Our hypothesis is that individuals with schizophrenia seem to have difficulties using internal representations of emotional experiences, previous rewards, and motivational goals to drive current and future behavior that should allow them to obtain desired outcomes, a deficit that has major clinical significance in terms of functional capacity. However, there are many processes that contribute to linking internal representations to behavior, and it is important to understand which of these are impaired in schizophrenia so as to design appropriate intervention strategies. Fortunately, a burgeoning affective neuroscience literature in humans and animals has begun to outline the core neural systems that serve to process and integrate reward and penalty signals and then translate these signals into value and/or utility estimates that can be used to drive action selection and goal planning. Although an oversimplification, it helps to organize this large literature by thinking of 4 major components to the translation of appetitive or reward information into behavioral responses
3,24,27–29 (see ). The first component, referred to as “hedonics or liking,” reflects the ability of the organism to “enjoy” the stimulus or event that may provide pleasure or reward. For many years, it was suggested that the neurotransmitter dopamine (DA) was the primary substrate of liking.
24 However, more recent research has shown that experimental depletion of DA does not reduce liking when it can be measured by facial expression and/or subjective reports.
24 Instead, hedonic responses (at least to primary sensory stimuli) seem to be mediated by activation of the opioid and gamma amino butyric acidergic systems in the nucleus accumbens shell and its projections to the ventral pallidum as well as in the orbital frontal cortex (OFC).
30–33A second component, called “reward prediction and wanting,” is thought to be mediated by the midbrain DA system, particularly the projections to ventral and dorsal striatal regions of the basal ganglia.
24,29 Many DA neurons in the substantia nigra and ventral tegmental area respond to stimuli that predict reward as well as to food and liquid rewards themselves. The degree to which these DA neurons respond to rewards seems to depend on reward predictability. If the reward was not predicted, then the DA neurons fire strongly (positive prediction error); if a predicted reward does not occur, then there is a transient depression in DA neuron firing (negative prediction error).
27–29,34–35 Furthermore, over time, DA neurons learn to fire to cues that predict reward rather than to rewards themselves. Similar effects have been found in humans in the ventral/dorsal striatum, with evidence from functional magnetic resonance imaging (fMRI) for activation of ventral and dorsal striatum to cues that predict reward
36,37 as well as both positive and negative prediction error responses.
38,39 These types of DA/striatal responses have been captured by temporal difference models that learn about stimuli in the environment that predict rewards.
40,41 These mechanisms are also thought to underlie basic aspects of reinforcement learning that may occur without conscious awareness.
42,43 A prominent, though slightly different theory, emphasizes the role of the DA-learning process in transferring incentive salience from the reward itself to reward-predicting cues, thus imbuing these cues with motivational properties themselves (eg, a wanting response
24).
A third component is “cost-benefit analysis” or the ability to integrate information from different sources to derive and update the value of potentially rewarding outcomes (figure 1). One aspect, thought to be mediated at least in part by OFC, is the ability to “represent value information,” ie, to take into account not only the hedonic properties of a stimulus but also the internal or motivational state of the organism (eg, value of juice when thirsty vs not),
44 the delay before the reward occurs,
45,46 the different reward options available (eg, juice vs wine after a hard day),
47,48 and the changing contingencies associated with a stimulus (a previously rewarded response is now punished).
49 Some researchers have described the OFC as being involved in “working memory for value” or the ability to maintain, update, and integrate different sources of information about value over a short period of time.
3,50 Human functional neuroimaging studies also highlight activation of OFC under conditions requiring value representations,
51,52 including those in which response contingencies need to be updated, such as reversal learning.
51,53,54 In addition, humans with OFC lesions can show reversal learning impairments.
55–57Another aspect of representing value information is “effort computation,” ie, determining the cost of engaging in whatever actions it will take to obtain that outcome. For example, one may really want to obtain chocolate cookies and may perceive eating these cookies as rewarding, but the effort associated with having to go to the store may prevent the person from pursuing actions to obtain the cookies. A growing body of research suggests that the dorsal anterior cingulate cortex (ACC) may be important for evaluating the effort associated with different action plans, in concert with DA input from nucleus accumbens and related forebrain circuitry.
58–61 For example, research has shown that ACC lesions as well as depletions of accumbens DA lead animals to choose low effort but low reward options over higher reward but higher effort options.
46,58,59,62–65 The potential role of ACC in computing effort may fit nicely with its suggested role in responding to conflict and error-related signals,
66–68 as feedback about conflict and errors may be an important source of information about the amount of effort a particular course of action is likely to require. Indeed, some work in healthy populations has suggested that error/conflict effects in ACC are modulated by motivational/affective and reward variables.
69,70 However, it is not yet clear whether the same regions of ACC that respond to conflict/error are those involved in effort computations or whether these represent different functional subdivisions of ACC, though both types of studies have shown activation of similar regions of dorsal anterior cingulate.
60,61,66,71 Nonetheless, even if it should turn out that this reflects a common mechanism, it helps to outline the role that ACC may play in a range of decision-making domains.
A fourth component is the ability to “generate and execute goal-directed action plans necessary to achieve the valued outcome.” Wallis and others have suggested that this function is carried out by the lateral prefrontal cortex (PFC) (in particular, dorsolateral PFC [DLPFC]).
3,72,73 Such a role for the DLPFC in motivated behavior would be consistent with its role in top-down control of cognitive processing, planning, and response execution is consistent with models suggesting that the DLPFC provides a bias signal that helps to facilitate goal-directed behavior
72 and is consistent with evidence for impaired action planning following lateral prefrontal lesions.
74,75 In other words, intact DLPFC function may be necessary to translate information about value into goal representations that can be implemented as action plans to achieve the desired outcome. Furthermore, some theories of goal maintenance in DLPFC emphasize the importance of phasic DA input as a gating signal that serves to update the contents of DLPFC and protect against interference.
76–78 There is also growing evidence from the human and nonhuman primate literature that the potential for reward can enhance firing in DLPFC neurons and increase fMRI responses in DLPFC during cognitive control tasks
79–82 and that such changes may mediate improved performance as a function of reward.
83,84