Getting Formal with Dopamine and Reward

Schultz, Wolfram

doi:10.1016/s0896-6273(02)00967-4

Cited by 2,208 publications

(1,830 citation statements)

References 183 publications

(7 reference statements)

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“…More specifically, the results suggest that reward processing systems determine whether an outcome is favorable or unfavorable on the basis of the range of possible outcomes encountered in a particular setting-judging the best possible outcome to be favorable and the worst possible outcome to be unfavorable, regardless of the absolute magnitudes of these outcomes. The scaling of the reward by the range of possible outcomes is consistent with reward prediction error theory, according to which brain areas are sensitive to deviations from expected reward rather than to absolute magnitude of reward (Holroyd and Coles, 2002;Montague and Berns, 2002;Schultz, 2002). Our findings are also consistent with previous research that has identified brain areas showing modulation of reward-sensitive activity by the context of the recent history of monetary rewards and punishments (Akitsuki et al, 2003;Elliott et al, 2000;Nakahara et al, 2004).…”

Section: Discussionsupporting

confidence: 90%

Activity in human reward-sensitive brain areas is strongly context dependent

et al. 2005

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Functional neuroimaging research in humans has identified a number of brain areas that are activated by the delivery of primary and secondary reinforcers. The present study investigated how activity in these reward-sensitive regions is modulated by the context in which rewards and punishments are experienced. Fourteen healthy volunteers were scanned during the performance of a simple monetary gambling task that involved a bwinQ condition (in which the possible outcomes were a large monetary gain, a small gain, or no gain of money) and a bloseQ condition (in which the possible outcomes were a large monetary loss, a small loss, or no loss of money). We observed reward-sensitive activity in a number of brain areas previously implicated in reward processing, including the striatum, prefrontal cortex, posterior cingulate, and inferior parietal lobule. Critically, activity in these reward-sensitive areas was highly sensitive to the range of possible outcomes from which an outcome was selected. In particular, these regions were activated to a comparable degree by the best outcomes in each condition-a large gain in the win condition and no loss of money in the lose condition-despite the large difference in the objective value of these outcomes. In addition, some rewardsensitive brain areas showed a binary instead of graded sensitivity to the magnitude of the outcomes from each distribution. These results provide important evidence regarding the way in which the brain scales the motivational value of events by the context in which these events occur. D

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Section: Discussionsupporting

confidence: 90%

Activity in human reward-sensitive brain areas is strongly context dependent

et al. 2005

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“…For instance, an active neuron primes its neighbor which causes its neighbor to become more active following that priming which in turn causes the neighbor to prime its neighbor and so on. Dopamine-like neurons are used in our model since they are fairly ubiquitous and can prime one another in 50-100 ms (Schultz 2002), which is well within the time span suggested for longrange contour integration of about 250 ms (Braun 1999). We state this because contour detection performance saturates at 12 Gabor elements.…”

Section: Featuresmentioning

confidence: 93%

“…Figure 3 shows a frame-by-frame example of this process. We reason for this method of propagation by observing that this process of priming has been observed and simulated in the brain, for instance in striatal neurons (Schultz 2002;Suri et al 2001). Additionally, we should note that we emphasize the term dopamine-like.…”

Section: Featuresmentioning

confidence: 99%

“…Additionally, we should note that we emphasize the term dopamine-like. This is because other systems such as norepinephrine neurons in the locus coeruleus and Cholinergic neurons in basal forebrain also exhibit similar behavior (Schultz 2002), and while fast plasticity has been observed in higher cortical areas such as the prefrontal cortex (Hemple et al 2000) and the rat visual cortex (Varela et al (4) When the neuron on the left receives another input, it is more likely to cross its firing threshold. This allows contour elements to propagate activity to other contour neurons that are not directly connected 1997) the time course and underlying mechanisms seem not to be understood well enough at the moment for our simulation.…”

Section: Featuresmentioning

confidence: 99%

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Computational modeling and exploration of contour integration for visual saliency

Mundhenk

Itti

2005

Biol Cybern

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We propose a computational model of contour integration for visual saliency. The model uses biologically plausible devices to simulate how the representations of elements aligned collinearly along a contour in an image are enhanced. Our model adds such devices as a dopamine-like fast plasticity, local GABAergic inhibition and multi-scale processing of images. The fast plasticity addresses the problem of how neurons in visual cortex seem to be able to influence neurons they are not directly connected to, for instance, as observed in contour closure effect. Local GABAergic inhibition is used to control gain in the system without using global mechanisms which may be non-plausible given the limited reach of axonal arbors in visual cortex. The model is then used to explore not only its validity in real and artificial images, but to discover some of the mechanisms involved in processing of complex visual features such as junctions and end-stops as well as contours. We present evidence for the validity of our model in several phases, starting with local enhancement of only a few collinear elements. We then test our model on more complex contour integration images with a large number of Gabor elements. Sections of the model are also extracted and used to discover how the model might relate contour integration neurons to neurons that process end-stops and junctions. Finally, we present results from real world images. Results from the model suggest that it is a good current approximation of contour integration in human vision. As well, it suggests that contour integration mechanisms may be strongly related to mechanisms for detecting end-stops and junction points. Additionally, a contour integration mechanism may be involved in finding features for objects such as faces. This suggests that visual cortex may be more information efficient and that neural regions may have multiple roles.

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“…FRN is a negative deflection of the ERP in response to feedback to performance on a given task, and it is hypothesized to reflect evaluation of feedback in a given comparative context (Foti & Hajcak, 2009). This has been understood as mirroring a basic reinforcement prediction and learning mechanism, highly relevant to reward processing (Schultz, 2002). The FRN response is known to vary with the valence of feedback, to the effect that worse-than-expected feedback yields a more negative response.…”

Section: Introductionmentioning

confidence: 99%

Anhedonia and emotional numbing in treatment-seeking veterans: behavioural and electrophysiological responses to reward

Eskelund¹,

Karstoft

Andersen

2018

European Journal of Psychotraumatology

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Background: Anhedonia is a common symptom following exposure to traumatic stress and a feature of the PTSD diagnosis. In depression research, anhedonia has been linked to deficits in reward functioning, reflected in behavioural and neural responses. Such deficits following exposure to trauma, however, are not well understood.Objective: The current study aims to estimate the associations between anhedonia, PTSD symptom-clusters and behavioural and electrophysiological responses to reward.Methods: Participants (N = 61) were recruited among Danish treatment-seeking veterans at the Department of Military Psychology in the Danish Defence. Before entering treatment, participants were screened with symptom measurement instruments and participated in a joint behavioural-electrophysiological experiment. The experimental paradigm consisted of a signal-detection task aimed at assessing reward-driven learning. Simultaneous electrophysiological-recordings were analysed to evaluate neural responses upon receiving reward, as indicated by the Feedback-Related Negativity (FRN) component.Result: Anhedonia as conceptualized in depression correlated with behavioural learning (r = -0.28, p = .032). Neither anhedonia nor behavioural learning correlated with FRN. However, the anhedonia symptom cluster of PTSD did correlate with FRN (r = 0.29, p = .023). Extending upon this in an exploratory analysis, the specific PTSD-symptom emotional numbing was found to correlate moderately with FRN (r = 0.38, p = .003).Conclusion: The present data suggest that anhedonia in trauma-exposed individuals is related to the anticipatory aspect of reward, whereas the neural consummatory reward response seems unlinked. Interestingly, emotional numbing in the same population is related to the consummatory phase of reward, correlating with the FRN response. This suggests that anhedonia and emotional numbing in response to trauma might pertain to different phases of reward processing.

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Getting Formal with Dopamine and Reward

Cited by 2,208 publications

References 183 publications

Activity in human reward-sensitive brain areas is strongly context dependent

Activity in human reward-sensitive brain areas is strongly context dependent

Computational modeling and exploration of contour integration for visual saliency

Anhedonia and emotional numbing in treatment-seeking veterans: behavioural and electrophysiological responses to reward

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