predictive coding schemes, proposed in the literature, essentially model the residuals with discrete distributions. However, real-valued residuals can arise in predictive coding, for example, from the usage of an r or...
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predictive coding schemes, proposed in the literature, essentially model the residuals with discrete distributions. However, real-valued residuals can arise in predictive coding, for example, from the usage of an r order linear predictor specified by r real-valued coefficients. In this paper, we propose a symbol-by-symbol coding scheme for the Laplace distribution, which closely models the distribution of real-valued residuals in practice. To efficiently exploit the real-valued predictions at a given precision, the proposed scheme essentially combines the process of residual computation and coding, in contrast to conventional schemes that separate these two processes. In the context of adaptive predictive coding framework, where the source statistics must be learnt from the data, the proposed scheme has the advantage of lower 'model cost' as it involves learning only one parameter. In this paper, we also analyze the proposed parametric coding scheme to establish the relationship between the optimal value of the coding parameter and the scale parameter of the Laplace distribution. Our experimental results demonstrated the compression efficiency and computational simplicity of the proposed scheme in adaptive coding of residuals against the widely used arithmetic coding, Rice-Golomb coding, and the Merhav-Seroussi-Weinberger scheme adopted in JPEG-LS. (C) 2015 Elsevier Inc. All rights reserved.
In this paper, we regard the sequence of returns as outputs from a parametric compound source. Utilizing the fact that the coding rate of the source shows the amount of information about the return, we describe l-lear...
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In this paper, we regard the sequence of returns as outputs from a parametric compound source. Utilizing the fact that the coding rate of the source shows the amount of information about the return, we describe l-learning algorithms based on the predictive coding idea for estimating an expected information gain concerning future information and give a convergence proof of the information gain. Using the information gain, we propose the ratio w of return loss to information gain as a new criterion to be used in probabilistic action-selection strategies. In experimental results, we found that our w-based strategy performs well compared with the conventional Q-based strategy.
The memory color effect and Spanish castle illusion have been taken as evidence of the cognitive penetrability of vision. In the same manner, the successful decoding of color-related brain signals in functional neuroi...
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The memory color effect and Spanish castle illusion have been taken as evidence of the cognitive penetrability of vision. In the same manner, the successful decoding of color-related brain signals in functional neuroimaging studies suggests the retrieval of memory colors associated with a perceived gray object. Here, we offer an alternative account of these findings based on the design principles of adaptive resonance theory (ART). In ART, conscious perception is a consequence of a resonant state. Resonance emerges in a recurrent cortical circuit when a bottom-up spatial pattern agrees with the top-down expectation. When they do not agree, a special control mechanism is activated that resets the network and clears off erroneous expectation, thus allowing the bottom-up activity to always dominate in perception. We developed a color ART circuit and evaluated its behavior in computer simulations. The model helps to explain how traces of erroneous expectations about incoming color are eventually removed from the color perception, although their transient effect may be visible in behavioral responses or in brain imaging. Our results suggest that the color ART circuit, as a predictive computational system, is almost never penetrable, because it is equipped with computational mechanisms designed to constrain the impact of the top-down predictions on ongoing perceptual processing. (C) 2020 Elsevier Ltd. All rights reserved.
This paper reviews the main algorithms Sor speech coning at low, and very low, bit rates, from 50 bps to 4000 bps. Then the HSX technique for coding at 1200 bps and a new segmental method with automatically derived un...
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This paper reviews the main algorithms Sor speech coning at low, and very low, bit rates, from 50 bps to 4000 bps. Then the HSX technique for coding at 1200 bps and a new segmental method with automatically derived units for very low bit rate coding are presented in details.
Efficient compression techniques are required for animated mesh sequences with fixed connectivity and time-varying geometry. In this paper, we propose a key-frame-based technique for three-dimensional dynamic mesh com...
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Efficient compression techniques are required for animated mesh sequences with fixed connectivity and time-varying geometry. In this paper, we propose a key-frame-based technique for three-dimensional dynamic mesh compression. First, key-frames are extracted from the animated sequence. Extracted key-frames are then linearly combined using blending weights to predict the vertex locations of the other frames. These blending weights play a key role in the proposed algorithm because the prediction performance and the required number of key-frames greatly depend on these weights. We present a novel method in order to compute the optimum blending weight that makes it possible to predict location of the vertices of the non-key frames with the minimum number of key-frames. The residual prediction errors are finally quantized and encoded using Huffman coding and another heuristic method. Experimental results on different test sequences with various sizes, topologies, and geometries demonstrate the privileged performance of the proposed method compared with the previous techniques. Copyright (c) 2015 John Wiley & Sons, Ltd.
Background and Hypothesis Humans develop a constellation of different representations of the external environment, even in the face of the same sensory exposure. According to the Bayesian framework, these differentiat...
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Background and Hypothesis Humans develop a constellation of different representations of the external environment, even in the face of the same sensory exposure. According to the Bayesian framework, these differentiations could be grounded in a different weight assigned to prior knowledge vs. new external inputs in predictive inference. Since recent advances in computational psychiatry suggest that autism (ASD) and schizophrenia (SSD) lie on the two diametric poles of the same predictive continuum, the adoption of a specific inferential style could be routed by dispositional factors related to autistic and schizotypal traits. However, no studies have directly investigated the role of ASD-SSD dimension in shaping the neuro-behavioral markers underlying perceptual inference. Study Design We used a probabilistic detection task while simultaneously recording EEG to investigate whether neurobehavioral signatures related to prior processing were diametrically shaped by ASD and SSD traits in the general population (n = 80). Results We found that the position along the ASD-SSD continuum directed the predictive strategies adopted by the individuals in decision-making. While proximity to the positive schizotypy pole was associated with the adoption of the predictive approach associated to the hyper-weighting of prior knowledge, proximity to ASD pole was related to strategies that favored sensory evidence in decision-making. Conclusions These findings revealed that the weight assigned to prior knowledge is a marker of the ASD-SSD continuum, potentially useful for identifying individuals at-risk of developing mental disorders and for understanding the mechanisms contributing to the onset of symptoms observed in ASD and SSD clinical forms.
During face-to-face conversational speech listeners must efficiently process a rapid and complex stream of multisensory information. Visual speech can serve as a critical complement to auditory information because it ...
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During face-to-face conversational speech listeners must efficiently process a rapid and complex stream of multisensory information. Visual speech can serve as a critical complement to auditory information because it provides cues to both the timing of the incoming acoustic signal (the amplitude envelope, influencing attention and perceptual sensitivity) and its content (place and manner of articulation, constraining lexical selection). Here we review behavioral and neurophysiological evidence regarding listeners' use of visual speech information. Multisensory integration of audiovisual speech cues improves recognition accuracy, particularly for speech in noise. Even when speech is intelligible based solely on auditory information, adding visual information may reduce the cognitive demands placed on listeners through increasing the precision of prediction. Electrophysiological studies demonstrate that oscillatory cortical entrainment to speech in auditory cortex is enhanced when visual speech is present, increasing sensitivity to important acoustic cues. Neuroimaging studies also suggest increased activity in auditory cortex when congruent visual information is available, but additionally emphasize the involvement of heteromodal regions of posterior superior temporal sulcus as playing a role in integrative processing. We interpret these findings in a framework of temporally-focused lexical competition in which visual speech information affects auditory processing to increase sensitivity to acoustic information through an early integration mechanism, and a late integration stage that incorporates specific information about a speaker's articulators to constrain the number of possible candidates in a spoken utterance. Ultimately it is words compatible with both auditory and visual information that most strongly determine successful speech perception during everyday listening. Thus, audiovisual speech perception is accomplished through multiple stages of integration, su
Ketamine, a non-competitive NMDA receptor antagonist, is used as a fast-acting antidepressant therapy in depressive disorders. This treatment provokes dissociative effects associating derealization and deperso-nalizat...
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Ketamine, a non-competitive NMDA receptor antagonist, is used as a fast-acting antidepressant therapy in depressive disorders. This treatment provokes dissociative effects associating derealization and deperso-nalization, and a synaptogenic signaling cascade promoting brain plasticity. Despite several preliminary studies suggesting the usefulness of its combination with psychotherapy, administration of ketamine isn???t generally combined with per-and post-infusion psychotherapy protocols in its clinical antidepressant use. However, the phenomenology of psychodysleptic experiences and the synaptogenic effect could potentiate cognitive and behavioral therapies (CBT). In this article, we purpose a practical protocol to Ketamine Augmented Psychotherapy (KAP) synthesizing contemporary data from the literature and our clinical experience. We detail proposals for clinical practice, and propose four important steps for the use of a psychodysleptic molecule for antidepressant purposes: preparation, administration, integration, and prolongation. Finally, we discuss the limits and prospects of this combination in the management of mood disorders. ?? 2021 L???Enc??phale, Paris.
Background People react very differently when sick, and there are only poor correlations between the intensity of the immune response and sickness behavior. Yet, alternative predictors of the individual differences in...
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Background People react very differently when sick, and there are only poor correlations between the intensity of the immune response and sickness behavior. Yet, alternative predictors of the individual differences in sickness are under-investigated. Based on the predictive coding model of placebo responses, where health outcomes are function of bottom-up sensory information and top-down expectancies, we hypothesized that individual differences in behavioral changes during sickness could be explained by individual top-down expectancies and prediction errors. Methods: Twenty-two healthy participants were made sick by intravenously administering lipopolysaccharide (2 ng/kg body weight). Their expectations of becoming sick were assessed before the injection. Results: Participants having lower expectations of becoming sick before the injection reacted with more emotional distress (i.e., more negative affect and lower emotional arousal) than those with high expectations of becoming sick, despite having similar overall sickness behavior (i.e., a combined factor including fatigue, pain, nausea and social withdrawal). In keeping with a predictive coding model, the "prediction error signal", i.e., the discrepancy between the immune signal and sickness expectancy, predicted emotional distress (reduction in emotional arousal in particular). Conclusion: The current findings suggest that the emotional component of sickness behavior is, at least partly, shaped by top-down expectations. Helping patients having a realistic expectation of symptoms during treatment of an illness may thus reduce aggravated emotional responses, and ultimately improve patients' quality of life and treatment compliance.
Visual perception of actions is supported by a network of brain regions in the occipito- temporal, parietal, and premotor cortex in the primate brain, known as the Action Observation Network (AON). Although there is a...
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Visual perception of actions is supported by a network of brain regions in the occipito- temporal, parietal, and premotor cortex in the primate brain, known as the Action Observation Network (AON). Although there is a growing body of research that charac- terizes the functional properties of each node of this network, the communication and direction of information flow between the nodes is unclear. According to the predictive coding account of action perception (Kilner, Friston, & Frith, 2007a;2007b), this network is not a purely feedforward system but has backward connections through which prediction error signals are communicated between the regions of the AON. In the present study, we investigated the effective connectivity of the AON in an experimental setting where the human subjects' predictions about the observed agent were violated, using fMRI and Dynamical Causal Modeling (DCM). We specifically examined the influence of the lowest and highest nodes in the AON hierarchy, pSTS and ventral premotor cortex, respectively, on the middle node, inferior parietal cortex during prediction violation. Our DCM results suggest that the influence on the inferior parietal node is through a feedback connection from ventral premotor cortex during perception of actions that violate people's predictions. (c) 2020 Elsevier Ltd. All rights reserved.
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