Implicit Aging: Masked Age Primes Influence Effort-Related Cardiovascular Response in Young Adults
ZAFEIRIOU, Athina, GENDOLLA, Guido H.E.
Based on the Implicit-Affect-Primes-Effort model and evidence that aging is associated with cognitive difficulties, this experiment investigated the effect ofmasked age primes on young adults' effort-related cardiovascular response during a mental arithmetic task.We predicted that elderly primes should activate the aging stereotype and thus render the performance difficulty concept accessible, while youth primes should activate the performance ease concept—similarly, as affect primes do. The accessible difficulty or ease concepts, in turn, should influence experienced demand and thus effort-related cardiovas- cular response during cognitiveperformance.Aneutral prime control condition should fall into these conditions.We found the expected effects on performance-related responses of heart rate (HR) and diastolic blood pressure (DBP): For bothmeasures, the elderly primes led to the strongest reactivity, the youth primes led to the weakest reactivity, and the neutral-prime control condition fell in between these conditions.
ZAFEIRIOU, Athina, GENDOLLA, Guido H.E. Implicit Aging: Masked Age Primes Influence Effort-Related Cardiovascular Response in Young Adults. Adaptive Human Behavior and Physiology, 2018, vol. 4, no. 1, p. 1-20
DOI : 10.1007/s40750-017-0074-z
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O R I G I N A L A RT I C L E
Implicit Aging: Masked Age Primes Influence
Effort-Related Cardiovascular Response in Young Adults
Athina Zafeiriou1&Guido H. E. Gendolla1
Received: 14 April 2017 / Revised: 12 July 2017 / Accepted: 13 July 2017
#Springer International Publishing AG 2017
Abstract Based on the Implicit-Affect-Primes-Effort model and evidence that aging is associated with cognitive difficulties, this experiment investigated the effect of masked age primes on young adults’effort-related cardiovascular response during a mental arithmetic task. We predicted that elderly primes should activate the aging stereotype and thus render the performance difficulty concept accessible, while youth primes should activate the performance ease concept—similarly, as affect primes do. The accessible difficulty or ease concepts, in turn, should influence experienced demand and thus effort-related cardiovas- cular response during cognitive performance. A neutral prime control condition should fall into these conditions. We found the expected effects on performance-related responses of heart rate (HR) and diastolic blood pressure (DBP): For both measures, the elderly primes led to the strongest reactivity, the youth primes led to the weakest reactivity, and the neutral-prime control condition fell in between these conditions.
Keywords Effort . Aging . Automaticity . Cardiovascular response
Social stereotypes are fixed, simplified, and overgeneralized cognitive structures (Rust and See 2010) that help humans to organize the perception of their complex social reality (Oakes et al.1994; Macrae et al. 1994). A typical and strong stereotype in the Western culture concerns the elderly: Most Western people associate aging with cognitive diffi- culties and a decline of mental capacities (e.g., Cuddy et al.2005; Harada et al.2013; Kite
* Guido H. E. Gendolla email@example.com Athina Zafeiriou
1 Geneva Motivation Lab, FPSE, Section of Psychology, University of Geneva, 40, Bd. du Pont-d’Arve, CH-1211 Geneva 4, Switzerland
and Wagner2002; Kite et al.2005). Apart from Westerners’mere stereotypic belief about an association between aging and cognitive difficulties, there is also evidence that older individuals’basic cognitive functioning indeed becomes less efficient and that the elderly face more difficulties than younger adults when they are confronted with a cognitive task (Hess and Ennis 2012). Accordingly, older people mobilize more resources and show stronger effort-related responses of the cardiovascular system than younger adults when they cognitively perform on a similar level (Smith and Hess 2015). Moreover, older people experience cognitive tasks as subjectively more demanding and mobilize more effort than younger people, and they disengage due to subjective over-challenge at high levels of perceived difficulty (Hess et al. 2016). That is, aging seems indeed to be associated with cognitive performance difficulties.
Based on the evidence discussed so far, this study tested the idea that the implicit activation of the aging stereotype is sufficient to systematically influence effort-related cardiovascular responses during a cognitive challenge—even in young, cognitively well-functioning individuals like university students. In first support to this idea, it was found that implicit elderly primes during a difficult cognitive task lead to an effort mobilization deficit which could be compensated by high success incentive (Zafeiriou and Gendolla2017). On the theoretical level, the present study extends a recent research program on the impact of the implicit activation of emotion concepts (see Niedenthal 2008)—mental representations of affective states—on effort mobilization to the realm of implicitly activated social stereotypes. That research has been guided by the Implicit- Affect-Primes-Effort (IAPE) model (Gendolla 2012, 2015), which posits that people learn in everyday life that coping with challenges is easier in some affective states than in others. Consequently, performance ease or difficulty become features of individuals’
mental representations of different affective states. The IAPE model posits that render- ing this affect knowledge accessible by implicitly processed affective stimuli—affect primes—during task performance leads to experiences of low or high task demand.
Specifically, happiness and anger are posited to be associated with high coping potential and thus ease, while sadness and fear are associated with low coping potential and thus difficulty. The resulting level of experienced task demand during performance deter- mines then the resources people mobilize according to the principles of motivational intensity theory: Effort is mobilized proportionally to subjective demand as long as success is possible and the necessary effort is justified (Brehm and Self1989).
Several studies have supported the IAPE model and found clear and replicated evidence for the systematic impact of implicitly processed affect primes on effort- related cardiovascular response (e.g., Chatelain and Gendolla 2015; Gendolla and Silvestrini2011; Lasauskaite et al. 2013; Silvestrini and Gendolla2011a). The present research extended the IAPE model logic. Based on the evidence that aging is associated with cognitive difficulties and that youth should therefore be associated with cognitive ease, we tested the idea that age-primes that are implicitly processed during a cognitive task should systematically influence effort-related response. That is, aging primes should increase and youth primes should decrease effort.
Effort-Related Cardiovascular Response
Integrating the predictions of motivational intensity theory (Brehm and Self1989) with Obrist’s (1981) active coping approach, Wright (1996) posited that β-adrenergic
sympathetic impact on the heart responds proportionally to the level of experienced task demand as long as success is possible and justified. Non-invasively,β-adrenergic impact on the heart is best reflected by increased cardiac contractility and thus shortened cardiac pre-ejection period (PEP)—the time interval between the beginning of left ventricular excitation and the opening of the left ventricular cardiac valve in a cardiac cycle (Berntson et al.2004). Supporting Wright’s integrative hypothesis, it has been found that PEP sensitively responds to variations in experienced task demand (e.g., Richter et al. 2008), incentive value (e.g., Richter and Gendolla 2009), and combinations of both (e.g., Silvestrini and Gendolla 2011b).
Several studies have also assessed responses of systolic blood pressure (SBP)—the maximal pressure in the vascular system between two heart beats—which is system- atically influenced by cardiac contractility through its impact on cardiac output (see Gendolla and Richter 2010; Wright and Kirby 2001). However, SBP and diastolic blood pressure (DBP)—the minimal arterial pressure between two heart beats—are also influenced by peripheral vascular resistance, which is not systematically influenced by β-adrenergic impact (Levick 2003). Still other studies have used heart rate (HR) to assess effort. Given that HR is determined by both sympathetic and parasympathetic nervous system activity, it should reflect effort mobilization when the sympathetic impact is stronger. In any case, some studies found that responses of HR (e.g., Brinkmann and Gendolla 2007; Eubanks et al. 2002; Freydefont et al. 2012;
Freydefont and Gendolla 2012; Gendolla 1998; Richter and Gendolla 2007) and DBP (e.g., Frazier et al. 2008; Gendolla and Richter 2006; Hui et al. 2009; Nolte et al. 2008; Wright et al. 2007; Wright et al. 2008) described patterns that had been predicted for effort mobilization.
The Present Experiment
Extending the IAPE model logic (Gendolla2012,2015) and considering our previous findings on implicit aging (Zafeiriou and Gendolla 2017), we tested the effect of implicitly processed age primes (elderly vs. youth) on effort-related cardiovascular response including a neutral prime control condition to which the effects of the age primes could be compared. Participants, who were young adults, worked on a mental arithmetic task adapted from Bijleveld et al. (2010). During that task, they processed very briefly presented pictures of faces of elderly vs. young individuals. In the additional neutral prime control condition, we presented pictures of dotted silhouettes.
We hypothesized that participants who processed elderly primes during the cognitive task should show stronger effort-related cardiovascular reactivity than participants who processed youth primes. Cardiovascular responses of participants processing neutral primes in the control condition should fall in between the elderly and youth-prime conditions. As outlined above, we expected this effort pattern because performance difficulty should be a feature of the aging stereotype, while performance ease should be a feature of the youth-stereotype. Making these features accessible by implicitly processing the age primes should thus result in high vs. low experienced demand during the cognitive task. Consequently, we predicted a linear increase in cardiovas- cular response from the youth prime via the neutral prime to the elderly prime condition. We assessed PEP, HR, SBP, and DBP—and to get a fuller picture of cardiovascular responses also total peripheral resistance (TPR) and heart rate variability
(HRV). As outlined above, our primary dependent variable reflecting effort was cardiac PEP. The analyses of TPR and HRV were run to better understand some of the cardiovascular effects we had found—as will be outlined below.
Participants and Design
Sixty-two healthy university students from various disciplines of the University of Geneva (49 women, 13 men, mean age 25 years) were randomly assigned to a 3 cell between-persons design: elderly prime vs. neutral prime vs. youth prime.
Participation was anonymous, voluntary, and recompensed with 10 Swiss Francs (approximately 10 USD). We had to remove one participant because she indicated taking medication that could have influenced her cardiovascular responses and three participants because of incomplete cardiovascular data. Although we had aimed at collecting valid data of at least 20 participants per cell (see Simmons et al. 2011) this left a final sample of N = 58 participants. Sex and age were balanced across conditions for the PEP measure: elderly-prime condition (16 women, 5 men, mean age 25 years), neutral-prime condition (15 women, 4 men, mean age 25 years), and youth-prime condition (14 women, 4 men, mean age 25 years). Concerning the other cardiovascular measures, we excluded two more participants for the HR analysis, because their HR responses exceeded the grand mean for more than 2.58 SDs and were thus considered as outliers. The final sample for this measure thus consisted of N = 56 participants―elderly-prime condition (16 women, 5 men, mean age 25 years), neutral-prime condition (14 women, 4 men, mean age 24.5 years), and youth-prime condition (13 women, 4 men, mean age 25 years). Likewise, we excluded three participants from the SBP analysis, because their systolic responses were more than 2.53 SDs above the grand mean, leaving a sample of N = 55 participants for the SBP analyses―elderly-prime condition (15 women, 5 men, mean age 25.5 years), neutral-prime condition (14 women, 4 men, mean age 24.5 years), and youth-prime condition (13 women, 4 men, mean age 25 years).
Finally, one participant was excluded from the DBP analysis, because his diastolic responses were 2.54 SDs above the grand mean, leaving a final sample of N = 57 participants for the DBP analyses―elderly-prime condition (16 women, 5 men, mean age 25 years), neutral-prime condition (15 women, 3 men, mean age 25 years), and youth-prime condition (14 women, 4 men, mean age 25 years).1
1We had more women than men in all conditions because of the gender distribution of psychology students at the University of Geneva—there are much more women than men. Concerning the outlier elimination in the analyses of the cardiovascular measures, we have also tested if the effects later reported in the Results section would be moderated if the eliminated outliers would be included in the analysis. These analyses revealed that the association between diastolic baselines and reactivity scores does not remain significant (p= 0.066), but that the linear contrast for DBP reactivity does (p= 0.038). Concerning HR, there is a significant association between baseline and reactivity values (p= 0.033), but the linear contrast on baseline-adjusted HR reactivity falls short of significance (p= 0.10).
Highly standardized front perspective greyscale pictures of young (age 19 to 25 years) and old (age 71 to 84 years) individuals of the Lifespan-Adult-Faces database (Minear and Park 2004) were used as age primes (picture codes: Wfemale19, Wfemale20, Wfemale22, Wmale20–2, Wmale22–2, Wmale25–2, Wfemale71, Wfemale76, Wfemale84, Wmale78, Wmale79, Wmale82). Half the pictures showed female faces;
half showed male faces. For the neutral condition, a dotted silhouette forming a bust of a person was used (see Fig.1).
Apparatus and Physiological Measures
PEP (in milliseconds [ms]) and HR (in beats per minute [bpm]) were assessed using a Cardioscreen® 1000 (Medis, Ilmenau, Germany) haemodynamic monitoring-system (for a validation study see Scherhag et al. 2005), that continuously measured electro- cardiogram (ECG) and impedance cardiogram (ICG) signals. Four pairs of spot electrodes (Medis-ZTECT™) were placed on the right and the left side of the base of participants’ neck and on the right and left middle axillary line at the height of the xiphoid to sample (1000 Hz) thoracic impedance and electrocardiogram signals. ECG and ICG signals were analyzed offline with Bluebox 2 V1.22 software (Richter2010) applying a 50 Hz low pass filter. R-peaks in the ECG signal were identified using a threshold peak-detection algorithm and visually confirmed (ectopic beats were deleted).
Réponse Enregistrée 1000 ms
5500 ms – RT 7 + 5 + 3 = 19
133 ms 133 ms
Fig. 1 Trial structure of the implicit age priming procedure in the context of the mental arithmetic task. The pictures of faces, except the dotted silhouettes, stem from the Lifespan-Adult-Faces database (Minear and Park 2004)
The first derivative of the change in thoracic impedance was calculated and the resulting dZ/dt-signal was ensemble averaged over periods of 1 min using the detected R-peaks (Kelsey and Guethlein 1990). Only artifact-free cardiac cycles were included in the ensemble averages. R-peak onset and B-point were automatically scored for each ensemble average. B-point location was estimated based on the RZ interval, as proposed by Lozano et al. (2007), visually inspected, and―if necessary―corrected as recommended (Sherwood et al.1990). PEP was determined as the interval (in ms) between ECG R-peak onset and ICG B-point (Berntson et al. 2004). Shorter PEP indicates a stronger beta adrenergic impact on the heart, and therefore greater reactivity in terms of effort intensity. HR (in beats per minute [bpm]) was determined by means of the same software.
Additionally, SBP and DBP (in millimeters of mercury [mmHg]) were assessed with a Dinamap ProCare monitor (GE Medical Systems, Information Technologies Inc., Milwaukee, WI) that uses oscillometry. The Dinamap’s blood pressure cuff was placed over the brachial artery above the elbow of participant’s non-dominant arm and automatically inflated in 1-min intervals to assess arterial pressure. Assessed values were stored on computer disk.
To get a fuller picture of hemodynamics during task performance, we also assessed mean arterial pressure (MAP) with the blood pressure monitor and cardiac output (CO) with the ICG monitor to calculate total peripheral resistance (TPR). CO was calculated by the Cadioscreen system according to the Sramek and Bernstein formula (see Bernstein, 1986). TPR was derived from CO and MAP by the formula TPR = (MAP/CO) * 80 (Sherwood et al.1990). To avoid redundancy, we report only the TPR effects—the outcome variable of interest—in the results section.
Based on the ECG signals, we also assessed heart rate variability (HRV) as an estimate of parasympathetic activation. HRV reflects variation in the time interval between consecutive heartbeats and can mirror HR variations due to respiratory sinus arrhythmia (RSA). For the present analysis of HRV, we have chosen the frequency domain method, which assigns frequency bands and counts the number of RR intervals that match each band (Task Force of the European Society of Cardiology and North American Society of Pacing and Electrophysiology 1996). The high frequency (HF) component of HRV (0.15 to 0.4 Hz) is centered at respiratory frequency and is almost exclusively mediated by the parasympathetic nervous system. HF-HRV was calculated using the power spectral density (PSD) analysis in the Kubios HRV analysis software (version 3.0, Biomedical Signal and Medical Imaging Analysis Group, Department of Applied Physics, University of Kuopio, Finland).
Procedure and Experimental Task
The study was conducted in accordance with the ethical guidelines of the University of Geneva and the procedure had been approved by the local ethical committee. The protocol was run in individual sessions, which took 30 min each. After having obtained signed consent, participants took a seat in a comfortable chair in front of a computer.
The experimenter applied the electrodes for ECG and ICG signals and the blood pressure cuff and went to an adjacent control room. The procedure was computerized with a script running in E-Prime 2.0 (Psychology Software Tools, Pittsburgh, PA), which controlled the presentation of instructions and stimuli and collected participants’
responses. The session started with some introductory information and a self-report measure of participants’affective state with two items assessing positive affect (happy, joyful), one item assessing sadness (downcast), and one item assessing anger (angry).
Participants rated the four affect items (BRight now, I’m feeling…^) on 7-point scales (1 – not at all to 7 – very much) to assess their global affective state before the age primes’ manipulation. This was followed by a cardiovascular baseline assessment period (8 min). During this period, participants watched an extract of a hedonically neutral documentary film about Portugal and cardiovascular activity was assessed continuously.
After baseline assessment, participants received instructions (BPlease respond as quickly and accurately as possible^) for a mental arithmetic task adapted from Bijleveld et al. (2010). As depicted in Fig. 1, each trial started with a fixation cross (1000 ms), followed by a pre-mask consisting of a random dot pattern (133 ms). In dependence on the prime-condition, the pre-mask was followed by an age-prime (i.e. a face picture of a young vs. old individual) in the youth-prime and elderly-prime conditions, or a picture of a dotted silhouette in the neutral-prime control condition (27 ms; i.e. 2 frames on a 75 Hz monitor). Presentation of the age-prime pictures was randomized in each condition and the same picture did not appear successively. The face pictures were immediately backward masked, presenting the same random dot pattern as the premask (133 ms). Next, a mathematical equation of three single-digits adding up to a sum appeared for maximally 4.5 s. Participants indicated whether this equation was correct (e.g., 7 + 5 + 7 = 19) or incorrect (e.g., 3 + 5 + 7 = 16) by pressing aByes^ or aBno^
key on the numerical keyboard with the fingers of their choice of their dominant hand.
The mathematical equation remained on the screen until participants gave a response.
After responding, the message Bresponse entered^ appeared. If participants did not respond during the maximal response interval of 4.5 s, the message Bplease answer more quickly^appeared for 1 s. To assure that all participants worked for the same time on the task (5 min) and processed the same number of primes independently of their working speed, the respective message appeared for 5.5 s minus participants’reaction time. The inter-trial interval randomly varied between 1 and 2.5 s.
Before onset of the main task, which consisted of 36 trials, participants worked on 10 training trials including only the dotted silhouettes of the neutral prime-condition as primes. During the training trials, participants received correctness feedback. No correctness feedback was given during the main task to prevent feedback-related affective reactions (e.g., Kreibig et al. 2012) that could interfere with the age prime effects.
After the task, participants retrospectively rated the level of experienced task difficulty (BWas it difficult for you to succeed on the task?^), the amount of mobilized effort (BHow much effort did you invest to succeed on the task?^), their subjective capability (BDid you feel able to succeed the task?^), success importance (BHow important was it for you to succeed the task?^), the subjective value of success (BWhat value had succeeding the task for you?^), their subjective capacity in mathe- matics (BHow do you evaluate your mathematical abilities?^), and how comfortable they feel in general with mental calculations (BIn general, do you feel comfortable with mental calculations?^). All ratings were made on 7-point scales (1– very low, 7– very high). The last two items were assessed to control for participants’ arithmetic ability beliefs, which can systematically influence perceived task demand and thus effort (see
Wright 1998; Wright and Kirby 2001). Then, participants evaluated their global affective state again with the same four affect items as at the procedure’s onset to assess whether the processed age primes had influenced participants’ conscious feelings.
Moreover, participants indicated possible medication and hypertension family history.
Finally, the experimenter entered the room and interviewed the participants in a standardized funnel debriefing procedure about the study’s purpose and what they had seen during the task. Participants who mentionedBflickers^ orBflashes^ were asked to describe their content. At the end of each experimental session, participants indicated some personal data (e.g., sex, age), were debriefed, thanked for their participation, and received their remuneration.
We applied contrast analysis to test our theory-based predictions about the age prime effects on effort-related cardiovascular response, which is the most powerful and thus the most appropriate statistical tool to test predicted patterns of means (Rosenthal and Rosnow 1985; Wilkinson and the Task Force for Statistical Inference 1999). We had predicted a linear increase of effort-related cardiovascular response from the youth- prime via the neutral prime to the elderly prime condition and tested this pattern with linear contrasts. Other measures, for which we had no theory-based predictions, were analyzed with conventional oneway ANOVAs. The affect ratings were subjected to a 3 (Prime)×2 (Time) mixed model ANOVA. The alpha-error level for all tests was 5%.
For the cardiovascular measures, for which we had directed hypotheses, we applied 1- tailed tests for follow-up cell comparisons. For the sake of an easier interpretability, effect sizes for 1 degree of freedom tests were transformed toη2.
For PEP and DBP, cardiovascular baseline scores were created by averaging the 1- min scores of the last 5 min of the habituation period. These scores did not differ signifi- cantly according to repeated measures ANOVAs,Fs < 0.98,ps > 0.42, and proved high internal consistency (Cronbach’s αs > 0.97). However, the HR values of the first minute significantly differed from the values of the last minute (p = 0.01).
Consequently, we calculated the HR baseline scores by averaging the last 4 min, which did not differ significantly according to Tukey tests (ps > 0.08), and were internally highly consistent (Cronbach’sα= 0.99). The SBP baseline values were also calculated by averaging the last 4 min of the habituation period, because values of the first minute differed significantly from the values of the third minute (p = 0.04). Values of the last 4 min did not significantly differ according to Tukey tests (ps > 0.69), and were internally highly stable (Cronbach’s α = 0.98). Means and standard errors of the baseline scores appear in Table1.
Oneway between-persons ANOVAs did not reveal significant a priori differences in baseline scores between the later three age-prime conditions (ps > 0.06). Due to the limited number of men in our sample, we did not include sex as a factor in the analyses.
We first created cardiovascular task scores for each participant by averaging the five 1- min scores of PEP, HR, SBP, and DBP assessed during performance (Cronbach s αs > 0.97). Then we created cardiovascular reactivity scores by subtracting the baseline values from these averaged task scores. Preliminary ANCOVAs of the reactivity scores revealed a significant association between baseline values and reactivity scores for DBP,F(1, 53)= 4.59,p= 0.04,η2= 0.08, but not for any other cardiovascular measure (Fs < 3.52, ps> 0.06, η2 < 0.06). Consequently, we introduced the DBP baselines as covariate in the analysis of the DBP reactivity to prevent law of initial values or carry over effects.2
PEP and SBP Reactivity
Cell means and standard errors appear in Table 2. In contrast to our effort-related prediction, the linear contrast for PEP reactivity was far from significance, F(1, 55) = 0.10, p = 0.75. The linear contrast was also not significant for SBP reactivity, F(1, 52) = 0.41, p = 0.84. For both measures, also the orthogonal quadratic contrasts were not significant (ps > 0.60).
For HR reactivity, the linear contrast was significant, F(1, 53) = 4.85, p = 0.03, η2 = 0.08. The orthogonal quadratic contrast was not significant (p = 0.89), meaning that the linear contrast left no significant variance in HR that could be explained by the quadratic contrast. As presented in Fig.2, the pattern of HR reactivity fully corresponds to the anticipated effort mobilization pattern. Accordingly, participants in the elderly- prime condition showed stronger HR reactivity (M= 3.82,SE= 0.80) than those in the youth-prime condition (M = 1.46, SE = 0.93). The neutral-prime control condition (M= 2.51,SE= 0.47) fell in between these cells, although it did not differ significantly from them (ps > 0.10).
The linear a priori contrast was also significant for baseline-adjusted DBP responses, F(1, 54) = 7.25,p = 0.01,η2 = 0.12, while the orthogonal quadratic contrast was not (p = 0.84). As depicted in Fig. 3, also diastolic reactivity corroborated our predicted effort-related pattern: DBP reactivity was the strongest in the elderly-prime condition (M = 3.57, SE = 0.50) and the weakest in the youth-prime condition (M = 1.43, SE = 0.61). The neutral-prime control condition fell in between these conditions (M= 2.64,SE= 0.59), although it did not significantly differ from the other two cells (ps > 0.15).
2We additionally tested if the reported effects on HR and SBP responses would be moderated by using 5-min baselines for these indices, as we did for PEP and DBP. Using those baselines did not moderate the effects.
The linear contrast remained significant on HR reactivity (p= .001), and non-significant on SBP reactivity (p= 0.89).
Given that we found evidence for the predicted effort-related pattern on responses of HR and DBP, but not on PEP—our primary effort-related measure—we ran follow-up analyses to get a fuller picture of the hemodynamic effects of the age primes that were processed during task performance. First, we tested whether the HR effects could have been caused by parasympathetic withdrawal rather than beta-adrenergic sympathetic activation. Second, further considering the lack of PEP effects, we also analyzed whether the DBP reactivity effects could have been caused by total peripheral resistance (TPR), which is beside cardiac output the second factor determining blood pressure—especially in DBP. Baseline values of HF-HRV and TPR appear in Table 3. HF-HRV baseline values were constituted of the last 4 min of the habituation period—as those of HR—whereas TPR baselines were created by averaging the last 5 min of that period—as those of DBP.
Reactivity scores of both measures were calculated in the same fashion as those of the other cardiovascular indices.
Table 2 Means and standard errors (in parentheses) of cardiac pre-ejection period and systolic blood pressure reactivity during task performance
Elderly Primes Neutral Primes Youth Primes
3.41 (0.66) PEPpre-ejection period (in ms) andSBPsystolic blood pressure (in mmHg)
Table 1 Means and standard errors (in parentheses) of the cardiovascular baseline values Primes
Elderly Primes Neutral Primes Youth Primes
54.11 (1.79) PEPpre-ejection period (in ms),HRheart rate (in beats/min),SBPsystolic blood pressure (in mmHg),DBP diastolic blood pressure (in mmHg)
Heart Rate Variability Reactivity
Cell means and standard errors appear in Table4. Neither the linear contrast (p= 0.86) nor the orthogonal quadratic contrast (p = 0.25) were significant. Rather than supporting the idea that the observed HR effects could be attributed to parasympathetic withdrawal during task performance, the HF-HRV values depicted in Table 4 suggest that there was only a small general decrease in parasympathetic activity.
Total Peripheral Resistance Reactivity
As for HF-HRV, neither the linear (p = 0.34), nor the orthogonal quadratic contrast (p = 0.72) were significant for TPR reactivity. The pattern of cell means and standard errors in Table4 only suggests a general increase in TPR during task performance.
To complete our analysis of hemodynamic effects, we run an additional correlation analysis to assess the general associations between the different cardiovascular reac- tivity scores. Results are depicted in Table5. Accordingly, HR was positively correlated with DBP and negatively correlated with TPR, which is not surprising. DBP was also positively correlated with SBP, whereas PEP was significantly and negatively correlat- ed with SBP and DBP. The negative correlation between PEP and HR fell short of significance. However, regarded overall, the correlations reflect that SBP, DBP, and also HR increased while PEP became shorter. This suggests links between beta-
Fig. 2 Cell means and standard errors of heart rate reactivity (in beats/min) during task performance
adrenergic sympathetic impact on the heart and responses in blood pressure and also in HR.
An oneway ANOVA of participants’ age revealed no significant differences between the conditions (p = 0.97, averageM= 25.07,SE = 0.53). Furthermore, ANCOVAs of HR and DBP―the cardiovascular measures on which the manipulation had significant effects―revealed no significant associations with participants’age (ps > 0.14). Thus, the effects reported above are hardly explicable by differences in participants’physical age between the different conditions.
Table 3 Means and standard errors (in parentheses) of high frequency heart rate variability and total peripheral resistance baseline values
Elderly Primes Neutral Primes Youth Primes
1002.21 (42.24) HF-HRVhigh frequency heart rate variability (in nu) andTPRtotal peripheral resistance (in dynes second per centimeter to the 5th power)
Fig. 3 Baseline-adjusted cell means and standard errors of diastolic blood pressure reactivity (in mmHg) during task performance
Given that we had two items assessing positive affect, we computed happiness scores by averaging these two pre-task and post-task items (rs > 0.88). A 3 (Prime)×2 (Time) mixed model ANOVA of these scores revealed a significant main effect of Time,F(1, 55) = 17.42,p < 0.001,η2= 0.24, reflecting a general decrease in happiness between the pre-task (M= 4.75,SE= 0.18) and post-task measures (M= 4.32,SE= 0.19). The Prime main effect and the interaction were not significant (ps > 0.12). Sadness and anger had been assessed with single items. 3 (Prime) × 2 (Time) mixed model ANOVAs of these ratings revealed no significant effects (ps > 0.34; sadness average M= 2.25,SE= 0.22; anger averageM= 1.50,SE= 0.15). These results do not provide any evidence for the possibility that the significant HR and DBP effects occurred, because the age prime manipulation elicited positive or negative feelings.
An oneway ANCOVA of the percentages of correct responses during the task—with correct responses during the practice trials as covariate to control for general individual
Table 5 Average means, standard errors and zero-order correlations between the analyzed cardiovascular reactivity scores
M (SE) HR SBP DBP HF-HRV TPR
PEP −1.15 0.37 −0.21 −0.48** −0.34* −0.07 0.22
HR 2.62 0.45 / 0.25 0.38** −0.07 −0.29*
SBP 3.45 0.44 / 0.50** −0.07 0.19
DBPa 2.31 0.31 / 0.19 0.20
HF-HRV −4.10 1.84 / 0.03
TPR 23.78 8.40 /
N= 53, PEP = pre-ejection period (in ms), SBP = systolic blood pressure (in mmHg),DBPdiastolic blood pressure (in mmHg),HRheart rate (in beats/min),HF-HRVhigh frequency heart rate variability (in nu),TPR total peripheral resistance (in dynes second per centimeter to the 5th power)
a Baseline adjusted
*p< 0.05, **p< 0.01
Table 4 Means and standard errors (in parentheses) of high-frequency heart rate variability and total peripheral resistance reactivity during task performance
Elderly Primes Neutral Primes Youth Primes
31.95 (21.90) HF-HRVhigh frequency heart rate variability (in nu) andTPRtotal peripheral resistance (in dynes second per centimeter to the 5th power)
differences—revealed only a strong covariate effect, F(1, 54) = 31.72, p < 0.001, η2 = 0.37 (averageM = 83%,SE= 1.39). The prime effect did not reach significance (p = 0.16).
Likewise, the ANCOVA of the reaction times for correct responses in milliseconds revealed a strong association between reaction times during the task and during the practice trials, F(1, 54) = 89.76, p < 0.001, η2 = 0.62 (average M = 2702.51, SE= 48.90). The prime main effect was far from significance (p = 0.81).
We created a subjective demand index by averaging the ratings of subjective task difficulty and reverse-coded subjective ability, which were positively correlated (r = 0.53, p < 0.001). An oneway ANOVA of this index was not significant (p = 0.56; average M = 3.30, SE = 0.16). We also averaged the ratings of success importance and success value, which were strongly correlated (r= 0.70,p< 0.001) to create a success importance index. However, an oneway ANOVA of this index was not significant (p= 0.94; averageM= 5.53, SE= 0.15). The analysis of self-reported effort was also not significant (p = 0.37; averageM = 4.66,SE = 0.16).
Finally, the ANOVA of the ratings of perceived capacity in mathematics was also not significant (p = 0.16), whereas the analysis of participants’ ratings of comfort with mental calculations was, F(2, 55) = 3.45, p = 0.04, η2 = 0.11. Post- hoc comparisons performed with Tukey tests found a difference between the elderly-prime (M = 4.71, SE = 0.41) and the youth-prime (M = 3.17, SE = 0.43) conditions (p = 0.03), whereas no other significant differences emerged (ps > 0.20). The self-evaluation of comfort with mental calculations in the neutral- prime condition fell in between the elderly- and youth-prime cells (M = 3.68, SE = 0.44). This suggests that there were by chance some participants that felt more comfortable with mental calculations in the elderly-prime condition than in the youth-prime condition. An ANCOVA of HR reactivity and the ratings of comfort with mental calculations revealed a covariate effect F(1, 52) = 6.07, p = 0.02, η2 = 0.10. Despite this finding, the above reported linear contrast on HR reactivity remained significant, F(1, 52) = 8.78, p = 0.005, η2 = 0.14, after controlling for the comfort with mental calculations. That is, we cannot attribute the observed significant difference between the age prime conditions to this variable. An ANCOVA of baseline-adjusted DBP reactivity and the comfort with mental calculation ratings as covariate revealed no significant associations (p = 0.80).
The funnel debriefing procedure revealed that no participant could properly report the aim of the study. This supports the idea that the age primes influenced effort mobili- zation implicitly. When asked to describe a trial of the mental arithmetic task, 57% of the participants mentioned having seen faces. 24% of them reported to have identified men and women, whereas only 2% of them indicated to have seen faces of old people.
This suggests that the vast majority of the participants had processed the primes without awareness of their age-related content.
The present experiment tested whether the mere implicit processing of age primes can systematically influence effort-related cardiovascular response during a cognitive task.
A previous experiment (Zafeiriou and Gendolla 2017) had revealed an effort mobili- zation deficit of young participants who were exposed to elderly primes during an objectively difficult cognitive task. This was predicted, because the activation of the performance difficulty concept—a feature of the elderly stereotype—should lead to subjective over-challenge in an objectively difficult task and thus to reduced effort due to disengagement. However, this effort-mobilization deficit could be compensated by high monetary success-contingent incentive—as it was previously the case for young people who were primed with fear or sadness (Chatelain and Gendolla 2016;
Freydefont and Gendolla 2012) or pain-related stimuli (Silvestrini 2015). In the Zafeiriou and Gendolla (2017) study, the predicted effort-related effects emerged for responses of PEP and HR. However, that experiment included only elderly vs. youth prime conditions, leaving it open if exposure to youth primes indeed results in low effort because youth is associated with performance ease. Answering this question necessitates a neutral prime control condition to which the effects of both youth and elderly primes can be compared. The present experiment suggest that this is the case—
at least concerning responses of HR and DBP.
Surprisingly, we did not find evidence for the expected age-prime effect on PEP reactivity—our primary measure of effort mobilization. By contrast, effects on HR response supported our effort-related hypotheses. HR reactivity described the linear effort-related pattern: Elderly primes led to the strongest reactivity, youth primes led to the weakest reactivity, and the neutral-prime control condition fell in between these conditions. The same effect emerged for DBP reactivity. Given that these effects on HR and DBP responses occurred in absence of the anticipated effects on PEP—the purest index of beta-adrenergic sympathetic impact on the heart—we ran further analyses:
Given the lack of PEP effects, we tested whether the HR effects could have been caused by parasympathetic withdrawal rather than sympathetic impact and if the DBP effects could have emerged due to increased alpha-adrenergic impact. We did so by analyzing responses of HF-HRV and TPR. However, these analyses did not lend support to the possibilities that the HR effects were caused by parasympathetic withdrawal or that the DBP effects emerged due to responses in alpha-adrenergic vasoconstriction. Rather, a correlation analysis revealed that blood pressure and also HR increased when PEP became shorter, while HR and DBP were not significantly associated with HF-HRV or TPR, respectively. This suggests that the HR and DBP effects were associated with cardiac contractility. However, contractility effects themselves were masked in the present study, revealing a rather complex pattern of cardiovascular responses.
However, we note that there are also other studies that have found effort-related effects on HR (e.g., Brinkmann and Gendolla 2007; Eubanks et al. 2002; Freydefont et al.
2012; Freydefont and Gendolla2012; Gendolla1998; Richter and Gendolla2007) and DBP responses (e.g., Frazier et al. 2008; Gendolla and Richter2006; Hui et al.2009;
Nolte et al. 2008; Wright et al. 2007, 2008). That is, the here reported effects have precursors.
The present study did not find age prime effects on cognitive performance in terms of response accuracy or reaction times in the mental arithmetic task. Although some of
our previous studies found that affect primes had corresponding effects on cardiovas- cular effort measures and performance (e.g., Chatelain and Gendolla 2016; Gendolla and Silvestrini 2010; Lasauskaite et al. 2013), most others had not. But this is not surprising, because effort and performance are not conceptually interchangeable. Effort refers to the mobilization of resources to carry out instrumental behavior, whereas performance describes the outcome of the instrumental behavior (see Gendolla and Richter2010). Moreover, beside effort, performance depends at least, and maybe more, on ability and strategy use (Locke and Latham 1990) and can be moderated by these variables. Consequently, one cannot expect that variations in effort automatically result in variations in performance. For the present study, the high percentage of correct responses—more than 80%—suggests that participants’capacity was largely sufficient to succeed on the task, meaning that high effort was not necessary to perform well.
Effort should especially contribute to better performance in more taxing tasks—i.e.
more objectively difficult tasks—where performance is less capacity-driven (see Silvestrini and Gendolla2013).
Applying the IAPE model logic (Gendolla 2012, 2015) to the effect of implicit aging, age primes should influence effort-mobilization by influencing subjective task demand during task performance. However, we did not find manipulation effects on participants’ ratings of subjective task demand or self-reported effort, which were assessed after performance. Some of our previous studies on implicit affect and effort found effects on these self-report measures (e.g., Chatelain and Gendolla 2015;
Gendolla and Silvestrini 2011; Lasauskaite et al. 2013; Lasauskaite Schüpbach et al.
2014; Silvestrini and Gendolla 2011a), whereas others did not (e.g., Chatelain and Gendolla2016; Chatelain et al.2016; Freydefont and Gendolla2012; Freydefont et al.
2012). However, we should keep in mind that the IAPE model predicts a prime impact on subjective demand during task performance rather than thereafter. Unfortunately, retrospective self-report measures can only mirror the latter. Moreover, the fact that these ratings were assessed retrospectively makes them vulnerable to several biases (Robinson and Clore 2002). Interestingly, recent studies by Lasauskaite et al. (2017) applied a sequential priming paradigm to directly test for implicit associations between implicit affect and the concepts of performance ease and difficulty. These studies found replicated evidence for these associations. Thus, future studies should test if the same emerges for associations between age primes and ease and difficulty. It could also be the case that the activation of the ease and difficulty concepts happens on the implicit level and does not necessarily have to become explicit—and thus accessible to self- report. This would mean a strictly automatic influence of effort mobilization. However, we have to leave it for future research to test this possibility.
In summary, we cautiously interpret the present findings as another demonstration (see Zafeiriou and Gendolla2017) of the systematic influence of age primes on effort mobilization during cognitive performance, even in young adults—although we acknowledge that the prime effects on PEP responses—our primary measure of effort—did not emerge. Nevertheless, the observed significant effects on HR and DBP responses described the anticipated effort-related pattern and seem to have been related to beta-adrenergic sympathetic impact, as the correlation analysis suggests.
Moreover, the very low percentage of recognition of the elderly primes’ content reported in the funnel debriefing allows us the conclusion that the reported effects indeed emerged implicitly.
In a broader perspective, the present findings contribute to the evidence that activation of the aging stereotype—even without awareness—can lead people to behave in stereotype-consistent ways with effects on behavior, performance, and cardiovascular responses (e.g., Bargh et al. 1996; Dijksterhuis et al. 2001; Hausdorff et al. 1999; Hsu et al. 2010; Levy 1996; Levy et al. 2000). However, there is a controversy on whether it is necessary to belong to the stereotype-relevant group in order to behave consistently with an activated stereotype. Some researchers speak for the possibility of aging stereotype activation and stereotype-consistent behaviors in young adults (e.g., Bargh et al. 1996), whereas others posit that stereotype consistent behavior only occurs among members of the stereotyped group (e.g., Levy1996). We cautiously interpret the present study as supporting the idea that the mere activation of the aging stereotype can elicit stereotype-consistent effects on effort mobilization in general—also in physically young adults.
Acknowledgements This research was supported by a grant from the Swiss National Science Foundation (SNF 100014-140251) awarded to Guido Gendolla. We thank Alina Miny for her help as hired experimenter.
Compliance with Ethical Standards
Conflict of Interest On behalf of all authors, the corresponding author states that there is no conflict of interest.
Bargh, J. A., Chen, M., & Burrows, L. (1996). Automaticity of social behavior: Direct effects of trait construct and stereotype-activation on action. Journal of Personality and Social Psychology, 71, 230–244.
Bernstein, D.P., (1986). A new stroke volume equation for thoracic electrical bioimpedance: Theory and rationale.Critical Care Medicine 14,(10), 904–909.
Berntson, G. G., Lozano, D. L., Chen, Y. J., & Cacioppo, J. T. (2004). Where to Q in PEP.Psychophysiology, 41, 333–337. doi:10.1111/j.1469-8986.2004.00156.x.
Bijleveld, E., Custers, R., & Aarts, H. (2010). Unconscious reward cues increase invested effort, but do not change speed–accuracy tradeoffs. Cognition, 115, 330–335. doi:10.1016/j.cognition.2009.12.012.
Brehm, J. W., & Self, E. A. (1989). The intensity of motivation.Annual Review of Psychology, 40, 109–131.
Brinkmann, K., & Gendolla, G. H. E. (2007). Dysphoria and mobilization of mental effort: Effects on cardiovascular reactivity.Motivation and Emotion, 31, 71–82. doi:10.1007/s11031-007-9054-0.
Chatelain, M., & Gendolla, G. H. E. (2015). Implicit fear and effort-related cardiac response. Biological Psychology, 111, 73–82. doi:10.1016/j.biopsycho.2015.08.009.
Chatelain, M., & Gendolla, G. H. E. (2016). Monetary incentive moderates the effect of implicit fear on effort- related cardiovascular response. Biological Psychology, 117, 150–158. doi:10.1016/j.
Chatelain, M., Silvestrini, N., & Gendolla, G. H. E. (2016). Task difficulty moderates implicit fear and anger effects on effort-related cardiac response. Biological Psychology, 115, 94–100. doi:10.1016/j.
Cuddy, A. J. C., Norton, M. I., & Fiske, S. T. (2005). This old stereotype: The pervasiveness and persistence of the elderly stereotype.Journal of Social Issues, 61, 267–285. doi:10.1111/j.1540-4560.2005.00405.x.
Dijksterhuis, A., Spears, R., & Lépinasse, V. (2001). Reflecting and deflecting stereotypes: Assimilation and contrast in impression formation and automatic behavior.Journal of Experimental Social Psychology, 37, 286–299. doi:10.1006/jesp.2000.1449.
Eubanks, L., Wright, R. A., & Williams, B. J. (2002). Reward influence on the heart: Cardiovascular response as a function of incentive value at five levels of task demand. Motivation and Emotion, 26, 139–152.
Frazier, B. D., Barreto, P., & Wright, R. A. (2008). Gender, perceptions of incentive value, and cardiovascular response to a performance challenge.Sex Roles, 59, 14–20. doi:10.1007/s11199-008-9440-4.
Freydefont, L., & Gendolla, G. H. E. (2012). Incentive moderates the impact of implicit anger vs. sadness cues on effort-related cardiac response. Biological Psychology, 9, 120–127. doi:10.1016/j.
Freydefont, L., Gendolla, G. H. E., & Silvestrini, N. (2012). Beyond valence: The differential effect of masked anger and sadness stimuli on effort-related cardiac response.Psychophysiology, 49, 665–671. doi:10.1111 /j.1469-8986.2011.01340.x.
Gendolla, G. H. E. (1998). Effort as assessed by motivational arousal in identity-relevant tasks.Basic and Applied Social Psychology, 20, 111–121. doi:10.1207/s15324834basp2002_3.
Gendolla, G. H. E. (2012). Implicit affect primes effort: theory and research on cardiovascular response.
International Journal of Psychophysiology, 86, 123–135. doi:10.1016/j.ijpsycho.2012.05.003.
Gendolla, G. H. E. (2015). Implicit affect primes effort: Basic processes, moderators, and boundary conditions.
Social and Personality Psychology Compass, 9, 606–619. doi:10.1111/spc3.12208.
Gendolla, G. H. E., & Richter, M. (2006). Cardiovascular reactivity during performance under social observation: The moderating role of task difficulty. International Journal of Psychophysiology, 62, 185–192. doi:10.1016/j.ijpsycho.2006.04.002.
Gendolla, G. H. E., & Richter, M. (2010). Effort mobilization when the self is involved: Some lessons from the cardiovascular system.Review of General Psychology, 14, 212–226. doi:10.1037/a0019742.
Gendolla, G. H. E., & Silvestrini, N. (2010). The implicit Go: Masked action cues directly mobilize mental effort.Psychological Science, 21, 1389–1393. doi:10.1177/0956797610384149.
Gendolla, G. H. E., & Silvestrini, N. (2011). Smiles make it easier and so do frowns: Masked affective stimuli influence mental effort.Emotion, 11, 320–328. doi:10.1037/a0022593.
Harada, C. N., Love, M. C. N., & Triebel, K. L. (2013). Normal cognitive aging. Clinics in Geriatric Medicine, 29, 737–752. doi:10.1016/j.cger.2013.07.002.
Hausdorff, J. M., Levy, B. R., & Wei, J. Y. (1999). The power of ageism on physical function of older persons:
Reversibility of age-related gait changes. Journal of the American Geriatrics Society, 47, 1346–1349.
Hess, T. M., & Ennis, G. E. (2012). Age differences in the effort and costs associated with cognitive activity.
The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 67, 447–455.
Hess, T. M., Smith, B. T., & Sharifian, N. (2016). Aging and effort expenditure: The impact of subjective perceptions of task demands.Psychology and Aging, 31, 653–660. doi:10.1037/pag000012.
Hsu, L. M., Chung, J., & Langer, E. J. (2010). The influence of age-related cues on health and longevity.
Perspectives on Psychological Science, 5, 632–648. doi:10.1177/1745691610388762.
Hui, S. A., Wright, R. A., Stewart, C. C., Simmons, A., Eaton, B., & Nolte, R. N. (2009). Performance, cardiovascular, and health behavior effects of an inhibitory strength training intervention.Motivation and Emotion, 33, 419–434. doi:10.1007/s11031- 009-9146-0.
Kelsey, R. M., & Guethlein, W. (1990). An evaluation of the ensemble averaged impedance cardiogram.
Psychophysiology, 27, 24–33. doi:10.1111/j.1469-8986.1990.tb02173.x.
Kite, M. E., & Wagner, L. S. (2002). Attitudes toward older adults. In T. Nelson (Ed.),Ageism: Stereotyping and prejudice against older persons(pp. 129–161). Cambridge: The MIT Press.
Kite, M. E., Stockdale, G. D., Whitley, B. E., & Johnson, B. T. (2005). Attitudes toward younger and older adults: An updated meta-analytic review. Journal of Social Issues, 61, 241–266. doi:10.1111/j.1540- 4560.2005.00404.x.
Kreibig, S. D., Gendolla, G. H. E., & Scherer, K. R. (2012). Goal relevance and goal conduciveness appraisals lead to differential autonomic reactivity in emotional respondingto performance feedback. Biological Psychology, 91, 365–375. doi:10.1016/j.biopsycho.2012.08.007.
Lasauskaite, R., Gendolla, G. H. E., Bolmont, M., & Freydefont, L. (2017). Implicit happiness and sadness are associated with ease and difficulty: Evidence from sequential priming.Psychological Research, 81, 321–
Lasauskaite, R., Gendolla, G. H. E., & Silvestrini, N. (2013). Do sadness primes make me work harder, because they make me sad?Cognition and Emotion, 27, 158–165. doi:10.1080/02699931.2012.689756.
Lasauskaite Schüpbach, R., Gendolla, G. H. E., & Silvestrini, N. (2014). Contrasting the effects of suboptimally versus optimally presented affect primes on effort-related cardiac response. Motivation and Emotion, 38, 748–758. doi:10.1007/s11031-014-9438-x.
Levick, J. R. (2003).Introduction to cardiovascular physiology(4th ed.). New York: Oxford University Press.
Levy, B. R. (1996). Improving memory in old age through implicit self-stereotyping.Journal of Personality and Social Psychology, 71, 1092–1107. doi:10.1037/0022-35188.8.131.522.
Levy, B. R., Hausdorff, J. M., Hencke, R., & Wei, J. Y. (2000). Reducing cardiovascular stress with positive self-stereotypes of aging. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 55, 205–213. doi:10.1093/geronb/55.4.P205.
Locke, E. A., & Latham, G. P. (1990).A theory of goal setting and task performance. Upper Saddle River:
Lozano, D. L., Norman, G., Knox, D., Wood, B. L., Miller, D., Emery, C. F., & Berntson, G. G. (2007). Where to B in dZ/dt.Psychophysiology, 44, 113–119. doi:10.1111/j.1469 8986.2006.00468.x.
Macrae, C. N., Milne, A. B., & Bodenhausen, G. V. (1994). Stereotypes as energy-saving devices: a peek inside the cognitive toolbox.Journal of Personality and Social Psychology, 66, 37. doi:10.1037/0022- 35184.108.40.206.
Minear, M., & Park, D. C. (2004). A lifespan database of adult facial stimuli.Behavior Research Methods, Instruments, & Computers, 36, 630–633. doi:10.3758/BF03206543.
Niedenthal, P. M. (2008). Emotion concepts. In M. Lewis, J. M. Haviland-Jones, & L. M. Barrett (Eds.), Handbook of emotion(3rd ed., pp. 587–600). New York: Guilford.
Nolte, R. N., Wright, R. A., Turner, C., & Contrada, R. J. (2008). Reported fatigue, difficulty, and cardiovascular response to a memory challenge. International Journal of Psychophysiology, 69, 1–8.
Oakes, P. J., Haslam, S. A., & Turner, J. C. (1994). Stereotyping and social reality. Oxford: Blackwell Publishing.
Obrist, P. A. (1981).Cardiovascular psychophysiology: A perspective. New York: Plenum Press. doi:10.1007 /978-1-4684-8491-5.
Richter, M. (2010). BlueBox 2 v1.22. [computer software]. University of Geneva.
Richter, M., Friedrich, A., & Gendolla, G. H. E. (2008). Task difficulty effects on cardiac activity.
Psychophysiology, 45, 869–875. doi:10.1111/j.1469-8986.2008.00688.x.
Richter, M., & Gendolla, G. H. E. (2007). Incentive value, unclear task difficulty, and cardiovascular reactivity in active coping. International Journal of Psychophysiology, 63, 294–301. doi:10.1016/j.
Richter, M., & Gendolla, G. H. E. (2009). The heart contrast to reward: Monetary incentives and preejection period.Psychophysiology, 46, 451–457. doi:10.1111/j.14698986.2009.00795.x.
Robinson, M. D., & Clore, G. L. (2002). Belief and feeling: Evidence for an accessibility model of emotional self-report.Psychological Bulletin, 128, 934–960. doi:10.1037/0033-2909.128.6.934.
Rosenthal, R., & Rosnow, R. L. (1985).Contrast analysis: focused comparisons in the analysis of variance.
New York: Cambridge University Press.
Rust, T. B., & See, S. T. K. (2010). Beliefs about aging and Alzheimer’s disease in three domains.Canadian Journal on Aging/La Revue Canadienne du Vieillissement, 29, 567–575.
Scherhag, A. W., Kaden, J. J., Kentschke, E., Sueselbeck, T., & Borggrefe, M. (2005). Comparison of impedance cardiography and thermodilution-derived measurements of stroke volume and cardiac output at rest and during exercise testing. Cardio-vascular Drugs and Therapy, 19, 141–147. doi:10.1007 /s10557-005-1048-0.
Sherwood, A., Allen, M. T., Fahrenberg, J., Kelsey, R. M., Lovallo, W. R., & van Dooren, L. J. P. (1990).
Methodological guidelines for impedance cardiography.Psychophysiology, 27, 1–23. doi:10.1111/j.1469- 8986.1990.tb02171.x.
Silvestrini, N. (2015). The effort-related cost of implicit pain.Motivation Science, 1, 151–164. doi:10.1037 /mot0000020.
Silvestrini, N., & Gendolla, G. H. E. (2011a). Masked affective stimuli moderate task difficulty effects on effort-related cardiovascular response. Psychophysiology, 48, 1157–1164. doi:10.1111/j.1469- 8986.2011.01181.x.
Silvestrini, N., & Gendolla, G. H. E. (2011b). Beta-adrenergic impact underlies the effect of mood and hedonic instrumentality on effort related cardiovascular response. Biological Psychology, 87, 209–217.
Silvestrini, N., & Gendolla, G. H. E. (2013). Automatic effort mobilization and the principle of resource conservation: One can only prime the possible and justified. Journal of Personality and Social Psychology, 104, 803–816. doi:10.1037/a0031995.
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant.Psychological Science, 22, 1359–
Smith, B. T., & Hess, T. M. (2015). The impact of motivation and task difficulty on resource engagement:
Differential influences on cardiovascular responses of young and older adults.Motivation Science, 1, 22–
Task Force of the European Society of Cardiology. (1996). Heart rate variability standards of measurement, physiological interpretation, and clinical use.European Heart Journal, 17, 354–381.
Wilkinson, L., & The Task Force on Statistical Inference. (1999). Statistical methods in psychology journals.
American Psychologist, 54, 594–604. doi:10.1037/0003-066X.54.8.594.
Wright, R. A. (1996). Brehm's theory of motivation as a model of effort and cardiovascular response. In P. M.
Gollwitzer & J. A. Bargh (Eds.),The psychology of action: Linking cognition and motivation to behavior (pp. 424–453). New York: Guilford.
Wright, R. A. (1998). Ability perception and cardiovascular response to behavioral challenge. In M. Kofta, G.
Weary, & G. Sedek (Eds.),Control in action: Cognitive and motivational mechanisms(pp. 197–232).
New York: Plenum. doi:10.1007/978-1-4757-2901-6_8.
Wright, R. A., Junious, T. R., Neal, C., Avello, A., Graham, C., Herrmann, L., Junious, S., & Walton, N.
(2007). Mental fatigue influence on effort-related cardiovascular response: Difficulty effects and exten- sion across cognitive performance domains.Motivation and Emotion, 31, 219–231. doi:10.1007/s11031- 007-9066-9.
Wright, R. A., & Kirby, L. D. (2001). Effort determination of cardiovascular response: An integrative analysis with applications in social psychology. In M. P. Zanna (Ed.),Advances in experimental social psychology (Vol. 33, pp. 255–307). San Diego: Academic.
Wright, R. A., Stewart, C. C., & Barnett, B. R. (2008). Mental fatigue influence on effort- related cardiovas- cular response: Extension across the regulatory (inhibitory)/non- regulatory performance dimension.
International Journal of Psychophysiology, 69, 127–133. doi:10.1016/j.ijpsycho.2008.04.00.
Zafeiriou, A., & Gendolla, G. H. E. (2017). Implicit activation of the aging stereotype influences effort-related cardiovascular response: The role of incentive.International Journal of Psychophysiology. doi:10.1016/j.