402 Iv. COGNITI vE AND M OTIvATIONAL P ROCESSES
Motives Energize Behavior Aimed 
at Incentive  Attainment
After a motive has become aroused by the 
presence of cues signaling a possible en -
counter with a (dis)incentive, behavior di -
rected at attaining the incentive or avoiding 
the disincentive—  a core characteristic of 
wanting (Berridge, 1996)—becomes highly 
energized, as reflected by the recruitment 
of physiological systems supporting active 
behavioral engagement with the environ -
ment (e.g., SNS activation) and both quicker 
onset and more effective (e.g., faster, more 
frequent, more forceful) execution of instru -
mental behavior (e.g., Ikemoto & Panksepp, 
1999; McClelland, 1987; Wright & Brehm, 
1989). Evidence for an energizing function 
has been obtained for all three frequently 
studied motives.
Bäumler’s (1975) previously mentioned 
finding that dopamine agonists increase, 
and antagonists decrease, PSE achievement 
imagery suggests that n Achievement en-
gages the dopamine system, which invigo -
rates responding (Ikemoto & Panksepp, 
1999). Furthermore, high-  achievement in -
dividuals show greater SNS activation when 
faced with a recall task of medium difficulty 
compared to individuals low in this motive 
(Brunstein & Schmitt, 2010; see also Ma -
zeres et al., 2019).
Behavioral studies using measures of re -
sponse speed, persistence, and performance 
output also suggest that n Achievement ener -
gizes behavior aimed at the mastery of chal -
lenges. For instance, compared to others, 
high-  achievement individuals show shorter 
response latencies on mental concentration 
tasks, particularly in response to negative 
feedback (Brunstein & Maier, 2005, Ra -
wolle et al., 2017); persist longer on chal -
lenging arithmetic tasks (Wendt, 1955); and 
solve more items on word-  puzzle (Brunstein 
& Schmitt, 2010) and arithmetic (Biernat, 
1989) tasks in a fixed amount of time.
Energizing effects have also been docu -
mented for n Power. Individuals high in this 
motivational need, relative to others, show 
signs of increased SNS activation when con -
fronted with power challenges (see Fodor, 
2010, for a review of relevant studies). For in -
stance, individuals whose n Power is aroused 
show an increase in salivary alpha amylase, a marker of SNS activation (Wiemers, Schult -
heiss, & Wolf, 2015). Steele (1977) found 
that power-  arousing speeches lead to greater 
subjective activation in high-power individu -
als as compared to low power-  individuals or 
high-power individuals in a neutral speech 
control condition. Schult  heiss and Brunstein 
(1999) observed that power-  motivated indi -
viduals felt more activated and performed 
better while playing a competitive computer 
game.
Finally, individuals high in n Affilia -
tion seem to engage in behaviors that allow 
them to connect with other people in posi -
tive, friendly ways more frequently or in -
tensively. For instance, they interact more 
frequently with others (Craig et al., 1994) 
and show more lively and warm nonverbal 
social behavior toward others (Hagemeyer 
et al., 2016; McAdams, Jackson, & Kirsh -
nit, 1984; McAdams & Powers, 1981). If the 
incentives are right, an energizing effect of 
n Affiliation on task performance can also 
be observed: When male participants were 
instructed by a female experimenter, their n 
Affiliation predicted superior performance 
on a challenging digit–  letter substitution 
task (Atkinson & O’Connor, 1966).
Taken together, these findings document 
that implicit motives predict energized be -
havior aimed at incentive attainment. The 
available evidence supports this conclusion 
for all three motives, based on physiological, 
performance, and subjective criteria.
Conclusion
When revising this chapter, we were im -
pressed by how much 13 years of research 
have added to our understanding of implicit 
motives. The recent growth of the field is 
reflected in a broadening of methodological 
tools, including operant learning paradigms 
(Stoeckart et al., 2017, 2018), fMRI (Schult -
heiss, Wirth, et al., 2008), ERP (Wang et 
al., 2014, 2017), facial EMG (Dufner et al., 
2015), and neuropsychological methods 
(Schult  heiss et al., 2012); sophisticated sta -
tistics, such as dyadic analyses (Denzinger, 
Backes, & Brandstätter, 2018; Hagemayer et 
al., 2015) and structural equation modeling 
(Hagemayer et al., 2016); and a renewed in -
terest in assessment issues (e.g., Slabbinck et 