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Neuroeconomic studies of decision making have emphasized reward learning as critical in the representation of value-driven choice behaviour. However, it is readily apparent that punishment and aversive learning are also significant factors in motivating decisions and actions. In this paper, we review the role of the striatum and amygdala in affective learning and the coding of aversive prediction errors (PEs). We present neuroimaging results showing aversive PE-related signals in the striatum in fear conditioning paradigms with both primary (shock) and secondary (monetary loss) reinforcers. These results and others point to the general role for the striatum in coding PEs across a broad range of learning paradigms and reinforcer types.

In the mammalian brain, dopamine is a critical neuromodulator whose actions underlie learning, decision-making, and behavioral control. Degeneration of dopamine neurons causes Parkinson’s disease, whereas dysregulation of dopamine signaling is believed to contribute to psychiatric conditions such as schizophrenia, addiction, and depression. Experiments in animal models suggest the hypothesis that dopamine release in human striatum encodes reward prediction errors (RPEs) (the difference between actual and expected outcomes) during ongoing decision-making. Blood oxygen level-dependent (BOLD) imaging experiments in humans support the idea that RPEs are tracked in the striatum; however, BOLD measurements cannot be used to infer the action of any one specific neurotransmitter. We monitored dopamine levels with subsecond temporal resolution in humans (n = 17) with Parkinson’s disease while they executed a sequential decision-making task. Participants placed bets and experienced monetary gains or losses. Dopamine fluctuations in the striatum fail to encode RPEs, as anticipated by a large body of work in model organisms. Instead, subsecond dopamine fluctuations encode an integration of RPEs with counterfactual prediction errors, the latter defined by how much better or worse the experienced outcome could have been. How dopamine fluctuations combine the actual and counterfactual is unknown. One possibility is that this process is the normal behavior of reward processing dopamine neurons, which previously had not been tested by experiments in animal models. Alternatively, this superposition of error terms may result from an additional yet-to-be-identified subclass of dopamine neurons.

Theoretical frameworks such as predictive coding suggest that the perception of the body and world – interoception and exteroception – involve intertwined processes of inference, learning, and prediction. In this framework, attention is thought to gate the influence of sensory information on perception. In contrast to exteroception, there is limited evidence for purely attentional effects on interoception. Here, we empirically tested if attentional focus modulates cortical processing of single heartbeats, using a newly-developed experimental paradigm to probe purely attentional differences between exteroceptive and interoceptive conditions in the heartbeat evoked potential (HEP) using EEG recordings. We found that the HEP is significantly higher during interoceptive compared to exteroceptive attention, in a time window of 524–620 ms after the R-peak. Furthermore, this effect predicted self-report measures of autonomic system reactivity. Our study thus provides direct evidence that the HEP is modulated by pure attention and suggests that this effect may provide a clinically relevant readout for assessing interoception. [Display omitted]

We provide a unified analysis of the predictive risk of ridge regression and regularized discriminant analysis in a dense random effects model. We work in a high-dimensional asymptotic regime where p,n → ∞ and p/n → γ > 0, and allow for arbitrary covariance among the features. For both methods, we provide an explicit and efficiently computable expression for the limiting predictive risk, which depends only on the spectrum of the feature-covariance matrix, the signal strength and the aspect ratio γ. Especially in the case of regularized discriminant analysis, we find that predictive accuracy has a nuanced dependence on the eigenvalue distribution of the covariance matrix, suggesting that analyses based on the operator norm of the covariance matrix may not be sharp. Our results also uncover an exact inverse relation between the limiting predictive risk and the limiting estimation risk in high-dimensional linear models. The analysis builds on recent advances in random matrix theory.

The question of how the brain represents reward prediction errors is central to reinforcement learning and adaptive, goal‐directed behavior. Previous studies have revealed prediction error representations in multiple electrophysiological signatures, but it remains elusive whether these electrophysiological correlates underlying prediction errors are sensitive to valence (in a signed form) or to salience (in an unsigned form). One possible reason concerns the loose correspondence between objective probability and subjective prediction resulting from the optimistic bias, that is, the tendency to overestimate the likelihood of encountering positive future events. In the present electroencephalography (EEG) study, we approached this question by directly measuring participants' idiosyncratic, trial‐to‐trial prediction errors elicited by subjective and objective probabilities across two experiments. We adopted monetary gain and loss feedback in Experiment 1 and positive and negative feedback as communicated by the same zero‐value feedback in Experiment 2. We provided electrophysiological evidence in time and time‐frequency domains supporting both reward and salience prediction error signals. Moreover, we showed that these electrophysiological signatures were highly flexible and sensitive to an optimistic bias and various forms of salience. Our findings shed new light on multiple presentations of prediction error in the human brain, which differ in format and functional role. We measured subjective and objective prediction error signals across two EEG tasks. We found prediction error representations in multiple neural signatures. The variety of neural prediction errors is further modulated by optimistic bias.

This paper deals with recursive continuous-time system identification using fractional-order models. Long-memory recursive prediction error method is proposed for recursive estimation of all parameters of fractional-order models. When differentiation orders are assumed known, least squares and prediction error methods, being direct extensions to fractional-order models of the classic methods used for integer-order models, are compared to our new method, the long-memory recursive prediction error method. Given the long-memory property of fractional models, Monte Carlo simulations prove the efficiency of our proposed algorithm. Then, when the differentiation orders are unknown, two-stage algorithms are necessary for both parameter and differentiation-order estimation. The performances of the new proposed recursive algorithm are studied through Monte Carlo simulations. Finally, the proposed algorithm is validated on a biological example where heat transfers in lungs are modeled by using thermal two-port network formalism with fractional models.

Spatial statistics for very large spatial data sets is challenging. The size of the data set, n, causes problems in computing optimal spatial predictors such as kriging, since its computational cost is of order [graphic removed] . In addition, a large data set is often defined on a large spatial domain, so the spatial process of interest typically exhibits non-stationary behaviour over that domain. A flexible family of non-stationary covariance functions is defined by using a set of basis functions that is fixed in number, which leads to a spatial prediction method that we call fixed rank kriging. Specifically, fixed rank kriging is kriging within this class of non-stationary covariance functions. It relies on computational simplifications when n is very large, for obtaining the spatial best linear unbiased predictor and its mean-squared prediction error for a hidden spatial process. A method based on minimizing a weighted Frobenius norm yields best estimators of the covariance function parameters, which are then substituted into the fixed rank kriging equations. The new methodology is applied to a very large data set of total column ozone data, observed over the entire globe, where n is of the order of hundreds of thousands.

When a person fails to obtain an expected reward from an object in the environment, they face a credit assignment problem: Did the absence of reward reflect an extrinsic property of the environment or an intrinsic error in motor execution? To explore this problem, we modified a popular decision-making task used in studies of reinforcement learning, the two-armed bandit task. We compared a version in which choices were indicated by key presses, the standard response in such tasks, to a version in which the choices were indicated by reaching movements, which affords execution failures. In the key press condition, participants exhibited a strong risk aversion bias; strikingly, this bias reversed in the reaching condition. This result can be explained by a reinforcement model wherein movement errors influence decision-making, either by gating reward prediction errors or by modifying an implicit representation of motor competence. Two further experiments support the gating hypothesis. First, we used a condition in which we provided visual cues indicative of movement errors but informed the participants that trial outcomes were independent of their actual movements. The main result was replicated, indicating that the gating process is independent of participants’ explicit sense of control. Second, individuals with cerebellar degeneration failed to modulate their behavior between the key press and reach conditions, providing converging evidence of an implicit influence of movement error signals on reinforcement learning. These results provide a mechanistically tractable solution to the credit assignment problem.

Experiences are stored in the mind as discrete mental units, or ‘events,’ which influence—and are influenced by—attention, learning, and memory. In this way, the notion of an ‘event’ is foundational to cognitive science. However, despite tremendous progress in understanding the behavioral and neural signatures of events, there is no agreed-upon definition of an event. Here, we discuss different theoretical frameworks of event perception and memory, noting what they can and cannot account for in the literature. We then highlight key aspects of events that we believe should be accounted for in theories of event processing––in particular, we argue that the structure and substance of events should be better reflected in our theories and paradigms. Finally, we discuss empirical gaps in the event cognition literature and what the future of event cognition research may look like.

Predictive coding (PC) has become a central framework in contemporary cognitive neuroscience, proposing that the brain operates as a hierarchical inference system that continuously minimizes the mismatch between predicted and actual sensory input. Its extension into clinical neuroscience has been accompanied by considerable enthusiasm, yet attempts to translate its computational principles into explanations of psychiatric and neurological disorders have yielded uneven results. The present review critically examines the clinical applicability of PC across three diagnostic domains: schizophrenia, autism spectrum disorder (ASD), and mood and anxiety disorders. Drawing on findings from neuroimaging, electrophysiology, and computational modeling, the discussion evaluates how disturbances in prediction error signaling, the precision weighting of sensory evidence relative to prior beliefs, and hierarchical inference have been proposed to relate to core clinical phenomena such as hallucinations, sensory hypersensitivity, and affective dysregulation. Particular attention is given to persistent theoretical tensions, including debates surrounding prior precision, the mapping between neural proxies and behavior, and the inconsistent use of PC terminology across diagnostic contexts. By adopting a structured and comparative approach, this review aims to clarify where predictive coding offers testable mechanistic insight into psychopathology, and where its explanatory scope remains limited or provisional.