Information Processing Framework Research Articles

Revealing the information contents of memory within the stimulus information representation framework.

The information contents of memory are the cornerstone of the most influential models in cognition. To illustrate, consider that in predictive coding, a prediction implies that specific information is propagated down from memory through the visual hierarchy. Likewise, recognizing the input implies that sequentially accrued sensory evidence is successfully matched with memorized information (categorical knowledge). Although the existing models of prediction, memory, sensory representation and categorical decision are all implicitly cast within an information processing framework, it remains a challenge to precisely specify what this information is, and therefore where, when and how the architecture of the brain dynamically processes it to produce behaviour. Here, we review a framework that addresses these challenges for the studies of perception and categorization–stimulus information representation (SIR). We illustrate how SIR can reverse engineer the information contents of memory from behavioural and brain measures in the context of specific cognitive tasks that involve memory. We discuss two specific lessons from this approach that generally apply to memory studies: the importance of task, to constrain what the brain does, and of stimulus variations, to identify the specific information contents that are memorized, predicted, recalled and replayed.This article is part of the Theo Murphy meeting issue ‘Memory reactivation: replaying events past, present and future’.

Direct Generation of Tailored Pulse-Mode Entanglement.

Photonic quantum technology increasingly uses frequency encoding to enable higher quantum information density and noise resilience. Pulsed time-frequency modes (TFM) represent a unique class of spectrally encoded quantum states of light that enable a complete framework for quantum information processing. Here, we demonstrate a technique for direct generation of entangled TFM-encoded states in single-pass, tailored down-conversion processes. We achieve unprecedented quality in state generation-high rates, heralding efficiency, and state fidelity-as characterized via highly resolved time-of-flight fiber spectroscopy and two-photon interference. We employ this technique in a four-photon entanglement swapping scheme as a primitive for TFM-encoded quantum protocols.

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. Machine learning (ML) offers a wealth of techniques to extract information from data that can be translated into knowledge about the underlying fluid mechanics. Moreover, ML algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of ML for fluid mechanics. We outline fundamental ML methodologies and discuss their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experiments, and simulations. ML provides a powerful information-processing framework that can augment, and possibly even transform, current lines of fluid mechanics research and industrial applications.

Developing Device-Equivalent and Effective Measures of Complex Thinking with an Information Processing Framework and Mobile First Design Principles

Organizations are increasingly offering pre-employment assessments on mobile devices to evaluate candidates. The aim of this study is to investigate whether employing a mobile first responsive web design based on an information processing framework will result in device-equivalent measures of cognitive ability. Tests of numerical and deductive reasoning composed of interactive item types were tested for measurement equivalence across device types. Hypotheses were tested using data collected from paid participants over 3 weeks in 2018. Participants completed the test on both a PC and a mobile device. Paired samples t-tests indicated no mean differences in scores or number of items completed across device type. Additional analyses indicated that these item types demonstrated configural invariance and at least partial metric and scalar invariance across device types. The results of this study provide strong support that mobile first design can yield a valid and reliable test that can be used on any device and for any job level.

Autonomous driving: cognitive construction and situation understanding

Autonomous vehicle is a kind of typical complex artificial intelligence system. In current research of autonomous driving, the most widely adopted technique is to use a basic framework of serial information processing and computations, which consists of four modules: perception, planning, decision-making, and control. However, this framework based on data-driven computing performs low computational efficiency, poor environmental understanding and self-learning ability. A neglected problem has long been how to understand and process environmental perception data from the sensors referring to the cognitive psychology level of the human driving process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring and experiential updating, enabling it to cope with traffic scenarios with high noise, dynamic, and randomness. In addition, for the process of understanding traffic scenes, the efficiency of event-related mechanism is more significant than single-attribute scenario perception data. Therefore, an effective self-driving method should not be confined to the traditional computing framework of `perception, planning, decision-making, and control. It is necessary to explore a basic computing framework that conforms to human drivers attention, reasoning, learning, and decision-making mechanism with regard to traffic scenarios and build an autonomous system inspired by biological intelligence.In this article, we review the basic methods and main progress in current data-driven autonomous driving technologies, deeply analyze the limitations and major problems faced by related algorithms. Then, combined with authors research, this study discusses how to implement a basic cognitive computing framework of self-driving with selective attention and an event-driven mechanism from the basic viewpoint of cognitive science. It further describes how to use multi-sensor and graph data with semantic information (such as traffic maps and a spatial correlation of events) to realize the associative representations of objects and drivable areas, as well as the intuitive reasoning method applied to understanding the situations in different traffic scenarios.The computing framework of autonomous driving based on a selective attention mechanism and intuitive reasoning discussed in this study can adapt to a more complex, open, and dynamic traffic environment.

Behavioral economics and the Virginia school of political economy: overlaps and complementarities

A variety of complementarities and overlaps exist between the psychological strand of behavioral economics and the subjectivist strand of Virginia Political Economy. This paper provides an overview of those commonalities and places them in a common information processing framework. The framework can account for systematic mistakes, framing effects, subjectivity, individual variety, and several issues in constitutional political economy. It also reveals many commonalities between these two quite different approaches to human behavior.

THEORETICAL-METHODOLOGICAL ANALYSIS OF THE PROBLEM OF THINKING IN PSYCHOLOGICAL SCIENCE

The theoretical and methodological analysis of the problem of thinking in psychological science is proposed. The classification of thinking forms and their subspecies is presented. The theoretical analysis of well-known scientists concerning the characteristics of the thinking essence and functions of the cerebral hemispheres in the framework of information processing is presented. The scheme of anatomical and psychophysiological system of thinking is offered. Understanding is revealed as a result of human thinking, as a complex analytical and synthetic activity of the brain, aimed at revealing the inner essence of objects, processes and representations of objective, surrounding reality. Consideration of forms of thinking and their varieties, especially conclusions, characterized in the presentation of information and communication technologies in the educational process of preschool institutions, which was noted in the prospects for further research . Key words: thinking, brain activity, anatomical and psychophysiological system, forms of thinking, preschool education. Клак В. О. Теоретико-методологічний аналіз проблеми мислення в психологічній науці/ Переяслав-Хмельницький державний педагогічний університет імені Григорія Сковороди, Україна, Переяслав-Хмельницький Запропоновано теоретико-методологічний аналіз проблеми мислення в психологічній науці. Представлена класифікація форм мислення і їх підвидів. Подано теоретичний аналіз відомих учених стосовно характеристики сутності мислення і функцій великих півкуль мозку у рамках обробки інформації. Запропонована схема анатомо-психофізіологічної системи мислення. Розуміння розкрите як результат мислення людини, як складну аналітико-синтетичну діяльність мозку, спрямовану на розкриття внутрішньої сутності предметів, процесів і явищ об'єктивної, оточуючої дійсності . Розгляд форм мислення і їх різновидів, особливо умовиводів, охарактеризовано у рамках представлення інформаційно-комунікаційних технологій у освітньому процесі дошкільних закладах, що було зазначено в перспективах подальшого дослідження. Ключові слова: мислення, діяльність мозку, анатомо-психофізіологічна система, форми мислення, дошкільна освіта.

Plant behaviour in response to the environment: information processing in the solid state.

Information processing and storage underpins many biological processes of vital importance to organism survival. Like animals, plants also acquire, store and process environmental information relevant to their fitness, and this is particularly evident in their decision-making. The control of plant organ growth and timing of their developmental transitions are carefully orchestrated by the collective action of many connected computing agents, the cells, in what could be addressed as distributed computation. Here, we discuss some examples of biological information processing in plants, with special interest in the connection to formal computational models drawn from theoretical frameworks. Research into biological processes with a computational perspective may yield new insights and provide a general framework for information processing across different substrates.This article is part of the theme issue ‘Liquid brains, solid brains: How distributed cognitive architectures process information’.

Pieces of Mind: The Proper Domain of Psychological Predicates by Carrie Figdor

Reviewed by: Pieces of Mind: The Proper Domain of Psychological Predicates by Carrie Figdor Stephen K. Reed FIGDOR, Carrie. Pieces of Mind: The Proper Domain of Psychological Predicates. Oxford: Oxford University Press, 2018. x + 220 pp. Cloth, $55.00 The objective of Pieces of Mind is to defend the literalist claim that the best interpretation of the extension of psychological predicates to other domains is a literal one. The literal interpretation justifies the use of terminology such as plants “decide,” bacteria communicate “linguistically,” and neurons “prefer.” A literal extension of psychological predicates would provide a unified account across a wide range of new discoveries in the biological sciences. In chapter 2 on qualitative analogy the author discusses several cases in which psychological predicates are extended to nonhuman domains. One case is the signaling capacity of plants, which have many components found in the animal neural system including voltage-gated channels and hormones that play a qualitatively similar role to neural chemicals found in animals. The result, according to the author, is that analogies between [End Page 598] plants and animals can now be conceptualized within an information-processing framework that launched cognitive science. Another case is communication among bacteria. Communication allows colonies to develop collective memory, generate common knowledge, organize themselves, and learn from experience. In chapter 3 on quantitative analogy Figdor identifies similarity relations based on mathematical models and equations. She extends the Lotka-Volterra model of population dynamics to the decision-making of fruit flies. She next extends a drift-diffusion model of human decision making to the anticipating of neurons. The extent to which such quantitative analogies reveal semantic similarities is a complex issue that is advanced by the author’s arguments. For a different perspective see my “Constraints on the Abstraction of Solutions” (1989). Three chapters are devoted to defending the literal view against semantic alternatives that the author labels the nonsense view, the metaphor view, and the technical view. The metaphor view is the most applicable to cognitive science because of the many studies that have investigated it. Studies in cognitive science typically involve relationships among terms, which Figdor argues is consistent with literalism. Consider the statement “resonator neurons prefer inputs having frequencies that resonate with the frequency of other oscillations.” Use of the term “prefer” is not a similarity mapping between neurons and humans but a mapping between what neurons do and what humans do. I found myself in agreement with the many examples that Figdor uses to support the literal interpretation of psychological terms. But she has selected the low-hanging fruit—the clear cases—to support her position. In my collaboration with ontologists to build cognitive ontologies we have had to make many decisions regarding which cognitive terms generalize across different domains and which terms do not. Our efforts begin with definitions, and here is where I have a problem with Figdor’s analysis. In her concluding summary she states, “Literalism agrees that psychological concepts are anchored in human experience and allows that we can still gain insight from phenomenology but these concepts were never defined within a closed formal system.” The extension of psychological terms should begin with their definitions, but it is difficult to find definitions in Pieces of Mind. Definitions are where the rubber hits the road. One challenge is that words often have multiple definitions. In our article “A Framework for Constructing Cognition Ontologies Using WordNet, FrameNet, and SUMO” (2015), Adam Pease and I began by deciding which definitions of a word are relevant for cognitive theories. For instance, WordNet has six definitions of “attention,” but we selected only two as theoretically important: (1) attention is the faculty or power of mental concentration, and (2) attention is the process whereby a person concentrates on some features of the environment to the (relative) exclusion of others. A second challenge is that tools in the information sciences may have different definitions of the same word. Fortunately, some tools have [End Page 599] coordinated definitions, such as for “seeing” and “looking” in WordNet, FrameNet, and the Suggested Upper Merged Ontology (SUMO). Each defines seeing as perceiving by sight and looking as seeing with attention. They all make a corresponding distinction between...

Quantum optical microcombs

A key challenge for quantum science and technology is to realise large-scale, precisely controllable, practical systems for non-classical secured communications, metrology and ultimately meaningful quantum simulation and computation. Optical frequency combs represent a powerful approach towards this, since they provide a very high number of temporal and frequency modes which can result in large-scale quantum systems. The generation and control of quantum optical frequency combs will enable a unique, practical and scalable framework for quantum signal and information processing. Here, we review recent progress on the realization of energy-time entangled optical frequency combs and discuss how photonic integration and the use of fiber-optic telecommunications components can enable quantum state control with new functionalities, yielding unprecedented capability.

Long range quantum coherence, quantum & classical correlations in Heisenberg XX chain

A comparative study of pairwise quantum coherence, quantum and classical correlations is addressed for non-nearest spin pairs of the 1D Heisenberg spin-12 XX chain. Following the Jordan–Wigner mapping, we diagonalize the hamiltonian of the chain and we check this procedure numerically as well. Using the “Pauli basis expansion” formalism we get the pairwise quantities studied in this work at any distance. We then, show the role of quantum correlations in revealing quantum phase transitions, the robustness of quantum discord to the temperature and the dominance of quantum correlations over their classical counterpart in the magnetic and thermal interval in quantum spin chains. We conclude the paper by shedding light from a resource-driven point of view on the new born quantity “quantum coherence” where we discuss its role in detecting quantum phase transitions being a long-range quantity, and how it outclasses the usual quantum correlations measures in the robustness against the temperature, which indicates potential uses in the framework of quantum information processing.

From location tracking to personalized eco-feedback: A framework for geographic information collection, processing and visualization to promote sustainable mobility behaviors

Nowadays, most people carry around a powerful smartphone which is well suited to constantly monitor the location and sometimes even the activity of its user. This makes tracking prevalent and leads to a large number of projects concerned with trajectory data. One area of particular interest is transport and mobility, where data is important for urban planning and smart city-related activities, but can also be used to provide individual users with feedback and suggestions for personal behavior change. As part of a large-scale study based in Switzerland, we use activity tracking data to provide people with eco-feedback on their own mobility patterns and stimulate them to adopt more energy-efficient mobility choices. In this paper we explore the opportunities offered by smartphone based activity tracking, propose a general framework to exploit location data to foster more sustainable mobility behavior, describe the technical solutions chosen and discuss a range of outcomes in terms of user perception and sustainability potential. The presented approach extracts mobility patterns from users’ trajectories, computes credible alternative transport options, and presents the results in a concise and clear way. The resulting eco-feedback helps people to understand their mobility choices, discover the most non-ecological parts of their travel behavior, and explore feasible alternatives.

An Information Processing Framework Facilitating the Implementation of Condition Monitoring in Cyber-Physical Systems

Implementing condition monitoring functionality in production machinery often proves to be a difficult task. Device-and process-specific algorithms must be created while inhomogeneous industrial communication networks hinder the integration of control signals and process variables. Further challenges arise from the advance of flexible Cyber-Physical Systems (CPS) and the Industrial Internet of Things (IIoT). They demand a service-oriented condition monitoring architecture, which seamlessly adapts to quickly changing production topologies. Existing condition monitoring systems (CMS) and reference architectures for CMS however do not possess the capabilities to meet the requirements originating from CPS and the IIoT. This paper presents a software module serving as a generic framework to ease the implementation of decentralized condition monitoring functionalities. A decentralized component, the monitoring module, constitutes a part of a holistic condition monitoring architecture managed by a central server deployed on an edge or cloud server. Therefore, the monitoring module offers an interface through which its data processing flow and algorithms are entirely remotely configurable during operation. Algorithms are encapsulated in function blocks, which can easily be setup and interconnected. Scalability is ensured by the use of web technology optimized for efficient data handling and parallel data-processing. The examined use cases show the potential of the developed software module for its deployment in the use case of monitoring turn-mill-centers.

Research on the Prewarning Method for the Safety of South-to-North Water Transfer Project Driven by Monitoring Data

In order to solve the prewarning problem of South-to-North Water Transfer Project safety, an intelligent cooperative prewarning method based on machine learning was proposed under the framework of intelligent information processing. Driven by the monitoring data of the South-to-North Water Transfer Project, the single sensor in typical scenes was studied, and the security threshold was predicted along the vertical axis of time, firstly. With the support of the data correlation calculation, the sensors in the typical scene were intelligently grouped, and the study objectives were changed into sensor grouping, secondly. Then, the nonlinear regression model between the single sensor and the multisensors was built on the time cross section, and the model was used to dynamically calculate the safety threshold of the current sensor for the second time. Finally, in the framework of intelligent information processing, a double verification mechanism was proposed to support the construction of the intelligent prewarning method for the safety of South-to-North Water Transfer Project. The paper collected the monitoring data from November 2015 to September 2016 in the typical scenarios. The experimental results showed that the methods constructed in the paper can be able to identify the abnormal causes of data sudden jump effectively and give the different level prewarning. The method provides a strong theoretical support for further manual investigation work.

The impact of information security threat awareness on privacy-protective behaviors

In this study, we examine how to motivate computer users to protect themselves from potential security and privacy threats. We draw on the Information Processing framework which posits that threat mitigation commonly occurs before full cognitive threat assessment and we conduct an empirical study to evaluate the effects of an exposure to general information security threats on the strength of passwords and the disclosure of personal information. Through an online experiment, we compare immediate computer user reactions to potential non-individually specific security and privacy threats in an extra-organizational context. We find evidence consistent with automatic security and privacy protective actions in response to these threats. Computer users exposed to news stories about corporate security breaches limit the disclosure of sensitive personal information and choose stronger passwords. The study complements the existing behavior modification research in information security by providing the theoretical and empirical foundation for the exploration of automatic security and privacy threat mitigation strategies across different contexts.

Fault detection for nonlinear systems with unreliable measurements based on hierarchy cubature Kalman filter

AbstractThis paper is concerned with fault detection of a kind of nonlinear dynamic system. Based on the framework of hierarchy information processing, the scope of the fault is first located by use of the presented windowing cubature Kalman filter (WCKF), followed by point‐by‐point fault detection to locate the fault by use of the residuals of each moment within the suspected windows. Theoretical analysis and experiments show that the presented algorithm is more effective than the traditional point‐by‐point fault detection method that only uses the moment residuals. The presented algorithm has potential value in many application fields, such as fault detection, reliability evaluation, fault tolerant control, etc.

Learning the hard way: Need for Cognition influences attitudes toward and self-reported use of desirable difficulties

We investigate the relationship between Need for Cognition (NFC), attitude towards and self-reported application of different desirable difficulties in self-regulated learning. Students with a higher NFC should be more appreciative and prone to use desirable difficulties because of a match between the learner’s attributes and the learning task requirements: cognitively effortful learning conditions will be preferred by students with a higher propensity for cognitive challenges. Supporting our hypotheses, we show that indeed a higher NFC positively relates to attitudes and use of different desirable difficulties (Study 1–2), especially to self-generating of materials and predictions, but weaker and inconsistently to interleaving/spacing and self-testing/practicing. While self-generating of contents and predictions loaded on one factor, and interleaving/spacing together with self-testing/practicing loaded on another factor, this two-factor structure was reliably obtained for self-reported use, yet for attitudes a one-factor structure did fit. Most importantly though, the observed relationship between NFC and different desirable difficulties could neither be explained by high school graduation grade, indicating academic competence (Study 1), nor by academic self-concept as one’s perception of one’s academic abilities (Study 2). Results are discussed within an information-processing framework related to implications for desirable difficulties as learning strategies.

What Causes Ecological Shifts?

A new information-processing framework helps researchers tease out the factors driving ecological shifts over short timescales.

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'.

Modality and Perceptual-Motor Experience Influence the Detection of Temporal Deviations in Tap Dance Sequences.

Accurate temporal information processing is critically important in many motor activities within disciplines such as dance, music, and sport. However, it is still unclear how temporal information related to biological motion is processed by expert and non-expert performers. It is well-known that the auditory modality dominates the visual modality in processing temporal information of simple stimuli, and that experts outperform non-experts in biological motion perception. In the present study, we combined these two areas of research; we investigated how experts and non-experts detected temporal deviations in tap dance sequences, in the auditory modality compared to the visual modality. We found that temporal deviations were better detected in the auditory modality compared to the visual modality, and by experts compared to non-experts. However, post hoc analyses indicated that these effects were mainly due to performances obtained by experts in the auditory modality. The results suggest that the experience advantage is not equally distributed across the modalities, and that tap dance experience enhances the effectiveness of the auditory modality but not the visual modality when processing temporal information. The present results and their potential implications are discussed in both temporal information processing and biological motion perception frameworks.