262 research outputs found

    Categorical evidence, confidence and urgency during the integration of multi-feature information

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    Includes bibliographical references.2015 Summer.The present experiment utilized a temporally-extended categorization task to investigate the neural substrates underlying our ability to integrate information over time and across multiple stimulus features. Importantly, the design allowed differentiation of three important decision functions: 1) categorical evidence, 2) decisional confidence (the choice-independent probability that a decision will lead to a desirable state), and 3) urgency (a hypothetical signal representing a growing pressure to produce a behavioral response within each trial). In conjunction with model-based fMRI, the temporal evolution of these variables were tracked as participants deliberated about impending choices. The approach allowed investigation of the independent effects of urgency across the brain, and also the investigation of how urgency might modulate representations of categorical evidence and confidence. Representations associated with prediction errors during feedback were also investigated. Many cortical and striatal somatomotor regions tracked the dynamical evolution of categorical evidence, while many regions of the dorsal and ventral attention networks (Corbetta and Shulman, 2002) tracked decisional confidence and uncertainty. Urgency influenced activity in regions known to be associated with flexible control of the speed-accuracy trade-off (particularly the pre- SMA and striatum), and additionally modulated representations of categorical evidence and confidence. The results, therefore, link the urgency signal to two hypothetical mechanisms underling flexible control of decision thresholding (Bogacz et al., 2010): gain modulation of the striatal thresholding circuitry, and gain modulation of the integrated categorical evidence

    Anterior cingulate and medial prefrontal cortex oscillations underlie learning alterations in trait anxiety in humans

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    Anxiety has been linked to altered belief formation and uncertainty estimation, impacting learning. Identifying the neural processes underlying these changes is important for understanding brain pathology. Here, we show that oscillatory activity in the medial prefrontal, anterior cingulate and orbitofrontal cortex (mPFC, ACC, OFC) explains anxiety-related learning alterations. In a magnetoencephalography experiment, two groups of human participants pre-screened with high and low trait anxiety (HTA, LTA: 39) performed a probabilistic reward-based learning task. HTA undermined learning through an overestimation of volatility, leading to faster belief updating, more stochastic decisions and pronounced lose-shift tendencies. On a neural level, we observed increased gamma activity in the ACC, dmPFC, and OFC during encoding of precision-weighted prediction errors in HTA, accompanied by suppressed ACC alpha/beta activity. Our findings support the association between altered learning and belief updating in anxiety and changes in gamma and alpha/beta activity in the ACC, dmPFC, and OFC

    Modulation of motor vigour by expectation of reward probability trial-by-trial is preserved in healthy ageing and Parkinson's disease patients

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    Motor improvements, such as faster movement times or increased velocity, have been associated with reward magnitude in deterministic contexts. Yet whether individual inferences on reward probability influence motor vigour dynamically remains undetermined. We investigated how dynamically inferring volatile action-reward contingencies modulated motor performance trial-by-trial. We conducted three studies that coupled a one-armed bandit decision-making paradigm with a motor sequence task and used a validated hierarchical Bayesian model to fit trial-by-trial data. In Study 1, we tested healthy younger (HYA, 37 [13 males]) and older adults (HOA, 37 [20 males]), and medicated Parkinson's Disease patients (PD, 20 [13 males]). We showed that stronger predictions about the tendency of the action-reward contingency led to faster performance tempo-commensurate with movement time-on a trial-by-trial basis without robustly modulating reaction time (RT). Using Bayesian linear mixed models, we demonstrated a similar invigoration effect on performance tempo in HYA, HOA and PD, despite HOA and PD being slower than HYA. In Study 2 (HYA, 39 [10 males]), we additionally showed that retrospective subjective inference about credit assignment did not contribute to differences in motor vigour effects. Last, Study 3 (HYA, 33 [6 males]) revealed that explicit beliefs about the reward tendency (confidence ratings) modulated performance tempo trial-by-trial.Our study is the first to reveal that the dynamic updating of beliefs about volatile action-reward contingencies positively biases motor performance through faster tempo. We also provide robust evidence for a preserved sensitivity of motor vigour to inferences about the action-reward mapping in ageing and medicated PD.SIGNIFICANCE STATEMENT:Navigating a world rich in uncertainty relies on updating beliefs about the probability that our actions lead to reward. Here we investigated how inferring the action-reward contingencies in a volatile environment modulated motor vigour trial-by-trial in healthy younger and older adults, and in Parkinson's Disease patients on medication. We found an association between trial-by-trial predictions about the tendency of the action-reward contingency and performance tempo, with stronger expectations speeding the movement. We additionally provided evidence for a similar sensitivity of performance tempo to the strength of these predictions in all groups. Thus, dynamic beliefs about the changing relationship between actions and their outcome enhanced motor vigour. This positive bias was not compromised by age or Parkinson's disease

    Modulation of motor vigour by expectation of reward probability trial-by-trial is preserved in healthy ageing and Parkinson's disease patients

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    Motor improvements, such as faster movement times or increased velocity, have been associated with reward magnitude in deterministic contexts. Yet whether individual inferences on reward probability influence motor vigour dynamically remains undetermined. We investigated how dynamically inferring volatile action-reward contingencies modulated motor performance trial-by-trial. We conducted three studies that coupled a one-armed bandit decision-making paradigm with a motor sequence task and used a validated hierarchical Bayesian model to fit trial-by-trial data. In Study 1, we tested healthy younger (HYA, 37 [13 males]) and older adults (HOA, 37 [20 males]), and medicated Parkinson’s Disease patients (PD, 20 [13 males]). We showed that stronger predictions about the tendency of the action-reward contingency led to faster performance tempo—commensurate with movement time—on a trial-by-trial basis without robustly modulating reaction time (RT). Using Bayesian linear mixed models, we demonstrated a similar invigoration effect on performance tempo in HYA, HOA and PD, despite HOA and PD being slower than HYA. In Study 2 (HYA, 39 [10 males]), we additionally showed that retrospective subjective inference about credit assignment did not contribute to differences in motor vigour effects. Last, Study 3 (HYA, 33 [6 males]) revealed that explicit beliefs about the reward tendency (confidence ratings) modulated performance tempo trial-by-trial. Our study is the first to reveal that the dynamic updating of beliefs about volatile action-reward contingencies positively biases motor performance through faster tempo. We also provide robust evidence for a preserved sensitivity of motor vigour to inferences about the action-reward mapping in ageing and medicated PD

    Activation and modulation of automatic response tendencies

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    The Feedback-Related Negativity is a Time-Dependent Brain Mechanism that Facilitates Aversive Learning: Implications for the Reinforcement Learning FRN Hypothesis

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    Organisms encode rewarding and aversive experiences through reinforcement learning, capitalizing on prediction errors (PEs), which adapt action strategies over time. Computational theories are explicit that PE signals should update action weights continuously over the course of a behavioral task, an important time-dependent variation that is eschewed in traditional neuroscience studies that average over large numbers of trials. I examined variation in reaction times and feedback-locked cortical activity over time as a function of PE to critically examine theories indicating that PE signals drive time-dependent learning. We recorded EEG while participants completed a novel reinforcement task that varied prediction error on a trial-by-trial basis. I applied a computational framework that modeled reaction time changes over the task as a function of prediction error and time. In positive reinforcement conditions, reaction times improved over the course of the task regardless of the PE. For negative reinforcement, learning effects were moderated by PE. For better than expected outcomes, more positive prediction errors (further from expectation) drove faster reaction times over the course of the task, and for worse than expected outcomes, more negative prediction errors (further from expectation) drove faster reaction times over the course of the task. Behavioral analyses were supplemented by single-trial robust regression of feedback-locked EEG. The feedback-related negativity (FRN), a mediofrontal ERP component thought to convey a PE signal, showed robust changes in activation over time but did not respond to trial-by-trial magnitude of prediction errors. This time-dependent change was evident only for reward delivery and aversive stimulus delivery, which represent on average the most salient outcomes in the task. Mediofrontal brain activity during this same time window and at the same scalp location drove subsequent reaction time improvements over the course of the task following aversive stimulus delivery. I suggest that the standard approach of examining the ERP as an average across conditions obscures important adaptation effects of the FRN that reflect reinforcement learning as outcomes are learned

    Neural Processes Underlying the Flexible Control and Learning of Attentional Selection

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    In every-day life we are usually surrounded by a plethora of stimuli, of which only some may be relevant to us at a given moment in time. The dynamic interaction between internal factors, such as our previous experience and current goals, and external factors, such as salient sensory stimulation, determine where, how and what we attend to in our environment. This dissertation investigated some of the neural mechanisms that underlie successful goal-directed behavior in two conditions 1. when attention was actively cued to a target stimulus, and 2. when the attentional target had to be actively and repeatedly learned, in macaque monkeys and in humans. In Chapter 2, I investigated inter-areal spiketrain correlations in neuron pairs across the fronto-cingulate cortex when macaque monkeys are cued to shift their attention to one of two target stimuli. I found that neuron pairs in anterior cingulate cortex (ACC) and dorsal prefrontal cortex (PFC) with similar spatial preferences correlate their spiketrains at the time when attention needs to be actively shifted, suggesting that the flexible interaction between these two areas may support successful covert attention shifts. In Chapter 3, I show that when the attentional target stimulus needs to be repeatedly learned and is defined by only one of several stimulus features, neurons in macaque frontal and striatal regions encode prediction error signals that carry specific information about the stimulus feature that was selected in the preceding choice. These signals may be involved in identifying those synapses that require updating to allow flexible adjustments in goal-directed behavior. In Chapter 4, I found that when humans must repeatedly learn the identity of an attentional target, a human event-related potential over visual cortex that is thought to index attentional target selection, selectively decreases after successful learning, in particular for the distracting stimulus, and selectively increases for the target stimulus following negative feedback during learning. Overall, this dissertation provides novel insights into some of the complex neural mechanisms that support flexible control and learning of attention across brain regions of the human and non-human primate brain
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