211.57) 1070.50 (213.96) 1381.01 (316.02)RTV – 51,986.13 (25,648.90) 75,417.87 (29,511.26) 151,541.32 (81,553.25)EVMS – .40 (.20) .52 (.26) .73 (.20)EC – 2.77 (.56) 2.62 (.70) 2.63 (.42)PD – 10.19 (2.51) 11.92 (2.60) 14.56 (2.09)TLX – 5.63 (1.07) 6.17 (1.07) 6.71 (1.19)RSME – 62.36 (15.70) 69.64 (16.98) 78.39 (16.53)HR 76.18
(12.50)75.77 (12.06) 75.94 (11.50) 77.91 (12.12)
MFB 1.65(.17)
1.55 (.20) 1.52 (.16) 1.48 (.15)
HFB 1.49(.22)
1.57 (.20) 1.58 (.20) 1.62 (.17)
CS 89.66(28.92)
92.80 (37.94) 103.44 (78.29) 106.39 (79.44)
Note: Standard deviations are in parentheses. MRT=mean reaction time; RTV=reactionvariance; EVMS=error in visual memory search; EC=error in counting; PD=perceiveddifficulty; TLX=task load index; RSME=rating scale for mental effort; HR=heart rate;MFB=midfrequency band; HFB=high-frequency band; CS=corrugator supercilii.
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procedure (smoothness prior to trend and eye model) and for theinterpolation rate (2 Hz). To approach normal distribution of the datafor statistical analysis, the spectral powervalueswere transformed intologarithmic values.
EMG activity of the corrugator supercilii muscle pertaining to theleft side of the face was recorded. Electrode locations were chosenaccording to the guidelines presented by Fridlund and Cacioppo(1986). EMG activity was recorded by Ag/AgCl surface electrodes withelectrode size, contact area, and housing of 3, 5, and 13 mm diameter,respectively. Electrodes were attached to the skin with centers 15 mmapart. The reference electrode was an anti-static bracelet placed onthe non-dominant ankle. The EMG was continuously monitored (gain1000), filtered (3 Hz–1 kHz) (Model P511K, Grass Instrument Co., USA),and digitized on-line (A/D rate: 500Hz). Artifacts such as coughing,yawing, stretching or other gross movements detected by theexperimenter were omitted for the analysis. Themean EMG amplitudescore was calculated and presented in microvolts.
1.6. Procedure
Before the experimental task, participants received specificbehavioral instructions which informed them to resist the consum-mation of psychoactive substances (e.g., tobacco, coffee, tea, oralcohol) and also to avoid stressful events or physical exercise during4 h prior to the experimental session. Upon entering the laboratory,participants received information on the experimental protocol andwere required to give written consent. Two EMG electrodes and thePOLAR Heart Rate Monitor were attached to the participants who satquietly for 10 min whilst baseline data were collected. Following thebaseline period, participants filled out the achievement motivationsubscale of the PRF, the STAI, as well the motive to achieve successscale and the motive to avoid failure scale. Results are presented inTables 1 and 2. At the end of this period and after receiving 32 practicetrials for each memory load, participants were randomly assigned toone of the three experimental conditions. After each task block,participants filled out the perceived difficulty scale, the task loadindex, and the rating scale for mental effort.
1.7. Data analysis
All the data were entered into a repeated-measures ANOVAdesign with one between-participant variable (i.e., approach-driven and avoidance-driven participants) and one within-partici-pant variable (i.e., three levels of memory load conditions: 1, 2, or 4consonants). The Greenhouse–Geisser correction, an adjustmentused in univariate repeated measures when the sphericityassumption is violated, was applied to study the effect of taskdifficulty and interaction. Reactivity was calculated by subtractingvalues for each baseline period from the values of each correspond-ing condition.
2. Results
Means and standard deviations of the baseline of physiologicalmeasures, the behavioral data, the subjective data, and the physiolo-gical data across stimuli conditions are presented in Table 3.
2.1. Behavioral data
In order to detect potential changes in speed-accuracy trade-offbetween groups, a 2×3 ANOVA was carried out on the arcsinus-transformed proportion of errors in the visual memory search taskand in the counting task. Since no group differences were found forthe proportion of errors in the visual memory search task and in thecounting task (all p valuesN .12), we compared the mean reaction timeand reaction time variance of the approach-driven participants withthose of the avoidance-driven participants.
A 2×3 ANOVA revealed a significant interaction between groupsand task difficulty in the mean reaction time, F(1.34,45.60)=4.33,pb .03, ηP2= .11 (Fig. 2). As expected, approach-driven participants had afaster mean reaction time, especially during the difficult task.Students' t tests revealed significant differences between theapproach-driven and the avoidance-driven groups in the memoryload of 1, 2, and 4 consonants, t(17)=2.70, pb .01, d= .39, t(17)=2.46,pb .02, d= .28, and t(17)=3.61, pb .002, d= .41, respectively.
A 2×3 ANOVA revealed the predicted effect of group, and taskdifficulty based on the mean reaction time and reaction time variance.
Fig. 2. Mean reaction times (ms) as a function of approach-driven vs. avoidance-driven participants and memory load. Error bars give standard errors of cell means.
148 R.L. Capa et al. / International Journal of Psychophysiology 70 (2008) 144–150
Specifically, approach-driven participants showed faster mean reac-tion time and lower reaction time variance (M=994.91 ms andM=75,550.32 ms2, respectively) than avoidance-driven participants(M=1221.39 ms and M=110,413.23 ms2, respectively), F(1,34)=11.23,pb .002, ηP2= .11, and F(1,34)=8.30, pb .007, ηP2= .20, respectively. Meanreaction time and reaction time variance increased as task difficultyincreased, F(1.34,45.60)=166.36, pb .001, ηP2=.83, and F(1.16,39.38)=58.17,pb .001, ηP2=.63, respectively. Mean reaction time and reaction timevariancewerehigher in thememory loadof 2 consonants compared to the1 consonant load, t(35)=10.7, pb .001, d=1.31, and t(35)=5.42, pb .001,d=1.20, respectively. Mean reaction time and reaction time variancewerehigher in thememory loadof 4 consonants compared to the2 consonants,t(35)=10.47, pb .001, d=1.63, and t(35)=7.34, pb .001, d=1.76, respectively.The proportion of errors in the visual memory search task increased astask difficulty increased, F(1.91,65.10)=35.74, pb .001, ηP2=.51. The propor-tion of errors was lower for the 1 consonant load compared to the 2consonants load, t(35)=2.63, pb .01, d=.69, and higher for the 4consonants load compared to the 2 consonants load, t(35)=5.48,pb .001, d=1.36. No other effect was significant.
2.2. Subjective data
Before testing the interactive effect of achievement motivationand task difficulty on the effort scale of the rating scale formental effort, the effect of task difficulty on the sum of perceiveddifficulty scores and the workload score of the task load index wasexamined with a 2×3 ANOVA. Perceived difficulty and workload
Fig. 3. Cell means and standard errors of midfrequency band reactivity as a funct
scores increased as task difficulty increased, reflecting a successfultask difficulty manipulation, F(1.83,62.40)=62.40, pb .001, ηP2= .65,and F(1.66,56.52)=23.33, pb .001, ηP2= .41, respectively. The sum ofperceived difficulty scores was lowest for the 1 consonant loadcompared to the 2 consonants load, t(35)=5.30, pb .001, d= .95, andhighest for the 4 consonants load compared to the 2 consonants load,t(35)=6.65, pb .001, d=1.58. The workload score of the task loadindex was lowest for the 1 consonant load compared to the 2consonants load, t(35)=3.64, pb .001, d= .71, and highest for the 4consonants load compared to the 2 consonants load, t(35)=4.20,pb .001, d= .58.
A 2×3 ANOVA revealed that the effort score of the rating scale formental effort was only sensitive to the task difficulty manipulationand increased as memory load increased, F(1.73,58.76)=34.75,pb .001, ηP2= .51. The effort score of the rating scale for mental efforttask was lowest for the 1 consonant load compared to the 2consonants load, t(35)=2.63, pb .01, d= .69, and highest for the 4consonants load compared to the 2 consonants load, t(35)=5.48,pb .001, d=1.36. No other effect was significant.
2.3. Physiological reactivity
The first step in the analysis was to examinewhether the two groupsdiffered between rest periods. AMANOVAwas conductedwith group asbetween-subject factor and baseline of high-frequency band, midfre-quency band, heart rate, and corrugator supercilii activity as dependentvariables. There were no baseline differences between groups, Wilks'
ion of approach-driven vs. avoidance-driven participants and memory load.
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Λ=.82, F(4,31)=1.69, p=.18, ηP2=.18. Since no group differences werefound, we compared the reactivity score of the approach-drivenparticipants with those of the avoidance-driven participants.
In order to detect differences of respiratory activity betweengroups and/or across stimuli conditions, a 2×3 ANOVA was carriedout on the high-frequency band. Since no effect was significant (allp valuesN .14), we can use the midfrequency band as a good index ofinvested mental effort. A 2×3 ANOVA of midfrequency bandrevealed a significant interaction between groups and task diffi-culty, F(1.75,59.42)=3.53, pb .04, ηP2= .09 (Fig. 3). Approach-drivenparticipants showed a decrease of midfrequency band reactivitywhen task difficulty increased but not avoidance-driven partici-pants. Students' t tests revealed a significant difference betweenapproach-driven and avoidance-driven groups in the memory loadof 4 consonants, t(17)=3.06, pb .008, d=1.55. Only the midfrequencyband of the approach-driven group seems to be affected by the taskdifficulty. Thiswas confirmed by a supplementary analysis. Scores of themidfrequency band reactivity were analyzed with a linear polynomialcontrast. Contrasts weights were +1 for the approach-driven group and0 for the avoidance-drivengroup. A significant linear trendbetween taskdifficulty and midfrequency band reactivity emerged, F(1,34)=10.02,pb .003, ηP2=.38. No other effect was significant.
A 2×3 ANOVA revealed the predicted effect of group onmidfrequency band reactivity, F(1,34)=4.83, pb .03, ηP2= .12. Specifi-cally, approach-driven participants had a stronger decrease ofmidfrequency band reactivity than (M=− .18) the avoidance-drivenparticipants (M=− .07). A 2×3 ANOVA of the midfrequency band, andheart rate reactivity revealed the experimental effect of task difficulty,F(1.75,59.42)=3.49, pb .04, ηP
2= .09, and F(1.91,64.95)=7.76, pb .001,ηP2= .19, respectively. Heart rate reactivity was higher in the 2
consonants load compared to the 4 consonants load, t(35)=3.88,pb .004, d= .46. No other effect was significant. A 2 (group)×3 (taskdifficulty) ANOVAof corrugator supercilii revealedno significant effect.
3. Discussion
In the present study, the interactive effect of achievementmotivation and task difficulty on effort mobilization, postulated byHumphreys and Revelle (1984), was investigated using behavioral,subjective, and physiological measures. We assumed that approach-driven participants would invest more effort than avoidance-drivenparticipants as task difficulty increased.
3.1. Behavioral and subjective results
The performance deterioration (i.e., mean reaction time) was lessfor approach-driven participants than for avoidance-driven partici-pants, especially during the difficult condition (i.e., the memory loadof 4 consonants). Analyses of the proportion of errors in the visualmemory search and counting tasks revealed no significant differencebetween groups. In consequence, we can conclude there was nospeed-accuracy trade-off between groups. The better performanceshowed by approach-driven participants compared to avoidance-driven participants is interpreted as a higher mobilization of effort. Itis possible that approach-driven participants had greater ability toperform the experimental task than avoidance-driven participants.Correspondingly, for the same level of invested mental effort,approach-driven participants showed better performance than avoid-ance-driven participants. However, if approach-driven participantshad greater ability to perform the task, then they should haveperceived the task asmore easy than avoidance-driven participants. Inthe present study, no group difference was found for the perceiveddifficulty scale and the task load index. We concluded that as no groupdifference was found for the perceived difficulty scale and the taskload index that there was likely no significant difference of perceiveddifficulty between groups.
3.2. Midfrequency band reactivity
The better performance showed by approach-driven participantscompared to avoidance-driven participants can also be interpreted asa greater mobilization of mental effort because approach-drivenparticipants had a stronger decrease of midfrequency band reactivitythan avoidance-driven participants during the difficult condition. Inseveral studies it was found that midfrequency band diminishedduring mental task performance (Miyake, 2001; Mulder et al., 1995;Ryu andMyung, 2005). This decrease is related to the amount of effortthat is invested in task performance. We concluded that approach-driven participants invested more mental effort than avoidance-driven participants, especially during the difficult task. This inter-pretation is in accordance with the interactive effect of achievementmotivation and task difficulty on mental effort postulated byHumphreys and Revelle (1984). Another interpretation of the resultsis that achievement motivation has influenced potential motivation.The importance of success determines the level of potential motiva-tion which is the amount of resources that is maximally justified forgoal attainment (see Brehm and Self, 1989; Gendolla and Wright,2005; Wright and Kirby, 2001; for reviews). Puca and Schmalt (1999)postulated that approach-driven participants should focus on thepositive emotional consequences of success, whereas avoidance-driven participants should focus on the negative emotional conse-quences of failure. One possible implication is that approach-drivenparticipants should have a high potential motivation. This hypothesisis in accordance with the results obtained. Approach-driven partici-pants showed a linear decrease of midfrequency band reactivity whentask difficulty increased. Potential motivation was probably so highthat even for the highest difficulty level effort was justified. Anotherpossible implication is that avoidance-driven participants should havea low potential motivation. This hypothesis is in accordance with theabsence of effect of task difficulty on the midfrequency band for theapproach-driven participants. Potential motivation was probably solow that even for the lowest difficulty level effort was not justified.
3.3. Heart rate reactivity
No significant interactive effect of achievement motivation andtask difficulty on heart rate reactivity was found. Moreover, nodifference between the 1 consonant load in comparison with the 2consonants loadwas significant. Heart rate reactivity increased only inthe memory load of 4 consonants compared to the 2 consonants load.The experimental task was probably too easy to highlight theinteractive effect of achievement motivation and task difficulty onheart rate reactivity. A relatively weak sympathetic (beta-adrenergic)reactivity may not affect heart rate reactivity significantly underconditions of low to moderate effort demand (Berntson et al., 1993).Subjective measures confirmed this possibility. Participants perceivedthe 1 and 2 consonants load as relatively easy and the 4 consonantsload as a moderate level of difficulty. In future studies, the test of theinteractive effect of achievement motivation and task difficulty onheart rate reactivity requires comparing tasks perceived as highlydifficult to tasks perceived as weakly difficult rather than comparingtasks of moderate difficulty to tasks of low difficulty. Moreover, asdifficulty of the 4 consonant load was moderate, we could not test thehypothesis that both approach and avoidance-driven participantsshould disengage when task difficulty is extremely high. In futurestudies, to test this hypothesis, participants will have to perform anextremely difficult task.
4. Conclusion
In a previous study, Capa et al. (in press) provided the first evidencefor the interactive effect of achievement motivation and task difficultyon effort-related physiological reactivity in a sensory-motor processing
150 R.L. Capa et al. / International Journal of Psychophysiology 70 (2008) 144–150
task. In the present study, we wished to conceptually replicate andextend the Capa et al. (in press) experiment. In contrast to Capa et al.(in press), whowere concerned by the effect of achievementmotivation,as well as task and goal difficulty, we exclusively focused on theinteractive effect of achievement motivation and task difficulty oninvested mental effort. Consequently, we opted for a less complex andmore direct experimental design. We chose an experimental taskcontaining three levels of task difficulty (rather than two). This allowedus to extend the effect of achievement motivation on the linearrelationship between subjective difficulty and invested mental effort.The interaction between groups and task difficulty on mean reactiontimeandmidfrequency bandwasonly significantwhen thedifficult task(i.e., memory load of 2 consonants) was compared to the very difficulttask (i.e., memory load of 4 consonants). However, no interactive effectwas significant when the easy task (i.e., memory load of 1 consonant)was compared to the difficult task. In conclusion, the test of theinteractive effect of achievement motivation and task difficulty requirescomparing very difficult tasks to difficult or easy tasks rather thancomparing difficult tasks to easy tasks. Moreover, a new experimentaltaskwasused. Contrary to the sensory-motor processing task used in theprevious study, we chose a cognitive task that demanded aminimum ofmotor movement from the participants to ensure that physiologicalreactivity could be attributed to the mobilization of mental effort ratherthan to metabolic movement effects. In addition, in the presentexperiment, the cognitive task was a visual memory search task withcounting that involved executive functions such as updating of workingmemory. Thus, the present study has for the first time established a linkbetween cardiovascular reactivity, performance task that taxed execu-tive functions, and achievement motivation. In conclusion, the fact thatthis interactive effect on cardiovascular reactivity and mean reactiontime holds true for two different types of cognitive tasks andexperimental designs makes us confident that the findings can begeneralized.
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