Individual Differences in Reward Processing

Individual differences in learning performance are considered central in the field of

educational neuroscience, in which changes in the brain function are associated to learning

trajectories (Goswami, 2006). One of the main research questions proposed in this thesis is to

behaviourally examine how individuals differ in reward based learning across the lifespan.

of individual differences and how they enable emotion and cognition.

In a study by Mobbs et al. (2005) significant correlational differences in BOLD activity and

personality differences were found despite the absence of correlation within NBO-FFI scores

(Five-Factor Inventory Questionnaire). However, due to high extraversion scores no

correlation emerged with whole brain activation and neuroticism. Interestingly, it is difficult

to find one brain area related to any NEO FFI scores. On the other hand, IQ differences are

strongly linked to executive control functions and associated brain activity in adults. Existing

neuroimaging studies have demonstrated that adults with enhanced fluid intelligence

exhibited a sturdier activation of specific brain regions during executive control performance

(Duncan, 2003; Gray et al., 2003). Results from Duncan, (2003) and Gray et al. (2003)

provide the framework that IQ individual differences are linked to developmental time

courses of neural circuitry supporting feedback-based learning. Additionally, in probabilistic

learning tasks, adults and children demonstrated distinctive patterns of neural activity when

processing reward stimuli. Therefore, there is a noticeable context of slowly developing

executive control functions with steady advances in adolescence (Crone, 2009; van den Bos

et al., 2012; Eppinger et al., 2009).

There are numerous inter-individual differences relative to rewards and punishments.

Interestingly, the connection in affective processes, learning and memory is central to reward

processing, motivated behaviour and decision making in individuals (Camara et al., 2009).

Individuals with enhanced tendency to chase previously rewarded behaviours instead of

impulsive behaviours display sturdier structural connectivity in the striatum and the

prefrontal cortex (Cohen et al., 2008). These aspects are considered to influence and motivate

1995). Based on personality theories individual traits reflect sensitivity to rewards (Digman,

1990; Eysenck, 1981; Gray, 1972; 1973; 1981; Tellegen & Waller, 1992; Zuckerman, 1983).

Sensitivity to reward differs substantively from one individual to another (Gray, 1987).

Individual differences in regards to reward sensitivity can predict food cravings, hyperphagia

and body weight. Moreover, behavioural studies have revealed association of reward

measures and food craving, overeating and body weight, in both healthy and overweight

populations whereas, neurobiological research in animals showed that pharmacological

stimulation of this circuit can override satiety and cause overeating of highly palatable foods

(Davis et al., 2004; Dawe & Loxton, 2004; Franken & Muris, 2005).

Individuals with high sensitivity to rewards are more likely to experience frequent and

intense food cravings resulting in overeating and most importantly eating disorders.

Excessive food intake and selection are strongly influenced by reward properties (e.g. taste,

smell, sight) (Toates, 1981; Berridge, 1996; Saper et al., 2002). Comparative studies on food

reward have implicated a network of interconnected brain regions comprising frontal, ventral

striatal, amygdala, and midbrain regions in aspects of food reward (Berridge, 2003; Ikemoto

& Panksepp, 1999; Kelley, 2004; Balleine, 2005; Di Chiara, 2005; Kelley et al., 2005).

Findings by Beaver (2006) demonstrated that individual differences in reward sensitivity

predict activation to pictures of appetising food in a fronto-striatal, amygdala and mid-brain

network, which are involved in food motivation.

This research is in line with the concept that individuals require specific psychological

mechanisms, which could guide their behaviour in certain situations (Buss, 1995; Tooby &

Cosmides, 1992). Such psychological mechanisms are considered to be cognitive processes

during evolutionary history. Furthermore, these mechanisms are activated by certain type of

information in the environment and thus, change environmental inputs through decision rules

to behavioural outputs that solve certain adaptive problems dependently (Campbell et ah,

2003). Therefore, psychological mechanisms require certain types of environmental inputs to

activate and guide behaviour. Based on this notion, Campbell et al. (2003) discovered that

high extraverted men exhibited leader behaviour only when specific type of rewards existed.

These results indicated that extraversion relative to individual differences provides significant

information about associations between personality traits and reward processing (Campbell et

al., 2003). Therefore, psychological mechanisms necessitate certain types o f environmental

inputs to activate and guide behaviour. Decision-making paradigms (Cohen et al., 2010; Ernst

et al., 2005; Eshel et al., 2007; Forbes et al., 2010; van Leijenhorst et al., 2010a) influence

neural sensitivity to incentive value differently in adults and adolescents during both reward

anticipation and receipt (Jarcho et al., 2012).

People respond differently to rewards and punishment due several aspects of human

behaviour. Reward processing is connected to reward functional activation in the Nucleus

Accumbens (NAc) (Camara et al., 2010). Its action is modulated by the occurrence of

positive and negative reward outcomes (e.g., monetary gains and losses) and by

manipulations in learning, decision-making and motivation (Camara et al., 2010). Individual

differences in terms of self-regulation (e.g. reward seeking, fear avoidance, and inhibitory

control) have been connected to the probability o f engaging in addictive behaviours such as

pathological gambling or substance abuse (Camara et al., 2010).

Generally, individuals desire to diminish negative outcomes based on the signals in the

rewarding or punishing events establishes a strong learning signal. Individual’s future

decisions are affected by feedback signals in the environment. In abstract conditions,

individuals make risky decisions for instant pleasure without considering the negative

outcomes (Camara et al., 2009). Moreover, in feedback assessment emotional responses have

an important effect on future reward driven behaviour. When individuals evaluate their

choices, the anticipated reward is realized as prediction error or not moreover, whether the

alternative choice is considered better or worse. In the presence o f a non-negative prediction

error individuals tend to assess the choice in positive manner and tend to keep this strategy. If

not they readjust the choices for future responses. In terms of negative assessment a wrong

choice significantly employs the bilateral superior temporal pole extending to the anterior

insula. The role of anterior insula in negative emotions such as regret (Kuhnen & Knutson,

2005) and disgust (Sanfey et al., 2003) in reward behaviour may affect people’s decision

strategy (Liu et al., 2007).