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Cognitive theories of depression, and mindfulness theories of well-being, converge on the notion that self-judgment plays a critical role in mental health. However, these theories have rarely been tested via tasks and computational modeling analyses that can disentangle the information processes operative in self-judgments. We applied a drift-diffusion computational model to the self-referential encoding task (SRET) collected before and after an 8-week mindfulness intervention (n = 96). A drift-rate regression parameter representing positive-relative to negative-self-referential judgment strength positively related to mindful awareness and inversely related to depression, both at baseline and over time; however, this parameter did not significantly relate to the interaction between mindful awareness and nonjudgmentalness. At the level of individual depression symptoms, at baseline, a spectrum of symptoms (inversely) correlated with the drift-rate regression parameter, suggesting that many distinct depression symptoms relate to valenced self-judgment between subjects. By contrast, over the intervention, changes in only a smaller subset of anhedonia-related depression symptoms showed substantial relationships with this parameter. Both behavioral and model-derived measures showed modest split-half and test-retest correlations. Results support cognitive theories that implicate self-judgment in depression and mindfulness theories, which imply that mindful awareness should lead to more positive self-views.

Computational psychiatry is a rapidly growing field attempting to translate advances in computational neuroscience and machine learning into improved outcomes for patients suffering from mental illness. It encompasses both data-driven and theory-driven efforts. Here, recent advances in theory-driven work are reviewed. We argue that the brain is a computational organ. As such, an understanding of the illnesses arising from it will require a computational framework. The review divides work up into three theoretical approaches that have deep mathematical connections: dynamical systems, Bayesian inference and reinforcement learning. We discuss both general and specific challenges for the field, and suggest ways forward.

We present three versions of the Lax-Milgram theorem in the framework of Hilbert C*-modules, two for those over W*-algebras and one for those over C*-algebras of compact operators. It is remarkable that while the Riesz theorem is not valid for certain Hilbert C*-modules over C*-algebras of compact operators, our Lax-Milgram theorem turns out to be valid for all of them. We also give several examples to illustrate our results, in particular, we show that the main theorem is not true for Hilbert modules over arbitrary C*-algebras., 13 pages

Dopamine is thought to provide reward prediction error signals to temporal lobe memory systems, but the role of these signals in episodic memory has not been fully characterized. Here we developed an incidental memory paradigm to (i) estimate the influence of reward prediction errors on the formation of episodic memories, (ii) dissociate this influence from surprise and uncertainty, (iii) characterize the role of temporal correspondence between prediction error and memoranda presentation and (iv) determine the extent to which this influence is dependent on memory consolidation. We found that people encoded incidental memoranda more strongly when they gambled for potential rewards. Moreover, the degree to which gambling strengthened encoding scaled with the reward prediction error experienced when memoranda were presented (and not before or after). This encoding enhancement was detectable within minutes and did not differ substantially after 24 h, indicating that it is not dependent on memory consolidation. These results suggest a computationally and temporally specific role for reward prediction error signalling in memory formation. Jang and colleagues show that positive reward prediction errors elicited during incidental encoding enhance the formation of episodic memories.

The interplay of dopaminergic striatal D1-D2 circuits is thought to support working memory (WM) by selectively filtering information that is to be remembered versus information to be ignored. To test this theory, we conducted an experiment in which healthy participants performed a visuospatial working memory (VSWM) task after ingesting the D2-receptor agonist cabergoline (or placebo), in a randomized, double-blinded, crossover design. Results showed greater interference from distractors under cabergoline, particularly for individuals with higher baseline dopamine (indicated by WM span). These findings support computational theories of striatal D1-D2 function during WM encoding and distractor-filtering, and provide new evidence for interactive cortico-striatal systems that support VSWM capacity and their dependence on WM span.

We show that if A is a non-unital $$C^*$$-algebra of compact operators, which is $$ *$$-isomorphic to $$\oplus _{i \in I} K(H_{i})$$, where I is an arbitrary index set and for every $$ i \in I $$, $$H_{i} $$ is a separable Hilbert space, then there exists a Hilbert $$A_1$$-module admitting no frames, where $$A_1$$ is the unitization of A.

Two studies invite us to reconsider the nature of striatal dopamine signals. Accumbens dopamine appears to signal the value of overt action and prediction errors arise from deviations in these signals.

In the future, climate change will strongly influence our environment and living conditions. Weather and Climate simulations that predict possible changes produce big data sets. The combination of various variables of climate models with spatial data from different sources helps to identify correlations and to study key processes. In this paper, the results of the Weather Research and Forecasting model are visualized for two regions. For this purpose, a continuous workflow that leads from the integration of heterogeneous raw data to 3D visualizations that can be displayed on a desktop computer or in an interactive virtual reality environment is developed. These easy-to-understand visualizations of complex data are the basis for scientific communication and for the evaluation and verification of models as well as for interdisciplinary discussions of the research results.

Patients with schizophrenia (SZ) show cognitive impairments on a wide range of tasks, with clear deficiencies in tasks reliant on prefrontal cortex function and less consistently observed impairments in tasks recruiting the striatum. This study leverages tasks hypothesized to differentially recruit these neural structures to assess relative deficiencies of each. Forty-eight patients and 38 controls completed two reinforcement learning tasks hypothesized to interrogate pre-frontal and striatal functions and their interaction. In each task, participants learned reward discriminations by trial and error and were tested on novel stimulus combinations to assess learned values. In the task putatively assessing fronto-striatal interaction, participants were (inaccurately) instructed that one of the stimuli was valuable. Consistent with prior reports and a model of confirmation bias, this manipulation resulted in overvaluation of the instructed stimulus after its true value had been experienced. Patients showed less susceptibility to this confirmation bias effect than did controls. In the choice bias task hypothesized to more purely assess striatal function, biases in endogenously and exogenously chosen actions were assessed. No group differences were observed. In the subset of participants who showed learning in both tasks, larger group differences were observed in the confirmation bias task than in the choice bias task. In the confirmation bias task, patients also showed impairment in the task conditions with no prior instruction. This deficit was most readily observed on the most deterministic discriminations. Taken together, these results suggest impairments in fronto-striatal interaction in SZ, rather than in striatal function per se.

In this report, we describe how common brain networks within the medial frontal cortex facilitate adaptive behavioral control in rodents and humans. We demonstrate that low frequency oscillations below 12 Hz are dramatically modulated after errors in humans over mid-frontal cortex and in rats within prelimbic and anterior cingulate regions of medial frontal cortex. These oscillations were phase-locked between medial frontal cortex and motor areas in both rats and humans. In rats, single neurons that encoded prior behavioral outcomes were phase-coherent with low-frequency field oscillations particularly after errors. Inactivating medial frontal regions in rats led to impaired behavioral adjustments after errors, eliminated the differential expression of low frequency oscillations after errors, and increased low-frequency spike-field coupling within motor cortex. Our results describe a novel mechanism for behavioral adaptation via low-frequency oscillations and elucidate how medial frontal networks synchronize brain activity to guide performance.

It takes effort and time to tame one's impulses. Although medial prefrontal cortex (mPFC) is broadly implicated in effortful control over behavior, the subthalamic nucleus (STN) is specifically thought to contribute by acting as a brake on cortico-striatal function during decision conflict, buying time until the right decision can be made. Using the drift diffusion model of decision making, we found that trial-to-trial increases in mPFC activity (EEG theta power, 4-8 Hz) were related to an increased threshold for evidence accumulation (decision threshold) as a function of conflict. Deep brain stimulation of the STN in individuals with Parkinson's disease reversed this relationship, resulting in impulsive choice. In addition, intracranial recordings of the STN area revealed increased activity (2.5-5 Hz) during these same high-conflict decisions. Activity in these slow frequency bands may reflect a neural substrate for cortico-basal ganglia communication regulating decision processes.

Aversively motivated learning is more poorly understood than appetitively motivated learning in many aspects, including the role of dopamine receptors in different regions of the striatum. The present study investigated the roles of the D1-like DA receptors in the nucleus accumbens (NAc) and dorsolateral striatum (DLS) on learning and performance of conditioned avoidance responses (CARs). Adult male Wistar rats received intraperitoneal (i.p.), intra-NAc, or intra-DLS injections of the D1 dopamine receptor agonist SKF 81297 or the D1 receptor antagonist SCH 23390 20 min before or immediately after a training session in the CAR task two-way active avoidance, carried out 24 h before a test session. Pre-training administration of SCH 23390, but not SKF 81297, caused a significant decrease in the number of CARs in the test, but not in the training session, when injected into the DLS, or in either session when injected into the NAc. It also caused a significant increase in the number of escape failures in the training session when injected into the NAc. Systemic administration caused a combination of these effects. Post-training administrations of these drugs caused no significant effect. The results suggest that the D1-like receptors in the NAc and DLS play important, though different, roles in learning and performance of CAR.

Reinforcement learning models have provided insight into the functions of dopamine and cortico-basal ganglia-thalamo-cortical circuits. Here the authors review the literature suggesting that these models can also be applied to improving our understanding of dysfunction in this system, particularly in the context of disease. Over the last decade and a half, reinforcement learning models have fostered an increasingly sophisticated understanding of the functions of dopamine and cortico-basal ganglia-thalamo-cortical (CBGTC) circuits. More recently, these models, and the insights that they afford, have started to be used to understand important aspects of several psychiatric and neurological disorders that involve disturbances of the dopaminergic system and CBGTC circuits. We review this approach and its existing and potential applications to Parkinson's disease, Tourette's syndrome, attention-deficit/hyperactivity disorder, addiction, schizophrenia and preclinical animal models used to screen new antipsychotic drugs. The approach's proven explanatory and predictive power bodes well for the continued growth of computational psychiatry and computational neurology.

Many of the individual differences in cognition, motivation, and learning—and the disruption of these processes in neurological conditions—are influenced by genetic factors. We provide an integrative synthesis across human and animal studies, focusing on a recent spate of evidence implicating a role for genes controlling dopaminergic function in frontostriatal circuitry, including COMT, DARPP-32, DAT1, DRD2, and DRD4. These genetic effects are interpreted within theoretical frameworks developed in the context of the broader cognitive and computational neuroscience literature, constrained by data from pharmacological, neuroimaging, electrophysiological, and patient studies. In this framework, genes modulate the efficacy of particular neural computations, and effects of genetic variation are revealed by assays designed to be maximally sensitive to these computations. We discuss the merits and caveats of this approach and outline a number of novel candidate genes of interest for future study.

The basal ganglia support learning to exploit decisions that have yielded positive outcomes in the past. In contrast, limited evidence implicates the prefrontal cortex in the process of making strategic exploratory decisions when the magnitude of potential outcomes is unknown. Here we examine neurogenetic contributions to individual differences in these distinct aspects of motivated human behavior, using a temporal decision-making task and computational analysis. We show that two genes controlling striatal dopamine function, DARPP-32 (also called PPP1R1B) and DRD2, are associated with exploitative learning to adjust response times incrementally as a function of positive and negative decision outcomes. In contrast, a gene primarily controlling prefrontal dopamine function (COMT) is associated with a particular type of 'directed exploration', in which exploratory decisions are made in proportion to Bayesian uncertainty about whether other choices might produce outcomes that are better than the status quo. Quantitative model fits reveal that genetic factors modulate independent parameters of a reinforcement learning system.

The dopaminergic system, particularly D2-like dopamine receptors, has been strongly implicated in reward processing. Animal studies have emphasized the role of phasic dopamine (DA) signaling in reward-related learning, but these processes remain largely unexplored in humans.To evaluate the effect of a single, low dose of a D2/D3 agonist--pramipexole--on reinforcement learning in healthy adults. Based on prior evidence indicating that low doses of DA agonists decrease phasic DA release through autoreceptor stimulation, we hypothesized that 0.5 mg of pramipexole would impair reward learning due to presynaptic mechanisms.Using a double-blind design, a single 0.5-mg dose of pramipexole or placebo was administered to 32 healthy volunteers, who performed a probabilistic reward task involving a differential reinforcement schedule as well as various control tasks.As hypothesized, response bias toward the more frequently rewarded stimulus was impaired in the pramipexole group, even after adjusting for transient adverse effects. In addition, the pramipexole group showed reaction time and motor speed slowing and increased negative affect; however, when adverse physical side effects were considered, group differences in motor speed and negative affect disappeared.These findings show that a single low dose of pramipexole impaired the acquisition of reward-related behavior in healthy participants, and they are consistent with prior evidence suggesting that phasic DA signaling is required to reinforce actions leading to reward. The potential implications of the present findings to psychiatric conditions, including depression and impulse control disorders related to addiction, are discussed.

We test our neurocomputational model of fronto-striatal dopamine (DA) and noradrenaline (NA) function for understanding cognitive and motivational deficits in attention deficit/hyperactivity disorder (ADHD). Our model predicts that low striatal DA levels in ADHD should lead to deficits in 'Go' learning from positive reinforcement, which should be alleviated by stimulant medications, as observed with DA manipulations in other populations. Indeed, while nonmedicated adult ADHD participants were impaired at both positive (Go) and negative (NoGo) reinforcement learning, only the former deficits were ameliorated by medication. We also found evidence for our model's extension of the same striatal DA mechanisms to working memory, via interactions with prefrontal cortex. In a modified AX-continuous performance task, ADHD participants showed reduced sensitivity to working memory contextual information, despite no global performance deficits, and were more susceptible to the influence of distractor stimuli presented during the delay. These effects were reversed with stimulant medications. Moreover, the tendency for medications to improve Go relative to NoGo reinforcement learning was predictive of their improvement in working memory in distracting conditions, suggestive of common DA mechanisms and supporting a unified account of DA function in ADHD. However, other ADHD effects such as erratic trial-to-trial switching and reaction time variability are not accounted for by model DA mechanisms, and are instead consistent with cortical noradrenergic dysfunction and associated computational models. Accordingly, putative NA deficits were correlated with each other and independent of putative DA-related deficits. Taken together, our results demonstrate the usefulness of computational approaches for understanding cognitive deficits in ADHD.

Conflict has been proposed to act as a cost in action selection, implying a general function of medio-frontal cortex in the adaptation to aversive events. Here we investigate if response conflict acts as a cost during reinforcement learning by modulating experienced reward values in cortical and striatal systems. Electroencephalography recordings show that conflict diminishes the relationship between reward-related frontal theta power and cue preference yet it enhances the relationship between punishment and cue avoidance. Individual differences in the cost of conflict on reward versus punishment sensitivity are also related to a genetic polymorphism associated with striatal D1 versus D2 pathway balance (DARPP-32). We manipulate these patterns with the D2 agent cabergoline, which induces a strong bias to amplify the aversive value of punishment outcomes following conflict. Collectively, these findings demonstrate that interactive cortico-striatal systems implicitly modulate experienced reward and punishment values as a function of conflict.

The frontal cortex and the basal ganglia interact via a relatively well understood and elaborate system of interconnections. In the context of motor function, these interconnections can be understood as disinhibiting, or "releasing the brakes," on frontal motor action plans: The basal ganglia detect appropriate contexts for performing motor actions and enable the frontal cortex to execute such actions at the appropriate time. We build on this idea in the domain of working memory through the use of computational neural network models of this circuit. In our model, the frontal cortex exhibits robust active maintenance, whereas the basal ganglia contribute a selective, dynamic gating function that enables frontal memory representations to be rapidly updated in a task-relevant manner. We apply the model to a novel version of the continuous performance task that requires subroutine-like selective working memory updating and compare and contrast our model with other existing models and theories of frontal-cortex-basal-ganglia interactions.

The aim of the present paper is to solve some major open problems of Hilbert C*-module theory by applying various aspects of multiplier C*-theory. The key result is the equivalence established between positive invertible quasi-multipliers of the C*-algebra of “compact” operators on a Hilbert C*-module {ℳ, 〈., 〉} and A-valued inner products on ℳ, inducing an equivalent norm to the given one. The problem of unitary isomorphism of C*-valued inner products on a Hilbert C*-module is considered and new criteria are formulated. Countably generated Hilbert C*-modules turn out to be unitarily isomorphic if they are isomorphic as Banach C*-modules. The property of bounded module operators on Hilbert C*-modules of being “compact” and/or adjointable is unambiguously connected to operators with respect to any choice of the C*-valued inner product on a fixed Hilbert C*-module if every bounded module operator possesses an adjoint operator on the module. Every bounded module operator on a given full Hilbert C*-module turns out to be adjointable if the Hilbert C*-module is orthogonally complementary. Moreover, if the unit ball of the Hilbert C*-module is complete with respect to a certain locally convex topology, then these two properties are shown to be equivalent to self-duality.

The main goal of the present paper is to generalize the results of~\cite{TroLNM,TroBoch} in the following way: To be able to define $K_0(A)o\C$-valued Lefschetz numbers of the first type of an endomorphism $V$ on a C*-elliptic complex one usually assumes that $V=T_g$ for some representation $T_g$ of a compact group $G$ on the C*-elliptic complex. We try to refuse this restriction in the present paper. The price to pay for this is twofold: (i) $ $ We have to define Lefschetz numbers valued in some larger group as $K_0(A)o\C$. (ii) We have to deal with W*-algebras instead of general unital C*-algebras. To obtain these results we have got a number of by-product facts on the theory of Hilbert W*- and C*-modules and on bounded module operators on them which are of independent interest.

Corrigendum: Prefrontal and striatal dopaminergic genes predict individual differences in exploration and exploitation

Let A be a C*-algebra, K be a compact space, A(K) be the C*-algebra of all continuous maps from K into A, 12(A) be the standard countably generated Hilbert A-module. We investigate a set of maps from K into EndA(12(A)), which is isomorphic to EndA(K)(12(A(K))). We describe the subsets which are isomorphic to EndfA(K)*(12(A(K))). GLA(K)(12(A(K))) and GLfA(K)*(12(A(K))), respectively. As an application we deduce a criterion for the self-duality of 12(A) in the commutative case.