This paper presents a new generalized particle model (GPM) to generate the prediction coding for lossless data compression. We discuss the GPM-based parallel algorithm, its properties and realization scheme. The propo...
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This paper presents a new generalized particle model (GPM) to generate the prediction coding for lossless data compression. We discuss the GPM-based parallel algorithm, its properties and realization scheme. The proposed GPM approach has advantages in terms of parallelism, scalability and easy hardware implementation over other sequential lossless compression methods
This paper describes a speech coder which exploits the combination of subband splitting, adaptive predictive coding, vector quantization and dynamic bit allocation in order to yield very good quality at 16 kbit/s. The...
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This paper describes a speech coder which exploits the combination of subband splitting, adaptive predictive coding, vector quantization and dynamic bit allocation in order to yield very good quality at 16 kbit/s. The coder has been evaluated by means of objective measures and informal listening tests. Some experiments have also been conducted with input signals degraded by acoustic background noise. Results confirm that the proposed coder is a viable approach for such applications as end-to-end transmission circuits, digital mobile radio and voice mail systems.
A new video coding paradigm (PRISM) built on distributed coding principles has recently been proposed in R. Puri and K. Ramchandran, (2002, 2003). The PRISM framework rests heavily on channel coding concepts. The basi...
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A new video coding paradigm (PRISM) built on distributed coding principles has recently been proposed in R. Puri and K. Ramchandran, (2002, 2003). The PRISM framework rests heavily on channel coding concepts. The basic idea is to do intra-frame coding but to approach the coding efficiency of motion-compensated inter-frame coding by using channel codes to derive a coding rate rebate by "exploiting" the correlated side-information present in the form of previous temporal frames at the decoder. The coding strategy described in R. Puri and K. Ramchandran, (2002, 2003) was limited to rate-1/2 trellis coset codes, which limit the granularity at which the correlated side-information can be exploited. In this work, we generalize the coset coding framework to multi-level codes which allow more flexible encoding rates and the capability to exploit correlations with much finer granularity. Simulation results validate the better compression performance (of the order of 1 dB for typical tests) of the multilevel framework.
In this paper, a quantization scheme for haptic data compression is proposed and the results of its application to a motion copying system are described. The data compression of image and audio data has been researche...
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In this paper, a quantization scheme for haptic data compression is proposed and the results of its application to a motion copying system are described. The data compression of image and audio data has been researched a lot. However, the data compression of haptic data has not been much researched yet although the amount of haptic data is large in general. The discrete cosine transform (DCT) is used for the lossy compression of haptic data in this paper. A new quantization scheme using characteristics of DCT is proposed. By using a proposed method, a larger signal-to-noise ratio compared to conventional method is achieved. In the experiment, the validity of the proposed method was verified.
Hohwy (2013) proposed an account of conscious access that integrates the global neuronal workspace (GNW) into the framework of predictive processing, a view that I term the predictive global neuronal workspace (PGNW)....
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Hohwy (2013) proposed an account of conscious access that integrates the global neuronal workspace (GNW) into the framework of predictive processing, a view that I term the predictive global neuronal workspace (PGNW). Whilst promising, the PGNW is theoretically underdeveloped and empirically underexplored. The aim of this article is to outline the empirical predictions that distinguish the PGNW from other workspace models. I do so by (i) placing the PGNW in close contact with experimental work and cashing out a set of predictions that distinguish it from the standard formulation of the GNW, (ii) exploring the evidence for the first of these predictions in the context of bistable perception and the conscious processing of auditory regularities, and (iii), contrasting the PGNW with Chanes and Barrett (2016) limbic workspace. Combined, these arguments show that the PGNW is both testable and supported by current evidence.
The extraction of cooccurrences between two events, A and B, is a central learning mechanism shared by all species capable of associative learning. Formally, the cooccurrence of events A and B appearing in a sequence ...
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The extraction of cooccurrences between two events, A and B, is a central learning mechanism shared by all species capable of associative learning. Formally, the cooccurrence of events A and B appearing in a sequence is measured by the transitional probability (TP) between these events, and it corresponds to the probability of the second stimulus given the first (i.e., p(B|A)). In the present study, nonhuman primates (Guinea baboons, Papio papio) were exposed to a serial version of the XOR (i.e., exclusive-OR), in which they had to process sequences of three stimuli: A, B, and C. In this manipulation, first-order TPs (i.e., AB and BC) were uninformative due to their transitional probabilities being equal to .5 (i.e., p(B|A) = p(C|B) = .5), while second-order TPs were fully predictive of the upcoming stimulus (i.e., p(C|AB) = 1). In Experiment 1, we found that baboons were able to learn second-order TPs, while no learning occurred on first-order TPs. In Experiment 2, this pattern of results was replicated, and a final test ruled out an alternative interpretation in terms of proximity to the reward. These results indicate that a nonhuman primate species can learn a nonlinearly separable problem such as the XOR. They also provide fine-grained empirical data to test models of statistical learning on the interaction between the learning of different orders of TPs. Recent bioinspired models of associative learning are also introduced as promising alternatives to the modeling of statistical learning mechanisms.
Slower perceptual alternations, a notable perceptual effect observed in psychiatric disorders, can be alleviated by antidepressant therapies that affect serotonin levels in the brain. While these phenomena have been w...
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Slower perceptual alternations, a notable perceptual effect observed in psychiatric disorders, can be alleviated by antidepressant therapies that affect serotonin levels in the brain. While these phenomena have been well documented, the underlying neurocognitive mechanisms remain to be elucidated. Our study bridges this gap by employing a computational cognitive approach within a Bayesian predictive coding framework to explore these mechanisms in depression. We fitted a prediction error (PE) model to behavioral data from a binocular rivalry task, uncovering that significantly higher initial prior precision and lower PE led to a slower switch rate in patients with depression. Furthermore, serotonin-targeting antidepressant treatments significantly decreased the prior precision and increased PE, both of which were predictive of improvements in the perceptual alternation rate of depression patients. These findings indicated that the substantially slower perception switch rate in patients with depression was caused by the greater reliance on top-down priors and that serotonin treatment's efficacy was in its recalibration of these priors and enhancement of PE. Our study not only elucidates the cognitive underpinnings of depression, but also suggests computational modeling as a potent tool for integrating cognitive science with clinical psychology, advancing our understanding and treatment of cognitive impairments in depression.
Serial dependence effects have been observed using a variety of stimuli and tasks, revealing that the recent past can bias current percepts, leading to increased similarity between two. The aim of this study is to det...
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Serial dependence effects have been observed using a variety of stimuli and tasks, revealing that the recent past can bias current percepts, leading to increased similarity between two. The aim of this study is to determine whether this temporal integration occurs in egocentric or allocentric coordinates. We asked participants to perform an orientation reproduction task using grating stimuli while the head was kept at a fixed position, or after a 40 degrees yaw rotation between trials, from left (-20 degrees) to right (+20 degrees), putting the egocentric and allocentric cues in conflict. Under these conditions, allocentric cues prevailed.
Cognitive neuropsychiatry is a branch of cognitive psychology that seeks to explain neuropsychiatric symptoms in terms of disruptions or damage to normal cognitive processes. A key objective of this approach is to use...
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Cognitive neuropsychiatry is a branch of cognitive psychology that seeks to explain neuropsychiatric symptoms in terms of disruptions or damage to normal cognitive processes. A key objective of this approach is to use insights derived from the study of pathological symptoms to inform accounts of premorbid cognitive systems. Delusions, in particular, can be considered to represent dysfunction of the cognitive processes underlying belief formation, so studying delusions may provide unique insights into nonpathological belief. While this approach has provided compelling accounts for a range of delusions in terms of putative cognitive dysfunctions, it is less clear that it has achieved progress in its reciprocal goal of informing understanding of belief more generally. In this review, we trace the origins of the cognitive neuropsychiatric approach and consider the reasons for the lack of progress. We propose a tentative framework to overcome these challenges and suggest directions for future research.
According to the cultural distance hypothesis (CDH), individuals learn culture-specific statistical structures in music as internal stylistic models and use these models in predictive processing of music, with musical...
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According to the cultural distance hypothesis (CDH), individuals learn culture-specific statistical structures in music as internal stylistic models and use these models in predictive processing of music, with musical structures closer to their home culture being easier to predict. This cultural distance effect may be affected by domain -specific (musical ability) and domain-general individual characteristics (openness, implicit cultural bias). To test the CDH and its modulation by individual characteristics, we recruited Chinese and Western adults to categorize stylistically ambiguous and unambiguous Chinese and Western melodies by cultural origin. Catego-rization performance was better for unambiguous (low CD) than ambiguous melodies (high CD), and for in -culture melodies regardless of ambiguity for both groups, providing evidence for CDH. Musical ability, but not other traits, correlated positively with melody categorization, suggesting that musical ability refines internal stylistic models. Therefore, both cultures show musical enculturation in their home culture with a modulatory effect of individual musical ability.
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