As one of the important 21st-century skills, collaborative problem solving (CPS) has caught much attention in the assessment area. Two initiative approaches have been created: the human-to-human and human-to-agent modes. Between the two modes, the human-to-human interaction is much closer to the real-world situation and its process stream data can reveal more detailed information about the cognitive processes. In order to measure CPS ability effectively by this mode, how to extract indicators from the data and model (...) it is crucial, while the existing methods all have some disadvantages. In the present study, we proposed a new paradigm for extracting indicators and modeling the dyad data in the human-to-human mode. Specifically, both individual and group indicators were extracted from the data stream, which served as evidence for demonstrating CPS skills. In addition, a within-item multidimensional Rasch model was used to fit the dyad data. To test the validity of the paradigm, we developed five tasks following the asymmetric mechanism and integrated them into an online testing platform. Four hundred thirty-four students from China participated in the assessment and the online platform recorded their crucial actions with time stamps. The new paradigm was used to deal with the generated process stream data. Results showed that the model fitted well. The item parameter estimates and fitting indexes were acceptable, and students were well differentiated. In general, the new paradigm of extracting indicators and modeling the dyad data is feasible for improving the human-to-human assessment of CPS. Finally, the limitations of the current study and further research directions are discussed. (shrink)

Processing data streams arriving at high speed requires the development of models that can provide fast and accurate predictions. Although deep neural networks are the state-of-the-art for many machine learning tasks, their performance in real-time data streaming scenarios is a research area that has not yet been fully addressed. Nevertheless, much effort has been put into the adaption of complex deep learning (DL) models to streaming tasks by reducing the processing time. The design of the asynchronous dual-pipeline DL (...) framework allows making predictions of incoming instances and updating the model simultaneously, using two separate layers. The aim of this work is to assess the performance of different types of DL architectures for data streaming classification using this framework. We evaluate models such as multi-layer perceptrons, recurrent, convolutional and temporal convolutional neural networks over several time series datasets that are simulated as streams at different speeds. In addition, we evaluate how the different architectures react to concept drifts typically found in evolving data streams. The obtained results indicate that convolutional architectures achieve a higher performance in terms of accuracy and efficiency, but are also the most sensitive to concept drifts. (shrink)

The use of sensors has had an enormous increment in the last years, becoming a valuable tool in many different areas. In this kind of scenario, the quality of data becomes an extremely important issue; however, not much attention has been paid to this specific topic, with only a few existing works that focus on it. In this paper, we present a proposal for managing data streams from sensors that are installed in patients’ homes in order to monitor (...) their health. It focuses on processing the sensors’ data streams, taking into account data quality. In order to achieve this, a data quality model for this kind of data streams and an architecture for the monitoring system are proposed. Moreover, our work introduces a mechanism for avoiding false alarms generated by data quality problems. (shrink)

It is now a common belief that the truths of our lives are hidden in the databases streamed from our interactions in smart environments. In this current hype of big data, the Internet of Things has been suggested as the idea to embed small sensors and actuators everywhere to unfold the truths beneath the surfaces of everything. However, remaining the technology that promises more than it can provide thus far, more important for the IoT’s actual expansion to various social (...) domains than the actual discovery of hidden truths has been people’s speculations about the unknown problems, such as hidden security issues or lifestyle concerns, beyond the narrow human knowability but assumed to leave their traces in the IoT-collected big data. This paper discuss this speculation as the concealed cognitive labor of IoT users that projects some fictitious values to the big data IoT companies accumulate. By the term pan-kinetics, the systemic operation of smart actuators is analyzed as the process through which fictitious values of data are converted to the real values as these actuators draw some profitable correlations from physical domains of the IoT. Analyzing smart electroencephalogram headsets as the unique IoT devices operating on human brains, it argues how the IoT translates this speculative realism of unknown problems into its big data, which the IoT developers believe to be full of machine-learnable correlations that would lead to the smart solutions of the problems. (shrink)

Low heart rate causes a risk of death, heart disease, and cardiovascular diseases. Therefore, monitoring the heart rate is critical because of the heart’s function to discover its irregularity to detect the health problems early. Rapid technological advancement allows healthcare sectors to consolidate and analyze massive health-based data to discover risks by making more accurate predictions. Therefore, this work proposes a real-time prediction system for heart rate, which helps the medical care providers and patients avoid heart rate risk in (...) real time. The proposed system consists of two phases, namely, an offline phase and an online phase. The offline phase targets developing the model using different forecasting techniques to find the lowest root mean square error. The heart rate time-series dataset is extracted from Medical Information Mart for Intensive Care. Recurrent neural network, long short-term memory, gated recurrent units, and bidirectional long short-term memory are applied to heart rate time series. For the online phase, Apache Kafka and Apache Spark have been used to predict the heart rate in advance based on the best developed model. According to the experimental results, the GRU with three layers has recorded the best performance. Consequently, GRU with three layers has been used to predict heart rate 5 minutes in advance. (shrink)

Parkinson’s Disease can be treated with the use of microelectrode recording and stimulation. This paper presents a data stream classifier that analyses raw data from micro-electrodes and decides whether the measurements were taken from the subthalamic nucleus (STN) or not. The novelty of the proposed approach is based on the fact that distances based on raw data are used. Two distances are investigated in this paper, i.e. Normalized Compression Distance (NCD) and Lempel-Ziv Jaccard Distance (LZJD). No (...) new features needed to be extracted due to the fact that in the case of high-dimensional data the process is extremely time-consuming. The k-nearest neighbour (k-NN) was chosen as the classifier due to its simplicity, which is essential in data stream classification. Results obtained from classifiers based on k-NN: k-NN, k-NN were compared with Probabilistic Approximate Window (k-NN with PAW); k-NN with Probabilistic Approximate Window and Adaptive Windowing (k-NN with PAW and ADWIN); and Self Adjusting Memory k-NN (SAM k-NN), which use the proposed distances, with the performance of the same classifiers but using standard Euclidean distance. Prequential accuracy was chosen as the performance measure. The results of the experiments performed with the described approach are in most cases better, i.e. the performance measures for kNN classifiers that use NCD and LZJD distances are better by up to 8.5 per cent and 14 per cent, respectively. Moreover, the proposed approach performs better when compared with other stream classification algorithms, i.e. Hoeffding Tree, Naive Bayes, and Leveraging Bagging. In the discussed case, an improvement of classification rate of up to 17.9 per cent when using Lempel-Ziv Jaccard Distance instead of the Euclidean was noted. (shrink)

There is a bias in neuroscience toward localizing and modularizing brain functions. Single cell recording, imaging studies, and the study of neurological deficits all feed into the Gallian view that different brain areas do different things and the things being done are confined to particular processing streams. At the same time, there is a growing sentiment that brains probably don’t work like that after all; it is better to conceive of them as fundamentally distributed units, multi‐tasking at every level. This (...) sentiment, however, is much less congenial to the tried‐and‐true experimental protocols available today and to theorizing about the brain in general. This essay examines the tension between current experimental methods and large-scale views of the brain. We argue that this disconnection between experiment and what really are guiding theoretical metaphors seriously impedes progress in neuroscience. (shrink)

This article explores the process by which intelligent transport system technologies have further advanced a data-driven culture in public transport and traffic control. Based on 12 interviews with transport engineers and fieldwork visits to three control rooms, it follows the implementation of Real-Time Passenger Information in Dublin and the various technologies on which it is dependent. It uses the concept of ‘data ratcheting’ to describe how a new data-driven rational order supplants a gradualist, conservative ethos, creating (...) technological dependencies that pressure organisations to take control of their own data and curate accessibility to outside organisations. It is argued that the implementation of Real-Time Passenger Information forms part of a changing landscape of urban technologies as cities move from a phase of opening data silos and expanded communication across departments and with citizens towards one in which new streams of digital data are recognised for their value in stabilising novel forms of city administration. (shrink)

An experiment is reported examining dual-process models of belief bias in syllogistic reasoning using a problem complexity manipulation and an inspection-time method to monitor processing latencies for premises and conclusions. Endorsement rates indicated increased belief bias on complex problems, a finding that runs counter to the “belief-first” selective scrutiny model, but which is consistent with other theories, including “reasoning-first” and “parallel-process” models. Inspection-time data revealed a number of effects that, again, arbitrated against the selective scrutiny model. The (...) most striking inspection-time result was an interaction between logic and belief on premise-processing times, whereby belief - logic conflict problems promoted increased latencies relative to non-conflict problems. This finding challenges belief-first and reasoning-first models, but is directly predicted by parallel-process models, which assume that the outputs of simultaneous heuristic and analytic processing streams lead to an awareness of belief - logic conflicts than then require time-consuming resolution. (shrink)

The technological possibilities for new data sources in media psychology, such as online live recordings, called Live Streaming, are growing continuously. These sources do not only offer plentiful quantitative material but also a fairly new access to ecologically valid and unobtrusive observation of problem-solving and decision-making processes. However, to exploit these potentials, epistemological and methodological reflection should guide research. The availability of Big Data and naturally occurring data sets allows to revise the historical controversies on the eligibility (...) of self-description. Drawing on such reflections, media psychology can contribute to renovate well established research methods, such as think aloud protocols, in order to enhance their empirical claims. Apart from other confident attempts to improve these methods, phenomenology and ethnomethodology offer a fruitful account to develop innovative data sources for self-description. Yet, this approach does not support a recurrence of self-description’s previous application but proposes an epistemological shift towards more subtle observations. In order to convey the potentials of media psychology, the risk of repeating classical mistakes, such as introspectionism, have to be regarded. Beyond these fallacies, however, modern digital technology holds encouraging potentials which have already partly been sighted by video gaming research. Due to the similarity of digital environments to laboratory setups, there remains to be a continuity from offline to online research, from traditional data to Big Data. Nevertheless, a true advance into new possibilities requires understanding the qualitative meaning of such data. (shrink)

This article explores the ways in which data centre operators are currently reconfiguring the systems of energy and heat supply in European capitals, replacing conventional forms of heating with data-driven heat production, and becoming important energy suppliers. Taking as an empirical object the heat generated from server halls, the article traces the expanding phenomenon of ‘waste heat recycling’ and charts the ways in which data centre operators in Stockholm and Paris direct waste heat through metropolitan district heating (...) systems and urban homes, and valorise it. Drawing on new materialisms, infrastructure studies and classical theory of production and destruction of value in capitalism, the article outlines two modes in which this process happens, namely infrastructural convergence and decentralisation of the data centre. These modes arguably help data centre operators convert big data from a source of value online into a raw material that needs to flow in the network irrespective of meaning. In this conversion process, the article argues, a new commodity is in a process of formation, that of computation traffic. Altogether data-driven heat production is suggested to raise the importance of certain data processing nodes in Northern Europe, simultaneously intervening in the global politics of access, while neutralising external criticism towards big data by making urban life literally dependent on power from data streams. (shrink)

The rapid growth of covert activities via communications network brought about an increasing need to provide an efficient method for data hiding to protect secret information from malicious attacks. One of the options is to combine two approaches, namely steganography and compression. However, its performance heavily relies on three major factors, payload, imperceptibility, and robustness, which are always in trade-offs. Thus, this study aims to hide a large amount of secret message inside a grayscale host image without sacrificing its (...) quality and robustness. To realize the goal, a new two-tier data hiding technique is proposed that integrates an improved exploiting modification direction method and Huffman coding. First, a secret message of an arbitrary plain text of characters is compressed and transformed into streams of bits; each character is compressed into a maximum of 5 bits per stream. The stream is then divided into two parts of different sizes of 3 and 2 bits. Subsequently, each part is transformed into its decimal value, which serves as a secret code. Second, a cover image is partitioned into groups of 5 pixels based on the original EMD method. Then, an enhancement is introduced by dividing the group into two parts, namely k1 and k2, which consist of 3 and 2 pixels, respectively. Furthermore, several groups are randomly selected for embedding purposes to increase the security. Then, for each selected group, each part is embedded with its corresponding secret code by modifying one grayscale value at most to hide the code in a -ary notational system. The process is repeated until a stego-image is eventually produced. Finally, the χ2 test, which is considered one of the most severe attacks, is applied against the stego-image to evaluate the performance of the proposed method in terms of its robustness. The test revealed that the proposed method is more robust than both least significant bit embedding and the original EMD. Additionally, in terms of imperceptibility and capacity, the experimental results have also shown that the proposed method outperformed both the well-known methods, namely original EMD and optimized EMD, with a peak signal-to-noise ratio of 55.92 dB and payload of 52,428 bytes. (shrink)

The nature of farming is – still – an essentially biological, and thus volatile, system, which poses substantial challenges to its integration into financialized capitalism. Financial investors often seek stability and predictability of returns that are hardly compatible with agriculture – but which are increasingly seen as achievable through data and digital farming technologies. This paper investigates how farmland investment brokers engage with, perceive, and produce farming data for their investors within a co-constructive process. Tackling land’s ‘stubborn (...) materiality’ for investment, I argue, has material and immaterial components: it includes the re-imagination of farming as a financial asset that delivers reliable income streams for investors; and the re-engineering of farmland’s concrete materialities with digital farming technologies. Farmland investment brokers develop investor-suitable farmland imaginaries, underpinned by storytelling as well as the calculative ‘evidence’ of (digital) data. At the same time, digital technologies have become a key tool for transforming farms into ‘investment grade assets’ endowed with the rich data on farm performance and financial returns requested by investors. I conclude that the assetization and digitization of farmland need to be seen as closely intertwined and mutually reinforcing processes and identify key areas for future research on this intersection. (shrink)

This study investigates the joint influences of three factors on the discovery of new word‐like units in a continuous artificial speech stream: the statistical structure of the ongoing input, the initial word‐likeness of parts of the speech flow, and the contextual information provided by the earlier emergence of other word‐like units. Results of an experiment conducted with adult participants show that these sources of information have strong and interactive influences on word discovery. The authors then examine the ability of (...) different models of word segmentation to account for these results. PARSER (Perruchet & Vinter, 1998) is compared to the view that word segmentation relies on the exploitation of transitional probabilities between successive syllables, and with the models based on the Minimum Description Length principle, such as INCDROP. The authors submit arguments suggesting that PARSER has the advantage of accounting for the whole pattern of data without ad‐hoc modifications, while relying exclusively on general‐purpose learning principles. This study strengthens the growing notion that nonspecific cognitive processes, mainly based on associative learning and memory principles, are able to account for a larger part of early language acquisition than previously assumed. (shrink)

The processes whereby our brains continue to learn about a changing world in a stable fashion throughout life are proposed to lead to conscious experiences. These processes include the learning of top-down expectations, the matching of these expectations against bottom-up data, the focusing of attention upon the expected clusters of information, and the development of resonant states between bottom-up and top-down processes as they reach an attentive consensus between what is expected and what is there in the outside world. (...) It is suggested that all conscious states in the brain are resonant states and that these resonant states trigger learning of sensory and cognitive representations. The models which summarize these concepts are therefore called Adaptive Resonance Theory, or ART, models. Psychophysical and neurobiological data in support of ART are presented from early vision, visual object recognition, auditory streaming, variable-rate speech perception, somatosensory perception, and cognitive-emotional interactions, among others. It is noted that ART mechanisms seem to be operative at all levels of the visual system, and it is proposed how these mechanisms are realized by known laminar circuits of visual cortex. It is predicted that the same circuit realization of ART mechanisms will be found in the laminar circuits of all sensory and cognitive neocortex. Concepts and data are summarized concerning how some visual percepts may be visibly, or modally, perceived, whereas amodal percepts may be consciously recognized even though they are perceptually invisible. It is also suggested that sensory and cognitive processing in the What processing stream of the brain obey top-down matching and learning laws that are often complementary to those used for spatial and motor processing in the brain's Where processing stream. This enables our sensory and cognitive representations to maintain their stability as we learn more about the world, while allowing spatial and motor representations to forget learned maps and gains that are no longer appropriate as our bodies develop and grow from infanthood to adulthood. Procedural memories are proposed to be unconscious because the inhibitory matching process that supports these spatial and motor processes cannot lead to resonance. (shrink)

This article presents a new interpretation of the consciousness-related neuroscientific findings using the framework of stochastic electrodynamics (SED), a branch of physics that sheds light on the basic principles underlying quantum systems. It is propounded that SED supplemented by two well-founded hypotheses leads to a satisfying explanation of the neural correlates of consciousness. The theoretical framework thus defined is based on the notion that all conceivable shades of phenomenal awareness are woven into the frequency spectrum of a universal background field, (...) called zero-point field (ZPF), implying that the fundamental mechanism underlying conscious systems rests upon the access to information available in the ZPF. The body of evidence can be interpreted such that in the extroverted, stimulus-oriented operating mode the brain produces streams of consciousness by periodically writing persistent information states into the ZPF (theta cycle). In the introspective operating mode, which goes along with activations of the default mode network, the brain is receptive to the flow of ZPF information states that constitutes the record of conscious experiences, suggesting that the sense of self and the retrieval of memories is accomplished by periodically reading (filtering) persistent information states from the ZPF (alpha cycle). Moreover, the data support the conclusion that meditative practices and psychedelics detune the filter, thus preventing the instantiation of self-referential conscious states, which leads to the dissolution of the ego. Instead, the brain taps into a wider spectrum of ZPF modes and, hence, gains access to an extended phenomenal color palette, resulting in expanded consciousness. (shrink)

While the effects of pattern learning on language processing are well known, the way in which pattern learning shapes exploratory behavior has long gone unnoticed. We report on the way in which individual differences in statistical pattern learning affect performance in the domain of language along multiple dimensions. Analyzing data from healthy monolingual adults' performance on a serial reaction time task and a self‐paced reading task, we show how individual differences in statistical pattern learning are reflected in readers' knowledge (...) of linguistic co‐occurrence patterns and in their exploration and exploitation of content‐specific and task‐general information. First, we investigated the extent to which an individual's pattern learning correlates with his or her sensitivity to systematic morphological and syntactic co‐occurrences, as evidenced while reading authentic sentences. We found that the stream of morphological and syntactic information has a more pronounced effect on the reading speed of, as we will label them, content‐sensitive learners in that the more probable the co‐occurrence pattern, the faster their reading of that pattern will be. Next, we investigated how differences in pattern learning are reflected in the ways in which individuals approach the reading task itself and adapt to it. Casting this relation in terms of exploration/exploitation strategies, known from Reinforcement Learning, we conclude that content‐sensitive learners are also more likely to initially probe (explore) a wider range of directly relevant patterns, which they can later use (exploit) to optimize their reading performance further. By affecting exploratory behavior, pattern learning influences the information that is gathered and becomes available for exploitation, thereby increasing the effect pattern learning has on language cognition. (shrink)

The detection and categorization of animate motions is a crucial task underlying social interaction and perceptual decision making. Neural representations of perceived animate objects are partially located in the primate cortical region STS, which is a region that receives convergent input from intermediate-level form and motion representations. Populations of STS cells exist which are selectively responsive to specific animated motion sequences, such as walkers. It is still unclear how and to what extent form and motion information contribute to the generation (...) of such representations and what kind of mechanisms are involved in the learning processes. The article develops a cortical model architecture for the unsupervised learning of animated motion sequence representations. We demonstrate how the model automatically selects significant motion patterns as well as meaningful static form prototypes characterized by a high degree of articulation. Such key poses are selectively reinforced during learning through a cross talk between the motion and form processing streams. Furthermore, we show how sequence-selective representations are learned in STS by fusing static form and motion input from the segregated bottom-up driving input streams. Cells in STS, in turn, feed their activities recurrently to their input sites along top-down signal pathways. We show how such learned feedback connections enable predictions about future input as anticipation generated by sequence-selective STS cells. Network simulations demonstrate the computational capacity of the proposed model by reproducing several experimental findings from neurosciences and by accounting for recent behavioral data. (shrink)

Recent findings in autopsy studies, neuroimaging, and neurophysiology indicate that dyslexia is accompanied by fundamental changes in brain anatomy and physiology, involving several anatomical and physiological stages in the processing stream, which can be attributed to anomalous prenatal and immediately postnatal brain development. Epidemiological evidence in dyslexic families led to the discovery of animal models with immune disease, comparable anatomical changes and learning disorders, which have added needed detail about mechanisms of injury and plasticity to indicate that substantial changes (...) in neural networks concerned with perception and cognition are present. It is suggested that the disorder of language, which is the cardinal finding in dyslexic subjects, results from early perceptual anomalies that interfere with the establishment of normal cognitive-linguistic structures, coupled with primarily disordered cognitive processing associated with developmental anomalies of cortical structure and brain asymmetry. This notion is supported by electrophysiological data and by findings of anatomical involvement in subcortical structures close to the input as well as cortical structures involved in language and other cognitive functions. It is not possible at present to determine where the initial insult lies, whether near the input or in high-order cortex, or at both sites simultaneously. (shrink)

Boundary completion and surface filling-in are computationally complementary processes whose multiple processing stages form processing streams that realize a hierarchical resolution of uncertainty. Such complementarity and uncertainty principles provide a new foundation for philosophical discussions about visual perception, and lead to neural explanations of difficult perceptual data.

Connectionist views in psychology and neuroscience give the impression that there is no one place in the brain into which all information funnels. If these impression are accurate, then we will have great difficulty picking out a point in neuronal or psychological time at which phenomena become conscious. If so, pointing to one place in which we are conscious of a particular event and expecting a psychophysical correlation between qualitative and neural events seems hopeless. In response to this worry, I (...) argue that ERP research can bridge the psychology and neuroscience such that we can identify when qualitative experiences occur relative to other cognitive events. I present data suggesting that accessing an early implicit priming system gives rise to a qualitatively different kind of ERP wave than does accessing a later episodic memory system. These results illustrate how it is possible to parse psychological events finely enough in (neuro-)psychological investigations in order to determine when particular psychological events occur in the head. So, if we could align consciousness with some psychological event, then we should be able to articulate when that event occurs in the processing stream (relative to other events) as long as that event can be correlated with some ERP waveform. (shrink)

In their model, Findlay & Walker propose that where and when the eyes move is determined by two relatively independent processing streams. Whereas both saccade direction and amplitude result from a low-level visual analysis of the peripheral visual stimulation, saccade latency results mainly from higher-level processes related to processing of the central information. In the present commentary, reading eye movement data are put forward as evidence against a strict autonomy of “Where” and “When” processing streams. First, saccade direction and (...) amplitude might be modified by high-level processes related to word identification. Second, the direction of a saccade directly affects its latency. (shrink)

Measures of visual cortical development in children demonstrate high variability and inconsistency throughout the literature. This is partly due to the specificity of the visual system in processing certain features. It may then be advantageous to activate multiple cortical pathways in order to observe maturation of coinciding networks. Visual stimuli eliciting the percept of apparent motion and shape change is designed to simultaneously activate both dorsal and ventral visual streams. However, research has shown that such stimuli also elicit variable visual (...) evoked potential (VEP) morphology in children. The aim of this study was to describe developmental changes in VEPs, including morphological patterns, and underlying visual cortical generators, elicited by apparent motion and shape change in school-aged children. Forty-one typically developing children underwent high-density EEG recordings in response to a continuously morphing, radially modulated, circle-star grating. VEPs were then compared across the age groups of 5-7, 8-10, and 11-15 years according to latency and amplitude. Current density reconstructions were performed on VEP data in order to observe activated cortical regions. It was found that two distinct VEP morphological patterns occurred in each age group. However, there were no major developmental differences between the age groups according to each pattern. Current density reconstructions further demonstrated consistent visual generators across age and pattern. These results describe two novel VEP morphological patterns in typically developing children, but with similar underlying cortical sources. The importance of these morphological patterns is discussed in terms of future studies and the investigation of a relationship to visual cognitive performance. (shrink)

The etiology of visual hallucinations is largely undetermined in schizophrenia. Collerton et al.'s PAD model partly concurs with what we know about neurocognition in schizophrenia, but we need to specify the types of perceptual and attentional abnormalities that are implicated in recurrent complex visual hallucinations (RCVH). Available data suggest that abnormal attentional control and top-down processing play a larger role than the ventral stream deficits.

We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural-language corpora. Given a stream of linguistic input, our model incrementally learns a grammar that captures its statistical patterns, which can then be used to parse or generate new data. The grammar constructed (...) in this manner takes the form of a directed weighted graph, whose nodes are recursively (hierarchically) defined patterns over the elements of the input stream. We evaluated the model in seventeen experiments, grouped into five studies, which examined, respectively, (a) the generative ability of grammar learned from a corpus of natural language, (b) the characteristics of the learned representation, (c) sequence segmentation and chunking, (d) artificial grammar learning, and (e) certain types of structure dependence. The model's performance largely vindicates our design choices, suggesting that progress in modeling language acquisition can be made on a broad front—ranging from issues of generativity to the replication of human experimental findings—by bringing biological and computational considerations, as well as lessons from prior efforts, to bear on the modeling approach. (shrink)

Impressions, energy radiated by phenomena in the momentary environmental scene, enter sensory neurons, creating in afferent nerves a data stream. Following Kant, by our inner sense the mind perceives its own thoughts as it ties together sense data into an internalized scene. The mind, residing in the brain, logically a Language Machine, processes and stores items as coded grammatical entities. Kantian synthetic unity in the linguistic brain is able to deliver our experience of the scene as we (...) appear to see it. Uniquely, the brain records its own history, synthesizing a Movie-in-the-Brain, called the Noumenal Cosmos. Attempting thereby to represent the actual Universe, this makes for a sovereign brain that governs itself. The brain is domicile of an Ego, with its selfhood at stake at all times. Yet, it can know itself only by its actions, in which it appears as an actor in its own movie. Phenomena enter garbled, as confused apparitions, and must be put in good form using top–down feedback control by Ego, so that each movie frame makes rational sense within the overall context of the Noumenal Cosmos. A stack of frames is processed typically in 40 Hz rhythm with 300 ms process time each, for about 12 in the stack at any time. Successive neural centers are processing the stack in the brain assembly line, based on data from increasingly global receptive fields. Ego stitches together the movie frames, but only the top frame is in consciousness for 25 ms. The top frame contains the whole scene where the Ego makes an appearance as the actor that imposes Kantian synthetic unity on the scene, merely an assembly of grammatical texts, in a system-internal coded process language, fitting the scene into the Noumenal Cosmos. But Ego observes Ego only to the extent permitted by the objectivity rule, only what it does and thinks, not its true face. From the Noumenal Cosmos, the Ego receives grammatical messages in the internal sense code. They are integrated into a whole in the reaction of the Ego to the momentary scene. The voluntary nature of Ego’s decisions is explained, based on its ability to code in advance its own actions sequentially in time, as it sees fit with a view to an orderly Noumenal Cosmos, records of code being arranged spatially in neural structures. (shrink)

In the article, we undertook an analysis of the prospects described in the research literature in connection with overcoming the linear interpretations of time and the transition to the concept of multiple times. We argue that it is possible to link two relatively autonomous objects on the basis of which they are discussed: the sphere of historical knowledge and the theme of memory. We argue that, overcome at the level of theories of the historical process, the linear idea of (...) time is preserved at the level of the creation of national histories and finds its expression in ethnocentrism or methodological individualism. Overcoming ethnocentrism should be associated not so much with the search for a more balanced attitude to specific interpretations of the historical past, associated with the inclusion of injustice shown in relation to other peoples in their own national histories, but with the development of alternative concepts of time that constitute the very formats of writing stories. We believe that the prospects for overcoming linear interpretations of time are associated with the development of the theory of multiple times. We argue that the constructivist approach should be considered the most heuristic theoretical and methodological basis for its implementation. We believe that modern interpretations of the heterogeneity of time imply the recognition of the mobility of time boundaries between the past, present, and future. We put forward the thesis that the criterion for distinguishing the modes of time should be considered the difference in the formats of the organization of knowledge. Correspondingly, we offer an interpretation of the historical past in the format of historical experience. As one of the consequences of this thesis, we affirm the need to revise ideas about the forms of influence of the past on the present. We argue that the recognition of the theory of the heterogeneity of time gives rise to relativism in determining the significance of different time streams in different social groups and cultures. We believe that the recognition of the theory of the diversity of temporal flows for different social and cultural groups presupposes the implementation of a procedure for synchronizing the data of temporal flows, which, on the one hand, makes it possible to overcome relativism, and, on the other hand, allochronism. Furthermore, we emphasize that synchronization should be based on the concept of modernity, which in turn is built on the basis of the project of the future and thus allows for the selection of various ideas about time. (shrink)

The main function of vision in many animals is to control movement. In rodents, some visuomotor acts require the construction of models of the external world while others rely on Gibsonian invariants. Such findings support Norman's dual processing approach but it is not clear that the two types of processing rely on homologs of visual processing streams described in primates.

This article derives from data collected over a six-month period between February and August 2022. Its sampling pertains to members of two general Twitter Lists of philosophy professionals: “Philosophers on Twitter”, limited to a maximum of 4500 active accounts, and “Philosophers”, restricted to accounts surpassing 1000 followers and currently including over 1,100 individuals. The totality of members of these two Lists is referenced in this article as “Philosophy Twitter”. -/- Data was collected in five principal ways from members (...) of these two Lists: 1) Monitoring the List streams, 2) addressing members, including following, retweeting, liking, endorsing, asking, commenting, and replying, 3) probing members’ Twitter activities in their Profiles (“Tweets & replies”), 4) reviewing members’ Twitter Bios, CVs, professional profiles, and websites, and 5) network analysis of members’ quantitative and qualitative association and interaction profiles. -/- The study of this material aimed at revealing interpersonal social structures and processes of philosophy professionals by their Twitter conduct. Its personal purview focused on creators, teachers, researchers, and students and thus excepted schools, colleges, universities, formal associations, and publishers. Particular attention was given to gaining insights on substantive orientation, cooperation, and constructive dialogue versus hierarchic and tribal characteristics. (shrink)

This study adds to the emergent stream of work examining the micro-level antecedents of pay dispersion by focusing on how business founders’ personal characteristics influence pay dispersion in their organizations. We leverage stakeholder theory and the motivated information processing perspective to predict pathways between founders’ self- versus other-oriented motivations, their perceptions of employee and shareholder salience, and pay dispersion in their organizations. We test our hypotheses on data from a two-wave survey of founders. We find that a high (...) level of motivation to benefit others on the part of a founder reduces the salience of shareholder concerns in decision-making, which in turn reduces pay dispersion. In contrast, a high level of motivation to benefit oneself heightens the salience of shareholder concerns in decision-making, increasing pay dispersion. Our results inform the debate on pay dispersion by elucidating the role played by founders’ self- versus other-oriented motivations and stakeholder salience perceptions. (shrink)

This paper presents social data and knowledge collective intelligence platform for TRaining Ethical AI Models (STREAM) to address the challenge of aligning AI models with human moral values, and to provide ethics datasets and knowledge bases to help promote AI models “follow good advice as naturally as a stream follows its course”. By creating a comprehensive and representative platform that accurately mirrors the moral judgments of diverse groups including humans and AIs, we hope to effectively portray cultural (...) and group variations, and capture the dynamic evolution of moral judgments over time, which in turn will facilitate the Establishment, Evaluation, Embedding, Embodiment, Ensemble, and Evolvement (6Es) of the moral capabilities of AI models. Currently, STREAM has already furnished a comprehensive collection of ethical scenarios, and amassed substantial moral judgment data annotated by volunteers and various popular Large Language Models, collectively portraying the moral preferences and performances of both humans and AIs across a range of moral contexts. This paper will outline the current structure and construction of STREAM, explore its potential applications, and discuss its future prospects. (shrink)

In order to extract the regularities underlying a continuous sensory input, the individual elements constituting the stream have to be encoded and their transitional probabilities (TPs) should be learned. This suggests that variance in statistical learning (SL) performance reflects efficiency in encoding representations as well as efficiency in detecting their statistical properties. These processes have been taken to be independent and temporally modular, where first, elements in the stream are encoded into internal representations, and then the co‐occurrences between (...) them are computed and registered. Here, we entertain a novel hypothesis that one unifying construct—the rate of information in the sensory input—explains learning performance. This theoretical approach merges processes related to encoding of events and those related to learning their regularities into a single computational principle. We present data from two large‐scale experiments with over 800 participants tested in support for this hypothesis, showing that rate of information in a visual stream clearly predicts SL performance, and that similar rate of information values leads to similar SL performance. We discuss the implications for SL theory and its relation to regularity learning. (shrink)

In 2013 the Health Research Council of New Zealand began a stream of funding titled 'Explorer Grants', and in 2017 changes were introduced to the funding mechanisms of the Volkswagen Foundation 'Experiment!' and the New Zealand Science for Technological Innovation challenge 'Seed Projects'. All three funding streams aim at encouraging novel scientific ideas, and all now employ random selection by lottery as part of the grant selection process. The idea of funding science by lottery has emerged independently in (...) several corners of academia, including in philosophy of science. This paper reviews the conceptual and institutional landscape in which this policy proposal emerged, how different academic fields presented and supported arguments for the proposal, and how these have been reflected in actual policy. The paper presents an analytical synthesis of the arguments presented to date, notes how they support each other and shape policy recommendations in various ways, and where competing arguments highlight need for further analysis or more data. In addition, it provides lessons for how philosophers of science can engage in shaping science policy, and in particular highlights the importance of mixing complementary expertise: it takes a village to raise policy. (shrink)

Binding can be described at three different levels: In neuroscience it refers to the integration of single-cell activities to form functional neural assemblies, especially in response to global stimulus properties; in cognitive science it refers to the integration of distributed modular input processing to form unified representations for memory and action, and in consciousness studies it refers to the unity of phenomenal consciousness . To describe and explain the unity of consciousness, detailed phenomenological descriptions of binding at the phenomenal level (...) and clarification of the underlying cognitive and neural mechanisms are required. The disunity of consciousness during dreaming is a fruitful avenue to study phenomenal binding and its mechanisms. The notion of the 'bizarreness' of dreams is closely related to the concept of 'binding': bizarreness can be reconceptualized as referring to different types of unusual combinations of features in the binding of dream images coherently together. The present study concentrates on the representation of human characters and the bizarreness found in these representations. We developed a rating scale that distinguishes different types of bizarreness on the basis of the unusual combinations of elements that are manifested in dream images. The data consisted of 592 dream reports in the home-based dream diaries of 52 students. The results indicate that about half of the human characters appearing in our dreams contain bizarre elements, and that certain types of bizarreness are more frequent than others. Phenomenal features intrinsic to the representation of a person are less frequently bizarre than is the external relation between the person and the context . Thus, binding the local features of a representation coherently together appears to be less prone to errors than binding several different information streams together into a coherent phenomenal model of the world. (shrink)

When searching for concepts in memory—as in the verbal fluency task of naming all the animals one can think of—people appear to explore internal mental representations in much the same way that animals forage in physical space: searching locally within patches of information before transitioning globally between patches. However, the definition of the patches being searched in mental space is not well specified. Do we search by activating explicit predefined categories and recall items from within that category, or do we (...) activate and recall a connected sequence of individual items without using categorical information, with each item recalled leading to the retrieval of an associated item in a stream, or both? Using semantic representations in a search of associative memory framework and data from the animal fluency task, we tested competing hypotheses based on associative and categorical search models. Associative, but not categorical, patch transitions took longer to make than position-matched productions, suggesting that categorical transitions were not true transitions. There was also clear evidence of associative search even within categorical patch boundaries. Furthermore, most individuals' behavior was best explained by an associative search model without the addition of categorical information. Thus, our results support a search process that does not use categorical information, but for which patch boundaries shift with each recall and local search is well described by a random walk in semantic space, with switches to new regions of the semantic space when the current region is depleted. (shrink)

Cognitive scientists distinguish between automatic and controlled mental processes. Automatic processes are either innately involuntary or become automatized through extensive practice. For example, reading words is a purportedly automatic process for proficient readers and the Stroop effect is consequently considered the “gold standard” of automated performance. Although the question of whether it is possible to regain control over an automatic process is mostly unasked, we provide compelling data showing that posthypnotic suggestion reduced and even removed Stroop interference (...) in highly hypnotizable individuals. Drawing on a large sample of highly hypnotizable participants, we examined the effects of suggestion on Stroop performance both with and without a posthypnotic suggestion to perceive the input stream as meaningless symbols. We show that suggestion administered to highly hypnotizable persons significantly reduced Stroop interference and derailed a seemingly automatic process. (shrink)

This paper explores the relation of thought and the stream of consciousness in the light of an ontological argument raised against cognitive phenomenology views. I argue that the ontological argument relies on a notion of ‘processive character’ that does not stand up to scrutiny and therefore it is insufficient for the argument to go through. I then analyse two more views on what ‘processive character’ means and argue that the process-part account best captures the intuition behind the argument. (...) Following this view, I reconstruct the ontological argument and argue that it succeeds in establishing that some mental episodes like judging, understanding and occurrent states of thought do not enter into the stream but fails to exclude episodes like entertaining. Contrary to what it might seem, this conclusion fits well with cognitive phenomenology views, given that, as I show, there is a way for non-processive mental episodes to be fundamentally related to processive ones, such that they cannot be excl.. (shrink)

Corporate Reputation (CR) is essential to value generation and is co-created between a company and its stakeholders, including supply chain actors. Consequently, CR is a critical and valuable resource that should be managed carefully along supply chains. However, the current CR literature is fragmented, and a general definition of CR is elusive. Besides, the academic CR debate largely lacks a supply chain perspective. This is not surprising, as it is very difficult to collect reliable data along supply chains. When (...) supply chains span the globe, data collection is especially challenging, as the chain consists of multiple suppliers and subcontractors, positioned at different tier levels. Recognizing this, the paper examines firstly the current state of CR research through a systematic literature review from a business perspective. The review is combined with a bibliometric mapping approach to show the most influential research clusters, representative of CR research streams and their contributors. This process highlights that the connection between CR and supply chain issues represents a major research gap. Consequently, this paper introduces a research agenda connecting these the two traditionally separated research fields. (shrink)

A typical claim in anti-representationalist approaches to cognition such as ecological psychology or radical embodied cognitive science is that ecological information is sufficient for guiding behavior. According to this view, affordances are immediately perceptually available to the agent (in the so-called “ambient energy array”), so sensory data does not require much further inner processing. As a consequence, mental representations are explanatorily idle: perception is immediate and direct. Here we offer one way to formalize this direct-perception claim and identify some (...) important limits to it. We argue that the claim should be read as saying that successful behavior just implies picking out affordance-related information from the ambient energy array. By relying on the Partial Information Decomposition framework, and more concretely on its development of the notion of synergy, we show that in multimodal perception, where various energy arrays carry affordance-related information, the “just pick out affordance-related information” approach is very inefficient, as it is bound to miss all synergistic components. Efficient multimodal information combination requires transmitting sensory-specific (and not affordance-specific) information to wherever it is that the various information streams are combined. The upshot is that some amount of computation is necessary for efficient affordance reconstruction. (shrink)

It has become increasingly difficult for individuals to exercise meaningful control over the personal data they disclose to companies or to understand and track the ways in which that data is exchanged and used. These developments have led to an emerging consensus that existing privacy and data protection laws offer individuals insufficient protections against harms stemming from current data practices. However, an effective and ethically justified way forward remains elusive. To inform policy in this area, we (...) propose the Ethical Data Practices framework. The framework outlines six principles relevant to the collection and use of personal data—minimizing harm, fairly distributing benefits and burdens, respecting autonomy, transparency, accountability, and inclusion—and translates these principles into action-guiding practical imperatives for companies that process personal data. In addition to informing policy, the practical imperatives can be voluntarily adopted by companies to promote ethical data practices. (shrink)

We study the phenomenon of merging of opinions for computationally limited Bayesian agents from the perspective of algorithmic randomness. When they agree on which data streams are algorithmically random, two Bayesian agents beginning the learning process with different priors may be seen as having compatible beliefs about the global uniformity of nature. This is because the algorithmically random data streams are of necessity globally regular: they are precisely the sequences that satisfy certain important statistical laws. By virtue (...) of agreeing on what data streams are algorithmically random, two Bayesian agents can thus be taken to concur on what global regularities they expect to see in the data. We show that this type of compatibility between priors suffices to ensure that two computable Bayesian agents will reach inter-subjective agreement with increasing information. In other words, it guarantees that their respective probability assignments will almost surely become arbitrarily close to each other as the number of observations increases. Thus, when shared by computable Bayesian learners with different subjective priors, the beliefs about uniformity captured by algorithmic randomness provably lead to merging of opinions. (shrink)

Flooding is one of the most damaging natural hazards globally. During the past three years, floods have claimed hundreds of lives and millions of dollars of damage in Iran. In this study, we detected flood locations and mapped areas susceptible to floods using time series satellite data analysis as well as a new model of bagging ensemble-based alternating decision trees, namely, bag-ADTree. We used Sentinel-1 data for flood detection and time series analysis. We employed twelve conditioning parameters of (...) elevation, normalized difference’s vegetation index, slope, topographic wetness index, aspect, curvature, stream power index, lithology, drainage density, proximities to river, soil type, and rainfall for mapping areas susceptible to floods. ADTree and bag-ADTree models were used for flood susceptibility mapping. We used software of Sentinel application platform, Waikato Environment for Knowledge Analysis, ArcGIS, and Statistical Package for the Social Sciences for preprocessing, processing, and postprocessing of the data. We extracted 199 locations as flooded areas, which were tested using a global positioning system to ensure that flooded areas were detected correctly. Root mean square error, accuracy, and the area under the ROC curve were used to validate the models. Findings showed that root mean square error was 0.31 and 0.3 for ADTree and bag-ADTree techniques, respectively. More findings illustrated that accuracy was obtained as 86.61 for bag-ADTree model, while it was 85.44 for ADTree method. Based on AUC, success and prediction rates were 0.736 and 0.786 for bag-ADTree algorithm, in order, while these proportions were 0.714 and 0.784 for ADTree. This study can be a good source of information for crisis management in the study area. (shrink)

In this paper we reflect on the effectiveness of cognitive mapping as a method to study farm functioning in its complexity and its diverse forms in the framework of our own experiment with a diverse group of Flemish beef farmers. With a structured direct elicitation method we gathered 30 CMs. We analyzed the content of these maps both qualitatively and quantitatively. The central role of the concept “Income” in most maps indicated a shared concern for economic security. Further, the CMs (...) indicated that farmers dealt with this shared social reality differently, as the relationships included in their maps referred to different functional processes relating to revenue streams, marketing strategies, investment decisions, dependence on production inputs, on-farm resource management, and personal well-being. With a clustering algorithm we grouped farmers based on the relationships in their maps, which allowed us to trace some of the broader patterns within the data, such as the existence of more business- and investment-minded farmers, in contrast to farmers focused on their quality of life, and animal production-oriented in contrast to marketing-oriented farmers. Taking into account farmers’ comments, we find that the applied methods had limited capability to classify farmers based on their perspectives on farming. Still, the system presentations proved useful to study what aspects farmers were working on or towards, and how these aspects may actually fit together as a whole. CMing was therefore mostly effective in exploring farm functioning in its complexity, and less so in exploring farm functioning in its diversity. (shrink)

A goal of expanding life over time, space, and substrate requires that we look beneath the surface of technology and the universal norms placed on human nature to a vision of its future that could be realized. There is a visible fracturing of the personal and social behaviors of its hybrid users – a process that we might call data‐clatter. While life expansion seeks the continuation of persons over time and space and beyond the physical body, there is (...) no current method for backing up the brain and transferring its processes outside the biological system. The objectives of life expansion include developing a whole‐body prosthesis as both a device for mobility and sensory perception and a system that streams cognitive functions. These systems would provide the biosphere for biological systems of humans and the cybersphere for computational systems of digital persons. (shrink)

In the rapidly evolving landscape of multimodal communication research, this paper explores the transformative role of machine learning (ML), particularly using multimodal large language models, in tracking, augmenting, annotating, and analyzing multimodal data. Building upon the foundations laid in our previous work, we explore the capabilities that have emerged over the past years. The integration of ML allows researchers to gain richer insights from multimodal data, enabling a deeper understanding of human (and non-human) communication across modalities. In particular, (...) augmentation methods have become indispensable because they facilitate the synthesis of multimodal data and further increase the diversity and richness of training datasets. In addition, ML-based tools have accelerated annotation processes, reducing human effort while improving accuracy. Continued advances in ML and the proliferation of more powerful models suggest even more sophisticated analyses of multimodal communication, e.g., through models like ChatGPT, which can now “understand” images. This makes it all the more important to assess what these models can achieve now or in the near future, and what will remain unattainable beyond that. We also acknowledge the ethical and practical challenges associated with these advancements, emphasizing the importance of responsible AI and data privacy. We must be careful to ensure that benefits are shared equitably and that technology respects individual rights. In this paper, we highlight advances in ML-based multimodal research and discuss what the near future holds. Our goal is to provide insights into this research stream for both the multimodal research community, especially in linguistics, and the broader ML community. In this way, we hope to foster collaboration in an area that is likely to shape the future of technologically mediated human communication. (shrink)

An essential requirement for increasing human confidence in computer systems is knowing an event’s origin. Therefore, it is necessary to have an efficient method to record such information. It is especially challenging in robotics, where unexpected behaviours can have unpredictable consequences, endangering the interests of people or even their safety. Furthermore, to analyse an incident’s cause or anticipate future behaviours, we must identify the events that cause a specific action. Although it is common to use logging systems for such purposes, (...) issues such as partial recording of events, unhelpful data or the impact on robot performance suggest conceiving new accountability solutions that assist when determining the responsible entities or the provenance of specific information. This paper presents a general-purpose approach to developing an accountability system for autonomous robots. It consists of four main components: a system event logger, a message producer, a distributed event streaming platform and a database. Our proposal is completely decoupled from the monitored system and allows real-time analysis, improving flexibility, besides system protection and transparency. Finally, the need to reduce the impact of the audit process and logging tasks on robot performance has promoted the development of different assessment scenarios to determine the best strategy for providing accounting services. (shrink)

The exposure of scientific scandals and the increase of dubious research practices have generated a stream of studies on Questionable Research Practices (QRPs), such as failure to acknowledge co-authors, selective presentation of findings, or removal of data not supporting desired outcomes. In contrast to high-profile fraud cases, QRPs can be investigated using quantitative, survey-based methods. However, several design issues remain to be solved. This paper starts with a review of four problems in the QRP research: the problem of (...) precision and prevalence, the problem of social desirability bias, the problem of incomplete coverage, and the problem of controversiality, sensitivity and missing responses. Various ways to handle these problems are discussed based on a case study of the design of a large, cross-field QRP survey in the social and medical sciences in Sweden. The paper describes the key steps in the design process, including technical and cognitive testing and repeated test versions to arrive at reliable survey items on the prevalence of QRPs and hypothesized associated factors in the organizational and normative environments. Partial solutions to the four problems are assessed, unresolved issues are discussed, and tradeoffs that resist simple solutions are articulated. The paper ends with a call for systematic comparisons of survey designs and item quality to build a much-needed cumulative knowledge trajectory in the field of integrity studies. (shrink)