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The Sense of Agency$

Patrick Haggard and Baruch Eitam

Print publication date: 2015

Print ISBN-13: 9780190267278

Published to Oxford Scholarship Online: September 2015

DOI: 10.1093/acprof:oso/9780190267278.001.0001

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The Neural Basis Underlying the Experience of Control in the Human Brain

The Neural Basis Underlying the Experience of Control in the Human Brain

(p.145) 6 The Neural Basis Underlying the Experience of Control in the Human Brain
The Sense of Agency

Lauren A. Leotti

Catherine Cho

Mauricio R. Delgado

Oxford University Press

Abstract and Keywords

Converging evidence suggests the perception of control—or the set of beliefs in one’s ability to exert control over the environment and to produce desired results—is integral for forming a sense of agency, hence affecting an individual’s general well-being. A large literature has demonstrated that the presence or absence of a sense of agency can have a significant impact on the regulation of emotion, behavior, and physiology. This chapter discusses the recent efforts in neuroscience research on humans which investigates this important subject. Collectively, the findings lend support to the theory that choice and personal control are inherently rewarding and motivating, which is highly beneficial for survival. The chapter discusses the implications of this research for understanding how the presence or absence of personal control influences emotion regulation and contributes to maladaptive behaviour.

Keywords:   perception of control, sense of agency, emotion, behavior, reward, motivation


When people hear the word “choice,” they typically think of decisions that are mundane (what are we going to eat for dinner tonight?) or exceptionally difficult (do I take a job at University A or University B?). Yet most things we do involve making choices. When choices do not require much effort (e.g., choosing where to allocate attention in the environment), we may not even perceive them as personal decisions. If such choices were removed, however, we would be very aware of their absence. Choice is important because it affords us the opportunity to be causal agents, instrumental in achieving desired outcomes.

Through choice, we develop a sense of agency, which refers to beliefs in our ability to exercise control over the environment. Such beliefs in personal control are known to be highly adaptive, for their presence or absence can have a profound impact on the regulation of behavior, emotion, and physiology. While there is a rich history of research on beliefs in personal control and choice in humans and animals (for reviews, see Bandura, 1997; Leotti, Iyengar, & Ochsner, 2010; Ryan & Deci, 2006; Shapiro, Schwartz, & Astin, 1996), the neural mechanisms surrounding the experience of control are less well understood in humans. However, recent human neuroscience research has begun to (p.146) contribute to this gap in our knowledge by exploring the affective experience of control and its impact on affective and motivational processes. Disruptions to control beliefs are at the core of many psychiatric disorders (Beck, 1976; Mansell, 2005; Ryan, Deci, & Grolnick, 1995; Shapiro et al., 1996; Strupp, 1970; Taylor & Brown, 1988, 1994), which may be associated with abnormal processing of affective and motivational stimuli in the brain related to failures in self-regulation (Heatherton & Wagner, 2011; Johnstone, van Reekum, Urry, Kalin, & Davidson, 2007). As a result, it is critical to understand how control is experienced in the human brain, because such research has significant implications for understanding the psychological and neural mechanisms related to the origin, maintenance, and potential treatment of many psychiatric disorders.

In this chapter we argue that the capacity to choose between alternatives and to decide which of those options will occur increases feelings of control. In other words, individuals acquire a sense of agency through accumulated experiences of choosing and being the agent of determining the flow of events. Therefore, in this chapter we argue that a sense of control, as it is exercised through removal choice, is inherently valuable and serves adaptive functions. We review the literature examining the value of exercising control, presenting evidence from behavioral psychology as well as from recent neuroimaging studies that highlight the role of affective and motivational brain circuitry in the appraisal of choice opportunity. Additionally, we discuss potential influences to the value of choice, and address the implications for this line of research for future exploration.

Perception of Control Theories

There are several theories that attempt to explain the processes underlying humans’ perception of controllability, including “locus of control” (Rotter, 1966), “illusion of control” (Langer, 1975), “self-efficacy” (Bandura, 1997), and “self-determination theory” (Ryan & Deci, 2000). Drawing from several theories in psychology, White (1959) proposed competence as a mechanism to explain human and animal motivation. He defined competence/effectance as the ability to effectively interact with the environment, which can be considered as one of the sources of human motivation, drawing upon an important link between effectance and motivation. In line with White’s argument, Higgins (2012) also refers to effectance as one of the sources of the motivational aspect of competence. Instead of viewing human behavior as goal directed, individuals seek to be aroused and to maintain activity—hence, acting and thus engaging in a trend of behaviors can have meaning to an individual in and of themselves. Additionally, a recent study by Eitam, Kennedy, & Higgins (p.147) (2013) suggests that one’s motivation toward performance is enhanced when there is a direct contingency between one’s own action and the outcome. Thus, motivation levels are heightened when individuals feel in more control over the outcome.

Despite differences across such theories, the underlying themes uniformly tout the adaptiveness of perceiving control and agency. Most notably, the term “self-efficacy,” defined by Albert Bandura, refers to the set of beliefs in one’s capability to perform courses of action to accomplish one’s goals. According to Bandura, these self-efficacy beliefs influence individuals’ behavior by interacting with cognitive and affective processes, promoting persistence with difficult tasks in individuals with high self-efficacy, and increasing susceptibility to stress in individuals with low-self efficacy (Bandura & Wood, 1989). Extensive research supports the adaptiveness of perceived self-efficacy in different spheres of psychosocial functioning, including work-related performance (Stajkovic & Luthans, 1998), child development (Bandura, Caprar, Barbaranelli, Gerbino, & Pastorelli, 2003), academic achievement and persistence (Multon, Brown, & Lent, 1991), and health functioning (Holden, 1992).

Beliefs in self-efficacy depend on our ability to actually exert control over our environment. Averill (1973) posited that there are three main ways that one can exert control: cognitive, behavioral, and decisional control. Cognitive control refers to regulating the way a potentially threatening stimulus is interpreted, such as altering the meaning or significance of the event or stimulus. The capacity for cognitive control is important for self-regulation (Baumeister, Heatherton, & Tice, 1994; Ochsner & Gross, 2005; Vohs & Baumeister, 2011), and may be critical for fostering self-efficacy beliefs. The other two types of control involve overt behavior, and are most relevant to the study of agency. As defined by Averill (1973), behavioral control is the ability to prevent or modify certain aspects of an event through implementing direct action, and decisional control is the selection of a single course of action from possible alternatives. Although Averill sees the source under the influence of control as the event when referring to behavioral control, it is also worthwhile to note that this definition is arbitrary, depending on whether the object under the influence of control is the event itself or the behavior according to one’s goal. Both of these types of control involve an individual acting as a causal agent to achieve a desired goal. The theory of reinforcement learning (Skinner, 1953; Thorndike, 1933) states that when a specific behavior results in a desired outcome, that behavior is reinforced (i.e., it is more likely to be repeated in the future). Importantly, while the action is successful at producing desired results, the agent himself is also successful at choosing the appropriate action. As a consequence, the opportunity to choose may be reinforced as well, and choice opportunity, then, becomes desirable in and of itself.

(p.148) The opportunity to choose provides an individual with the opportunity to assert their preferences, thus enhancing motivation and performance (Patall, 2013; Patall, Cooper, & Robinson, 2008). Individuals feel more satisfied, competent, and engaged when they are able to express a preference through choice (Cordova & Lepper, 1996; Grolnick & Ryan, 1987; Langer & Rodin, 1976; Patall et al., 2008; Patall, Cooper, & Wynn, 2010; Ryan & Deci, 2000). Merely having an opportunity to choose, even over something inconsequential, has been shown to have a significant impact on quality and even duration of life (Langer & Rodin, 1976). Although the current chapter focuses on the idea that the capacity to choose between alternatives is an important aspect of feeling in control, individuals may also feel a sense of agency when only a single alternative exists and they decide to pursue that option. For example, consumption of the only available food in one’s refrigerator can also instill a feeling of control in an individual. In such a situation, they choose to consume the food, rather than not, and may even increase the value assigned to such food item as a result of having chosen it (albeit the lack of alternatives).

Human behavioral studies suggest that choice is not only highly motivating, but it is also valuable. A seminal paper by Langer (1975) investigated the “illusion of control,” which refers to the phenomenon in which an individual perceives control over an outcome when no true control exists. The study revealed that choice has a substantial impact on control beliefs, and provided some of the earliest direct evidence that choice is valuable. In Langer’s study, participants were offered the opportunity to purchase a lottery ticket for $1, and were either allowed to freely select their ticket (choice group) or were assigned a ticket (no choice group). On the day of the lottery, participants were asked if they would be willing to sell their tickets. Those in the no-choice group were willing to sell their tickets for an average of $1.96, but those in the choice group priced their tickets at a whopping $8.67. She also found that people in the choice group were less willing to switch to another lottery, even though it had better odds of winning. Other studies have shown that people prefer options that lead to additional choice (Bown, Read, & Summers, 2003; Leotti & Delgado, 2011; Suzuki, 1997, 2011), despite the fact that a secondary choice requires greater effort without any additional reward. This suggests that choice itself confers additional value, making it more desirable.

The simple act of choosing has been shown to elicit significant preference change. In the classic free-choice paradigm (Brehm, 1956), items that are selected (e.g., blender vs. toaster), as opposed to rejected, are rated as higher in value after selection, and those that are rejected are rated as lower in value. This post-choice preference shift cannot be explained simply by rationalization to minimize cognitive dissonance (i.e., if I chose the blender over the toaster, the blender must be better than I initially thought). In fact, a study by Lieberman (p.149) and colleagues (2001) revealed that post-choice preference shift could occur in amnesiacs, who could not remember their explicit choice. Additionally, such choice-induced preference change has been demonstrated in preschool-aged children and monkeys (Egan, Santos, & Bloom, 2007), can occur when the choice is made blindly without reviewing alternatives (Sharot, Velasquez, & Dolan, 2010), and can be long lasting, persisting years beyond the initial decision (Sharot, Fleming, Yu, Koster, & Dolan, 2012). These studies suggest that the act of choosing, itself, is an important modulator of affective valuation processes.

Neural Systems Underlying the Perception of Control

The plethora of data demonstrating the important role of perceiving control and the behavioral evidence discussed in the preceding section suggest that choice is desirable and may be inherently rewarding. A complement to the behavioral studies and theories is the introduction of neural data to the question of perceiving control. Identification of the neural substrates of control allows us to constrain our understanding of how perceiving and exercising control are beneficial and adaptive. Specifically, one can hypothesize that if expectations of control, via choice, are valuable and exert a rewarding feeling, then anticipation of choice opportunity should recruit brain structures involved in reward-related processes. A highly interconnected cortical-striatal network, modulated by dopaminergic neurons, has been implicated in processing reward information and fostering goal-directed behavior (Berridge & Kringelbach, 2008; Delgado, 2007; Haber & Knutson, 2010; Levy & Dubois, 2006; O’Doherty, 2004; Schultz et al., 1997). Brain regions that support such reward processes include subcortical regions such as the striatum (which includes the caudate, putamen, and nucleus accumbens), and prefrontal cortical (PFC) structures consisting of the orbitofrontal cortex (OFC) and medial prefrontal cortex (MPFC), which have some structural overlap (Ongur & Price, 2000). The striatum is the input unit of the larger basal ganglia complex and receives projections from several structures, including cortical regions, putting it in a prime position to process cognitive, motor, and motivational information and to influence behavior (Balleine, Delgado, & Hikosaka, 2007; Haber & Knutson, 2010; Middleton & Strick, 2000). While both subcortical and cortical regions have been linked to reward processing, the current discussion will focus largely on the striatum, due to the preponderance of evidence demonstrating its role in instrumental learning, when rewards are contingent upon behavior (McClure, Berns, & Montague, 2003; O’Doherty, Dayan, Friston, Critchley, & Dolan, 2003; O’Doherty et al., 2004; Yacubian (p.150) et al., 2006), which is most relevant for the discussion of the neural substrates of exercising behavioral control.

Functional magnetic resonance imaging (fMRI) studies in humans have supported a rich animal literature (e.g., Robbins & Everitt, 1996) and have implicated the striatum in response to the receipt and anticipation of rewards. Such studies have found increased striatal activity in response to the receipt of primary reinforcers such as food and drinks (O’Doherty, Rolls, Francis, Bowtell, & McGlone, 2001) and secondary reinforcers such as monetary rewards (Delgado, Nystrom, Fissell, Noll, & Fiez, 2000; Knutson & Cooper, 2005), as well as to the mere anticipation of rewards (Kirsch et al., 2003; Knutson, Adams, Fong, & Hommer, 2001; Knutson, Taylor, Kaufman, Peterson, & Glover, 2005; O’Doherty, Deichmann, Critchley, & Dolan, 2002). Additionally, the striatum differentiates between rewards and punishments and is sensitive to the magnitude and probability of rewards (Delgado, Locke, Stenger, & Fiez, 2003; Delgado et al., 2000; Delgado, Stenger, & Fiez, 2004; Kirsch et al., 2003; Knutson et al., 2005; Tobler, O’Doherty, Dolan, & Schultz, 2007; Yacubian et al., 2006).

Although various divisions of the striatum have been proposed based on anatomy and function in rodents, such as ventromedial to dorsolateral representing initial learning to habit formation (Balleine & O’Doherty, 2009; Voorn, Vanderschuren, Groenewegen, Robbins, & Pennartz, 2004), the most basic division in humans involves dorsal and ventral portions. The dorsal striatum includes the caudate and putamen and is thought to be preferentially activated for tasks or stimuli with increased motivational incentives (Delgado et al., 2004; Zink, Pagnoni, Martin-Skurski, Chappelow, & Berns, 2004). Furthermore, whereas the ventral striatum, including nucleus accumbens and ventral portions of caudate and putamen, responds to reward value irrespective of the actions leading to rewards (i.e. stimulus-outcome relationships), the dorsal striatum responds more for rewards that are contingent upon behavior (O’Doherty et al., 2004). Several studies have demonstrated that individuals recruit greater activity in the striatum when rewards are contingent upon their responses, than when they are just passively delivered, illustrating the important role of this region in processing contingency (Bjork & Hommer, 2007; Elliott, Newman, Longe, & William Deakin, 2004; O’Doherty et al., 2004; Tricomi, Delgado, & Fiez, 2004).

One such example is a study by Tricomi and colleagues (2004), which was among the first human neuroimaging studies to illustrate the role of the dorsal striatum in processing contingency between choice opportunity and outcomes. In this study, participants were led to believe that on some trials, outcomes were dependent on their actions (choice condition), and on other trials, their actions had no effect on potential outcomes (no-choice condition). (p.151) Participants reported perceiving greater control over outcomes in the choice condition (relative to no-choice), as well as greater motivation to win money. Further, the dorsal striatum was recruited only for the choice condition, when participants believed that the outcomes were dependent on their actions.

Another example is a study by Tanaka and colleagues (Tanaka, Balleine, & O’Doherty, 2008), which found that in addition to the dorsal striatum, the medial prefrontal cortex (MPFC) and medial OFC are also involved in computing contingency. Specifically, greater reports of subjective causality were associated with greater activity in MPFC, suggesting that this region is important for processing agency, consistent with research supporting the role of this region in processing self-relevance (Heatherton et al., 2006; Johnson et al., 2002; Kelley et al., 2002; Platek, Keenan, Gallup Jr., & Mohamed, 2004) and preference-based decision-making (Johnson et al., 2005; Paulus & Frank, 2003).

In sum, initial neuroimaging studies have supported an animal literature highlighting the role of the striatum and cortical regions in reward-related processing and motivated behavior. Further, such studies have delineated the involvement of the dorsal striatum and MPFC in action-contingency and perceiving control. The ventral striatum, a critical structure for reward processing, has been associated with computing the value of potential rewards and making predictions to aid goal-directed behavior, expressed through consummatory and anticipatory signals.

The Value of Choice: Neural and Behavioral Correlates

Studies demonstrating the sensitivity of the striatum to agency or behavioral contingency during reward processing (e.g., Bjork & Hommer, 2007; O’Doherty, Critchley, Deichmann, & Dolan, 2003; Tricomi et al., 2004) provided critical preliminary support to the hypothesis that exercising control, via choice, modulates activity in reward circuitry. However, these studies focused on the decision period and the outcomes following choice, where a true difference existed between choice options, so that having an opportunity to choose clearly afforded the subject the advantage of selecting the best outcome. From behavioral studies, we know, however, that choice seems to be desirable even if there is no true difference in outcomes (Bown et al., 2003; Leotti & Delgado, 2011; Suzuki, 1997, 2011). Therefore, it is critical to dissociate the positive effects of exercising control by having the opportunity to choose between options from merely selecting the option that leads to higher reward. Thus, to better characterize the affective experience of control and choice, it is necessary to directly examine whether expectancies of control opportunities are rewarding, while controlling for the various affective and cognitive (p.152) processes involved in decision-making tasks used in the previous studies, such as computation of expected value of potential outcomes at the time of choice and emotional responses related to perceived success following outcomes.

Leotti and Delgado (2011a) tested this hypothesis with a simple choice paradigm aimed at examining the affective experience when anticipating choice opportunity. In this study, participants viewed cues that predicted free choice or forced-choice, where a response would lead to a potential monetary reward. Unbeknownst to the participants, both available options (whether freely chosen or selected by the computer) would lead to the same average reward. We considered this manipulation of fundamental importance, since our primary hypothesis was concerned with the idea of the opportunity for choice itself having a rewarding feature, which is not necessarily dependent upon the outcomes associated with one’s choice (see Leotti & Delgado, 2014). Thus, any differences in reported liking of the different cue types (free vs. forced choice) and in associated blood-oxygen-level-dependent (BOLD) responses in reward circuitry could be attributed to differences in the appraisal of the cues themselves, and not to true differences in outcomes, or differences in perceived success. We found that participants liked cues predicting free choice better than those predicting forced choice (Figure 6.1a). Furthermore, choice cues recruited greater activity in reward-related regions previously implicated in the anticipation of reward, including the ventral striatum, midbrain, and dorsal anterior cingulate cortex (ACC). These regions have been linked to reward processing more generally (Delgado, 2007; Knutson et al., 2001; Knutson et al., 2005; O’Doherty, 2004), and have been linked to the voluntary engagement in risky decision-making (Rao, Korczykowski, Pluta, Hoang, & Detre, 2008).

The Neural Basis Underlying the Experience of Control in the Human Brain

Figure 6.1 Behavioral and fMRI correlates of choice value (adapted from Leotti & Delgado, 2011). (a) Participants reported greater subjective liking for cues predicting Choice (C) than those predicting No-choice (NC); (b) Main effects of cue type revealed significant effects in striatum bilaterally, dorsal ACC, and right amygdala; (c) in the right striatum, we observed significantly greater fMRI BOLD activity for Choice condition relative to No-choice and to the Non-informative (NI) and Predictable (P) control conditions. Error bars represent standard errors of the mean. The asterisk marks a significant difference (p < .05).

Choice may be desirable, and may recruit reward-related circuitry, because it is inherently valuable, or alternatively, because it increases the predictability of outcomes or decreases feelings of risk, both which have been theorized to contribute to feelings of control (Thompson, 1999). Additional experimental control conditions included in our study (Leotti & Delgado, 2011) allowed us to tease apart the contributions of these cognitive and affective components involved in decision-making. In our Non-informative (NI) condition, a cue was followed by choice 50% of the time, thus manipulating the uncertainty of choice opportunity. In a predictable (P) no-choice condition, the cue indicated that the participant would have no choice, but it also revealed which colored key would be selected by the computer, controlling for potential differences in predictability. Figure 6.1b displays the main effects of cue type in a whole-brain analysis, illustrating effects in the striatum, dorsal ACC, and amygdala. Figure 6.1c reveals that recruitment of the striatum was related to the probability of having a choice (Choice > Non-informative > No choice), but was not modulated by the predictability of the outcomes (i.e., no difference (p.153) (p.154) for the Predictable cue, which indicated what the selected option would be, in comparison to all other conditions). Hence, striatal activity depended upon the opportunity for choice, rather than a preference for a particular option that is being reflected in the predictable cue. In addition to the ventral striatum, the dorsal striatum also was preferentially activated for choice cues, consistent with previous studies of contingency effects and increased motivational salience, discussed earlier. The recruitment of both ventral and dorsal striatum suggests that the anticipation of choice opportunity involves processes related to action-outcome as well as stimulus-outcome contingencies.

Besides the striatum, our analyses revealed that patterns of BOLD activity differed for our experimental and control conditions in distinct regions of the brain. Heightened activity in the amygdala was observed for the Non-informative cue, potentially reflecting risk or uncertainty (Hsu, Bhatt, Adolphs, Tranel, & Camerer, 2005). Additionally, greater activity in the dorsal ACC was observed for cues predicting choice and possible choice (Non-informative) relative to the cues predicting no choice, which may reflect the motivational salience of choice opportunity when anticipating effortful decision-making (Rushworth, Walton, Kennerley, & Bannerman, 2004). The dissociation in the recruitment of these regions across conditions suggests that we can interpret the recruitment of reward regions, such as the striatum, to reflect reward processing, rather than other cognitive processes related to choice and control.

Although there was no true difference between options in our tasks, participants reported a preference for one option over the other. However, anticipation of the computer’s selection of the preferred option did not selectively recruit reward circuitry. Nonetheless, it is possible that individuals value choice opportunity because they believe, correctly or incorrectly, that choice will provide them access to the best option available. Although the mathematical expected value (average rewards) of the two available options are equal in our task, participants’ mis-estimations of expected value, due to trial-by-trial fluctuations in rewards, may contribute to perceived differences in the value of the colored keys. As a result, subjects may believe that choice is more valuable because it allows for the selection of the key that has the highest expected value at any given time.

The findings from Leotti and Delgado (2011a) suggest that reward-related brain circuitry is recruited when anticipating choice opportunity, potentially providing support for the hypothesis that choice is inherently valuable. Another way to quantify the value of choice opportunity is to determine how much participants were willing to pay for additional choice. An interesting study (Fujiwara et al., 2013) asked participants if they would rather receive a specific amount of money or an opportunity to choose from a set number (p.155) of objects. They found that when the amount of money offered was held constant, participants preferred to have more choices. Moreover, the value, or “willingness to pay,” increased with the number of choices available, as did associated activity in the ventral striatum. As we mentioned earlier, increasing the number of choice options should increase the value of choice, because it increases the likelihood of obtaining the best option. However, individuals in this study seemed to value choice above and beyond what would be expected due to mathematical increases in expected value, implying that choice has value in and of itself. One interesting line of research to extend these ideas is the implementation of computational models to better understand how the choice for preference may develop. For instance, reinforcement learning models have been extensively used in fMRI research (for a review, see O’Doherty, 2007; Frank & Fossella, 2011; Delgado & Dickerson, 2012; Doll et al., 2012), highlighting the involvement of regions such as the striatum in processing a prediction error signal. Such computational models may prove to be quite valuable in explaining the mechanisms through which one comes to prefer choice and to value it. For instance, a recent study posits that the preference for choice develops due to positive prediction errors experienced when one makes a successful choice (Cockburn et al., 2014).

The Value of Choice and Negative Context

Collectively, human neuroimaging studies of experiencing control over positive outcomes (e.g., contingency, free choice) suggest that brain regions involved in reward-related processes are important in the affective appraisal of choice opportunity. In the context of positive outcomes, choice may be desirable because it affords opportunities to optimize rewards. Often, however, people are faced with the challenge of making decisions to avoid potentially negative outcomes. In these cases, having an opportunity to exercise agency, through choice, may serve an important role for reducing the stress associated with uncertainty and threat. In the next section, we discuss the value of choice and control in the context of potentially negative outcomes.

When individuals are faced with potentially negative outcomes, they may experience stress and engage in avoidance behavior. However, perceived control has been shown to buffer the negative emotional response to aversive events. For example, belief in one’s ability to exercise control over a stressful situation has significant impact on autonomic arousal, release of stress hormones, and functioning of the immune system (Abelson, Khan, Liberzon, Erickson, & Young, 2008; Bandura, Taylor, Williams, Mefford, & Barchas, 1985; Kamen-Siegel, Rodin, Seligman, & Dwyer, 1991; Maier, Laudenslager, & Ryan, 1985). Behavioral control has been shown to mitigate arousal during (p.156) anticipation of aversive noise (Glass, Singer, & Friedman, 1969) or photographs (Geer & Maisel, 1972), and to increase tolerance to electric shock (Staub, Tursky, & Schwartz, 1971), and pain (Kanfer & Seider, 1973). Perceived control over painful stimuli also reduces subjective reports of pain and anxiety (Salomons, Johnstone, Backonja, & Davidson, 2004; Salomons, Johnstone, Backonja, Shackman, & Davidson, 2007; Salomons et al., 2010; Wiech et al., 2006). Importantly, the mere belief in control opportunities is sufficient to elicit benefits, even if control is never exercised (Corah & Boffa, 1970; Glass, Reim, & Singer, 1971; Gunnar-vonGnechten, 1978).

Having the opportunity to exercise control, through choice, may reduce negative affect induced by the threat of an aversive outcome. Thus, we would expect that expectancies of choice opportunity would lead to better coping, or self-regulation, when outcomes are uncertain and potentially aversive and, as a result, should recruit brain networks involved in cognitive control and successful emotion regulation. Regulation of negative affect involves the recruitment of cortical regions within the lateral and medial PFC that exert a modulatory influence over responses of regions involved in affective processing, such as the amygdala and insula, resulting in attenuation of negative emotional expression (Ochsner & Gross, 2008). We might expect a similar pattern of activity in the brain if the anticipation of control is comparable to other antecedent-focused emotion-regulation strategies. At the same time, neuroimaging research has demonstrated the critical role of the striatum as a mediator of the relationship between PFC activity and successful regulation of negative affect (Hare, Tottenham, Davidson, Glover, & Casey, 2005; Salomons et al., 2010; Wager, Davidson, Hughes, Lindquist, & Ochsner, 2008). As a result, we might expect the striatum to also play a key role in the modulation of emotional responses to choice opportunity when anticipating either potentially positive or negative outcomes.

Although there are only a handful of human neuroimaging studies investigating perceived control to date, the preliminary evidence supports the hypotheses that expectations of control influence brain activity involved in affective and motivational processes important for emotion regulation. Several studies have demonstrated that the exercise of behavioral control over pain reduces brain activity in pain-processing regions (Salomons et al., 2004; Wiech et al., 2006). Moreover, recruitment of the PFC during the anticipation of control over pain may contribute to these controllability effects, resulting in reduced pain processing and subjective reports of pain. Controllability over pain and related reductions in anxiety also have been associated with increased activity in the ventral striatum (Salomons et al., 2010). A related study by Kerr, McLaren, Mathy, and Nitschke (2012) demonstrated the important role of the ventromedial PFC in the anticipation of control over an (p.157) aversive event. In that study, when snake phobic participants had the opportunity to terminate a threatening video (as opposed to having no control over viewing duration), they recruited greater activity in the ventromedial PFC, a region that had previously been identified in rodents as critical for supporting controllability effects on stress regulation (Maier, Amat, Baratta, Paul, & Watkins, 2006). These results relate well with ideas associated with sensorimotor attenuation (Voss, Ingram, Haggard, & Wolpert, 2006), which highlight reductions in neural responses in sensory areas following voluntary actions. Interestingly, the mere expectation of a potential action has a significant effect on somatosensory perception, even in the absence of the execution of behavioral command (Voss et al., 2008). Taken together, findings in controllability over pain and sensorimotor attenuation support the idea that similar neural mechanisms may be involved in the expectancy of control over behavior and stressors (i.e., pain).

The studies described above represent opportunities to exercise behavioral control, via escape, such that an action can lead to the avoidance of a negative outcome. These studies highlight the role of regions of the PFC in the anticipation and exercise of control. On the other hand, when people anticipate decisional control (when a choice must be made between options), the ventral striatum seems to play an important role (Leotti & Delgado, 2014), consistent with previous findings when anticipating decisional control leading to potential gains (Leotti & Delgado, 2011). However, the recruitment of the ventral striatum seems to be dependent on the context in which losses are incurred. Specifically, when losses were incurred in the context of potential simultaneous gains, there was tremendous inter-subject variability in the reported liking of choice, as well as ventral striatum recruitment during anticipation of choice. In fact, half of all participants in the study reported that they preferred to have no choice if choice could lead to potential losses, and in these participants, we observed greater ventral striatum activity when anticipating the no-choice condition. However, in a separate experiment, when we presented losses in the absence of gains, participants more consistently reported that they preferred choice, and showed greater choice-related BOLD activity in the ventral striatum. While the impact of context effects (e.g., framing effects or endowment effects) on loss aversion and decision-making are well-known (De Martino, Kumaran, Holt, & Dolan, 2009; De Martino, Kumaran, Seymour, & Dolan, 2006; Kahneman & Tversky, 2000; Tversky & Kahneman, 1981), the current results extend these findings to the prospect of exercising control. These findings suggest that the value of choice may depend on various situational contexts, as well as individual differences, which require further exploration in future human neuroscience research.

The Neural Basis Underlying the Experience of Control in the Human Brain

Figure 6.2 Overview of neural circuitry involved in experience of choice (adapted from Leotti, Iyengar, & Ochsner, 2010). The opportunity to exercise control over positive outcomes, via choice, recruits brain regions commonly involved in reward processing, such as the striatum, which comprises the caudate (CAU), putamen (PUT), and nucleus accumbens (nAcc). Other reward regions in the medial prefrontal cortex (MPFC) may also be preferentially activated to reflect increased reward value under increased self-relevance and general processing of contingency. In the context of potential threats, the opportunity to exercise control may recruit regions of the brain that are critical for adaptive emotion regulation, such as the lateral PFC, MPFC, and striatum, and consequently result in decreases in brain regions involved in negative emotional responses, such as the amygdala (AMY). When faced with negative outcomes, the value of choice may be sensitive to individual differences and situational context.

(p.158) In summary, the MPFC and striatum play an important role in the affective experience of control and choice, when anticipating both rewarding and aversive outcomes. The putative role of these regions with respect to choice is summarized in Figure 6.2. Recruitment of the MPFC may be important for increasing motivational salience in the context of positive outcomes, and may be responsible for reducing threat in the context of negative outcomes. While choice opportunity seems to be rewarding in the context of positive outcomes, personal and situational factors may influence choice value, and associated modulation of the striatum, when anticipating potentially negative outcomes.

Influences to the Value of Choice and Control

In addition to the context effects described above, there are many potential influences to the value of choice. A recent study by Wenke, Fleming, and Haggard (2010) observed that subliminally priming one particular response over an alternative enhanced the sense of control. Intuitively, the results may (p.159) seem to imply that subjects felt heightened levels of agency when in fact they were least in control of the outcome, or that the sense of control is heightened when there is less interference in the selection process. One interesting idea is that subliminal priming of a specific key response may cause greater fluency in behavior; that is, a subliminally primed stimulus that is compatible with a motor response promotes easier and faster action selection, which thereby increases feelings of control (Chambon et al., 2013). People believe they are responsible for either the improvement (in the case of positive outcomes) or worsening (for negative outcomes) of their well-being (Beattie, Baron, Hershey, & Spranca, 1994; Botti, McGill, & Iyengar, 2003; Burger, 1989). As a result, choice seems to magnify the anticipated outcome. Because choice opens up the possibility of making a bad decision, and being responsible for a negative outcome, people may find choice undesirable in the context of potentially negative outcomes. This discrepancy in the preference for choice is consistent with the “self-serving bias,” such that individuals take credit for successes but tend to attribute failures to external sources beyond one’s control (Brewin & Shapiro, 1984; D. T. Miller & Ross, 1975; Rotter, 1966). Interestingly, part of the dorsal striatum, the caudate nucleus, has been associated with the self-serving bias attribution (Blackwood, Bentall, Simmons, Murray, & Howard, 2003).

There is also a tendency for people to prefer to accept the status quo, as opposed to overtly acting in a way that could lead to an error and potential regret (Baron & Ritov, 1994; Feldman, Miyamoto, & Loftus, 1999; Samuelson & Zeckhauer, 1988; Tsiros & Mittal, 2000). This is consistent with research demonstrating that people experience greater elation following actions that lead to positive events and greater regret following actions that lead to negative outcomes (Kahneman & Tversky, 1982; Landman, 1987). Regions involved in anticipation of control, such as the anterior insula and MPFC, have also been linked to the anticipation of regret (Coricelli et al., 2005; Fleming, Thomas, & Dolan, 2010; Nicolle, Fleming, Bach, Driver, & Dolan, 2011). It is unclear, however, whether this ambivalence toward, or even dislike of, control applies only to circumstances of decisional control, or whether it also extends to opportunities to exercise behavioral control.

If we argue that choice itself is valuable, then we might assume that the more choice we have, the happier we will be. While this may be true to a degree (Fujiwara et al., 2013), an excessive amount of choice is burdensome. In fact, a surfeit of choice proves to be highly demotivating, a phenomenon that has been referred to as the “tyranny of choice” (Schwartz, 2000). Though people may initially find a larger choice assortment to be more attractive, they tend to be less satisfied with their choice or to defer choice altogether (Iyengar & Lepper, 2000). Too much choice may be cognitively burdensome (Dhar, 1997; Iyengar & Lepper, 2000; Shiv & Fedorikhin, 1999) and depleting of self-control (p.160) resources (Vohs et al., 2008), particularly when it is difficult to differentiate between equally attractive options (Fasolo, Hertwig, Huber, & Ludwig, 2009; Sela, Berger, & Liu, 2009), which may engender feelings of dissatisfaction in choice and loss of confidence (Iyengar, Wells, & Schwartz, 2006) or buyer’s remorse and regret (Inman & Zeelenberg, 2002; Sagi & Friedland, 2007; Tsiros & Mittal, 2000). While this research suggests that excessive choice may be undesirable, at the same time, other research has demonstrated that people will work very hard to leave options open to them, for fear of losing additional choice (Shin & Ariely, 2004). It is this conflict between desire and disdain for choice that is appropriately coined the “paradox of choice” (Schwartz, 2009).

Nonetheless, there is a difference between having choices and making choices. Research suggests that when it comes to difficult decisions (e.g., medical decisions), whereas many people do not like making the final decision, the large majority of individuals prefer to have a choice, at the very least (Levinson, Kao, Kuby, & Thisted, 2005; Ogden, Daniells, & Barnett, 2008). However, choosing may be more stressful when there is insufficient information to make an informed selection (Paterson & Neufeld, 1995). At these times, it may be preferable to defer choice to someone with greater expertise, such as a doctor (Levinson et al., 2005). Individuals may still experience a sense of control, however, if they believe someone is acting in their best interest (i.e., control by proxy).

Other social factors may also contribute to the value of choice. An interesting study by Ybarra and colleagues (2012) suggests that security associated with positive social relationships may decrease the value of choice. In one experiment, participants were instructed to write about either a supportive or unsupportive social relationship, and then were asked to engage in a decision-making task in which they could purchase a cell phone. Participants decided whether they wanted to be assigned a cell phone (default mode option), or they could pay a small incremental fee to have additional options made available to them. Participants primed to think about a supportive relationship were less willing to pay for additional choice. Furthermore, a second experiment revealed that feelings of calmness and security associated with the social relationship were significant mediators of the observed decreased choice preference (Ybarra et al., 2012). One explanation for this finding is that feelings of calmness and security may result in a reduced desire to explore through choice.

Finally, there are individual differences that may contribute to the affective experience of choice. Differences in decision-making strategies may influence the value of choice and control. Individuals who are maximizers (i.e., those who look to find the “best” option available (Iyengar et al., 2006; Schwartz et al., 2002), for example, tend to experience greater dissatisfaction in choice (p.161) and greater regret than do people who are satisfiers (i.e., those who choose options that are “good enough”). Culturally motivated beliefs may also influence the value of choice, such that individuals with an independent focus (commonly associated with Western cultures) may prefer to make their own choices, whereas individuals with an interdependent focus (commonly associated with Eastern cultures) may prefer choices to be made by benevolent others (Iyengar & Lepper, 1999). More generally, individual differences in optimism, self-esteem, and mood have been linked to the tendency to demonstrate an illusion of control (Alloy & Abramson, 1982; Fontaine, Manstead, & Wagner, 1993; Taylor & Brown, 1988).

With respect to the brain, there are a few studies that highlight the existence of individual differences in the experience of control. For example, as we previously mentioned, individuals who reported liking choice better than no choice demonstrated greater recruitment of the striatum (Leotti & Delgado, 2014). Relatedly, individual differences in preference changes following choice (e.g., liking a vacation spot better after you chose it) correlate with extent of recruitment of the ventral striatum (Sharot, De Martino, & Dolan, 2009; Sharot, Shiner, & Dolan, 2010). Furthermore, pre-existing beliefs about one’s ability to exercise control over life events have been linked to the recruitment of PFC regions during controllable pain (Salomons et al., 2007; Wiech et al., 2006). An individual’s tendency to avoid cognitive demand in decision-making is associated with greater activity in the lateral PFC (McGuire & Botvinick, 2010), and may contribute to the value of choice when cognitive resources are taxed. Additional work in this area is necessary to better understand how individual differences, as well as situational factors, may contribute to behavioral and brain correlates of the affective experience of choice and control.

The Significance of Understanding Control and Its Neural Basis

Further research investigating the affective experience of control and its neural correlates is critical for understanding healthy functioning as well as maladaptive behavior. Struggles for control are at the core of many psychiatric disorders (Beck, 1976; Mansell, 2005; Ryan, Deci, & Grolnick, 1995; Shapiro et al., 1996; Taylor & Brown, 1988, 1994), including anxiety disorders (Abramson, Garber, & Seligman, 1980; Bandura, 1988; Reuven-Magril, Dar, & Liberman, 2008), depression (Deutsch, 1978; Mathews, 1977; Schwartz et al., 2002; Seligman, 1975), drug and alcohol addictions (Bandura, 1999; Shapiro Jr. & Zifferblatt, 1976), eating disorders (Fairburn, Shafran, & Cooper, 1999; Favaro & Santonastaso, 1998; Jeffrey, 1987; King, 1989; Shapiro Jr, Blinder, Hagman, & Pituck, 1993), and self-injurious behavior (Favazza, 1989; Herpertz, 1995).

(p.162) Beliefs about self-efficacy not only influence affect and behavior, but also physical health. Control has been shown to reduce stress hormone production following a stressful procedure (Abelson et al., 2008). Individuals with low perceived self-efficacy report and demonstrate lesser functional capacities as a result of chronic pain (Lackner & Corosella, 1999), are more likely to frequent the emergency room for asthmatic symptoms (Nouwen, Freeston, Labbe, & Boulet, 1999), are less likely to follow prescribed rehabilitation guidelines following a cardiac event (Lau-Walker, 2004), and are more likely to relapse following treatment for substance dependence (Litt, Kadden, Kabela-Cormier, & Petry, 2008; Wilson, 1987). Interestingly, while a lack of control is detrimental to adaptive functioning, overestimating one’s own control, or experiencing an “illusion of control” (Langer, 1975), may be somewhat protective from depression (Alloy & Abramson, 1979; Presson & Benassi, 2003).

When animals or humans perceive a sense of lack of control over a stressor, they exhibit exaggerated fear responses, increase in stress levels, and greater negative affect (Amat et al., 2005; Maier & Watkins, 2005; Mohr et al., 2012). Indeed, uncontrollability over stressful stimuli has been linked with a variety of negative consequences such as negative emotions and harmful psychological and motivational side effects (Amat et al., 2005; Jensen & Karoly, 1991; Jensen et al., 1991; Maier & Watkins, 2005), suggesting that perceived control might be important for regulating negative emotional responses (Delgado et al., 2008, 2009). More recently, neuroimaging studies have supported this idea by showing decreases in negative affect in a variety of tasks as a function of control, which is coupled with the modulation of brain regions involved in emotion regulation or control. For instance, when subjects were provided with either an uncontrollable or controllable cue indicating the duration of heat (long or short) to be applied to their forearms, brain regions associated with pain were significantly more activated during uncontrollable compared to controllable conditions (Salomons et al., 2004). Moreover, self-controlled stimulation of noxious stimuli induced less pain and anxiety based on subjective ratings, and pain intensity negatively correlated with enhanced neural processing in lateral prefrontal regions involved in emotion regulation (Wiech et al., 2006). Finally, perceived control of expected negative affect due to pain (Delgado et al., 2008, 2009; Jensen & Karoly, 1991; Jensen et al., 2003) or fear (Delgado et al., 2008; 2009) is decreased when participants perceive control, which can recruit striatum mechanisms when participants have the opportunity to avoid a negative outcome (LeDoux & Gorman, 2014; Jensen et al., 2003; Delgado et al., 2009) and more cortical-based mechanisms when using emotion regulation strategies (e.g., Kalisch et al., 2005; Delgado et al., 2008; for a review, see Ochsner & Gross, 2005).

(p.163) If disturbances to perceived control negatively influence psychosocial well-being, then methods to adaptively restore appropriate levels of control and feelings of self-efficacy should be an effective therapeutic technique, and indeed form a central tenet of various schools of psychotherapy (Ryan, Lynch, Vansteenkiste, & Deci, 2011; Strupp, 1970). Provision of choice may be one such way to reduce stress and bolster feelings of control. In fact, we know that when patients have control over analgesic administration (as opposed to nurse-dependent analgesic administration), they tend to consume less analgesic medicine, report less pain, and experience greater satisfaction in treatment (Ballantyne et al., 1993; Shiloh et al., 2003). Participants who are given choice over treatment show enhanced placebo analgesia as compared to participants who are given no choice over treatment (Geers & Rose, 2011; Geers, Rose, Fowler, Rasinski, Brown, & Helfer, 2013; Rose, Geers, Rasinski, & Fowler, 2012). Even simply giving participants a choice in coping strategies had been shown to improve tolerance for experimentally induced pain (Rokke & al’Absi, 1992; Rokke, Fleming-Ficek, Siemens, & Hegstad, 2004; Rokke & Lall, 1992). Additional research is necessary to extend these findings to address symptoms related to mood and anxiety, as well as maladaptive behaviors.

Preliminary findings suggest that choice is an important tool for shaping affective and behavioral responses to stressful events. However, we still have much to learn about how the experience of exercising control, through choice, may influence self-efficacy beliefs, and how this influences our neural responses to future opportunities to exercise control. Individuals with low perceived control demonstrate an impaired ability to control destructive thought patterns and to produce desired results (Bandura & Jourden, 1991). An inability to exercise control will lead to feelings of helplessness (Beck, Emery, & Greenberg, 1985) and depressed mood states, further reinforcing feelings of inefficacy (Kavanagh & Bower, 1985). As a result, individuals can experience “learned helplessness” (Seligman, 1972), the phenomenon that occurs when an individual has the available resources to cope with a stressor, but is unable to effectively use those resources as a result of previous unsuccessful attempts to exercise control over a similar stressor. This phenomenon has been extensively demonstrated in animal research (Maier & Seligman, 1976; Maier & Watkins, 2005), but also has been observed in humans (Hiroto & Seligman, 1975; I. W. Miller & Norman, 1979) and is predictive of depressive symptoms (Alloy et al., 1999). Rodents typically demonstrate stress-related behavior (e.g., freezing), and increased activity in brainstem nuclei mediating escape behavior when faced with uncontrollable stress (inescapable shock) but not when faced with controllable stress (escapable shock; Maier & Watkins, 2005). Yet, when rodents are faced with controllable stress after previously (p.164) having been exposed to uncontrollable stress, they fail to engage in escape behavior, demonstrating learned helplessness (Maier & Seligman, 1976). Interestingly, however, rodents respond to inescapable shock as if it were escapable, if they previously had experience with escapable shock, suggesting that previous experience with controllable stress provides some future resilience (Amat et al., 2005; Amat, Paul, Zarza, Watkins, & Maier, 2006; Maier et al., 2006).

The animal literature has made significant contributions to our understanding of the critical role of the MPFC to the experience of learned helplessness (Maier et al., 2006). In humans, research has demonstrated that controllability effects depend on regions of the MPFC (Kerr et al., 2012). Additionally, self-reports of helplessness in chronic pain patients are inversely related to cortical thickness of the mid-cingulate, which may explain the reduction in escape-motivated behavior in learned helplessness (Salomons et al., 2012). Moreover, the research outlined in this chapter suggests that brain regions involved in affective and motivational processes, such as the MPFC and the ventral striatum, are important in the affective appraisal of choice opportunity, as a vehicle for exercising control. These regions are also known to play critical roles in successful affective regulation (Delgado, Jou, Ledoux, & Phelps, 2009; Lieberman et al., 2007; Ochsner & Gross, 2005; Wager et al., 2008). Thus, the recruitment of these regions in the anticipation of control may suggest that choice opportunity involves processes important to behavioral and cognitive emotional regulation strategies.


Collectively, the findings suggest that a sense of agency, and opportunities to exercise control, through choice, are highly adaptive. Recent human neuroscience research suggests that the opportunity to control is desirable, and recruits brain circuitry involved in affective and motivational processes. Choice modulates this reward-related brain circuitry both when approaching positive outcomes and when avoiding negative outcomes. However, the value of exercising control may vary across individuals due to person-specific variables and may be sensitive to situational factors (e.g., context, type of control). Opportunities for control also recruit brain regions involved in the successful regulation of affect, implying that choice may be a powerful tool for regulating responses to both appetitive and aversive stimuli. Future human research will need to address whether simple provisions of choice can be a helpful tool for augmenting feelings of self-efficacy and agency, to prevent learned helplessness and to enhance coping under stressors that are both controllable and uncontrollable.

(p.165) Acknowledgments

This work was supported by funding from the National Institute on Drug Abuse to M.R.D. (DA027764) and a fellowship to L.A.L (F32-DA027308).


Bibliography references:

Abelson, J. L., Khan, S., Liberzon, I., Erickson, T. M., & Young, E. A. (2008). Effects of perceived control and cognitive coping on endocrine stress responses to pharmacological activation. Biological Psychiatry, 64(8), 701–707.

Abramson, L. Y., Garber, J., & Seligman, M. E. P. (1980). Learned helplessness in humans: an attributional analysis. Human Helplessness: Theory and Applications, 3, 34.

Alloy, L. B., & Abramson, L. Y. (1979). Judgment of contingency in depressed and non-depressed students: sadder but wiser? Journal of Experimental Psychology: General, 108(4), 441–485.

Alloy, L. B., & Abramson, L. Y. (1982). Learned helplessness, depression, and the illusion of control. Journal of Personality and Social Psychology, 42(6), 1114.

Alloy, L. B., Abramson, L. Y., Whitehouse, W. G., Hogan, M. E., Tashman, N. A., Steinberg, D. L., et al. (1999). Depressogenic cognitive styles: predictive validity, information processing and personality characteristics, and developmental origins. Behaviour Research and Therapy, 37(6), 503–531.

Amat, J., Baratta, M. V., Paul, E., Bland, S. T., Watkins, L. R., & Maier, S. F. (2005). Medial prefrontal cortex determines how stressor controllability affects behavior and dorsal raphe nucleus. Nature Neuroscience, 8(3), 365–371.

Amat, J., Paul, E., Zarza, C., Watkins, L. R., & Maier, S. F. (2006). Previous experience with behavioral control over stress blocks the behavioral and dorsal raphe nucleus activating effects of later uncontrollable stress: role of the ventral medial prefrontal cortex. Journal of Neuroscience, 26(51), 13264–13272.

Averill, J. R. (1973). Personal control over aversive stimuli and its relationship to stress. Psychological Bulletin, 80(4), 286.

Ballantyne, J. C., Carr, D. B., Chalmers, T. C., Dear, K. B., Angelillo, I. F., & Mosteller, F. (1993). Postoperative patient-controlled analgesia: meta-analyses of initial randomized control trials. Journal of Clinical Anesthesia, 5(3), 182–193.

Balleine, B. W., Delgado, M. R., & Hikosaka, O. (2007). The role of the dorsal striatum in reward and decision-making. Journal of Neuroscience, 27(31), 8161–8165.

Balleine, B. W., & O’Doherty, J. P. (2009). Human and rodent homologies in action control: corticostriatal determinants of goal-directed and habitual action. Neuropsychopharmacology, 35(1), 48–69.

Bandura, A. (1988). Self-efficacy conception of anxiety. Anxiety Research, 1(2), 77–98.

Bandura, A. (1997). Self-efficacy: the exercise of control. New York: Freeman.

Bandura, A. (1999). A sociocognitive analysis of substance abuse: an agentic perspective. Psychological Science, 10(3), 214–217.

Bandura, A., Caprar, G. V., Barbaranelli, C., Gerbino, M., & Pastorelli, C. (2003). Role of affective self-regulatory efficacy in diverse spheres of psychosocial functioning. Child Development, 74(3), 769–782. (p.166)

Bandura, A., & Jourden, F. J. (1991). Self-regulatory mechanisms governing the impact of social-comparison on complex decision making. Journal of Personality and Social Psychology, 55, 479–488.

Bandura, A., Taylor, C. B., Williams, S. L., Mefford, I. N., & Barchas, J. D. (1985). Catecholamine secretion as a function of perceived coping self-efficacy. Journal of Consulting and Clinical Psychology, 53, 406–414.

Bandura, A., & Wood, R. E. (1989). Effect of perceived controllability and performance standards on self-regulation of complex decision-making. Journal of Personality and Social Psychology, 56, 805–814.

Baron, J., & Ritov, I. (1994). Reference points and omission bias. Organizational Behavior and Human Decision Processes, 59, 475–498.

Baumeister, R. F., Heatherton, T. F., & Tice, D. M. (1994). Losing control: how and why people fail at self-regulation. San Diego, CA: Academic Press.

Beattie, J., Baron, J., Hershey, J. C., & Spranca, M. D. (1994). Psychological determinants of decision attitude. Journal of Behavioral Decision Making, 7(2), 129–144.

Beck, A. T. (1976). Cognitive therapy and the emotional disorders. New York: International Universities Press.

Beck, A. T., Emery, G., & Greenberg, R. L. (1985). Anxiety disorders and phobias. New York: Basic Books.

Berridge, K. C., & Kringelbach, M. L. (2008). Affective neuroscience of pleasure: reward in humans and animals. Psychopharmacology (Berlin), 199(3), 457–480.

Bjork, J. M., & Hommer, D. W. (2007). Anticipating instrumentally obtained and passively-received rewards: a factorial fMRI investigation. Behavioural Brain Research, 177(1), 165–170.

Blackwood, N. J., Bentall, R. P., Simmons, A., Murray, R. M., & Howard, R. J. (2003). Self-responsibility and the self-serving bias: an fMRI investigation of causal attributions. NeuroImage, 20(2), 1076–1085.

Botti, S., McGill, A., & Iyengar, S. S. (2003). Preference for control and its effect on the evaluation of consumption experiences. Advances in Consumer Research, 30, 127–128.

Bown, N. J., Read, D., & Summers, B. (2003). The lure of choice. Journal of Behavioral Decision Making, 16, 297–308.

Brehm, J. W. (1956). Post-decision changes in the desirability of alternatives. Journal of Abnormal and Social Psychology, 52, 384–389.

Brewin, C. R., & Shapiro, D. A. (1984). Beyond locus of control: attribution of responsibility for positive and negative outcomes. British Journal of Psychology, 75(1), 43–49.

Burger, J. (1989). Negative reactions to increases in perceived personal control. Journal of Personality and Social Psychology, 56(2), 246–256.

Chambon, V., Wenke, D., Fleming, S. M., Prinz, W., & Haggard, P. (2013). An online neural substrate for sense of agency. Cerebral Cortex, 23(5), 1031–1037.

Cockburn, J., Collins, A. G. E., & Frank, M. J. (2014). A reinforcement learning mechanism responsible for the valuation of free choice. Neuron, 83(3), 551–557.

Corah, N. L., & Boffa, J. (1970). Perceived control, self-observation, and response to aversive stimulation. Journal of Personality and Social Psychology, 16(1), 1–4.

Cordova, D. I., & Lepper, M. R. (1996). Intrinsic motivation and the process of learning: beneficial effects of contextualization, personalization, and choice. Journal of Educational Psychology, 88(4), 715–730. (p.167)

Coricelli, G., Critchley, H. D., Joffily, M., O’Doherty, J. P., Sirigu, A., & Dolan, R. J. (2005). Regret and its avoidance: a neuroimaging study of choice behavior. Nature Neuroscience, 8(9), 1255–1262.

De Martino, B., Kumaran, D., Holt, B., & Dolan, R. J. (2009). The neurobiology of reference-dependent value computation. Journal of Neuroscience, 29(12), 3833–3842.

De Martino, B., Kumaran, D., Seymour, B., & Dolan, R. J. (2006). Frames, biases, and rational decision-making in the human brain. Science, 313, 684–687.

Delgado, M. R. (2007). Reward-related responses in the human striatum. Annals of the New York Academy of Sciences, 1104, 70–88.

Delgado, M. R., & Dickerson, K. C. (2012). Reward-related learning via multiple memory systems. Biological Psychiatry, 72(2), 134–141.

Delgado, M. R., Jou, R. L., Ledoux, J. E., & Phelps, E. A. (2009). Avoiding negative outcomes: tracking the mechanisms of avoidance learning in humans during fear conditioning. Frontiers in Behavioral Neuroscience, 3, 33.

Delgado, M. R., Locke, H. M., Stenger, V. A., & Fiez, J. A. (2003). Dorsal striatum responses to reward and punishment: effects of valence and magnitude manipulations. Cognitive, Affective, & Behavioral Neuroscience, 3(1), 27–38.

Delgado, M. R., Nearing, K. I., LeDoux, J. E., & Phelps, E. A. (2008). Neural circuitry underlying the regulation of conditioned fear and its relation to extinction. Neuron, 59, 829–838.

Delgado, M. R., Nystrom, L. E., Fissell, C., Noll, D. C., & Fiez, J. A. (2000). Tracking the hemodynamic responses to reward and punishment in the striatum. Journal of Neurophysiology, 84(6), 3072–3077.

Delgado, M. R., Stenger, V. A., & Fiez, J. A. (2004). Motivation-dependent responses in the human caudate nucleus. Cerebral Cortex, 14(9), 1022–1030.

Deutsch, A.-M. (1978). Self-control and depression: an appraisal. Behavior Therapy, 9(3), 410–414.

Dhar, R. (1997). Consumer preference for a no-choice option. Journal of Consumer Research, 24(2), 215–231.

Doll, B. B., Simon, D. A., & Daw, N. D. (2012). The ubiquity of model-based reinforcement learning. Current Opinion in Neurobiology, 22(6), 1075–1081.

Egan, L. C., Santos, L. R., & Bloom, P. (2007). The origins of cognitive dissonance: evidence from children and monkeys. Psychological Science, 18(11), 978–983.

Eitam, B., Kennedy, P. M., & Higgins, E. T. (2013). Motivation from control. Experimental Brain Research, 229(3), 475–484.

Elliott, R., Newman, J. L., Longe, O. A., & William Deakin, J. F. (2004). Instrumental responding for rewards is associated with enhanced neuronal response in subcortical reward systems. NeuroImage, 21(3), 984–990.

Fairburn, C. G., Shafran, R., & Cooper, Z. (1999). A cognitive behavioural theory of anorexia nervosa. Behaviour Research and Therapy, 37(1), 1–13.

Fasolo, B., Hertwig, R., Huber, M., & Ludwig, M. (2009). Size, entropy, and density: what is the difference that makes the difference between small and large real‐world assortments? Psychology & Marketing, 26(3), 254–279.

Favaro, A., & Santonastaso, P. (1998). Impulsive and compulsive self-injurious behavior in bulimia nervosa: prevalence and psychological correlates. The Journal of Nervous and Mental Disease, 186(3), 157–165. (p.168)

Favazza, A. R. (1989). Why patients mutilate themselves. Hospital and Community Psychiatry, 40(2), 137–145.

Feldman, J., Miyamoto, J. M., & Loftus, E. F. (1999). Are actions regretted more than inactions? Organizational Behavior and Human Decision Processes, 78, 232–255.

Fleming, S. M., Thomas, C. L., & Dolan, R. J. (2010). Overcoming status quo bias in the human brain. Proceedings of the National Academy of Sciences USA, 107(13), 6005–6009.

Fontaine, K. R., Manstead, A. S., & Wagner, H. (1993). Optimism, perceived control over stress, and coping. European Journal of Personality, 7(4), 267–281.

Frank, M. J., & Fossella, J. A. (2011). Neurogenetics and pharmacology of learning, motivation, and cognition. Neuropsychopharmacology, 36(1), 133–152.

Fujiwara, J., Usui, N., Park, S. Q., Williams, T., Iijima, T., Taira, M., Tsutsui, K., & Tobler, P. N. (2013). Value of freedom to choose encoded by the human brain. Journal of Neurophysiology, 110(8), 1915.

Geer, J. H., & Maisel, E. (1972). Evaluating the effects of the prediction-control confound. Journal of Personality and Social Psychology, 23(3), 314.

Geers, A., & Rose, J. (2011). Treatment choice and placebo expectation effects. Social and Personality Psychology Compass, 5(10), 734–750.

Geers, A. L., Rose, J. P., Fowler, S. L., Rasinski, H. M., Brown, J. A., & Helfer, S. G. (2013). Why does choice enhance treatment effectiveness? Using placebo treatments to demonstrate the role of personal control. Journal of Personality and Social Psychology, 105(4), 549–566.

Glass, D. C., Reim, B., & Singer, J. E. (1971). Behavioral consequences of adaptation to controllable and uncontrollable noise. Journal of Experimental Psychology: General, 7, 244–257.

Glass, D. C., Singer, J. E., & Friedman, L. N. (1969). Psychic costs of adaptation to an environmental stressor. Journal of Personality and Social Behavior, 12, 200–210.

Grolnick, W. S., & Ryan, R. M. (1987). Autonomy in children’s learning: an experimental and individual difference investigation. Journal of Personality and Social Psychology, 52(5), 890.

Gunnar-vonGnechten, M. R. (1978). Changing a frightening toy into a pleasant toy by allowing the infant to control its actions. Developmental Psychology, 14, 147–152.

Haber, S. N., & Knutson, B. (2010). The reward circuit: linking primate anatomy and human imaging. Neuropsychopharmacology, 35(1), 4–26.

Hare, T. A., Tottenham, N., Davidson, M. C., Glover, G. H., & Casey, B. J. (2005). Contributions of amygdala and striatal activity in emotion regulation. Biological Psychiatry, 57(6), 624–632.

Heatherton, T. F., & Wagner, D. D. (2011). Cognitive neuroscience of self-regulation failure. Trends in Cognitive Sciences, 15(3), 132–139.

Heatherton, T. F., Wyland, C. L., Macrae, C. N., Demos, K. E., Denny, B. T., & Kelley, W. M. (2006). Medial prefrontal activity differentiates self from close others. Social Cognitive and Affective Neuroscience, 1(1), 18–25.

Herpertz, S. (1995). Self-injurious behaviour: psychopathological and nosological characteristics in subtypes of self-injurers. Acta Psychiatrica Scandinavica, 91(1), 57–68. (p.169)

Hiroto, D. S., & Seligman, M. E. (1975). Generality of learned helplessness in man. Journal of Personality and Social Psychology, 31(2), 311.

Holden, G. (1992). The relationship of self-efficacy appraisals to subsequent health related outcomes: a meta-analysis. Social Work in Health Care, 16(1), 53–93.

Hsu, M., Bhatt, M., Adolphs, R., Tranel, D., & Camerer, C. F. (2005). Neural systems responding to degrees of uncertainty in human decision-making. Science, 310(5754), 1680–1683.

Inman, J. J., & Zeelenberg, M. (2002). Regret in repeat purchase versus switching decisions: the attenuating role of decision justifiability. Journal of Consumer Research, 29, 116–128.

Iyengar, S. S., & Lepper, M. (2000). When choice is demotivating: can one desire too much of a good thing? Journal of Personality and Social Psychology, 79, 995–1006.

Iyengar, S. S., & Lepper, M. R. (1999). Rethinking the value of choice: a cultural perspective on intrinsic motivation. Journal of Personality and Social Psychology, 76(3), 349.

Iyengar, S. S., Wells, R. E., & Schwartz, B. (2006). Doing better but feeling worse: looking for the “best” job undermines satisfaction. Psychological Science, 17(2), 143–150.

Jeffrey, R. W. (1987). Behavioral treatment of obesity. Annals of Behavioral Medicine, 9(1), 20–24.

Jensen, M. P., & Karoly, P. (1991). Control beliefs, coping efforts, and adjustment to chronic pain. Journal of Consulting and Clinical Psychology, 59(3), 431.

Jensen, M. P., Turner, J. A., Romano, J. M., & Karoly, P. (1991). Coping with chronic pain: a critical review of the literature. Pain, 47(3), 249–283.

Jensen, J., McIntosh, A. R., Crawley, A. P., Mikulis, D. J., Remington, G., & Kapur, S. (2003). Direct activation of the ventral striatum in anticipation of aversive stimuli. Neuron, 40(6), 1251–1257.

Johnson, S. C., Baxter, L. C., Wilder, L. S., Pipe, J. G., Heiserman, J. E., & Prigatano, G. P. (2002). Neural correlates of self‐reflection. Brain, 125(8), 1808–1814.

Johnson, S. C., Schmitz, T. W., Kawahara-Baccus, T. N., Rowley, H. A., Alexander, A. L., Lee, J., et al. (2005). The cerebral response during subjective choice with and without self-reference. Journal of Cognitive Neuroscience, 17(12), 1897–1906.

Johnstone, T., van Reekum, C. M., Urry, H. L., Kalin, N. H., & Davidson, R. J. (2007). Failure to regulate: counterproductive recruitment of top-down prefrontal-subcortical circuitry in major depression. Journal of Neuroscience, 27(33), 8877–8884.

Kahneman, D., & Tversky, A. (1982). The psychology of preferences. Scientific American, 246(1), 160–173.

Kahneman, D., & Tversky, A. (2000). Choices, values, and frames. Cambridge: Cambridge University Press.

Kalisch, R., Wiech, K., Critchley, H. D., Seymour, B., O’Doherty, J. P., Oakley, D. A., et al. (2005). Anxiety reduction through detachment: Subjective, physiological, and neural effects. Journal of Cognitive Neuroscience, 17, 874–883.

Kamen-Siegel, L., Rodin, J., Seligman, M. E., & Dwyer, J. (1991). Explanatory style and cell-mediated immunity in elderly men and women. Health Psychology, 10(4), 229.

Kanfer, F., & Seider, M. L. (1973). Self-control: factors enhancing tolerance of noxious stimulation. Journal of Personality and Social Behavior, 25, 381. (p.170)

Kavanagh, D. J., & Bower, G. H. (1985). Mood and self-efficacy: impact of joy and sadness on perceived capabilities. Cognitive Therapy and Research, 9, 507–525.

Kelley, W. M., Macrae, C. N., Wyland, C. L., Caglar, S., Inati, S., & Heatherton, T. F. (2002). Finding the self? An event-related fMRI study. Journal of Cognitive Neuroscience, 14(5), 785–794.

Kerr, D. L., McLaren, D. G., Mathy, R. M., & Nitschke, J. B. (2012). Controllability modulates the anticipatory response in the human ventromedial prefrontal cortex. Frontiers in Emotion Science, 3(557), 1–11.

King, M. (1989). Locus of control in women with eating pathology. Psychological Medicine, 19, 183–187.

Kirsch, P., Schienle, A., Stark, R., Sammer, G., Blecker, C., Walter, B., et al. (2003). Anticipation of reward in a nonaversive differential conditioning paradigm and the brain reward system: an event-related fMRI study. NeuroImage, 20(2), 1086–1095.

Knutson, B., Adams, C. M., Fong, G. W., & Hommer, D. (2001). Anticipation of increasing monetary reward selectively recruits nucleus accumbens. Journal of Neuroscience, 21(16), RC159.

Knutson, B., & Cooper, J. C. (2005). Functional magnetic resonance imaging of reward prediction. Current Opinion in Neurology, 18(4), 411–417.

Knutson, B., Taylor, J., Kaufman, M., Peterson, R., & Glover, G. (2005). Distributed neural representation of expected value. Journal of Neuroscience, 25(19), 4806–4812.

Lackner, J. M., & Corosella, A. M. (1999). The relative influence of perceived pain control, anxiety, and functional self efficacy on spinal function among patients with chronic low back pain. Spine, 24(21), 2254–2261.

Landman, J. (1987). Regret and elation following action and inaction: affective responses to positive versus negative outcomes. Personality and Social Psychology Bulletin, 13, 524–536.

Langer, E. J. (1975). The illusion of control. Journal of Personality and Social Psychology, 32(2), 311–328.

Langer, E. J., & Rodin, J. (1976). The effects of choice and enhanced personal responsibility for the aged: a field experiment in an institutional setting. Journal of Personality and Social Psychology, 34(2), 191–198.

Lau-Walker, M. (2004). Relationship between illness representation and self-efficacy. Journal of Advanced Nursing, 48(3), 216–225.

LeDoux, J. E., & Gorman, J. M. (2014). A call to action: overcoming anxiety through active coping.

Leotti, L. A., & Delgado, M. R. (2011a). The inherent reward of choice. Psychological Science, 22(10), 1310–1318.

Leotti, L. A., & Delgado, M. R. (2014). The value of exercising control over monetary gains and losses. Psychological Science, 25(2), 596–604.

Leotti, L. A., Iyengar, S. S., & Ochsner, K. N. (2010). Born to choose: the origins and value of the need for control. Trends in Cognitive Sciences, 14(10), 457–463.

Levinson, W., Kao, A., Kuby, A., & Thisted, R. A. (2005). Not all patients want to participate in decision making. Journal of General Internal Medicine, 20(6), 531–535.

Levy, R., & Dubois, B. (2006). Apathy and the functional anatomy of the prefrontal cortex-basal ganglia circuits. Cerebral Cortex, 16(7), 916–928. (p.171)

Lieberman, M. D., Eisenberger, N. I., Crockett, M. J., Tom, S. M., Pfeifer, J. H., & Way, B. M. (2007). Putting feelings into words: affect labeling disrupts amygdala activity in response to affective stimuli. Psychological Science, 18(5), 421–428.

Lieberman, M. D., Ochsner, K. N., Gilbert, D. T., & Schacter, D. L. (2001). Do amnesics exhibit cognitive dissonance reduction? The role of explicit memory and attention in attitude change. Psychological Science, 12(2), 135–140.

Litt, M. D., Kadden, R. M., Kabela-Cormier, E., & & Petry, N. M. (2008). Coping skills training and contingency management treatments for marijuana dependence: exploring mechanisms of behavior change. Addiction, 103(4), 638–648.

Higgins, E. T. (2012). Beyond pleasure and pain: How motivation works. Oxford University Press.

Maier, S. F., Amat, J., Baratta, M. V., Paul, E., & Watkins, L. R. (2006). Behavioral control, the medial prefrontal cortex, and resilience. Dialogues in Clinical Neuroscience, 8(4), 397–406.

Maier, S. F., Laudenslager, M. L., & Ryan, S. M. (1985). Stressor controllability, immune function, and endogenous opiates. In F. R. Brush & J. B. Overmier (Eds.), Affect, conditioning, and cognition: essays on the determinants of behavior (pp. 183–201). Hillsdale, NJ: Erlbaum Associates.

Maier, S. F., & Seligman, M. E. (1976). Learned helplessness: theory and evidence. Journal of Experimental Psychology: General, 105(1), 3.

Maier, S. F., & Watkins, L. R. (2005). Stressor controllability and learned helplessness: the roles of the dorsal raphe nucleus, serotonin, and corticotropin-releasing factor. Neuroscience & Biobehavioral Reviews, 29(4), 829–841.

Mansell, W. (2005). Control theory and psychopathology: an integrative approach. Psychology and Psychotherapy: Theory, Research and Practice, 78(2), 141–178.

Mathews, C. O. (1977). A review of behavioral theories of depression and a self-regulation model for depression. Psychotherapy: Theory, Research & Practice, 14(1), 79.

McClure, S. M., Berns, G. S., & Montague, P. R. (2003). Temporal prediction errors in a passive learning task activate human striatum. Neuron, 38(2), 339–346.

McGuire, J. T., & Botvinick, M. M. (2010). Prefrontal cortex, cognitive control, and the registration of decision costs. Proceedings of the National Academy of Sciences USA, 107(17), 7922–7926.

Middleton, F. A., & Strick, P. L. (2000). Basal ganglia and cerebellar loops: motor and cognitive circuits. Brain Research Reviews, 31(2), 236–250.

Miller, D. T., & Ross, M. (1975). Self-serving biases in attribution of causality: fact or fiction? Psychological Bulletin, 82, 213–225.

Miller, I. W., & Norman, W. H. (1979). Learned helplessness in humans: a review and attribution-theory model. Psychological Bulletin, 86(1), 93.

Mohr, C., Leyendecker, S., Petersen, D., & Helmchen, C. (2012). Effects of perceived and exerted pain control on neural activity during pain relief in experimental heat hyperalgesia: a fMRI study. European Journal of Pain, 16(4), 496–508.

Multon, K. D., Brown, S. D., & Lent, R. W. (1991). Relation of self-efficacy beliefs to academic outcomes: A meta-analytic investigation. Journal of Counseling Psychology, 38(1), 30. (p.172)

Nicolle, A., Fleming, S. M., Bach, D. R., Driver, J., & Dolan, R. J. (2011). A regret-induced status quo bias. Journal of Neuroscience, 31(9), 3320–3327.

Nouwen, A., Freeston, M. H., Labbe, R., & Boulet, L.-P. (1999). Psychological factors associated with emergency room visits among asthmatic patients. Behavior Modification, 23(2), 217–233.

O’Doherty, J. P. (2004). Reward representations and reward-related learning in the human brain: insights from neuroimaging. Current Opinion in Neurobiology, 14(6), 769–776.

O’Doherty, J. P. (2007). Lights, camembert, action! The role of human orbitofrontal cortex in encoding stimuli, rewards, and choices. Annals of the New York Academy of Sciences, 1121(1), 254–272.

O’Doherty, J. P., Critchley, H., Deichmann, R., & Dolan, R. J. (2003). Dissociating valence of outcome from behavioral control in human orbital and ventral prefrontal cortices. Journal of Neuroscience, 23(21), 7931–7939.

O’Doherty, J. P., Dayan, P., Friston, K., Critchley, H., & Dolan, R. J. (2003). Temporal difference models and reward-related learning in the human brain. Neuron, 38(2), 329–337.

O’Doherty, J. P., Dayan, P., Schultz, J., Deichmann, R., Friston, K., & Dolan, R. J. (2004). Dissociable roles of ventral and dorsal striatum in instrumental conditioning. Science, 304(5669), 452–454.

O’Doherty, J. P., Deichmann, R., Critchley, H. D., & Dolan, R. J. (2002). Neural responses during anticipation of a primary taste reward. Neuron, 33(5), 815–826.

O’Doherty, J. P., Rolls, E. T., Francis, S., Bowtell, R., & McGlone, F. (2001). Representation of pleasant and aversive taste in the human brain. Journal of Neurophysiology, 85(3), 1315–1321.

Ochsner, K. N., & Gross, J. J. (2005). The cognitive control of emotion. Trends in Cognitive Sciences, 9(5), 242–249.

Ochsner, K. N., & Gross, J. J. (2008). Cognitive emotion regulation insights from social cognitive and affective neuroscience. Current Directions in Psychological Science, 17(2), 153–158.

Ogden, J., Daniells, E., & Barnett, J. (2008). The value of choice: development of a new measurement tool. British Journal of General Practice, 58(554), 614–618.

Ongur, D., & Price, J. L. (2000). The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans. Cerebral Cortex, 10(3), 206–219.

Patall, E. A. (2013). Constructing motivation through choice, interest, and interestingness. Journal of Educational Psychology, 105(2), 522.

Patall, E. A., Cooper, H., & Robinson, J. C. (2008). The effects of choice on intrinsic motivation and related outcomes: a meta-analysis of research findings. Psychological Bulletin, 134(2), 270–300.

Patall, E. A., Cooper, H., & Wynn, S. R. (2010). The effectiveness and relative importance of choice in the classroom. Journal of Educational Psychology, 102(4), 896.

Paterson, R. J., & Neufeld, R. W. (1995). What are my options? Influences of choice availability on stress and the perception of control. Journal of Research in Personality, 29(2), 145–167. (p.173)

Paulus, M. P., & Frank, L. R. (2003). Ventromedial prefrontal cortex activation is critical for preference judgments. Neuroreport, 14(10), 1311–1315.

Platek, S. M., Keenan, J. P., Gallup Jr, G. G., & Mohamed, F. B. (2004). Where am I? The neurological correlates of self and other. Cognitive Brain Research, 19(2), 114–122.

Presson, P. K., & Benassi, V. A. (2003). Are depressive symptoms positively or negatively associated with the illusion of control. Social Behavior and Personality: An International Journal, 31(5), 483–495.

Rao, H., Korczykowski, M., Pluta, J., Hoang, A., & Detre, J. A. (2008). Neural correlates of voluntary and involuntary risk taking in the human brain: an fMRI Study of the Balloon Analog Risk Task (BART). NeuroImage, 42(2), 902–910.

Reuven-Magril, O., Dar, R., & Liberman, N. (2008). Illusion of control and behavioral control attempts in obsessive-compulsive disorder. Journal of Abnormal Psychology, 117(2), 334.

Robbins, T. W., & Everitt, B. J. (1996). Neurobehavioural mechanisms of reward and motivation. Current Opinion in Neurobiology, 6(2), 228–236.

Rokke, P. D., & al’Absi, M. (1992). Matching pain coping strategies to the individual: a prospective validation of the cognitive coping strategy inventory. Journal of Behavioral Medicine, 15, 611–625.

Rokke, P. D., Fleming-Ficek, S., Siemens, N. M., & Hegstad, H. J. (2004). Self-efficacy and choice of coping strategies for tolerating acute pain. Journal of Behavioral Medicine, 27, 343–360.

Rokke, P. D., & Lall, R. (1992). The role of choice in enhancing tolerance to acute pain. Cognitive Therapy and Research, 16, 53–65.

Rose, J. P., Geers, A. L., Rasinski, H. M., & Fowler, S. L. (2012). Choice and placebo expectation effects in the context of pain analgesia. Journal of Behavioral Medicine, 35(4), 462–470.

Rotter, J. (1966). Generalised expectancies for internal versus external control of reinforcement. Psychological Monographs, 80(1, Whole no. 609), 1–28.

Rushworth, M. F., Walton, M. E., Kennerley, S. W., & Bannerman, D. M. (2004). Action sets and decisions in the medial frontal cortex. Trends in Cognitive Sciences, 8(9), 410–417.

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55, 68–78.

Ryan, R. M., & Deci, E. L. (2006). Self-regulation and the problem of human autonomy: does psychology need choice, self-determination, and will? Journal of Personality, 74(6), 1557–1585.

Ryan, R. M., Deci, E. L., & Grolnick, W. S. (1995). Autonomy, relatedness, and the self: their relation to development and psychopathology. In D. Cicchetti, & D. J. Cohen (Eds.), Developmental psychopathology, Vol. 1: Theory and methods. Wiley series on personality processes (pp. 618–655). Oxford, England: John Wiley & Sons, 787 pp.

Ryan, R. M., Lynch, M. F., Vansteenkiste, M., & Deci, E. L. (2011). Motivation and autonomy in counseling, psychotherapy, and behavior change: a look at theory and practice. The Counseling Psychologist, 39, 193–260. (p.174)

Sagi, A., & Friedland, N. (2007). The cost of richness: the effect of the size and diversity of decision sets on post-decision regret. Journal of Personality and Social Psychology, 93(4), 515.

Salomons, T. V., Johnstone, T., Backonja, M. M., & Davidson, R. J. (2004). Perceived controllability modulates the neural response to pain. Journal of Neuroscience, 24(32), 7199–7203.

Salomons, T. V., Johnstone, T., Backonja, M. M., Shackman, A. J., & Davidson, R. J. (2007). Individual differences in the effects of perceived controllability on pain perception: critical role of the prefrontal cortex. Journal of Cognitive Neuroscience, 19(6), 993–1003.

Salomons, T. V., Moayedi, M., Weissman-Fogel, I., Goldberg, M. B., Freeman, B. V., Tenenbaum, H. C., et al. (2012). Perceived helplessness is associated with individual differences in the central motor output system. European Journal of Neuroscience, 35, 1481–1487.

Salomons, T. V., Nusslock, R., Detloff, A., Johnstone, T., Abramson, L. Y., & Davidson, R. J. (2010). Perceived control over pain reduces anxiety: top down mechanisms. Abstract presented at International Association for the Study of Pain.

Samuelson, W., & Zeckhauer, R. (1988). Status quo bias in decision making. Journal of Risk and Uncertainty, 1, 7–59.

Schultz, W., Dayan, P., Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275(5306), 1593–1599.

Schwartz, B. (2000). Self-determination: the tyranny of freedom. American Psychologist, 55(1), 79.

Schwartz, B. (2009). The paradox of choice. New York: HarperCollins.

Schwartz, B., Ward, A., Monterosso, J., Lyubomirsky, S., White, K., & Lehman, D. R. (2002). Maximizing versus satisficing: happiness is a matter of choice. Journal of Personality and Social Psychology, 83(5), 1178.

Sela, A., Berger, J., & Liu, W. (2009). Variety, vice, and virtue: how assortment size influences option choice. Journal of Consumer Research, 35(6), 941–951.

Seligman, M. E. P. (1972). Learned helplessness. Annual Review of Medicine, 23(1), 407–412.

Seligman, M. E. P. (1975). Helplessness: on depression, development, and death. San Francisco: W. H. Freeman/Times Books/Henry Holt.

Shapiro, D. H., Jr., Schwartz, C. E., & Astin, J. A. (1996). Controlling ourselves, controlling our world: psychology’s role in understanding positive and negative consequences of seeking and gaining control. American Psychologist, 51(12), 1213–1230.

Shapiro, D. H., Jr., Blinder, B. J., Hagman, J., & Pituck, S. (1993). A psychological “sense-of-control” profile of patients with anorexia nervosa and bulimia nervosa. Psychological Reports, 73(2), 531–541.

Shapiro, D. H., Jr., & Zifferblatt, S. M. (1976). An applied clinical combination of Zen meditation and behavioral self-management techniques: reducing metadone dosage in drug addiction. Behavior Therapy, 7(5), 694–695.

Sharot, T., De Martino, B., & Dolan, R. J. (2009). How choice reveals and shapes expected hedonic outcome. Journal of Neuroscience, 29(12), 3760–3765. (p.175)

Sharot, T., Fleming, S. M., Yu, X., Koster, R., & Dolan, R. J. (2012). Is choice-induced preference change long lasting? Psychological Science, 23(10), 1123–1129.

Sharot, T., Shiner, T., & Dolan, R. J. (2010). Experience and choice shape expected aversive outcomes. Journal of Neuroscience, 30(27), 9209–9215.

Sharot, T., Velasquez, C. M., & Dolan, R. J. (2010). Do decisions shape preference? Evidence from blind choice. Psychological Science, 21(9), 1231–1235.

Shiloh, S., Zukerman, G., Butin, B., Deutch, A., Yardeni, I., Benyamini, Y., et al. (2003). Postoperative patient-controlled analgesia (PCA): how much control and how much analgesia? Psychology and Health, 18(6), 753–770.

Shin, J., & Ariely, D. (2004). Keeping doors open: the effect of unavailability on incentives to keep options viable. Management Science, 50(5), 575–586.

Shiv, B., & Fedorikhin, A. (1999). Heart and mind in conflict: the interplay of affect and cognition in consumer decision making. Journal of Consumer Research, 26(3), 278–292.

Skinner, B. F. (1953). Science and human behavior. New York: Macmillan.

Stajkovic, A. D., & Luthans, F. (1998). Self-efficacy and work-related performance: a meta-analysis. Psychological Bulletin, 124(2), 240.

Staub, E., Tursky, B., & Schwartz, G. E. (1971). Self-control and predictability: their effects on reactions to aversive stimulation. Journal of Personality and Social Behavior, 18, 157–162.

Strupp, H. H. (1970). Specific versus non-specific factors in psychotherapy and the problem of control. Archives of General Psychiatry, 23, 393–401.

Suzuki, S. (1997). Effects of number of alternatives on choice in humans. Behavioural Processes, 39, 205–214.

Suzuki, S. (2011). Choice between single-response and multichoice tasks in humans. The Psychological Record, 50(1), 7.

Tanaka, S. C., Balleine, B. W., & O’Doherty, J. P. (2008). Calculating consequences: brain systems that encode the causal effects of actions. Journal of Neuroscience, 28(26), 6750–6755.

Taylor, S. E., & Brown, J. D. (1988). Illusion and well-being: a social psychological perspective on mental health. Psychological Bulletin, 103(2), 193.

Taylor, S. E., & Brown, J. D. (1994). Positive illusions and well-being revisited: separating fact from fiction. Psychological Bulletin, 116(1), 21–27.

Thompson, S. (1999). Illusions of control: how we overestimate our personal influence. Current Directions in Psychological Science, 8(6), 187–190.

Thorndike, E. L. (1933). A theory of the action of the after-effects of a connection upon it. Psychological Review, 40, 434–439.

Tobler, P. N., O’Doherty, J. P., Dolan, R. J., & Schultz, W. (2007). Reward value coding distinct from risk attitude-related uncertainty coding in human reward systems. Journal of Neurophysiology, 97(2), 1621–1632.

Tricomi, E. M., Delgado, M. R., & Fiez, J. A. (2004). Modulation of caudate activity by action contingency. Neuron, 41(2), 281–292.

Tsiros, M., & Mittal, V. (2000). Regret: a model of its antecedents and conse- quences in consumer decision making. Journal of Consumer Research 26, 401–417.

Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453–458. (p.176)

Vohs, K. D., Baumeister, R. F., Schmeichel, B. J., Twenge, J. M., Nelson, N. M., & Tice, D. M. (2008). Making choices impairs subsequent self-control: a limited-resource account of decision making, self-regulation, and active initiative. Journal of Personality and Social Psychology, 94(5), 883.

Vohs, K. D., & Baumeister, R. F. E. (2011). Handbook of self-regulation: research, theory, and applications. New York: Guilford Press.

Voorn, P., Vanderschuren, L. J., Groenewegen, H. J., Robbins, T. W., & Pennartz, C. (2004). Putting a spin on the dorsal–ventral divide of the striatum. Trends in Neurosciences, 27(8), 468–474.

Voss, M., Ingram, J. N., Haggard, P., & Wolpert, D. M. (2006). Sensorimotor attenuation by central motor command signals in the absence of movement. Nature Neuroscience, 9(1), 26–27.

Voss, M., Ingram, J. N., Wolpert, D. M., & Haggard, P. (2008). Mere expectation to move causes attenuation of sensory signals. PLoS One, 3(8), e2866.

Wager, T. D., Davidson, M. L., Hughes, B. L., Lindquist, M. A., & Ochsner, K. N. (2008). Prefrontal-subcortical pathways mediating successful emotion regulation. Neuron, 59(6), 1037–1050.

Wenke, D., Fleming, S. M., & Haggard, P. (2010). Subliminal priming of actions influences sense of control over effects of action. Cognition, 115(1), 26–38.

Wiech, K., Kalisch, R., Weiskopf, N., Pleger, B., Stephan, K. E., & Dolan, R. J. (2006). Anterolateral prefrontal cortex mediates the analgesic effect of expected and perceived control over pain. Journal of Neuroscience, 26(44), 11501–11509.

Wilson, G. T. (1987). Cognitive processes in addiction. British Journal of Addiction, 82(4), 343–353.

White, R. W. (1959). Motivation reconsidered: the concept of competence. Psychological Review, 66(5), 297.

Yacubian, J., Glascher, J., Schroeder, K., Sommer, T., Braus, D. F., & Buchel, C. (2006). Dissociable systems for gain- and loss-related value predictions and errors of prediction in the human brain. Journal of Neuroscience, 26(37), 9530–9537.

Ybarra, O., Lee, D. S., & Gonzalez, R. (2012). Supportive social relationships attenuate the appeal of choice. Psychological Science, 23(10), 1186–1192.

Zink, C. F., Pagnoni, G., Martin-Skurski, M. E., Chappelow, J. C., & Berns, G. S. (2004). Human striatal responses to monetary reward depend on saliency. Neuron, 42(3), 509–517.