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The Oxford Handbook of Human Motivation (2nd edn)
Richard M. Ryan (ed.)
https://doi.org/10.1093/oxfordhb/9780190666453.001.0001
Published: 2019 Online ISBN: 9780190666484 Print ISBN: 9780190666453
CH AP TE R
https://doi.org/10.1093/oxfordhb/9780190666453.013.15 Pages 269–284
Published: 12 August 2019
Abstract
Keywords: rewards, unconscious processes, motivation, regulation, priming
Subject: Social Psychology, Psychology
Series: Oxford Library of Psychology
15 Does Goal Pursuit Require Conscious Awareness?
Ruud Custers,Stefan Vermeent,Henk Aarts
Human behavior is directed at goals. Although goal pursuit is traditionally regarded as an endeavor
that requires conscious awareness, experimental evidence in psychology suggests that human goal
pursuit can originate and unfold in the unconscious. Accordingly, goal-directed behavior could be
motivated outside conscious awareness in the current situation or environment. This chapter reviews
past and current research examining the evidence for such unconscious motivation of goal-directed
behavior. The review is organized around two themes. The rst theme deals with research that
analyzes goal pursuit as automated behaviors, thereby addressing the operational function of
repetition for motivated processes in directing and controlling behavior in the absence of conscious
awareness. The second theme concerns the quest of understanding the unconscious sources of human
goal pursuit and includes a discussion of recent work on reward cueing, aimed at addressing the
question of how reward signals in the environment can motivate behavior outside awareness.
Any meaningful behavior humans engage in is goal directed. Humans do not behave randomly; they behave
to realize specic states or outcomes they nd desirable. From taking a walk in the park to buying groceries
or making coee, they pursue outcomes, which requires keeping an eye on the prize, choosing the right
courses of action, and monitoring their progress. Although conscious processes play an important role all
the way from planning to the execution of behavior, this role may decrease as individuals plan and execute
particular goal-directed actions repeatedly in the same context. Most people make their morning coee
absentmindedly, pondering what the day is going to bring, instead of engaging in careful deliberation or
planning the process of coee making. Consciousness, then, may drop out of the equation for such well-
rehearsed goal-directed behaviors. Although such examples are numerous, it is less clear how goal pursuit
is possible without much assistance of consciousness.
p. 269
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In the current chapter, we aim to clarify how goal pursuit might emerge largely outside awareness. We will
depart from the literature on habits and ideomotor theory, arguing that stimuli in the environment can
activate outcome representations that, in turn, can trigger the associated actions that have produced these
outcomes in the past. However, we assume that for goal pursuit to be supported by the eort it requires,
rewarding properties of the outcome play a crucial role. We will review recent work that investigates how
such motivational properties can motivate behavior without much conscious intervention. We believe that a
more thorough understanding of how goal pursuit may operate under the radar of conscious awareness is
benecial for understanding and intervening in human behavior because the majority of the goals we
pursue day to day are repetitive in nature, in terms of how we aim to attain them as well as the context in
which we do so.
Goal Pursuit Without Awareness: Some Preliminary Thoughts and
Findings
p. 270
Modern theories of goals consider goal pursuit mainly a conscious aair: In the event of a challenge or
opportunity, we compare potential courses of actions to determine which one to pursue to produce the
desired outcome, mainly based on the expected value of the outcome that motivates the pursuit (Deci &
Ryan, 1985; Keeney & Raia, 1976). We then deliberate and select the means that will produce the outcome
and monitor the progress toward the goal as we engage in them (Gollwitzer, 1990). However, several lines of
research suggest that dierent aspects of goal pursuit may operate fairly automatically, without much
thought and under the radar of conscious awareness.
First, there is a rich literature on judgment and decision-making suggesting that aect plays a key role in
the formation of attitudes, expected value, and choice, often bypassing deliberative processes. Such an
aect-driven inuence on human behavior has been demonstrated in several research programs. For
instance, people can implicitly form attitudes on the basis of simple evaluative conditioning procedures in
which neutral stimuli (CS) are linked to aective stimuli (UCS), sometimes without being aware of the
conditioning process (de Houwer, Thomas, & Baeyens, 2001; Hofmann, de Houwer, Perugini, Baeyens, &
Crombez, 2010). Furthermore, studies employing decision-making tasks suggest that judgment and choice
are nearly impossible without emotional processes. Human subjects have been shown to approach decision
options and avoid others based on their bodily sensations and feelings that accompanied the decision-
making process, an eect that does not seem to rely on consciousness and has been dubbed the somatic
marker hypothesis (Damasio, 1994). A nice illustration of this hypothesis pertains to the act of ipping a
coin to decide what to do (e.g., when one needs a new car and one can choose between a Skoda or a Volvo). It
turns out that a certain positive or negative sensation can become manifest when tails or heads determines
one decision of a specic course of action, indicating a preference was already present.
Second, rooted in Murray’s (1938) concept of needs and its role in personality, a substantial research
program on social motivation suggests that specic patterns of preferences and decision-making can be
driven by implicit motives. Implicit motives are dened as motivational dispositions that operate outside of
people’s conscious awareness and are aimed at the attainment of specic classes of incentives (McClelland,
Koestner, & Weinberger, 1989; Schultheiss et al., 2008). These motives are presumed to build on
evolutionarily old systems (midbrain structures) that appear to develop in a preverbal stage and are guided
by what is pleasant and aversive during socialization experiences. Self-attributed motives, that is, motives
that a person can express and report on explicitly, are thought to depend on evolutionarily more recent
systems (cortical structures) that develop later in childhood and are sensitive to language via verbal
commands from others, self-instructions, and explicit knowledge about norms and values. As a result of the
early, nonverbal way in which they are acquired, implicit motives tend to develop independently from
conscious awareness and hence are dicult to articulate. Self-attributed motives, in contrast, are suggested
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to rely on consciousness and are therefore readily accessible to verbal reports. Accordingly, implicit motives
must be assessed indirectly, for example, with projective instruments such as the Thematic Apperception
Test (see Schultheiss & Brunstein, 2001). Self-attributed motives can be measured directly with instruments
that rely on the capacity for introspection, such as self-report questionnaires.
Research on implicit motives largely focuses on three main social needs, namely achievement (i.e., the
desire to prosper and gain success), power (i.e., the desire to inuence and control others), and aliation
(i.e., the desire for friendly social interactions). Once these motives are established, they orient, select, and
energize behavior (McClelland, 1985). Indeed, several studies reveal that people’s behavior can be reliably
predicted by achievement, aliation, and power motives that are measured by the Thematic Apperception
Test or alternative projection measures (for an overview of dierent measures of implicit motives, see
Schultheiss & Pang, 2007) and this predictive value does not necessarily correspond with the predictive
value of explicitly generated motives.
Finally, others have argued that such implicit motives can be acquired as a situational goal state. Situational
goal states are shaped by direct experience and other types of learning to act in a goal-directed way (e.g., I
want to earn money) in a specic context (e.g., when I enter the oce), such that after some repetition the
context is able to trigger the pursuit of the goal at hand (Bargh, Gollwitzer, Lee-Chai, Barndollar, &
Trötschel, 2001). Such priming eects (eects in which the mere exposure to information renders
knowledge, such as a psychological concept, ready for later use) are proposed to build on knowledge
structures including the context, the goal itself, and actions as well as opportunities that may aid goal
pursuit (Aarts & Dijksterhuis, 2000, 2003; Bargh & Gollwitzer, 1994; Kruglanski et al., 2002). For
example, the goal of consuming fruit may be related to eating a banana while having lunch in the university
cafeteria. Or, a visit to an exclusive restaurant or bar may be connected to interacting with good friends and
the desire to socialize and go out. Thus, when activating or priming a goal by the associated context (e.g.,
eating fruit when going for lunch in the cafeteria), we do not access a single concept, but rather a rich
structure containing, among other things, cognitive, aective, and behavioral information.
p. 271
There is a long list of studies that seem to underscore the notion that goal pursuit can be triggered by the
environment outside awareness. Such environment-driven goal pursuit can be evoked either directly, for
example, by exposure to goal information, such as words associated with achievement (Bargh et al., 2001;
Bongers, Dijksterhuis, & Spears, 2010; Eitam, Hassin, & Schul, 2008; Engeser, Wendland, & Rheinberg,
2006; Hart & Albarracín, 2009; Oikawa, 2004; Shantz & Latham, 2009), or indirectly, for example, by
exposure to specic aspects in the social environments, such as signicant others or observation of
another’s behavior (Aarts, Gollwitzer, & Hassin, 2004; Dik & Aarts, 2007; Friedman, Deci, Elliot, Moller, &
Aarts, 2010; Loersch, Aarts, Payne, & Jeeris, 2008).
Although the evidence for unconscious goal pursuit is mounting, the ndings have been criticized on two
grounds: First, whereas this research suggests that goal pursuit can be triggered by the environment, the
claim that this occurs outside awareness has been questioned. Specically, tests of unawareness rely on
checks that ask participants to assess whether they have seen goal-relevant stimuli (relevant for controlling
the unconscious processing of input) or to explicitly report in retrospect whether the presented
environment or stimuli inuenced them. These measures have two problems: (a) subjects might simply
have forgotten what happened before in the study or they are not willing to reveal their experiences of being
inuenced (or not); and (b) apart from recollection and motivational problems, the evidence for
unconscious processes is said to be provided when there is no relation between the manipulation, awareness
checks, and dependent variable. In other words, the test of awareness is based on a null eect. Whereas null
eects can exist, inadequate sample sizes and small eects might produce a Type 2 error (Vadillo,
Konstantinidis, & Shanks, 2016), such that one fails to reject a false null hypothesis.
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Second, in line with a general concern for robustness of research ndings (Gelman & Loken, 2014;
Ioannidis, 2005; Simmons, Nelson, & Simonsohn, 2011), it has become clear over recent years that one
should not take evidence of an eect as solid proof or facts. Several nonreplications of psychological
experiments have demonstrated that not all studies are as easily replicated (Doyen, Klein, Pichon, &
Cleeremans, 2012; Hagger et al., 2016; Klein et al., 2014; Newell & Shanks, 2014). In this, research on
unconscious processes in goal pursuit is no exception, with a recent joint replication eort involving many
labs nding that only 36% of studies psychology-wide produce the same results when repeated (Klein et al.,
2014). For one, these failures to replicate have statistical and methodological reasons. Most studies use
small samples and are thus susceptible to noise. In combination with selective reporting of successful
studies (i.e., the le drawer problem; Ioannidis, 2005) and researchers having too many degrees of freedom
to analyze and report data (Gelman & Loken, 2014; Simmons et al., 2011), the prevalence of environmental
control of goal-directed behavior may have been heavily overestimated. Nonetheless, a recent meta-
analysis of the eects of behavior priming established a small eect (Cohen’s d of 0.33) across several
studies that in one way or another show that action words can trigger actual behavior (Weingarten et al.,
2016). Most notably, priming eects were stronger when actions were more subjectively valued or desirable.
Despite the methodological concerns, this is encouraging.
Apart from these methodological concerns, the priming literature oers a fairly heterogeneous pallet of
studies. Although research participants are commonly exposed to priming-sensitive information, the
relation between the primes and the actual representation of the concept that is assumed to be activated
comprises substantial variation. Primes can be words, observed behavior of others, objects related to a goal
of actions, and so forth. At the same time, it is not always clear how goals or action-related concepts are
assumed to be represented (e.g., amodal knowledge, embodied action, or outcome representations), so it is
hard to predict whether a relevant representation will be activated in the rst place. Moreover, motivational
factors are often overlooked. That is, primed behavioral constructs may be related to an individual’s goal
and therefore priming eects may dier from one concept to another and from one person to the next (see,
e.g., Custers & Aarts, 2007; Ferguson, 2007). Because most studies do not take into account such
motivational moderators, priming eects on behavior may be present or absent. Finally, there is little
agreement about the behavioral measures to detect priming eects and these measures may dier in
their sensitivity, the exact properties of behavior they pick up (direction of behavior, invested eort, etc.),
and the match with the behavioral eects.
p. 272
It appears, then, that there is not consensus about either conceptual analyses or experimental procedures to
be able to oer a clear operational denition of the topic under investigation. Such clarity might help to
understand and appreciate (some of) the ndings on environmental control of goal pursuit that possibly
occurs outside awareness. But what, then, are the mechanisms that produce these eects? In the next
section we present a possible account for these ndings.
Goal Pursuit as Automated Behaviors
In recent years, we have developed an account in which goal pursuit as automated behaviors can be
understood. This account is called ADORE (goals as action–(desired) outcome representations) and has
been presented previously in dierent versions (see Figure 15.1; Custers & Aarts, 2010, 2014). The main goal
of ADORE is to promote a better understanding and examination of how the covertly operating brain–
cognition interface controls observable human behavior: behavior (e.g., pressing a lever or keys on the
keyboard) that can be clearly classied as goal directed; actions that are executed to obtain a valued
outcome (getting food or producing letters on the computer screen).
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In line with others (Hommel et al., 2001; Jeannerod, 1997; Prinz, 1997), ADORE assumes that action–
outcome representations are a natural consequence of repeatedly performing actions and experiencing their
outcomes. These action–outcomes form the basis for goal-directed behavior. More formally, action–
outcome representations are formed that allow responses (r) to occur and actions to be selected by
activating the representation of the outcome (o´), which eventually makes this outcome (o) happen (o´ → r
→ o). Crucially, the account also holds that when positive aective information is represented in the
outcome (o´), then this can act as a reward signal, which may boost eort invested in the behavior,
essentially motivating behavior to obtain the actual rewarding outcome (o). Thus, the acquisition of
genuine human goal pursuit relies on a representation of a rewarding outcome, a response, and the actual
experiences of the rewarding outcome (o´ → r → o; Custers & Aarts, 2010).
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Accordingly, priming the representation of the desired outcome moves the body to result in obtaining the
actual rewarding outcome. To understand how responses can be automatically triggered, we rst turn to the
literature on habits, spelling out the dierence between purely habitual responses and goal-directed
behavior that is initiated outside awareness.
Figure 15.1
Action–(desired) outcome representations (ADORE) framework for understanding priming eects on goal pursuit based on
Custers and Aarts (2010).
Habitual Action Selection
At the lowest level of analysis, habits can be regarded as stimulus–response (s–r) links that are established
and reinforced by rewards that follow certain responses to a stimulus. If, for example, one always wakes up
to the alarm clock, takes a shower, and nds comfort in doing so, the sound of the alarm clock may become
associated with the response of taking a shower. Eventually, when the behavior has repeatedly been
executed in response to the certain stimulus and the stimulus–response association has become well
established, the perception of the stimulus may automatically trigger the execution of the associated
behavior (s → r).
This view suggests that, after sucient practice, behavior becomes completely stimulus controlled and
eventually operates independent of the rewards (e.g., the comfort of the shower) that initially reinforced the
behavior. This perseverance of an s–r relation after reward removal or devaluation (e.g., one keeps walking
to the shower in response to the alarm clock even if the shower only provides cold water) is taken as
evidence that the behavior is no longer driven by rewards (or the anticipated desired outcome) and is truly
habitual (Dickinson, Balleine, Watt, Gonzales, & Boakes, 1995).
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Action–Outcome Learning
However, actions can also be triggered through outcome representations. Such action–outcome
learning capitalizes on the human capacity of learning to associate a stimulus-triggered response
with an outcome that follows the response (s → r → o; e.g., a sound triggers the act of moving one’s
arm to the left, which results in shutting down the alarm clock). Stimuli in the environment would,
then, trigger a response not directly, but through the representation of an outcome that is associated
with the response (s → o´ → r → o).
p. 273
Important support for such a perspective comes from the empirical observation that humans represent their
actions in terms of their observable eects or outcomes and establish associations between the outcomes
and the motor programs that produce the outcome (s–o; Hommel et al., 2001; Jeannerod, 1997; Prinz, 1997;
Vallacher & Wegner, 1987). As a consequence, action can follow from an ideomotor principle (James, 1890):
Merely thinking about or activating a representation of a certain outcome (o´) moves and programs the
human body in the service of achieving that outcome without a conscious decision to act (o´ → r → o). In
addition, representing actions in terms of their potentially desirable outcomes allows people to direct their
behavior at the level of the specic outcome, in that they serve as reference points that guide and adjust
ongoing actions toward producing the desired goal.
Evidence from such automatic eects comes from outcome-priming studies. In such paradigms,
participants usually must react to two dierent stimuli with two dierent responses that each produce their
own outcome. When outcomes are primed just before the imperative stimuli, action selection (speed and
accuracy) is biased by the primes. For instance, in a seminal study by Elsner and Hommel (2001),
participants learned that left/right keypresses yielded low/high tones in a learning phase. In the test phase,
presenting low/high tones just before people’s responses biased actions in the direction of the primes.
Such outcome-priming eects are also apparent in mimicry. Mimicry could be seen as a specic case of
ideomotor behavior in which perceived behavioral outcomes in others trigger the same behavior in the
observer. For instance, perceiving a smile may activate an outcome representation (o´) that is associated
with a particular pattern of motor responses (i.e., contracting specic facial muscles), which produces the
outcome in the observer. It has even been argued that mirror neurons that have been observed in monkeys
(i.e., neurons that are activated on execution of an action as well as perceiving its outcome) reect a learned
overlap in action and outcome representations on the single-cell level (Heyes, 2010).
In an attempt to extrapolate the principles of ideomotor behavior addressed above to a more conceptual
level of analysis, researchers have tested whether behavior and language comprehension are closely
connected, such that words (semantic information) referring to actions lead to activation of the motor
programs that produce them. Such semantic motor resonance (Zwaan & Taylor, 2006) has also been
demonstrated in functional magnetic resonance imaging studies where subjects were exposed to action
words. It was found that merely reading words related to nger, mouth, or foot movements produced
activation in the same areas in the premotor cortex that were activated when performing the actual
corresponding actions (Pulvermüller, 2005). This demonstrates that reading words related to action–
outcomes triggers the corresponding motor programs, which provides a mechanism by which more
complicated semantic information related to outcomes may trigger behavior that produces them.
It is worth noting that these studies demonstrate that motor resonance as a result of observing others’ goals
or reading abstract descriptions of action–outcomes implies that actions and their outcomes are
hierarchically organized. Consider that reading the word kick, for instance, triggers the corresponding
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Incentive Learning
action of bending the knee and then swinging the leg forward. The word kick, then, triggers not one
response, but a pattern of responses that are chained together (Custers & Veling, 2009), in that one
response (bending) triggers the next (swinging), and hierarchically, in the sense that the behavioral
outcome of kicking is the result of simpler actions (bending and swinging) that are, in turn, caused by even
simpler actions (contracting specic muscles). The more abstract the behavioral outcomes (e.g., earning
money, helping), the more steps it takes to cascade down to the motor level. This hierarchical organization
implies that each action can be seen as the outcome of simpler actions, and hence the concepts of goals and
means can be used interchangeably depending on the level of explanation. The proposal of such a
hierarchical structure of actions has been widely accepted in several areas of psychological research (Carver
& Scheier, 1981; Gallistel, 1985; G. A. Miller, Galanter, & Pribram, 1960; Mischel, 1974; Powers, 1973; Schank
& Abelson, 1977; Vallacher & Wegner, 1987).
The precise implementation of such abstract outcomes as earning money or helping can never be fully the
result of action–outcome associations that are retrieved from memory. This implementation must be
inuenced by bottom-up processes in the specic behavioral context as well. Through such situated
cognition (Barsalou, 2016), the specic instantiation of “helping” may depend on which behaviors are
aorded by the situation. Thus, depending on the (social) context, activating the mental representation of
helping can lead to picking up dropped pens or holding a door for someone. Assuming that both action
patterns are overlearned, it is still plausible that the context already determines the specic action pattern
that is triggered by the abstract outcome representation. Hence, abstract outcome representations could—
when activated—in principle, produce behavior in a rather exible way, rendering the behavior adaptive in
the context at hand.
p. 274
Can Stimuli Trigger Motivation?
Although the theories previously outlined explain how stimuli can trigger responses, mediated by outcome
representations or not, they are silent when it comes to the question of whether, besides directing behavior,
such stimuli can also motivate this behavior outside awareness. For goal pursuit to operate, behavior must
also be furnished with the necessary eort.
Early ndings from research on incentive learning provide some evidence showing that stimuli may
also incentivize or motivate behavior. Incentive learning theories were inspired by remarkable ndings
in dierent animal labs that shed new light on the role of reinforcement in learning processes
following the s–r habit paradigm (Skinner, 1953; J. B. Watson, 1925). For instance, operant stereotypes
or misbehaviors were discovered during operant conditioning experiments (Breland & Breland, 1961).
One such behavior is autoshaping (Brown & Jenkins, 1968; Williams & Williams, 1969). For example, it
has been shown that pigeons, for which free presentation of food is repeatedly paired with a light
signal, start to vigorously pick at the light bulb although this behavior is not explicitly reinforced. This
phenomenon, in which the animal’s behavior is automatically shaped without specic reinforcement,
occurs because the positive aect or pleasure aroused by the food has now become linked to the light
bulb, which triggers a motivational approach in the animal as if the stimulus were an incentive.
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Pavlovian-to-Instrumental Transfer
A similar phenomenon is the case of general Pavlovian-to-instrumental transfer in the animal-
learning literature (Estes, 1948; Rescorla & Solomon, 1967). In the classic Pavlovian-to-instrumental
transfer paradigm, an animal learns that a neutral stimulus predicts a particular positive outcome (an
s–o relation formed by Pavlovian conditioning, say, a bell predicts food). In addition, it also learns
separately that a particular response leads to the outcome (an r–o relation formed by instrumental
learning, say, that pushing a lever produces food). When the result of learning is tested under
extinction (neither the stimuli nor the responses produce the reward any longer), it is found that the
stimulus evokes the response (s → r), even though this association has never been explicitly reinforced.
That is, the Pavlovian conditioning transferred to the stimulus, so that it now is associated with, and
evokes, the response. In general Pavlovian-to-instrumental transfer, this eect is shown to be
nonspecic, in that the stimulus appears to motivate any response, even responses that have never
been associated with the outcome.
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Biological grounding of this transfer of motivational value from the outcome to the stimulus comes from
research suggesting that so-called pleasure centers in the brain (mainly targeting the nucleus accumbens)
are involved in the mechanism that creates incentives (Shizgal, 1999). For example, rats that have learned
to perform an arbitrary behavior such as pressing a lever in a cage that is followed by electrical stimulation
of the mesolimbic brain area become highly motivated to perform that behavior (as the behavior activates
the brain’s pleasure center and, hence, triggers positive aect; Olds & Milner, 1954). It appears as if pushing
the lever becomes a goal in itself. Illustrative of the motivational strength of this type of incentive learning,
it has been established that animals run uphill, leap over hurdles (Edmonds & Gallistel, 1974), and cross
electried grids (Olds, 1958) to engage in the behavior. Importantly, such enhanced eort eects occur even
in the absence of physiological deprivation states such as thirst or hunger (Shizgal, 1997). This research
demonstrates that practice not only leads to automatic stimulus–response rules, but also can endow stimuli
with motivational power.
Understanding the Unconscious Source of Human Goal Pursuit
In humans, motivation has been mostly studied as a function of monetary rewards. In daily life, most people
deal with money every day, several times a day, and in small and large amounts. Accordingly, it is no
surprise that money is a powerful motivator for human decision-making and behavior and that people
are willing to work for it (e.g., see Bijleveld & Aarts, 2014, for a summary on the role of money in people’s
lifes). Interestingly, money seems to act just as other more basic acquired rewards, such as food, water, and
sex. Neurobiological studies have shown that a variety of reward cues, including money, are encoded by the
same brain areas (specically, the ventral striatum accommodated in the mesolimbic system). Accordingly,
the striatum has also become known as the reward center implicated in the motivation of human cognition
and behavior.
p. 275
There is ample evidence showing that monetary rewards aect cognition and behavior in several ways. In
human subjects it has been demonstrated that money improves their performance on several cognitive
tasks, such as visual attention, working memory, and response conict (Bijleveld & Aarts, 2014). In
addition, the value of money has been shown to play a crucial role in the assessment of expected values and
decision-making and that one is more willing to spend eort when more money is at stake.
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It is important to emphasize that, despite its profound and pervasive eects, most research that addresses
the question of how money aect people’s mind, brain, and behavior explicitly conveys or communicates to
subjects what amount of money can be earned by increasing their task performance. Thus, the question of
whether reward cues also impinge on human performance outside conscious awareness has not been
directly addressed. In the section “The Power of Reward Cues,” we review a line of research that
investigates this, employing an experimental method in which task performance is tested after suboptimal
(i.e., hard to consciously perceive) presentation to reward cues (coins).
The Power of Reward Cues
A growing literature suggests that stimuli in the environment that signal an opportunity for obtaining a
reward can indeed motivate behavior in the absence of awareness. The rst study to demonstrate this was
provided by Pessiglione and colleagues (Pessiglione et al., 2007), who invited participants to perform a task
in which they could earn money by squeezing a handgrip. Before each squeeze, the money that could be
earned was indicated by displaying the picture of a 1-pound or 1-penny coin on the screen. Participants were
told that the harder they squeezed, the larger the percentage of the reward at stake they would earn. The
coin pictures were masked (for a review on masked priming, see van den Bussche, van den Noortgate, &
Reynvoet, 2009) and either presented for a very short (suboptimal) or long (optimal) interval. Regardless of
whether participants could consciously perceive how much money was at stake, they deployed more force
for the high-value coins. Congruently, skin conductance responses—used as an index of sympathetic
nervous system activity—were higher to suboptimally presented images of 1 pound compared to those of 1
penny.
A similar eect has been demonstrated for cognitive eort, looking at physiological markers of eort.
Bijleveld, Custers, and Aarts (2009) asked participants to memorize digits and then to recall them verbally.
At the beginning of each trial, a high reward (50 cents) or a low reward (1 cent) was at stake and was
presented either suboptimally or optimally. Pupil dilation, a physiological measure related to the
mobilization of mental eort, was used. Participants showed an increase of pupil dilation—related to an
increase of mental eort invested—on highly rewarded trials, and this held regardless of whether the
rewards were presented suboptimally or optimally.
Crucially, this eect was only obtained for dicult trials (memorize ve digits) and not for easy trials
(memorize three digits). This nding segues well with the classical features of eort mobilization (Brehm &
Self, 1989; Wright & Gendolla, 2012; Wright & Kirby, 2001): People mobilize no more energy than necessary
to achieve a goal when performing an easy task. However, when task diculty is high, individuals will strive
to reach the highest possible performance level that is necessary to ensure goal attainment.
Capa and colleagues (Capa, Bouquet, Dreher, & Dufour, 2013) obtain more direct evidence for the role of
eort mobilization in response to suboptimal reward cues. In their experiment, participants were instructed
that, if they responded correctly to each trial of a run of 13 trials, they would receive the money displayed at
the beginning of the run. Participants exhibited better performance, as shown by percentage of correct runs,
for a higher than for a lower reward displayed, regardless of whether they were presented optimally or
suboptimally. This better performance was likely to be the result of a greater mobilization of resources, as
suggested by a stronger suppression of frontocentral alpha activity. Reduced alpha activity over dierent
cortical areas, from frontal to parietal sites, has been reported during the performance of mental tasks
(Gevins, Smith, McEvoy, & Yu, 1997) and is inversely related to the amount of cortical resources allocated to
task performance. Because the mean time of the run was 40.74 seconds, this study also demonstrates
that suboptimal reward cues can have an eect lasting over several seconds. Importantly, no dierences in
performance and alpha activity were observed between the beginning and end of each run, suggesting that
p. 276
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although null-eects do not allow rm conclusions, the eect of unconscious reward had not collapsed over
time.
The nding that reward cues can mobilize mental eort outside awareness has been replicated by others
(Bijleveld et al., 2014; Bijleveld, Custers, & Aarts, 2010, 2011; Capa et al., 2013; Capa, Cleeremans, Bustin, &
Hansenne, 2011; Zedelius, Veling, & Aarts, 2011, 2012; Zedelius, Veling, Bijleveld, & Aarts, 2012). However,
the studies described above demonstrate a similar eect for conscious reward cues and reward cues that
were allegedly presented without being accompanied by awareness. Although at rst sight this seems to
provide evidence for the idea that conscious and unconscious reward cues motivate behavior in the same
way, it also leaves the door open for important criticism. That is, if conscious and unconscious reward cues
always have the same eect, it could also be the case that even though stimuli were presented briey
between masks, participants were still able to consciously perceive them. Stronger evidence for unconscious
eects would be provided by studies that seek to produce a dissociation between the eects of conscious and
unconscious reward cues.
Such a dissociation was provided by Zedelius, Veling, and Aarts (2012). In this experiment, not only the
presentation of the reward cues was manipulated, but also the attainability of the reward. That is, at the
beginning of every trial, participants were informed whether a subsequently presented reward would be
attainable or unattainable. Ecient performance thus required the trial-by-trial integration of reward
value and attainability. It was found that suboptimal cues signaling high rewards enhanced performance,
even when these rewards were unattainable. In contrast, optimal high-reward cues only improved
performance when the rewards could be attained. This suggests that optimally presented rewards are used
more strategically in eort preparation, whereas suboptimal cues just seem to boost eort regardless of
whether this is helpful in attaining the reward (Bijleveld, Custers, & Aarts, 2010; Veling & Bijleveld, 2015;
Zedelius, Veling, Bijleveld, et al., 2012).
The ndings discussed above have been captured in a framework for understanding and examining human
reward processing and its similar or distinctive eects on task performance (see Table 15.1; Bijleveld,
Custers, & Aarts, 2012b). This framework mainly addresses the processing of monetary reward cues, but it
can also be applied to other reward cues. In more detail, this framework proposes that people rst process
rewards in rudimentary, subcortical brain structures.
Table 15.1 Framework for Reward Processing
Type of
reward
processing
Required
intensity of
reward
Phenomenological
experience of
reward
Functionality and potentially
involved brain structures
Behavioral outcomes
Initial
Low
Reward is not
consciously
experienced
Rudimentary: VS and its
immediate outputs
Facilitation of performance
Full
High
Reward is
consciously
experienced
Rudimentary: VS and its
immediate outputs; higher level:
MPFC, ACC, DLPFC
Facilitation of performance;
strategic decision-making and
reflections on rewards
Note. VS = ventral striatum; MPFC = medial prefrontal cortex; ACC = anterior cingulate cortex; DLPFC = dorsolateral prefrontal
cortex. Based on Bijleveld, Custers, and Aarts (2012a).
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One of these structures is the striatum—a cerebral structure that is also activated by suboptimal reward
cues (Pessiglione et al., 2007). As observed in several studies, this initial processing can facilitate task
performance directly by prompting the recruitment of eort in the service of reward attainment. This initial
processing of reward cues requires little perceptual input and is not consciously experienced. When
participants are aware of the reward at stake, rewards may undergo full processing. In that case, brain
structures that are engaged may involve higher level cognitive functions located in the frontal brain, in
addition to the rudimentary structures already engaged by initial reward processing, such as the anterior
cingulate cortex, the dorsolateral prefrontal cortex, and the medial prefrontal cortex. These cerebral
structures are related to cognitive functions such as strategy and decision-making (LeDoux, 1996),
executive control, and maintenance of reward information over time (Haber & Knutson, 2010). Thus, full
reward processing may lead individuals to consciously choose a strategy.
p. 277
It is important to note that the framework briey discussed above is related to research in the area of
emotional processes and working memory. Specically, in a substantial contribution to theory and research
on fear processing and motivated behavior, LeDoux (1996) has distinguished two routes of emotional
processing: the low (direct) route and the high (indirect) route. The direct route is considered to operate
unconsciously and involves the limbic brain system that processes emotional information rapidly and
supercially. The high route involves conscious processing and is more slow and precise in dealing with
specic information processed in cortical areas. Accordingly, emotional information is initially processed in
a crude way to prepare the body and further processed in a more analytical way to produce a proper
response.
In another domain relevant for understanding goal-directed behavior, Baars (2002) proposed a global
workspace theory to account for the role of working memory in supporting the control and execution of
behavior. According to this theory, unconscious processes can run in local brain networks that operate in
parallel with limited communication between them, but they can form “coalitions” to broadcast messages
throughout the brain. This coalition formation and respective broadcasting is supposed to occur in the
global workspace, which co-occurs with consciousness. The global workspace might be used to exercise
executive control to perform voluntary actions, thereby increasing the likelihood of achieving goals.
In short, then, several lines of research suggest that goal pursuit can originate in the unconscious:
Motivational and emotional information is initially processed in the mesolimbic system and goal-relevant
information is handled by independently operating local brain networks that support goal-directed
behavior. Further processing in cortical areas and communication between local networks might promote
eective decision-making and goal pursuit that is accompanied by conscious awareness of the goals one
pursues.
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When Outcome + Reward Information Are (Re)presented in One
Instance
The notion that a variety of reward cues are encoded by the same brain system to motivate cognition and
action and can be processed unconsciously has led to the proposal that a positive reward signal associated
with outcomes plays a crucial role in the unconscious origins of goal pursuit (Custers & Aarts, 2010).
Specically, when a desired outcome or goal is primed, activation of the mental representation of this
outcome is immediately followed by the activation of an associated positive aective tag, which acts as a
reward signal for pursuing the primed goal. The positive reward signal attached to a goal thus unconsciously
facilitates the actual selection of the goal and the subsequent mobilization of eort and resources to
maintain the goal, unless other (e.g., more rewarding) goals gain priority. This aective–motivational
process relies on associations between the representations of outcomes and positive reward signals that are
shaped by one’s history (e.g., when a person was happy when making money or performing well). In this
case, the goal is said to preexist as a desired state in the mind. Priming this goal representation not only
prepares the appropriate instrumental actions associated with the goal, but also motivates behavior,
rendering it persistent and exible, directed at attaining the desired outcome.
A recent set of studies investigated the role of this positive reward signal attached to a goal in the eects of
goal priming in teenagers and young adults (Custers & Aarts, 2007; Ferguson, 2007). For instance, Custers
and Aarts (2007) exposed participants to suboptimally presented words related to the goal of going out
socially. Next, they performed a mouse-click task that, if sucient time was left, was followed by a lottery
in which they could win tickets for a popular student party. Thus, working hard (or fast) on the task can be
seen as a means to get to the goal of socializing. It was established that participants put more eort into the
instrumental task to attain the goal state when the goal concept of “socializing” was primed and that this
eect was more pronounced when the goal concept was more positive (which was assessed in a separate
implicit aective association task). These ndings show that goal-priming eects on motivated behavior
and action control are conditional on the positive valence attached to the primed goal. Similar eects of
positive reward value attached to a goal have been documented for other, perhaps more consequential
behaviors. Priming an egalitarian goal, for instance, changes people’s voting behavior to the extent that this
goal is represented as positive or rewarding (Ferguson, 2007).
The ndings presented above indicate that nonconscious goal pursuit may result when a preexisting desired
goal is activated, which, because of its association with positive aect, sets o a positive reward signal.
In theory, this process could be simulated by externally triggering the aective signal just after activation of
a neutral goal concept (i.e., a goal concept that provides a reference point for action but does not designate a
current desired state that people are motivated to pursue). This ability to respond to the mere coactivation
of goal representations and positive aective cues is thought to play a fundamental role in social learning
(N. E. Miller & Dollard, 1941) and is considered basic in motivational analyses of human behavior (Shizgal,
1997). Thus, when a child observes its mother’s smile upon munching homemade cookies, a student
witnesses a hilarious joke upon entering the classroom, or a person strolling around in the mall hears
people laugh while reading on a billboard “Start your holiday here,” this can cause the goal representations
that are primed by those situations (eating candy, achieving at school, booking a vacation) to acquire an
intrinsic reward value, which prepares and regulates goal-directed behavior.
p. 278
This hypothesis that mere coactivation of a neutral goal concept and positive aect simulates the
acquisition of desired goals and produces nonconscious goal pursuit has been tested as well (Aarts, Custers,
& Marien, 2008; Aarts, Custers, & Veltkamp, 2008; Aoyama et al., 2017; Blakemore, Neveu, & Vuilleumier,
2017; Custers & Aarts, 2005; Holland, Wennekers, Bijlstra, Jongenelen, & van Knippenberg, 2009; Takarada
& Nozaki, 2014, 2017; Veltkamp, Aarts, & Custers, 2008; Veltkamp, Custers, & Aarts, 2011). In these studies,
goal concepts were paired with positively valenced information outside conscious awareness by exploiting
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the evaluative conditioning paradigm (de Houwer et al., 2001). For instance, it has been shown that repeated
pairing of the representation of a neutral goal concept (e.g., words such as drinking, cleaning up, doing
puzzles) and positive aect (e.g., words such as summer or nice) motivates participants to work harder on an
intervening task to secure engagement in the behavior (Custers & Aarts, 2005). In another study, eects of
linking the behavioral concept of drinking to positive aect were compared with the deprivation of water on
the amount of water that was consumed in a tasting task. The results of this study showed that deprivation
increased the amount of drinking and that shaping drinking as more positive caused participants to drink
more water only when they were not deprived. These ndings indicate that linking neutral goal concepts to
positive aect simulates eects of actual needs (Veltkamp, Aarts, & Custers, 2009).
A recent study examined the eects of coactivating goal representations and positive reward signals on the
preparation and motivation of behavior in more detail. In this study, healthy young adults had to squeeze a
handgrip in response to a start sign while the timing and persistence of their behavior were measured
(Aarts, Custers, & Marien, 2008). Prior to this task, words pertaining to the goal of physical exertion were
suboptimally presented (or not) together with positive words that signal rewards (e.g., good, nice) or not. In
line with the ideomotor principle, research participants who were suboptimally primed with the goal of
exertion started to squeeze earlier. However, only participants for whom the goal was coactivated with a
positive reward signal recruited more resources to execute this goal, as evidenced by more forceful and
persistent squeezing. Consciously reported motivation did not show any relation with the suboptimal goal-
priming manipulation. Hence, activating a goal representation gives behavior a head start, whereas the
accompanying reward signal motivates behavior outside awareness (for replications, see Takarada &
Nozaki, 2014, 2017).
In an examination of the role of aective information in the context of ideomotor learning, Eder and
colleagues (Eder, Rothermund, de Houwer, & Hommel, 2015) taught their participants to process positive
and negative pictures after performing a specic action. Thus, the assumption here is that a positive and
negative event is represented in terms of an outcome of specic actions, and hence, participants should be
more motivated to select the action that leads to positive events than the action that leads to negative
events. Their research established two ndings. First, they showed a compatibility eect of priming in a
response facilitation paradigm. That is, they found that anticipating positive events automatically triggered
the associated action, but anticipating negative events did so as well. However, automatically selecting an
action in response to anticipating negative outcomes and then producing them does not seem functional for
an organism that controls action to satisfy needs and desires by realizing favorable events and avoiding
aversive ones (Eder & Hommel, 2013; Elliot, 2013). Interestingly, testing the ideomotor learning eect in a
free-choice task yielded a response selection bias toward positive consequences. This latter eect provides
suggestive evidence for the idea that positive events that follow actions are represented as desired
outcomes.
In another ideomotor learning study exploring a specic case of the Pavlovian-to-instrumental transfer
eect, Marien, Aarts, and Custers (2015) tested the interactive role of action–outcome learning and positive
signal processing in motivating human goal-directed behavior. Specically, they addressed dierent
stimulus–response order conditions to examine whether a specic action (e.g., pressing the spacebar with
the left index nger) that is followed (rather than preceded) by neutral stimuli motivates individuals to
obtain these stimuli when the neutral stimuli are accompanied by positive sensations. In an action–
outcome learning paradigm, they manipulated whether a neutral object (e.g., a pencil) appearing on the
computer screen was conceived of as an outcome (o) of response (r) by asking participants to press the
spacebar before or after the presentation of the stimulus. Furthermore, they independently presented
neutral (s) or positive (s) auditory stimuli upon visual presentation of the object (neutral auditory stimuli
were words such as because or there; positive auditory stimuli were words such as good or nice that are
central to the human nature of social learning and reinforcement; Bandura, 1986; N. E. Miller & Dollard,
p. 279
0 +
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1941). Results showed that actual eort to obtain the object was only enhanced when the action was
followed by the object and presented with a positive signal. The ndings suggest that people represent
stimuli as outcomes when a stimulus follows action performance and become motivated to attain them
when this stimulus is accompanied by a positive signal.
In sum, research has demonstrated that (a) priming people with outcomes causes them to perform the
relevant associated actions with more eort or vigor when these outcomes are more strongly associated
with positive aect and (b) priming neutral outcomes together with positive reward cues has the same
eect. Hence, this provides converging evidence for the idea that both the recruitment of associated action
patterns and the reward signals evoked by primes or cues in the environment related to outcomes play a
central role in the manifestation of goal pursuit.
Conclusion and Future Directions
In the present chapter, we presented our ADORE framework in an attempt to shed more light on the role of
conscious awareness in goal pursuit. In doing so, we brought together two largely independent lines of
research: rst, the literature on action–outcome learning, which claims that cues related to outcomes can
activate associated action representations, serving as an explanation for ideomotor eects on behavior; and
second, the literature on the motivating power of cues that signal prospective rewards. Drawing on
paradigms borrowed from evaluative conditioning research, studies from our own as well as other labs were
discussed that demonstrated that coactivating outcome representations and reward cues in a controlled
manner in the lab promotes goal pursuit, even when people are unaware of this coactivation.
The current framework advances our understanding of priming eects on motivated behavior, not only by
demonstrating such priming eects, but also by oering a testable denition of goal representations:
Although representing actions in terms of their outcomes may be enough for ideomotor action, our
framework holds that this outcome must have rewarding properties (i.e., be desirable) for motivational
eect to occur. Although the action–outcome learning literature has largely ignored the role of the
desirability of the outcome (but see Eder et al., 2015), rewards and outcomes have nearly always been
conated in research looking at the motivational power of cues (see, e.g., Talmi, Seymour, Dayan, & Dolan,
2008; P. Watson, Wiers, Hommel, & de Wit, 2014). By looking at how action–(desirable) outcome
representations are learned, the ADORE framework has opened up new avenues for research on how cues in
the environment may shape human behavior outside awareness.
Our account also oers that reward cues in the environment may be processed at dierent levels as a result
of the strength of the input of the reward signal. Although initially rewards may be processed in a
rudimentary way, mainly by the ventral striatum, full reward processing may involve the medial prefrontal
cortex, the anterior cingulate cortex, and the dorsolateral prefrontal cortex (see Bijleveld et al., 2012b).
Although it is hard to say whether conscious awareness plays a causal role in this spread of activation, it is
certainly in line with theories on the function of consciousness (Baars, 2002). Understanding this role of
conscious awareness may help to predict whether reactions to reward cues may be a boost in invested eort
or whether more complicated strategic behavior may unfold.
Although our framework builds on ndings from dierent paradigms and various labs, it is important to
note that much of the support for the motivating eects of cues that signal prospective rewards uses money
as reward. Although money is an important all-round motivator with which each of us has a long history,
we do not believe that the obtained eects are necessarily driven by preexisting associations between
money and eort. Although other studies suggest that just priming the concept of money may have an
eect on behavior (Vohs, Mead, & Goode, 2006), rewards in the work discussed here are always relative,
pitting the eects of a low reward (1 cent) against those of a higher reward (10 or 50 cents). It is worth
p. 280
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emphasizing that the eects of those high(er) rewards have been found to be dependent on the level of
diculty. Although, relative to the low reward, high-reward cues boost motivation in dicult conditions,
they do not in easy conditions where no eort is required. Therefore, whether reward cues boost motivation
depends not only on the reward cue itself, but also on interact with the situation at hand.
Although our framework maintains that cues can evoke motivated behavior outside awareness, the question
remains whether people are truly unaware of the cues that aect their behavior. Although the majority of
the studies we described use suboptimal presentation of cues, it is still possible that some results are driven
by some participants who consciously perceive the cues on some of the trials (Vadillo et al., 2016). Hence,
the question remains whether suboptimal presentation in those studies is, in fact, subliminal (i.e., below the
threshold of conscious awareness).
This assumption is debatable because it relies on the notion that there is one clear threshold for all
participants and is the same for all trials. Even if the threshold is determined for the actual participants in
the reported study (which is not always the case), it is often determined in a separate detection test that may
dier from the actual experiment in many ways (e.g., time of execution, goal of the task number of trials).
Hence, a failure to nd an eect on a cue-detection task does not necessarily have to mean that participants
were not aware of the (or some of the) cues in the actual task (Shanks, 2017). Moreover, it has been noted
that the strategy of relying on a null eect to demonstrate an absence of conscious awareness is inherently
problematic (Vadillo et al., 2016). Hence, stronger methods aiming to assess the involvement of
consciousness in more detail (Overgaard, 2015) are necessary to support claims about awareness and
separate conscious from unconscious processes.
Such methods would also help to look at the interplay between conscious and unconscious processes. It has,
for instance, been proposed that a potential role for consciousness may lie not in the starting and steering of
behavior, but in stopping it. Whereas the brain is designed to realize desired outcomes, in the early 21st
century the well-being of the individual may in large part be dependent on the ability to prevent oneself
from engaging in rewarding behaviors that have undesired personal and social long-term consequences
(e.g., eating junk food, derogating others). Although it is known that ongoing and impulsive behaviors can
be inhibited directly by environmental stimuli or well-learned stop rules (Chen, Veling, Dijksterhuis, &
Holland, 2016; Verbruggen & Logan, 2009), we do not yet know whether and how people can express an
unconscious volitional veto (Brass & Haggard, 2007) or whether consciousness as a relatively new knack of
human evolution is required to overrule the labor of the older reward system involved in unconscious goal
pursuit.
Recently, researchers have started to explore the role of negative aect in this process, and it turns out that
negative stimuli that are merely processed when having a goal in mind can put goal pursuit on hold (Aarts,
Custers, & Holland, 2007; Boksem, Ruys, & Aarts, 2011; Clore & Huntsinger, 2009; Knight, Brewer, Ball,
DeWitt, & Marsh, 2015; Veling, Aarts, & Stroebe, 2011; Zedelius et al., 2014). Importantly, this modulating
eect of negative aect on the cessation of goal pursuit may not be so general, because other studies
suggests that people can also be motivated by negative aect, such as when goals are associated with anger
(Aarts et al., 2010; Angus, Kemkes, & Schutter, 2015; Carver & Harmon-Jones, 2009). It remains to be seen,
then, whether and how negative aect accompanying the activation of goals serves as an unconscious veto
to not engage in goal pursuit itself.
Finally, ADORE may play a role not only in the instigation of goal pursuit, but also in the subjective
experiences arising from it. Activated representations of outcomes play a role in the predictive process that
shapes perception of the observed outcomes (Clark, 2013). Moreover, the match between a predicted and
experienced outcome may give rise to experiences of agency (Aarts, Custers, & Wegner, 2005). Hence,
people may ironically perceive agency over actions that are put in motion by external cues in the
environment and reward signals may only strengthen these eects (Aarts, Custers, & Marien, 2009). Hence,
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the control of the environment over our goal pursuits may be more prevalent than our experiences would
suggest.
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