Behavior is guided by the compatibility of expectations based on past experience and the outcome. In a recent study, Fouragnan and colleagues report that absolute prediction error (PE)-related ...heart-evoked potentials (HEPs) differ according to the cardiac cycle phase at outcome, and that the magnitude of this effect positively correlates with reward learning in healthy adults.
Behavior is guided by the compatibility of expectations based on past experience and the outcome. In a recent study, Fouragnan and colleagues report that absolute prediction error (PE)-related heart-evoked potentials (HEPs) differ according to the cardiac cycle phase at outcome, and that the magnitude of this effect positively correlates with reward learning in healthy adults.
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 prediction-error expansion (PEE) based reversible data hiding, better exploiting image redundancy usually leads to a superior performance. However, the correlations among prediction-errors are not ...considered and utilized in current PEE based methods. Specifically, in PEE, the prediction-errors are modified individually in data embedding. In this paper, to better exploit these correlations, instead of utilizing prediction-errors individually, we propose to consider every two adjacent prediction-errors jointly to generate a sequence consisting of prediction-error pairs. Then, based on the sequence and the resulting 2D prediction-error histogram, a more efficient embedding strategy, namely, pairwise PEE, can be designed to achieve an improved performance. The superiority of our method is verified through extensive experiments.
Reversible Data Hiding Techniques (RDH) play an increasingly pivotal role in the field of cybersecurity. Overlooking the properties of the carrier image and neglecting the influence of texture can ...lead to undesirable distortions and irreversible data hiding. In this paper, a novel block-based RDH technique is proposed that harnesses the relative correlation between multidirectional prediction error histograms (MPEH) and pixel fluctuation values to mitigate undesirable distortions and enable RDH, thereby ensuring heightened security and efficiency in the distribution process and improving the robustness of the block-based RDH technique. The proposed technique uses a combination of pixel fluctuation and local complexity measures to determine the best embedding locations within smooth regions based on the cumulative peak regions of the MPEH with the lowest fluctuation values. Similarly, during the extraction process, the same optimal embedding locations are identified within smooth regions. The multidirectional prediction error histograms are then used to accurately extract the hidden data from the pixels with lower fluctuation values. Overall, the experimental results highlight the effectiveness and superiority of the proposed technique in various aspects of data embedding and extraction, and demonstrate that the proposed technique outperforms other state-of-the-art RDH techniques in terms of embedding capacity, image quality, and robustness against attacks. The average Peak Signal-to-Noise Ratio (PSNR) achieved with an embedding capacity ranging from 0.5×104 bits to 5×104 bits is 52.72 dB. Additionally, there are no errors in retrieving the carrier image and secret data.
•We propose a novel RDH with pairwise PEE and 2D-PEH decomposition.•The proposed method can achieve good capacity-distortion performance.•The proposed method outperforms some state-of-the-art RDH ...methods.
Reversible data hiding (RDH) is an important topic of data hiding. In this paper, we exploit pixel-based pixel value ordering prediction (PPVO) and pairwise prediction-error expansion (PEE) to design a novel RDH with pairwise PEE and 2-dimensional prediction-error histogram (2D-PEH) decomposition. Specifically, for each pair of pixels, they are accurately predicted according to the maximum and minimum values of their neighbor pixels by using PPVO to generate a global 2D-PEH. After selecting negative 2D expansion bins, a series of sub 2D-PEHs can be obtained by decomposing the global 2D-PEH according to the errors between the maximum and minimum values of neighbor pixels. Finally, for each sub 2D-PEH, pairwise PEE mapping manner is selected to conduct data embedding according to the characteristic of the sub 2D-PEH. Experimental results demonstrate that the proposed method can achieve good capacity-distortion performance and outperforms some state-of-the-art RDH methods.
This article outlines how a core concept from theories of homeostasis and cybernetics, the inference-control loop, may be used to guide differential diagnosis in computational psychiatry and ...computational psychosomatics. In particular, we discuss 1) how conceptualizing perception and action as inference-control loops yields a joint computational perspective on brain-world and brain-body interactions and 2) how the concrete formulation of this loop as a hierarchical Bayesian model points to key computational quantities that inform a taxonomy of potential disease mechanisms. We consider the utility of this perspective for differential diagnosis in concrete clinical applications.
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.
Conscious perception and attention are difficult to study, partly because their relation to each other is not fully understood. Rather than conceiving and studying them in isolation from each other ...it may be useful to locate them in an independently motivated, general framework, from which a principled account of how they relate can then emerge. Accordingly, these mental phenomena are here reviewed through the prism of the increasingly influential predictive coding framework. On this framework, conscious perception can be seen as the upshot of prediction error minimization and attention as the optimization of precision expectations during such perceptual inference. This approach maps on well to a range of standard characteristics of conscious perception and attention, and can be used to interpret a range of empirical findings on their relation to each other.
To adapt to threats in the environment, animals must predict them and engage in defensive behavior. While the representation of a prediction error signal for reward has been linked to dopamine, a ...neuromodulatory prediction error for aversive learning has not been identified.
We measured and manipulated norepinephrine release during threat learning using optogenetics and a novel fluorescent norepinephrine sensor.
We found that norepinephrine response to conditioned stimuli reflects aversive memory strength. When delays between auditory stimuli and footshock are introduced, norepinephrine acts as a prediction error signal. However, temporal difference prediction errors do not fully explain norepinephrine dynamics. To explain noradrenergic signaling, we used an updated reinforcement learning model with uncertainty about time and found that it explained norepinephrine dynamics across learning and variations in temporal and auditory task structure.
Norepinephrine thus combines cognitive and affective information into a predictive signal and links time with the anticipation of danger.
Learning describes the process by which our internal expectation models of the world are updated by surprising outcomes (prediction errors PEs) to improve predictions of future events. However, the ...mechanisms through which error signals dynamically influence existing neural representations are unknown. Here, we use functional magnetic resonance imaging (fMRI) in humans solving a two-step Markov decision task to investigate changes in neural activation patterns following PEs. Using a dynamic multivariate pattern analysis, we can show that PE-related fMRI responses in error-coding regions predict trial-by-trial changes in multivariate neural patterns in the orbitofrontal cortex, the precuneus, and the ventromedial prefrontal cortex (vmPFC). Importantly, the dynamics of these pattern changes in the vmPFC also predicted upcoming changes in choice strategies and thus highlight the importance of these pattern changes for behavior.
Display omitted
•RPEs and SPEs elicit BOLD responses in distinct areas, replicating earlier findings•RPEs and SPEs predict short-term pattern changes in dissociable brain networks•Pattern change in vmPFC, ACC, and OFC predicts adaptations of the behavioral policy
Möhring and Gläscher discovered that distinct prediction error signals in the human brain modulate short-term reconfigurations of neural patterns in dissociable brain networks. Additionally, these pattern reconfigurations predict changes in the behavioral policy. These findings provide a comprehensive account of the interaction between prediction errors, neural patterns, and behavioral adaptations.
PDF
