Neural correlates of response reversal: Considering acquisition
Department of Psychology, University College London, London WC1E 6BT, UK. NeuroImage
(Impact Factor: 6.36).
03/2007; 34(4):1754-65. DOI: 10.1016/j.neuroimage.2006.08.060
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.
Available from: Hannes Ruge
- "Ghahremani et al. (2010) showed that the posterior lOFC was more strongly engaged during deterministic reversal learning than during initial learning. However, two other studies suggest the possibility that lOFC is also relevant for the feedback-driven initial acquisition of rules (Budhani et al., 2007; Tsuchida et al., 2010) – a view that is line with a recently emerging view endorsing a less specific functional role of the lateral OFC (Stalnaker et al., 2015). Besides the lOFC, previous studies also imply the dorsal striatum in feedback-driven reversal learning vs. initial learning (Bellebaum et al., 2008; Ghahremani et al., 2010) and single cell recording studies suggest the possibility that other regions like the dorsolateral prefrontal cortex might exhibit differences in the precise learning dynamics between initial learning and reversal learning (Cromer et al., 2011; Pasupathy and Miller, 2005). "
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ABSTRACT: A key element of behavioral flexibility is to quickly learn to modify or reverse previously acquired stimulus-response associations. Such reversal learning (RL) can either be driven by feedback or by explicit instruction, informing either retrospectively or prospectively about the changed response requirements. Neuroimaging studies have thus far exclusively focused either on feedback-driven RL or on instructed initial learning of novel rules. The present study examined the neural basis of instructed RL as compared to instructed initial learning, separately assessing reversal-related instruction-based encoding processes and reversal-related control processes required for implementing reversed rules under competition from the initially learned rules. We found that instructed RL is partly supported by similar regions as feedback-driven RL, including lateral orbitofrontal cortex (lOFC) and anterior dorsal caudate. Encoding-related activation in both regions determined resilience against response competition during subsequent memory-based reversal implementation. Different from feedback-driven RL, instruction-based RL relied heavily on the generic fronto-parietal cognitive control network – not for encoding but for reversal-related control processes during memory-based implementation. These findings are consistent with a model of partly decoupled, yet interacting, systems of (i) symbolic rule representations that are instantaneously updated upon instruction and (ii) pragmatic representations of reward-associated S-R links mediating the enduring competition from initially learned rules.
Available from: Zhong-Lin Lu
- "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 , , , , , . "
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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.
- "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). "
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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.
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