Probabilistic Information Processing Research Articles

Prestimulus α/β power in temporal-order judgments: individuals differ in direction of modulation but show consistency over auditory and visual tasks.

The processing of incoming sensory information can be differentially affected by varying levels of α-power in the electroencephalogram (EEG). A prominent hypothesis is that relatively low prestimulus α-power is associated with improved perceptual performance. However, there are studies in the literature that do not fit easily into this picture, and the reasons for this are poorly understood and rarely discussed. To evaluate the robustness of previous findings and to better understand the overall mixed results, we used a spatial TOJ task in which we presented auditory and visual stimulus pairs in random order while recording EEG. For veridical and non-veridical TOJs, we calculated the power spectral density (PSD) for 3 frequencies (5 Hz steps: 10, 15, and 20 Hz). We found on the group level: (1) Veridical auditory TOJs, relative to non-veridical, were associated with higher β-band (20 Hz) power over central electrodes. (2) Veridical visual TOJs showed higher β-band (10, 15 Hz) power over parieto-occipital electrodes (3) Electrode site interacted with TOJ condition in the β-band: For auditory TOJs, PSD over central electrodes was higher for veridical than non-veridical and over parieto-occipital electrodes was lower for veridical than non-veridical trials, while the latter pattern was reversed for visual TOJs. While our group-level result showed a clear direction of prestimulus modulation, the individual-level modulation pattern was variable and included activations opposite to the group mean. Interestingly, our results at the individual-level mirror the situation in the literature, where reports of group-level prestimulus modulation were found in either direction. Because the direction of individual activation of electrodes over auditory brain regions and parieto-occipital electrodes was always negatively correlated in the respective TOJ conditions, this activation opposite to the group mean cannot be easily dismissed as noise. The consistency of the individual-level data cautions against premature generalization of group-effects and suggests different strategies that participants initially adopted and then consistently followed. We discuss our results in light of probabilistic information processing and complex system properties, and suggest that a general description of brain activity must account for variability in modulation directions at both the group and individual levels.

Cross-platform hotel evaluation by aggregating multi-website consumer reviews with probabilistic linguistic term set and Choquet integral.

In order to adequately utilize and integrate both ratings and comments from multiple websites, this paper proposes a new hotel evaluation model with probabilistic linguistic information processing. Taking consumers' possible psychological activities when leaving their reviews into consideration, this paper adapts the Weber-Fechner Law with the linguistic scale function and develop a novel unbalanced linguistic scale function. This paper also attempts to develop a method that enables adjusting linguistic-term formations among different websites to make full use of information. Then, we learn the decision criteria and the corresponding weight of hotel evaluation based on analyzing rating rules and consumer comments. Moreover, considering the interrelationships among criteria, this paper extends the Choquet integral to the probabilistic linguistic term set (PLTS) environment and designs some novel fusion operators. Furthermore, considering the fact that different websites mostly focus on heterogeneous hotel criteria, this paper puts forward a weighted averaging linear assignment based ranking method with the aid of PLTS Choquet integral. Finally, a case study of hotel evaluation is given to illustrate the validity and applicability of our proposed approach.

Neuronal Sequence Models for Bayesian Online Inference.

Various imaging and electrophysiological studies in a number of different species and brain regions have revealed that neuronal dynamics associated with diverse behavioral patterns and cognitive tasks take on a sequence-like structure, even when encoding stationary concepts. These neuronal sequences are characterized by robust and reproducible spatiotemporal activation patterns. This suggests that the role of neuronal sequences may be much more fundamental for brain function than is commonly believed. Furthermore, the idea that the brain is not simply a passive observer but an active predictor of its sensory input, is supported by an enormous amount of evidence in fields as diverse as human ethology and physiology, besides neuroscience. Hence, a central aspect of this review is to illustrate how neuronal sequences can be understood as critical for probabilistic predictive information processing, and what dynamical principles can be used as generators of neuronal sequences. Moreover, since different lines of evidence from neuroscience and computational modeling suggest that the brain is organized in a functional hierarchy of time scales, we will also review how models based on sequence-generating principles can be embedded in such a hierarchy, to form a generative model for recognition and prediction of sensory input. We shortly introduce the Bayesian brain hypothesis as a prominent mathematical description of how online, i.e., fast, recognition, and predictions may be computed by the brain. Finally, we briefly discuss some recent advances in machine learning, where spatiotemporally structured methods (akin to neuronal sequences) and hierarchical networks have independently been developed for a wide range of tasks. We conclude that the investigation of specific dynamical and structural principles of sequential brain activity not only helps us understand how the brain processes information and generates predictions, but also informs us about neuroscientific principles potentially useful for designing more efficient artificial neuronal networks for machine learning tasks.

TOPSIS Method for Developing Supplier Selection with Probabilistic Linguistic Information

In this paper, we investigate the probabilistic linguistic multiple attribute group decision-making (MAGDM) with incomplete weight information. In this method, the linguistic term sets (LTSs) is converted into probabilistic linguistic term sets (PLTSs). For deriving the weight information of the attribute, an optimization model is built on the basis of the fundamental idea of conventional TOPSIS method, by which the attribute weights can be decided. In addition, the optimal alternative(s) is decided by computing the shortest distance from the probabilistic linguistic positive ideal solution (PLPIS) and on the other side the farthest distance of the probabilistic linguistic negative ideal solution (PLNIS). The method has precise trait in probabilistic linguistic information processing. The information distortion and losing was avoided which happen formerly in the probabilistic linguistic information processing. In the end, a case study for green supplier selection is given to demonstrate the merits of the developed method. The results display that the approach is uncomplicated, valid and simple to compute.

Quantum computing with classical bits

A bit-quantum map relates probabilistic information for Ising spins or classical bits to quantum spins or qubits. Quantum systems are subsystems of classical statistical systems. The Ising spins can represent macroscopic two-level observables, and the quantum subsystem employs suitable expectation values and correlations. We discuss static memory materials based on Ising spins for which boundary information can be transported through the bulk in a generalized equilibrium state. They can realize quantum operations as the Hadamard or CNOT-gate for the quantum subsystem. Classical probabilistic systems can account for the entanglement of quantum spins. An arbitrary unitary evolution for an arbitrary number of quantum spins can be described by static memory materials for an infinite number of Ising spins which may, in turn, correspond to continuous variables or fields. We discuss discrete subsets of unitary operations realized by a finite number of Ising spins. They may be useful for new computational structures. We suggest that features of quantum computation or more general probabilistic computation may be realized by neural networks, neuromorphic computing or perhaps even the brain. This does neither require small isolated entities nor very low temperature. In these systems the processing of probabilistic information can be more general than for static memory materials. We propose a general formalism for probabilistic computing for which deterministic computing and quantum computing are special limiting cases.

Quantum generalized observables framework for psychological data: a case of preference reversals in US elections.

Politics is regarded as a vital area of public choice theory, and it is strongly relying on the assumptions of voters' rationality and as such, stability of preferences. However, recent opinion polls and real election outcomes in the USA have shown that voters often engage in 'ticket splitting', by exhibiting contrasting party support in Congressional and Presidential elections (cf. Khrennikova 2014 Phys. ScriptaT163, 014010 (doi:10.1088/0031-8949/2014/T163/014010); Khrennikova & Haven 2016 Phil. Trans. R. Soc. A374, 20150106 (doi:10.1098/rsta.2015.0106); Smith et al. 1999 Am. J. Polit. Sci.43, 737-764 (doi:10.2307/2991833)). Such types of preference reversals cannot be mathematically captured via the formula of total probability, thus showing that voters' decision making is at variance with the classical probabilistic information processing framework. In recent work, we have shown that quantum probability describes well the violation of Bayesian rationality in statistical data of voting in US elections, through the so-called interference effects of probability amplitudes. This paper is proposing a novel generalized observables framework of voting behaviour, by using the statistical data collected and analysed in previous studies by Khrennikova (Khrennikova 2015 Lect. Notes Comput. Sci.8951, 196-209) and Khrennikova & Haven (Khrennikova & Haven 2016 Phil. Trans. R. Soc. A374, 20150106 (doi:10.1098/rsta.2015.0106)). This framework aims to overcome the main problems associated with the quantum probabilistic representation of psychological data, namely the non-double stochasticity of transition probability matrices. We develop a simplified construction of generalized positive operator valued measures by formulating special non-orthonormal bases with respect to these operators.This article is part of the themed issue 'Second quantum revolution: foundational questions'.

The role of linguistic experience in the processing of probabilistic information in production

ABSTRACTSpeakers track the probability that a word will occur in a particular context and utilise this information during phonetic processing. For example, content words that have high probability within a discourse tend to be realised with reduced acoustic/articulatory properties. Such probabilistic information may influence L1 and L2 speech processing in distinct ways (reflecting differences in linguistic experience across groups and the overall difficulty of L2 speech processing). To examine this issue, L1 and L2 speakers performed a referential communication task, describing sequences of simple actions. The two groups of speakers showed similar effects of discourse-dependent probabilistic information on production, suggesting that L2 speakers can successfully track discourse-dependent probabilities and use such information to modulate phonetic processing.

Allais for the poor: Relations to ability, information processing, and risk attitudes

This paper complements evidence on the Allais paradox from advanced countries and educated people by a novel investigation in a poor rural area. The share of Allais-type behavior is indeed high and related to indicators of “lacking ability,” such as poor education, unemployment, and little financial sophistication. Based on prospective reference theory, we extend these characteristics by biased processing of probabilistic information. Finally, we reveal that Allais-type behavior is linked to risk-related characteristics, such as risk tolerance and optimism. This indicates a potential problem as exactly the more dynamic among the poor tend to make inconsistent decisions under uncertainty.

統計的因果推論 : 因果効果の識別可能問題におけるベイジアンネットワークの役割( ベイジアンネットワークと確率的情報処理の新展開)

統計的因果推論 : 因果効果の識別可能問題におけるベイジアンネットワークの役割( ベイジアンネットワークと確率的情報処理の新展開)

Allais for the Poor: Relations to Ability, Information Processing and Risk Attitudes

This paper complements evidence on the Allais paradox from advanced countries and educated people by a novel investigation in a poor rural area. The share of Allais-type behavior is indeed high and related to characteristics of “lacking ability”, such as poor education, unemployment, and little financial sophistication. Based on prospective reference theory, we extend these characteristics by biased processing of probabilistic information. Finally, we reveal that Allais-type behavior is linked to risk-related characteristics, such as risk tolerance and optimism. This indicates a potential problem as exactly the more dynamic among the poor tend to make inconsistent decisions under uncertainty.

Processing of probabilistic information in weight perception and motor prediction.

We studied the effects of probabilistic cues, i.e., of information of limited certainty, in the context of an action task (GL: grip-lift) and of a perceptual task (WP: weight perception). Normal subjects (n = 22) saw four different probabilistic visual cues, each of which announced the likely weight of an object. In the GL task, the object was grasped and lifted with a pinch grip, and the peak force rates indicated that the grip and load forces were scaled predictively according to the probabilistic information. The WP task provided the expected heaviness related to each probabilistic cue; the participants gradually adjusted the object's weight until its heaviness matched the expected weight for a given cue. Subjects were randomly assigned to two groups: one started with the GL task and the other one with the WP task. The four different probabilistic cues influenced weight adjustments in the WP task and peak force rates in the GL task in a similar manner. The interpretation and utilization of the probabilistic information was critically influenced by the initial task. Participants who started with the WP task classified the four probabilistic cues into four distinct categories and applied these categories to the subsequent GL task. On the other side, participants who started with the GL task applied three distinct categories to the four cues and retained this classification in the following WP task. The initial strategy, once established, determined the way how the probabilistic information was interpreted and implemented.

Extracting nonlinear spatiotemporal dynamics in active dendrites using data-driven statistical approach

We propose a data-driven statistical method for extracting nonlinear spatiotemporal membrane dynamics of active dendrites. We employ a framework of probabilistic information processing to extract the nonlinear spatiotemporal dynamics obeying the reaction-diffusion equation from partially observable data. By employing sequential Monte-Carlo method and other statistical methods, membrane dynamics and their underlying electrical properties are simultaneously estimated in the proposed method. Using the proposed method, we show that nonlinear spatiotemporal dynamics in active dendrites can be extracted from partially observable data.

The 2013 Benjamin Franklin Medal in Computer and Cognitive Science presented to William Labov

The Franklin Institute, Philadelphia, Pennsylvania, awards the 2013 Benjamin Franklin Medal in Computer and Cognitive Science to Professor William Labov for pioneering contributions to the understanding of language variation and change through quantitative means, and for initiating the scientific study of non-standard dialects with significant social and cultural implications.Labov is widely recognized as the founder of sociolinguistics, the quantitative study of language structure and use. Through detailed analysis of speech across dialect boundaries and over time, Labov has identified general principles governing language variation and change, while shedding light on how the mind assimilates and responds to linguistic stimuli to form internalized linguistic knowledge and representations. He pioneered the use of statistical methods in the study of language, thereby contributing to the understanding of cognition as a probabilistic information processing system. Moreover, Labov׳s research on African American English has led to the recognition of non-standard dialects as complex linguistic systems in their own right, rather than imperfect deviations from the standard variety.

Infinite-range transverse field Ising models and quantum computation

We present a brief review on information processing, computing and inference via quantum fluctuation, and clarify the relationship between the probabilistic information processing and theory of quantum spin glasses through the analysis of the infinite-range model. We also argue several issues to be solved for the future direction in the research field.

Code-division multiple-access multiuser demodulator by using quantum fluctuations.

We examine the average-case performance of a code-division multiple-access (CDMA) multiuser demodulator in which quantum fluctuations are utilized to demodulate the original message within the context of Bayesian inference. The quantum fluctuations are built into the system as a transverse field in the infinite-range Ising spin glass model. We evaluate the performance measurements by using statistical mechanics. We confirm that the CDMA multiuser modulator using quantum fluctuations achieve roughly the same performance as the conventional CDMA multiuser modulator through thermal fluctuations on average. We also find that the relationship between the quality of the original information retrieval and the amplitude of the transverse field is somehow a "universal feature" in typical probabilistic information processing, viz., in image restoration, error-correcting codes, and CDMA multiuser demodulation.

Mathematical structures of loopy belief propagation and cluster variation method

The mathematical structures of loopy belief propagation are reviewed for graphical models in probabilistic information processing in the stand point of cluster variation method. An extension of adaptive TAP approaches is given by introducing a generalized scheme of the cluster variation method. Moreover the practical message update rules in loopy belief propagation are summarized also for quantum systems. It is suggested that the loopy belief propagation can be reformulated for quantum electron systems by using density matrices of ideal quantum lattice gas system.

Behavioral and Neural Effects of Nicotine on Visuospatial Attentional Reorienting in Non-Smoking Subjects

The cholinergic neurotransmitter system has been proposed to be involved in the processing of probabilistic top-down information provided by endogenous cues in location-cueing paradigms. It has been shown that the behavioral and neural effects of a nicotinic cholinergic stimulation resemble the effects obtained by manipulating the validity of the spatial cues: enhancing cortical nicotine levels and decreasing cue validity both reduce the reaction time difference between invalidly and validly cued targets (ie, the 'validity effect') as well as neural activity related to attentional reorienting in parietal brain regions. In the present study, we investigated whether the behavioral and neural effects of nicotine in location-cueing paradigms are dependent upon different a priori cue validities. Twenty-four subjects were investigated in a double-blind placebo-controlled between-subject design with functional magnetic resonance imaging. Nicotine was administered to non-smoking volunteers via polacrilex gums (Nicorette, 2 mg) before performing a location-cueing paradigm with valid and invalid cues in the context of 90 and 60% cue validity in the MR scanner. Nicotine significantly reduced the validity effect in the 90% but not in the 60% cue validity condition. Fronto-parietal and cingulate regions showed stronger nicotinic reductions of reorienting-related neural activity in the high than in the low cue validity condition. Our data reveal an interaction effect between the pharmacological and cognitive modulation of attentional reorienting, which is evident at both a behavioral as well as the neuronal level.

情報統計力学とは何か : 情報学でもMore Is Different( ベイジアンネットワークと確率的情報処理の新展開)

情報統計力学とは何か : 情報学でもMore Is Different( ベイジアンネットワークと確率的情報処理の新展開)

Maximum Marginal Likelihood Estimation and Constrained Optimization in Image Restoration.

In image restorations, two different mathematical frameworks for massive probabilistic models are given in the standpoint of Bayes statistics. One of them is formulated by assuming that a priori probability distribution has a form of Gibbs micro-canonical distribution and can be reduced to a constrained optimization. In this framework, we have to know a quantity estimated from the original image though we do not have to know the con.guration of the original image. We give a new method to estimate the length of boundaries between areas with different grey-levels in the original image only from the degraded image in grey-level image restorations. When the length of boundaries in the original image is estimated from the given degraded image, we can construct a probabilistic model for image restorations by means of Bayes formula. This framework can be regarded as probabilistic information processing at zero temperature. The other framework is formulated by assuming the a priori probability distribution has a form of Gibbs canonical distribution. In this framework, some hyperparameters are determined so as to maximize a marginal likelihood. In this paper, it is assumed that the a priori probability distribution is a Potts model, which is one of familiar statistical-mechanical models. In this assumption, the logarithm of marginal likelihood can be expressed in terms of free energies of some probabilistic models and hence this framework can be regarded as probabilistic information processing at finite temperature. The practical algorithms in the constrained optimization and the maximum marginal likelihood estimation are given by means of cluster variation method, which is a statistical-mechanical approximation with high accuracy for massive probabilistic models. We compare them with each others in some numerical experiments for grey-level image restorations.

Modeling effort decisions with behavioral decision theory: Toward an individual differences model of expectancy theory

Using behavioral decision theory, this paper describes four experiments which tested the multiplicative force model from V. H. Vroom's (1964, Work and Motivation, New York: Wiley) Expectancy Theory. In the first two experiments, the academic course effort decisions of 68 graduate students were examined using a decision-making exercise involving 24 hypothetical courses. Each course was described in terms of three instrumentalities and one expectancy (0, .4, or .8 in the first experiment, and 0, .2, or .6 in the second experiment). Using two different decision-making exercises, the last two experiments captured the decisions of 89 high school and undergraduate students concerning the exertion of effort to get 24 hypothetical jobs. For the third experiment, each job was described in terms of three instrumentalities and one expectancy (.05, .50, or .95). For the fourth experiment, each job was described in terms of four instrumentalities and one expectancy (0, .3, or .6). In each of the four experiments, the instrumentalities and expectancies were presented using factorial designs in order to increase the level of experimental control and to provide a sufficient number of decisions for each subject to facilitate individual within-person analyses of the data. Vroom's multiplicative force model was supported by only a minority of the subjects; a majority supported an additive model. It is hypothesized that the two different models are attributable to individual differences in cognitive processing of probabilistic information. This research demonstrates the theoretical, analytic, and psychometric advantages of the behavioral decision theory modeling approach to testing Expectancy Theory.