 18. Implicit Motives  395
ceptual sensitivity (Donhauser et al., 2015), 
attentional orienting (Schult  heiss & Hale, 
2007), event-  related brain potentials (Wang 
et al., 2014, 2017), instrumental learning 
(Schult  heiss, Pang, et al., 2005; Stoeckart et 
al., 2017, 2018) and brain activation (Schult -
heiss, Wirth, et al., 2008). Individuals high 
in n Affiliation are particularly sensitive to 
not only facial expressions signaling affilia -
tive intent (joy) but also expressions signal -
ing rejection and hostility (e.g., anger, dis -
gust) in studies on facial muscle activation 
(Kordik et al., 2012), attentional orienting 
(Schult  heiss & Hale, 2007), implicit learn -
ing (Schult  heiss, Pang, et al., 2005), and va -
lence evaluations (Rösch, Stanton, & Schult -
heiss, 2013). In summary, research provides 
mounting evidence for implicit motives’ sen -
sitivity to nonverbal signals.
Explicit motives, in contrast, respond 
preferentially to verbal cues. In a meta- 
analysis, Spangler (1992) found that high san  
Achievement predicted achievement- related 
behaviors particularly well in the presence 
of achievement-  focused instructions but 
failed to predict behavior in the absence of 
such verbal cues, or in the presence of strong 
task-  intrinsic cues, such as task-based feed -
back about one’s performance increases or 
decreases. Similarly, Engeser and Baumann 
(2014) provided experimental evidence that 
achievement- related words activate the ex -
plicit need to achieve but have no effect on n 
Achievement.
An Information Processing Model of Implicit 
and Explicit  Motives
Schult  heiss (2001, 2008) has presented an 
information processing account of implicit 
and explicit motives that aims to integrate 
these sets of findings. In a nutshell, the 
model states that implicit motives prefer -
entially respond to nonverbal cues and in -
centives  and, after arousal, are particularly 
likely to have an impact on nondeclarative 
measures of motivation, that is, measures 
of behaviors and processes that are not ac -
cessible to, or controlled by, a person’s self- 
concept or verbally represented intentions. 
Nondeclarative measures include physi -
ological responses aimed at promoting bio -
logically rooted needs (e.g., changes in blood 
pressure and heart rate, hormone release, muscle tone), acquisition of new stimulus– 
stimulus associations and goal-  directed be -
haviors through processes of Pavlovian and 
instrumental learning, and utilization of 
such learned stimulus connections and be -
haviors in the appropriate contexts. Explicit 
motives, on the other hand, preferentially re -
spond to verbal–  symbolic cues and influence 
declarative measures of motivation, that is, 
measures that tap into a person’s verbally 
represented sense of self and the attitudes, 
judgments, decisions, and goals associated 
with it. Evaluative judgments, choice behav -
ior, assessments of self-  regulatory control, 
and idiographic personal goal descriptions 
are all examples of declarative measures of 
motivation.
The model also specifies a mechanism by 
which verbal cues can interact with implicit 
motives to influence both declarative and non -
declarative measures of motivation: Referen -
tial processing,  the process through which 
words are assigned to nonverbal experience 
and, conversely, mental images are generated 
in response to words (Schult  heiss & Stras -
ser, 2012; Paivio, 1986). One way to employ 
referential processing is through the strategic 
translation of verbally communicated goals 
into experience-  like mental imagery. In a se -
ries of studies by Schult  heiss and Brunstein 
(1999, 2002), participants were assigned 
power- or affiliation-  related goals and then 
either had an opportunity to translate these 
verbally represented goals into an experien -
tial, nonverbal format through guided goal 
imagery procedures (goal imagery group) or 
not (control group). The authors found that 
declarative and nondeclarative measures of 
motivation were contingent on participants’ 
implicit motives in the goal imagery groups, 
but were independent of their implicit mo -
tives in the control groups (for a replication, 
see Rawolle et al., 2017). Research shows 
that in addition to such strategic use of ref -
erential processing, dispositional differences 
in the ability to quickly translate between 
verbal and nonverbal representations also 
moderate the degree to which individuals’ 
goal choices fit their implicit motivational 
needs (Schult  heiss et al., 2011). In summary, 
these findings show that referential process -
ing allows implicit motives to “understand” 
and respond to verbal stimuli, which would 
otherwise bypass them.