392 Iv. COGNITI vE AND M OTIvATIONAL P ROCESSES
1973). These ideas stuck, so much so that 
the version of this chapter in the previous 
edition even dedicated a detailed theoretical 
analysis to this issue (Schult  heiss, 2008).
Thirteen years later, we have become 
more skeptical about the validity of the 
distinction between incentive-  approach 
and disincentive-  avoidance in implicit mo -
tive research and conclude that it rests on a 
weak empirical foundation. The distinction 
was initially based on theoretical consider -
ations (e.g., Boyatzis, 1973; Heckhausen, 
1963; Veroff & Veroff, 1972), and subse -
quent experimental work has largely failed 
to demonstrate the expected differences 
between arousal of incentive-  approach and 
disincentive-  avoidance states (Wirth & 
Schult  heiss, 2006) or any motive arousal ef -
fect at all (see Hofer, Busch, Bender, Ming, 
& Hagemeyer’s [2010] failure to validate 
Heckhausen’s [1963] coding system). In one 
notable exception, results pointed toward 
greater functional similarity between the 
two motivational modes than originally an -
ticipated (Pang, 2010a). Another reason for 
caution is the observation that when mea -
sures that are presumed to assess hope and 
fear components of a given motive are used 
simultaneously in the same study, they typi -
cally show substantial variance overlap and 
predict either the same outcomes or, when 
they predict different outcomes, are difficult 
to map onto approach –avoidance concepts 
in a clear-cut manner (e.g., Hofer & Busch, 
2011a; Veroff, 1982).
What does appear to be valid is the distinc -
tion between hope and fear of an incentive  
(and, conversely, fear and hope of a disincen -
tive). Many experimental studies in which 
motivational incentives and disincentives 
were experimentally manipulated show dis -
ordinal interactions between experimental 
factors and implicit motive measures in their 
predictive effects on outcomes. Rather than 
an indifference toward both incentives and 
disincentives at very low motive levels, the 
opposite of what happens at very high motive 
levels is observed. For instance, Schult  heiss, 
Wirth, et al. (2005) reported that whereas 
individuals high in n Power showed better 
learning of behavior that was instrumental 
for beating an opponent (power incentive) 
and worse learning for behavior that was as -
sociated with being beaten (power disincen -tive), individuals low in n Power showed the 
opposite effect: worse learning after beating 
an opponent and better learning after being 
beaten. Similar disordinal effects were found 
in other studies involving other motivational 
needs (e.g., Brunstein & Maier, 2005; Kor -
dik, Eska, & Schult  heiss, 2012). Low scores 
on a given motive measure predicting the op -
posite of what high scores predict strongly 
suggest that motive measures based on pic -
ture story methods represent a continuum 
from incentive avoidance, based on fear and 
disliking,  at very low scores, through a neu -
tral midpoint at medium scores, to incentive 
approach, based on approach and liking,  at 
high scores.
Other  Motives
The research focus on the needs for achieve -
ment, power, and affiliation is due, in part, 
to serendipity and should not be taken to 
suggest an exclusive status of these needs 
in motivation science. Other motivational 
needs have been studied, too (see Atkinson, 
1958; C. Smith, 1992). However, because 
these forays into other motivational domains 
have so far not generated substantive bodies 
of research, we do not discuss them here.
How Implicit Motives Differ 
from Self-Attributed Needs and  Goals
One of the most striking and pervasive find -
ings to emerge from more than 60 years of 
research on implicit motives is that the cor -
relation between storytelling and self-  report 
measures of motivational needs is close to 
zero. Results from a sample of 309 partici -
pants reported by Rawolle, Schult  heiss, and 
Schult  heiss (2013; see Table 18.1) illustrates 
a typical finding: Explicit measures of mo -
tivation, like individuals’ sense of com -
mitment to personal goals or motivation 
questionnaire scores, converge with each 
other, but not substantially with the PSE. 
A meta-  analysis by Spangler (1992) yielded 
an average variance overlap of less than 1% 
between PSE and questionnaire measures of 
the achievement motive. This finding was 
replicated and extended to other motive 
domains in a recent meta-  analysis (Köllner 
& Schult  heiss, 2014). Here, average corre -