3,465 research outputs found

    Neural computations underlying action-based decision making in the human brain

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    Action-based decision making involves choices between different physical actions to obtain rewards. To make such decisions the brain needs to assign a value to each action and then compare them to make a choice. Using fMRI in human subjects, we found evidence for action-value signals in supplementary motor cortex. Separate brain regions, most prominently ventromedial prefrontal cortex, were involved in encoding the expected value of the action that was ultimately taken. These findings differentiate two main forms of value signals in the human brain: those relating to the value of each available action, likely reflecting signals that are a precursor of choice, and those corresponding to the expected value of the action that is subsequently chosen, and therefore reflecting the consequence of the decision process. Furthermore, we also found signals in the dorsomedial frontal cortex that resemble the output of a decision comparator, which implicates this region in the computation of the decision itself

    Substantia nigra activity level predicts trial-to-trial adjustments in cognitive control

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    Effective adaptation to the demands of a changing environment requires flexible cognitive control. The medial and the lateral frontal cortices are involved in such control processes, putatively in close interplay with the BG. In particular, dopaminergic projections from the midbrain (i.e., from the substantia nigra [SN] and the ventral tegmental area) have been proposed to play a pivotal role in modulating the activity in these areas for cognitive control purposes. In that dopaminergic involvement has been strongly implicated in reinforcement learning, these ideas suggest functional links between reinforcement learning, where the outcome of actions shapes behavior over time, and cognitive control in a more general context, where no direct reward is involved. Here, we provide evidence from functional MRI in humans that activity in the SN predicts systematic subsequent trial-to-trial RT prolongations that are thought to reflect cognitive control in a stop-signal paradigm. In particular, variations in the activity level of the SN in one trial predicted the degree of RT prolongation on the subsequent trial, consistent with a modulating output signal from the SN being involved in enhancing cognitive control. This link between SN activity and subsequent behavioral adjustments lends support to theoretical accounts that propose dopaminergic control signals that shape behavior both in the presence and in the absence of direct reward. This SN-based modulatory mechanism is presumably mediated via a wider network that determines response speed in this task, including frontal and parietal control regions, along with the BG and the associated subthalamic nucleus

    Value and prediction error in medial frontal cortex: integrating the single-unit and systems levels of analysis

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    The role of the anterior cingulate cortex (ACC) in cognition has been extensively investigated with several techniques, including single-unit recordings in rodents and monkeys and EEG and fMRI in humans. This has generated a rich set of data and points of view. Important theoretical functions proposed for ACC are value estimation, error detection, error-likelihood estimation, conflict monitoring, and estimation of reward volatility. A unified view is lacking at this time, however. Here we propose that online value estimation could be the key function underlying these diverse data. This is instantiated in the reward value and prediction model (RVPM). The model contains units coding for the value of cues (stimuli or actions) and units coding for the differences between such values and the actual reward (prediction errors). We exposed the model to typical experimental paradigms from single-unit, EEG, and fMRI research to compare its overall behavior with the data from these studies. The model reproduced the ACC behavior of previous single-unit, EEG, and fMRI studies on reward processing, error processing, conflict monitoring, error-likelihood estimation, and volatility estimation, unifying the interpretations of the role performed by the ACC in some aspects of cognition

    Role of Anterior Cingulate Cortex in Saccade Control

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    Cognitive control is referred to the guidance of behavior based on internal goals rather than external stimuli. It has been postulated that prefrontal cortex is mainly involved in higher order cognitive functions. Specifically, anterior cingulate cortex (ACC), which is part of the prefrontal cortex, is suggested to be involved in performance monitoring and conflict monitoring that are considered to be cognitive control functions. Saccades are the fast eye movements that align the fovea on the objects of interest in the environment. In this thesis, I have explored the role of ACC in control of saccadic eye movements. First, I performed a resting-state fMRI study to identify areas within the ACC that are functionally connected to the frontal eye fields (FEF). It has been shown that FEF is involved in saccade generation. Therefore, the ACC areas that are functionally connected to FEF could be hypothesized to have a role in saccade control. Then, I performed simultaneous electrophysiological recordings in the ACC and FEF. Furthermore, I explored whether ACC exerts control over FEF. My results show that ACC is involved in cognitive control of saccades. Furthermore, the ACC and FEF neurons communicate through synchronized theta and beta band activity in these areas. The results of this thesis shine light on the mechanisms by which these brain areas communicate. Moreover, my findings support the notion that ACC and FEF have a unique oscillatory property, and more specifically ACC has a prominent theta band, and to a lesser extent beta band activity

    The role of the lateral prefrontal cortex and anterior cingulate in stimulus–response association reversals

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    Many complex tasks require us to flexibly switch between behavioral rules, associations, and strategies. The prefrontal cerebral cortex is thought to be critical to the performance of such behaviors, although the relative contribution of different components of this structure and associated subcortical regions are not fully understood. We used functional magnetic resonance imaging to measure brain activity during a simple task which required repeated reversals of a rule linking a colored cue and a left/right motor response. Each trial comprised three discrete events separated by variable delay periods. A colored cue instructed which response was to be executed, followed by a go signal which told the subject to execute the response and a feedback instruction which indicated whether to ‘‘hold’’ or ‘‘f lip’’ the rule linking the colored cue and response. The design allowed us to determine which brain regions were recruited by the specific demands of preparing a rule contingent motor response, executing such a response, evaluating the significance of the feedback, and reconfiguring stimulus–response (SR) associations. The results indicate that an increase in neural activity occurs within the anterior cingulate gyrus under conditions in which SR associations are labile. In contrast, lateral frontal regions are activated by unlikely/unexpected perceptual events regardless of their significance for behavior. A network of subcortical structures, including the mediodorsal nucleus of the thalamus and striatum were the only regions showing activity that was exclusively correlated with the neurocognitive demands of reversing SR associations. We conclude that lateral frontal regions act to evaluate the behavioral significance of perceptual events, whereas medial frontal–thalamic circuits are involved in monitoring and reconfiguring SR associations when necessary

    Single neuron computations of cognition in the human brain

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    Understanding how information is encoded, processed, and decoded to produce behavior is a fundamental goal of neuroscience. In this dissertation, we aim to expand our understanding of our human decision-making processes at the single-neuronal level. We describe three studies exploring the neural substrate of decision-making in three separate brain regions. First, we describe a method for recording the activity of individual neurons in human subjects. The unique combination of behavioral and neurophysiological data will allow us to better understand the neural substrate of cognitive functions in humans. Second, we explored how decisions are represented in the brain. We recorded single neuronal responses in the human nucleus accumbens while subjects engaged in a financial decision-making task. We found that neurons in the nucleus accumbens predicted upcoming decisions well before the behavior was manifested. In addition, these neurons encoded a positive and negative prediction error signal, signaling the difference between expected and realized outcome. Third, we explored how the brain represents decision conflict and how it adapts to prime future decisions allowing tradeoff between speed and accuracy. We found that individual neurons in the human dorsal anterior cingulate cortex encode the level of decision conflict in a dose-dependent manner. In addition, these neurons encode historical conflict information, priming the neural circuit to future trials of the same or varying conflict levels. Following selective ablation of the dorsal anterior cingulate cortex, we found this signal was selectively abolished. Lastly, we explored how the brain represents decisions under conflict and if these decisions are malleable to external intervention. We found that neurons in the human subthalamic nucleus are selectively activated and encode the upcoming decision during situations of high decision conflict. Based on the physiological findings, we then applied intermittent stimulation through the implanted deep brain stimulation electrode during the same task, to demonstrate a causal interaction between the physiology and behavior. In conclusion, we describe a set of experiments that systematically explore human decision-making processes at the single-neuronal level

    Under pressure: Response urgency modulates striatal and insula activity during decision-making under risk

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    When deciding whether to bet in situations that involve potential monetary loss or gain (mixed gambles), a subjective sense of pressure can influence the evaluation of the expected utility associated with each choice option. Here, we explored how gambling decisions, their psychophysiological and neural counterparts are modulated by an induced sense of urgency to respond. Urgency influenced decision times and evoked heart rate responses, interacting with the expected value of each gamble. Using functional MRI, we observed that this interaction was associated with changes in the activity of the striatum, a critical region for both reward and choice selection, and within the insula, a region implicated as the substrate of affective feelings arising from interoceptive signals which influence motivational behavior. Our findings bridge current psychophysiological and neurobiological models of value representation and action-programming, identifying the striatum and insular cortex as the key substrates of decision-making under risk and urgency

    Decoding the neural substrates of reward-related decision making with functional MRI

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    Although previous studies have implicated a diverse set of brain regions in reward-related decision making, it is not yet known which of these regions contain information that directly reflects a decision. Here, we measured brain activity using functional MRI in a group of subjects while they performed a simple reward-based decision-making task: probabilistic reversal-learning. We recorded brain activity from nine distinct regions of interest previously implicated in decision making and separated out local spatially distributed signals in each region from global differences in signal. Using a multivariate analysis approach, we determined the extent to which global and local signals could be used to decode subjects' subsequent behavioral choice, based on their brain activity on the preceding trial. We found that subjects' decisions could be decoded to a high level of accuracy on the basis of both local and global signals even before they were required to make a choice, and even before they knew which physical action would be required. Furthermore, the combined signals from three specific brain areas (anterior cingulate cortex, medial prefrontal cortex, and ventral striatum) were found to provide all of the information sufficient to decode subjects' decisions out of all of the regions we studied. These findings implicate a specific network of regions in encoding information relevant to subsequent behavioral choice
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