Previous work on response reversal has typically used a single pair of stimuli that serially reverse. This conflation of acquisition and reversal processes has prevented an examination of the functional role of neural systems implicated in response reversal during acquisition despite the relevance of such data in evaluating accounts of response reversal. In the current study, participants encountered 16 independent reversing stimulus pairs in the context of a probabilistic response reversal paradigm. Functional regions of interest identified as involved in response reversal through a contrast used in the previous literature (punished errors made in the reversal phase versus rewarded correct responses), were interrogated across conditions. Consistent with suggestions that middle frontal cortex codes reward, this region showed significantly greater responses to rewarded rather than punished trials irrespective of accuracy or learning phase (acquisition or reversal). Consistent with the suggestion that this coding of the expectation of reinforcement is acquired via input from the amygdala, we observed significant positive connectivity between activity within the amygdala and a region of rostral anterior cingulate cortex highly proximal to this region of middle frontal/mesial prefrontal cortex. In contrast, inferior frontal cortex, anterior cingulate cortex and caudate showed greater responses to punished errors than to the rewarded correct responses. These three regions also showed significant activation to rewarded errors during acquisition, in contrast to positions suggesting that inferior frontal cortex represents punishment or suppresses previously rewarded responses. Moreover, a connectivity analysis with an anterior cingulate cortex seed revealed highly significant positive connectivity among them. The implications of these data for recent accounts of response reversal and of response reversal impairments in specific neuropsychiatric populations are discussed.
"Lesion studies on animals , , , ,  and humans ,  have consistently implicated the ventrolateral prefrontal cortex and lateral orbitofrontal cortex (OFC) in this type of reversal learning. Mirroring these findings, functional imaging studies have also identified the lateral OFC , , , and several other brain regions in reversal learning, including the inferior frontal gyrus (IFG) , , the dorsomedial prefrontal cortex (DMPFC), , the dorsolateral prefrontal cortex (DLPFC) , , the posterior parietal cortex , , and the striatum , , , , , . "
[Show abstract][Hide abstract] ABSTRACT: Impairments in flexible goal-directed decisions, often examined by reversal learning, are associated with behavioral abnormalities characterized by impulsiveness and disinhibition. Although the lateral orbital frontal cortex (OFC) has been consistently implicated in reversal learning, it is still unclear whether this region is involved in negative feedback processing, behavioral control, or both, and whether reward and punishment might have different effects on lateral OFC involvement. Using a relatively large sample (N = 47), and a categorical learning task with either monetary reward or moderate electric shock as feedback, we found overlapping activations in the right lateral OFC (and adjacent insula) for reward and punishment reversal learning when comparing correct reversal trials with correct acquisition trials, whereas we found overlapping activations in the right dorsolateral prefrontal cortex (DLPFC) when negative feedback signaled contingency change. The right lateral OFC and DLPFC also showed greater sensitivity to punishment than did their left homologues, indicating an asymmetry in how punishment is processed. We propose that the right lateral OFC and anterior insula are important for transforming affective feedback to behavioral adjustment, whereas the right DLPFC is involved in higher level attention control. These results provide insight into the neural mechanisms of reversal learning and behavioral flexibility, which can be leveraged to understand risky behaviors among vulnerable populations.
PLoS ONE 12/2013; 8(12):e82169. DOI:10.1371/journal.pone.0082169 · 3.23 Impact Factor
"It is important to remember though that this work was conducted with healthy individuals (just because a variable may slightly alter the level of antisocial behavior shown by a healthy individual does not mean that it is contributory to clinical levels of impairment). Moreover, this architecture is recruited when avoiding a suboptimal choice; i.e., when about to make a response choice associated with punishment or failing to make a response that would gain reward (Budhani et al. 2007; Casey et al. 2001; Kuhnen and Knutson 2005; Liu et al. 2007). Youth with ODD and CD have been shown to recruit these regions less when making suboptimal choices as a function of expected values (White et al. 2013c). "
[Show abstract][Hide abstract] ABSTRACT: The disruptive behavior disorders Disruptive behavior disorders include Conduct Disorder (CD) Conduct Disorder (CD) , Oppositional Defiant Disorder (ODD) Oppositional Defiant Disorder (ODD) , and Attention Deficit Hyperactivity Disorder (ADHD). These disorders are highly comorbid with each other as well as with mood and anxiety disorders and personality disorders (particularly borderline personality disorder). The goal of this chapter is to consider these disorders from an RDoC(ish) approach. In other words, we will outline four functional processes and the behavioral implications of dysfunction within these processes. Moreover, we will briefly consider how dysfunction in one might increase the risk for the development of rather different behavioral problems that have been previously associated with rather different disorders. Our goal is to identify neurocognitive-based functional targets for treatment.
Current Topics in Behavioral Neurosciences 09/2013; 16. DOI:10.1007/7854_2013_247
"Principal neural systems implicated in this process include dorsomedial frontal cortex and inferior frontal/ anterior insula cortex. These regions are consistently activated when behavior needs to be altered (Botvinick, Cohen, & Carter, 2004; Budhani, et al., 2007). As noted above, displays of anger are typically used to curtail/ change the behavior of others (Averill, 1982). "
[Show abstract][Hide abstract] ABSTRACT: In this paper, we will argue that (1) four classes of norm can be distinguished from a neuro‐cognitive perspective; (2) learning the prohibitive power of these norms relies on relatively independent emotional systems; (3) individuals with psychopathy show selective impairment for one of these emotional learning systems and two classes of norm: care based and justice based; and (4) while emotional learning systems are necessary for appropriate moral development/reasoning, they are not sufficient for moral development/reasoning.
Annals of the New York Academy of Sciences 09/2013; 1299(1). DOI:10.1111/nyas.12169 · 4.38 Impact Factor
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