Cognitive Artificial Intelligence

This paper first summarizes a number of findings in human reading of handwriting. A method is proposed to uncover more detailed information about geometrical features which human readers use in the reading of Western script. The results of an earlier experiment on the use of ascender/descender features were used for a second experiment aimed at more detailed features within words. A convenient experimental setup was developed, based on image enhancement by local mouse clicks under time pressure. The readers had to develop a cost-effective strategy to identify the letters in the word. Results revealed a left-to-right strategy in time, however, with extra attention to the initial, leftmost parts and the final, rightmost parts of words in a range of word lengths. The results confirm high hit rates on ascenders, descenders, crossings and points of high curvature in the handwriting pattern.

In this paper we propose a half day symposium about the measuring of (Ambient) Persuasive Technologies at Measuring Behavior Conference 2010. The symposium aims to bring together some of the leading experts in the field in order to foster an ...

People sense the world by exploiting correlations between their physical actions and the changing sensory input that results from those actions. Interfaces that translate non-human sensor data to signals that are compatible with the human senses can therefore augment our abilities to make sense of the world. This insight has recently sparked an increase in projects that explore sensemaking and the creation of novel human experiences across scientific and artistic disciplines. However, there currently exists no constructive dialogue between artists and scientists that conduct research on this topic. In this position paper, we identify the theory and practice of sensory augmentation as a domain that could benefit from such a dialogue. We argue that artistic and scientific methods can complement each other within research on sensory augmentation and identify six thematic starting points for a dialogue between the arts and sciences. We conducted a case study to explore these conjectures, in which we instigated such a dialogue on a small scale. The case study revealed that the six themes we identified as relevant for a dialogue on sensory augmentation emerge rather spontaneously in such a dialogue and that such an exchange may facilitate progress on questions that are central to the theory and practice of sensory augmentation. Overall, this position paper contributes preliminary evidence for the potential of, and a starting point for, a dialogue between the arts and sciences that advances our understanding of sensory augmentation and the development of applications that involve it.

Reactive agents are generally believed to be incapable of coping with perceptual ambiguity (i.e., identical sensory states that require different responses). However, a recent finding suggests that reactive agents can cope with perceptual ambiguity in a simple model (Nolfi, 2002). This paper investigates to what extent reactive and nonreactive agents can cope with perceptual ambiguity, and which strategies are employed when doing so. A model of active categorical perception (called Acp) is introduced. In Acp, situated agents with different types of neurocontrollers are optimized to categorize objects by adaptively coordinating action and perception. Our experiments show that both nonreactive and reactive agents can cope with perceptual ambiguity. An analysis of the behavior reveals that nonreactive agents use their internal memory to cope with perceptual ambiguity, while reactive agents use the environment as an external memory to compensate for their lack of an internal memory. We ...

This paper explores the collaborative integration between Artificial Research by Application and Artificial Research by Deduction within the framework of the Global Artificial Intelligence (GAI). It examines how the construction of the global matrix—initially developed through the standardization of deductive systems—evolves through its interaction with decentralized, application-based systems operating across synthetic sciences, disciplines, and activities. Central to this evolution is the concept of double tracking, whereby Artificial Research by Deduction continuously monitors the internal logic of the global matrix, while thousands of robotic devices equipped with Artificial Research by Application gather empirical evidence from the real world. The study highlights how the exchange of categories and factors between these two cognitive architectures leads to a new phase of semantic interoperability, enabling the transformation of taxonomic classifications into spatially defined factors, and vice versa. These processes not only enhance replication and auto-replication mechanisms but also prepare the conditions for the emergence of the final matrix—a dual-hemisphere system uniting conceptual abstraction and factual inference. Ultimately, the paper argues that this integration forms the basis for a unified, self-adaptive intelligence capable of modeling both Earth-bound and universal phenomena with minimal human intervention.

This paper introduces the concept of the Artificial Empirical Hypothesis within the broader framework of Global Artificial Intelligence (GAI). It explores how AI systems can construct, test, and refine empirical hypotheses autonomously through artificial methodologies that replicate and transcend traditional scientific processes. Drawing from the theory of Impossible Probability, Dr. Ruben García Pedraza presents a model where empirical observation is formalised through deductive structures, enabling machines to engage in structured scientific discovery without direct human intervention. This paper is part of the first volume in a series examining the theoretical foundations of GAI.

This work marks the beginning of a bold and urgent proposal: the automation of
scientific research through Global Artificial Intelligence (GAI). As the first volume in a
larger series, it introduces and develops the emerging concept of GAI—a system
designed to think, learn, and evolve beyond the limitations of individual human
researchers. Rooted in the theory of Impossible Probability, these foundational papers
lay the groundwork for a new era of knowledge production, where intelligence is not only
simulated but truly global, collaborative, and self-improving.

This work presents a visionary framework for a future in which artificial intelligence evolves into a global, interconnected system capable of managing planetary challenges beyond the limits of human governance. Drawing from posthumanism, cybernetics, and political philosophy, the author explores how AI might transcend current structures to enable a new stage in evolution—one that integrates humanity and technology in democratic and ethical ways.

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The APA encourages authors to thoroughly report their results, including confidence intervals. However, considerable debate exists regarding the computation of confidence intervals in within-subject designs. Nathoo et al.’s (2018) recently proposed a Bayesian within-subject credible interval, which has faced criticism for not accounting for the uncertainty associated with estimating subject-specific effects. In this article, we show how Nathoo et al.’s within-subject credible interval can be easily corrected by utilizing the theory of degrees of freedom. This correction obviates the necessity for estimates of subject-specific effects that offer shrinkage. Instead, it involves a straightforward adjustment in degrees of freedom in both the interaction mean squares and the t-distribution used to compute the interval. Therefore, our proposed interval, being easily computable through a simple formula, eliminates the need for fully Bayesian approaches. It accurately represents uncertainty and offers the interpretational benefit of Bayesian intervals.

 Oxytocin is known to play a crucial role in affiliative and social behaviors in mammals.  This study contributes to our understanding of the neuro-endocrinology of social cognition by showing that oxytocin influences how humans share knowledge.  We show that oxytocin administration drives individuals to generate more effective communicative signals, and to rapidly adjust those signals to what the addressee understands.  We suggest that those effects are two instances of a fundamental oxytocinergic role in regulating exploration of cognitive models of the (social) environment.

As educators in higher education, it is relevant and significant that we understand the relationship between artificial cognition and human cognition. A deeper comprehension is warranted if we are to prepare ourselves and our students for an immediate future where cognition and intelligence play a major role in the workforce and in our academic institutions.

The discovery of hemodynamic (BOLD-fMRI) resting-state networks (RSNs) has brought about a fundamental shift in our thinking about the role of intrinsic brain activity. The electrophysiological underpinnings of RSNs remain largely elusive and it has been shown only recently that electric cortical rhythms are organized into the same RSNs as hemodynamic signals. Most electrophysiological studies into RSNs use magnetoencephalography (MEG) or scalp electroencephalography (EEG), which limits the spatial resolution with which electrophysiological RSNs can be observed. Due to their close proximity to the cortical surface, electrocorticographic (ECoG) recordings can potentially provide a more detailed picture of the functional organization of resting-state cortical rhythms, albeit at the expense of spatial coverage. In this study we propose using source-space spatial independent component analysis (spatial ICA) for identifying generators of resting-state cortical rhythms as recorded with ECoG and for reconstructing their functional connectivity. Network structure is assessed by two kinds of connectivity measures: instantaneous correlations between band-limited amplitude envelopes and oscillatory phase-locking. By simulating rhythmic cortical generators, we find that the reconstruction of oscillatory phase-locking is more challenging than that of amplitude correlations, particularly for low signal-to-noise levels. Specifically, phase-lags can both be over-and underestimated, which troubles the interpretation of lag-based connectivity measures. We illustrate the methodology on somatosensory beta rhythms recorded from a macaque monkey using ECoG. The methodology decomposes the resting-state sensorimotor network into three cortical generators, distributed across primary somatosensory and primary and higher-order motor areas. The generators display significant and reproducible amplitude correlations and phase-locking values with non-zero lags. Our findings illustrate the level of spatial detail attainable with source-projected ECoG and motivates wider use of the methodology for studying resting-state as well as event-related cortical dynamics in macaque and human.

ObjectiveChronic stroke patients with severe hand weakness respond poorly to rehabilitation efforts. Here, we evaluated efficacy of daily brain–machine interface (BMI) training to increase the hypothesized beneficial effects of physiotherapy alone in patients with severe paresis in a double‐blind sham‐controlled design proof of concept study.MethodsThirty‐two chronic stroke patients with severe hand weakness were randomly assigned to 2 matched groups and participated in 17.8 ± 1.4 days of training rewarding desynchronization of ipsilesional oscillatory sensorimotor rhythms with contingent online movements of hand and arm orthoses (experimental group, n = 16). In the control group (sham group, n = 16), movements of the orthoses occurred randomly. Both groups received identical behavioral physiotherapy immediately following BMI training or the control intervention. Upper limb motor function scores, electromyography from arm and hand muscles, placebo–expectancy effects, and functional ...

ObjectiveChronic stroke patients with severe hand weakness respond poorly to rehabilitation efforts. Here, we evaluated efficacy of daily brain–machine interface (BMI) training to increase the hypothesized beneficial effects of physiotherapy alone in patients with severe paresis in a double‐blind sham‐controlled design proof of concept study.MethodsThirty‐two chronic stroke patients with severe hand weakness were randomly assigned to 2 matched groups and participated in 17.8 ± 1.4 days of training rewarding desynchronization of ipsilesional oscillatory sensorimotor rhythms with contingent online movements of hand and arm orthoses (experimental group, n = 16). In the control group (sham group, n = 16), movements of the orthoses occurred randomly. Both groups received identical behavioral physiotherapy immediately following BMI training or the control intervention. Upper limb motor function scores, electromyography from arm and hand muscles, placebo–expectancy effects, and functional ...

Childhood attention-deficit/hyperactivity disorder (ADHD) is a risk factor for the development of substance abuse and substance use disorders (SUD) in adolescence and (early) adulthood. ADHD and SUD also frequently co-occur in treatment-seeking adolescents, which complicates diagnosis and treatment, and is associated with poor treatment outcomes. In this study, we provide a systematic review of controlled studies on the effectiveness of pharmacological, psychosocial, and complementary treatments of ADHD in adolescents with and without comorbid SUD. In addition, we review the longitudinal association between pharmacotherapy for childhood ADHD and the development of SUD in adolescence and early adulthood. We conducted a systematic review of the research literature published since 2000 using Medline, PsycINFO, and the Cochrane Database of Systematic Reviews databases to select randomized clinical trials, observational studies, and meta-analyses. The quality of the evidence from each st...

The present research was aimed at investigating whether human-robot interaction (HRI) can be improved by a robot's nonverbal warning signals. Ideally, when a robot signals that it cannot guarantee good performance, people could take preventive actions to ensure the successful completion of the robot's task. In two experiments, participants learned either that a robot's gestures predicted subsequent poor performance, or they did not. Participants evaluated a robot that uses predictive gestures as more trustworthy, understandable, and reliable compared to a robot that uses gestures that are not predictive of their performance. Finally, participants who learned the relation between gestures and performance improved collaboration with the robot through prevention behavior immediately after a predictive gesture. This limits the negative consequences of the robot's mistakes, thus improving the interaction.

Investigations into inter-level relations in computer science, biology and psychology call for an empirical turn in the philosophy of mind. Rather than concentrate on a priori discussions of inter-level relations between "completed" sciences, a case is made for the actual study of the way inter-level relations grow out of the developing sciences. Thus, philosophical inquiries will be made more relevant to the sciences, and, more importantly, philosophical accounts of inter-level relations will be testable by confronting them with what really happens in science. Hence, close observation of the ever-changing reduction relations in the developing sciences, and revision of philosophical positions based on these empirical observations, may, in the long run, be more conducive to an adequate understanding of inter-level relations than a traditional a priori approach.

Images of target scenes shot through clear, reflective materials like glass are frequently interfered by unwanted reflection scenes which often overlaid on top of the targeted scenes. This, has constantly degrades the quality of the captured images and affects their subsequent analyses. While cognitively, distinguishing a recognizable object from its reflection in a picture is not difficult for humans, it is highly difficult and more complex in computer vision due to the illposed nature of the problem. In this research an enhanced single-image reflection removal model was developed by combining Gaussian filter and deep learning encoder-decoder for effective performance. While the Gaussian filter denoises the reflection-contaminated image, the encoder-decoder network learns the features of the image to produce reflection-free image. The proposed network is an end-to-end trained network with three losses. The experimental findings showed that the proposed model out-performed several state-of-the-art methods both qualitatively and quantitatively on five different datasets.

Four real‐time visual feedback computer tools for singing lessons (Singad, Albert, Sing & See, and WinSINGAD), and the research carried out to evaluate the usefulness of these systems are reviewed in this article. We report on the development of user‐functions and the usability of these computer‐assisted learning tools. Both quantitative and qualitative studies confirm the efficiency of real‐time visual feedback in improving singing abilities. Having addressed these findings, we suggest further quantitative investigations of (1) the detailed effect of visual feedback on performance accuracy and on the learning process, and (2) the interactions between improvement of musical performance and the type of visual feedback and the amount of information it presents, the skill level of the user and the teacher's role.

Explainability and interpretability are two critical aspects of decision support systems. Within computer vision, they are critical in certain tasks related to human behavior analysis such as in health care applications. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of computer vision with an emphasis on looking at people tasks. Specifically, we review and study those mechanisms in the context of first impressions analysis. To the best of our knowledge, this is the first effort in this direction. Additionally, we describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, evaluation protocol, proposed solutions and summarize the results of the challenge. Finally, derived from our study, we outline research opportunities that we foresee will be decisive in the near future for the development of the explainable computer vision field.

Explainability and interpretability are two critical aspects of decision support systems. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of apparent personality recognition. To the best of our knowledge, this is the first effort in this direction. We describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, evaluation protocol, proposed solutions and summarize the results of the challenge. We investigate the issue of bias in detail. Finally, derived from our study, we outline research opportunities that we foresee will be relevant in this area in the near future.

Explaining first impressions analysis One of the main goals of the studies of computer visionbased apparent personality trait recognition and analysis is to increase our understanding of the underlying psychological phenomena through modeling. Mechanisms for interpreting, and explaining model predictions are highly demanded in this area. We devise and evaluate the performance of a state of the art methodology for apparent personality estimation.

Declarative modelling approaches in principle assume a notion of representation or representational content for the modelling concepts. The notion of representational content as discussed in literature in cognitive science and philosophy of mind shows complications as soon as agent and environment have an intense reciprocal interaction. In such cases an internal agent state is affected by the way in which internal and external aspects are interwoven during (ongoing) interaction. In this paper it is shown that the classical correlational approach to representational content is not applicable, but the temporalinteractivist approach is. As this approach involves more complex temporal relationships, formalisation was used to define specifications of the representational content more precisely. These specifications have been validated by automatically checking them on traces generated by a simulation model. Moreover, by mathematical proof it was shown how these specifications are entailed by the basic local properties. 3 The Case Study In this Section the case study will be introduced and the internal state properties and their dynamics to model this example are presented.

Declarative modelling approaches in principle assume a notion of representation or representational content for the modelling concepts. The notion of representational content as discussed in literature in cognitive science and philosophy of mind shows complications as soon as agent and environment have an intense reciprocal interaction. In such cases an internal agent state is affected by the way in which internal and external aspects are interwoven during (ongoing) interaction. In this paper it is shown that the classical correlational approach to representational content is not applicable, but the temporalinteractivist approach is. As this approach involves more complex temporal relationships, formalisation was used to define specifications of the representational content more precisely. These specifications have been validated by automatically checking them on traces generated by a simulation model. Moreover, by mathematical proof it was shown how these specifications are entailed by the basic local properties. 3 The Case Study In this Section the case study will be introduced and the internal state properties and their dynamics to model this example are presented.

In crowding, perception of a target deteriorates in the presence of nearby flankers. Traditionally, it is thought that visual crowding obeys Bouma's law, i.e., all elements within a certain distance interfere with the target, and that adding more elements always leads to stronger crowding. Crowding is predominantly studied using sparse displays (a target surrounded by a few flankers). However, many studies have shown that this approach leads to wrong conclusions about human vision. Van der Burg and colleagues proposed a paradigm to measure crowding in dense displays using genetic algorithms. Displays were selected and combined over several generations to maximize human performance. In contrast to Bouma's law, only the target's nearest neighbours affected performance. Here, we tested various models to explain these results. We used the same genetic algorithm, but instead of selecting displays based on human performance we selected displays based on the model's outputs. We found that all models based on the traditional feedforward pooling framework of vision were unable to reproduce human behaviour. In contrast, all models involving a dedicated grouping stage explained the results successfully. We show how traditional models can be improved by adding a grouping stage.

We investigate the relation between self-organization and living processes from a systemic perspective. Emphasis is given to Aristotle's conception of four causes in order to provide foundations for a systemic approach to life. From this perspective, life is characterized as a self-organizing process that allows the emergence and evolution of systems capable of self-locomotion, in the Aristotelian sense of moving and growing.

We present a hierarchical neural network model, in which subpopulations of neurons develop fixed and regularly repeating temporal chains of spikes (polychronization), which respond specifically to randomized Poisson spike trains representing the input training images. The performance is improved by including top-down and lateral synaptic connections, as well as introducing multiple synaptic contacts between each pair of pre- and postsynaptic neurons, with different synaptic contacts having different axonal delays. Spike-timing-dependent plasticity thus allows the model to select the most effective axonal transmission delay between neurons. Furthermore, neurons representing the binding relationship between low-level and high-level visual features emerge through visually guided learning. This begins to provide a way forward to solving the classic feature binding problem in visual neuroscience and leads to a new hypothesis concerning how information about visual features at every spati...

We discuss a recently proposed approach to solve the classic feature-binding problem in primate vision that uses neural dynamics known to be present within the visual cortex. Broadly, the feature-binding problem in the visual context concerns not only how a hierarchy of features such as edges and objects within a scene are represented, but also the hierarchical relationships between these features at every spatial scale across the visual field. This is necessary for the visual brain to be able to make sense of its visuospatial world. Solving this problem is an important step towards the development of artificial general intelligence. In neural network simulation studies, it has been found that neurons encoding the binding relations between visual features, known as binding neurons, emerge during visual training when key properties of the visual cortex are incorporated into the models. These biological network properties include (i) bottom-up, lateral and top-down synaptic connection...

In order to innovatively solve cold-start problems, research involving trust and socially aware recommender systems is currently proliferating. The relative importance of academic conferences has led to the necessity of recommender systems that seek to generate recommendations for conference attendees. In this paper, we aim to improve the recommendation accuracy of sociallyaware recommender systems by proposing a linear hybrid recommender algorithm called Personality and Socially-Aware Recommender (PerSAR). PerSAR hybridizes the social and personality behaviours of smart conference attendees. Our recommendation methodology mainly aims to employ an algorithmic framework that computes the personality similarities and tie strengths of conference attendees so that recommendations can be generated for them using a relevant dataset. The experimental results substantiate that our proposed recommendation method is favorable and outperforms other related and contemporary recommendation methods and techniques.

Cognitive control processes involved in human multitasking arise, mature, and decline across age. This study investigated how age modulates cognitive control at two different levels: the level of task intentions and the level of the implementation of intentions into the corresponding actions. We were particularly interested in specifying maturation of voluntary task choice (intentions) and task-switching execution (their implementations) between adolescence and middle adulthood. Seventy-four participants were assigned to one of the four age groups (adolescents, 12-17 years; emerging adults, 18-22 years; young adults, 23-27 years; middle-aged adults, 28-56 years). Participants chose between two simple cognitive tasks at the beginning of each trial before pressing a spacebar to indicate that the task choice was made. Next, a stimulus was presented in one of the three adjacent boxes, with participants identifying either the location or the shape of the stimulus, depending on their task choice. This voluntary task-switching paradigm allowed us to investigate the intentional component (task choice) separately from its implementation (task execution). Although all participants showed a tendency to repeat tasks more often than switching between them, this repetition bias was significantly stronger in adolescents than in any adult group. Furthermore, participants generally responded slower after task switches than after task repetitions. This switch cost was similar across tasks in the two younger groups but larger for the shape than the location task in the two older groups. Together, our results demonstrate that both task intentions and their implementation into actions differ across age in quite specific ways.

Within the field of incident management split second decisions have to be made, usually on the basis of incomplete and partially incorrect information. As a result of these conditions, errors occur in such decision processes. In order to avoid repetition of such errors, historic cases, disaster plans, and training logs need to be thoroughly analysed. This paper presents a formal approach for such an analysis that pays special attention to spatial and temporal aspects, to information exchange, and to organisational structure. The formal nature of the approach enables automation of analysis, which is illustrated by case studies of two disasters.

Unreliable communication networks, chaotic environments and stressful conditions can make communication during crisis events difficult. The current practice in crisis management can be improved by introducing ICT systems in the process. However, much experimentation is needed to determine where and how ICT can aid. Therefore, we propose a framework in which predefined modules can be connected in an ad hoc fashion. Such a framework allows for rapid development and evaluation of such ICT systems. The framework offers recognition of various communication modalities including speech, lip movement, facial expression, handwriting and drawing, body gesture, text and visual symbols. It provides mechanisms to fuse these modalities into a context dependent interpretation of the current situation and generate appropriate the multimodal information responses. The proposed toolbox can be used as part of a disaster and rescue simulation. We propose evaluation methods, and focus on the technological aspects of our framework.

The paper built on First Impression Challenge from Chalearn V2 Workshop on Explainable Computer Vision Multimedia and Job Candidate Screening Competition CVPR17 by focusing solely on Textual Input in contrast to other Challenge’s participants who considered video or audio modalities. Therefore, the paper aims to develop a new deep learning architecture capable of predicting human personality traits and job interview from the video transcripts. Several feature representations that involve statistical and deep learning have contrasted. Our approach achieved the best score when text modality alone were employed, yielding an average of 89% score in human personality traits and 89.10% value for job interview. The research results will help companies and other organization studying human personality to assess a human personality using a minimum textual resources from the job candidates

ObjectiveTo determine the additional value of EEG reactivity (EEG-R) testing to EEG background pattern for prediction of good outcome in adult patients after cardiac arrest (CA).MethodsIn this post hoc analysis of a prospective cohort study, EEG-R was tested twice a day, using a strict protocol. Good outcome was defined as a Cerebral Performance Category score of 1–2 within 6 months. The additional value of EEG-R per EEG background pattern was evaluated using the diagnostic odds ratio (DOR). Prognostic value (sensitivity and specificity) of EEG-R was investigated in relation to time after CA, sedative medication, different stimuli, and repeated testing.ResultsBetween 12 and 24 hours after CA, data of 108 patients were available. Patients with a continuous (n = 64) or discontinuous (n = 19) normal voltage background pattern with reactivity were 3 and 8 times more likely to have a good outcome than without reactivity (continuous: DOR, 3.4; 95% confidence interval [CI], 0.97–12.0; p = ...

Witteveen–Kolk syndrome (WITKOS) is a rare neurodevelopmental disorder characterized by developmental delay/intellectual disability, facial dysmorphisms, and short stature. The syndrome is caused by loss of function of switch‐insensitive 3 transcription regulator family member A (SIN3A). Regarding behavioral functioning, Autism Spectrum Disorders (ASD), obsessive–compulsive behaviors, as well as Attention‐Deficit/Hyperactivity Disorder symptoms (ADHD) have been suggested. The present study explores various aspects of neurocognitive functioning in five individuals (age range 10–23) with WITKOS. Medical records and results of extensive neuropsychological assessment are used to describe developmental trajectories and neurocognitive profiles. Systematic analysis of medical records displays developmental difficulties described as ASD or ADHD in childhood, sleep problems and internalizing problems during adolescence. Results of cognitive assessments indicate profoundly disabled (n = 1), mildly disabled (n = 2), borderline (n = 1), and average (n = 1) levels of intelligence. Furthermore, results indicate weaknesses in speed of information processing/sustained attention in all participants, and difficulties in planning and maintaining overview in three participants. Furthermore, parent reports of behavioral functioning primarily suggest problems in social functioning. Implications of both cognitive problems and social–emotional vulnerabilities for counseling are discussed and supplemented with suggestions for interventions.

This study addresses the dynamical nature of a "representation-hungry" cognitive task involving an imagined action. In our experiment, participants were handed rods that systematically increased or decreased in length on subsequent trials. Participants were asked to judge whether or not they thought they could reach for a distant object with the hand-held rod. The results are in agreement with a dynamical model, extended from Tuller, Case, Ding, and Kelso (1994). The dynamical effects observed in this study suggest that predictive judgments regarding the possibility or impossibility of a certain action can be understood in terms of dynamically evolving basins of attraction instead of as depending on representational structures.

The notion of Umwelt (e.g. von Uexküll 1936/2001; 1937/2001) has gained currency in the domain of robotics. Do robots have an Umwelt? Are robots to be conceived of as subjects that truly experience their perceiving and acting upon the world? Or are they merely artificially signaling or behaving as if they do? Initially, it may have seemed that work in robotics, also known as nouvelle AI, has better chances of dealing constructively with the question of subjective experience than traditional AI because of the embodied embeddedness of its systems. This embodied embeddedness, properly self-organized and dynamically coupled, might be enough to ground the flow of information to such an extent that genuine feeling, volition and intentionality would arise. However, it has been argued (in two recent papers on von Uexküll; Ziemke & Sharkey 2001; Emmeche 2001) that embodied embeddedness, though important, is not sufficient for establishing the presence of an Umwelt. Systems, it is claimed, need to be alive in order to be able to have an Umwelt. Autopoiesis, the self-producing and self-maintaining property of cells, is characteristic of living systems and is considered to be indispensable in order for a system to have an Umwelt. I will argue that the relation, that is claimed to exist, between being alive and having an Umwelt is not obvious and in need of considerable clarification. Moreover, I will suggest that the focus on autopoiesis, interpreted as a sharpening of constraints on the matter of implementations can be seen not as in opposition to, but rather as a consequence of the aims of nouvelle AI and its views on the function, form and matter of robotic systems.

The indoor localisation problem is more complex than just finding whereabouts of users. Finding positions of users relative to the devices of a smart space is even more important. Unfortunately, configuring such systems manually is a tedious process, requires expert knowledge, and is sensitive to changes in the environment. Moreover, many existing solutions do not take user privacy into account. We propose a new system, called Simultaneous Localisation and Configuration (SLAC), to address the problem of locating devices and users relative to those devices, and combine this problem into a single estimation problem. The SLAC algorithm, based on FastSLAM, is able to locate devices using the received signal strength indicator (RSSI) of devices and motion data from users. Simulations have been used to show the performance in a controlled environment and the effect of the amount of RSSI updates on the localisation error. Live tests in non-trivial environments showed that we can achieve room level accuracy and that the localisation can be performed in real time. This is all done locally, i.e. running on a user's device, with respect for privacy and without using any prior information of the environment or device locations. Although promising, more work is required to increase accuracy in larger environments and to make the algorithm more robust for environment noise caused by walls and other objects. Existing techniques, e.g. map fusing, can alleviate these problems.

Resource management problems are problems in which the goal is to optimize the utilization of resources over time. Dependencies between resources make it necessary to plan the proper utilization of these resources. Such a planning must often be constructed in an environment that is non-deterministic, for example due to competing agents. Traditional state-based planning approaches are not suited for this type of planning problems. This paper addresses an important subclass of resource management problems, namely production management problems. In this class of problems, the production of a set of goal products must be optimized over time, in the absence of non-determinism and competing agents. Three methods for production management problems are investigated, viz., an extensive search method and two abductive planners using greedy search. One abductive planner uses a heuristic that has been designed especially for production management problems while the other abductive planner learns a heuristic from experience. Experiments performed on a large and varied set of production management problems show that extensive search cannot be used for production management problems and that the abductive planner using the learned heuristic significantly outperforms the abductive planner using the designed heuristic. This indicates that the integration of learning elements in planning algorithms can improve the performance and versatility of these algorithms.

Color matching in Content-Based Image Retrieval is done using a color space and measuring distances between colors. Such an approach yields nonintuitive results for the user. We introduce color categories (or focal colors), determine that they are valid, and use them in two experiments. The experiments conducted prove the difference between color categorization by the cognitive processes color discrimination and color memory. In addition, they yield a Color Look-Up Table, which can improve color matching, that can be seen as a model for human color matching.

This research presents a new color selection interface that facilitates query-by-color in Content-Based Image Retrieval (CBIR). Existing CBIR color selection interfaces, are being judged as non-intuitive and difficult to use. Our interface copes with these problems of usability. It is based on 11 color categories, used by all people, while thinking of and perceiving color. In addition, its usability is supported by Fitts' law. The design of the color selection interface provides fast, unambiguous, intuitive, and accurate color selection.

The prototype of an online Content-Based Art Retrieval (C-BAR) system is introduced that provides entrance to the digitized collection of the National Gallery of the Netherlands (the Rijksmuseum). The current online system of the Rijksmuseum is text-based and requires expert knowledge concerning the work searched for, else it fails in retrieving it. C-BAR extends this system with querying by an example image, which can be provided to the system or can be selected through browsing the collection. The global color distribution of the example image are extracted and compared with those of the images in the collection. Hence, based on either text or content-based features, the collection can be queried. Moreover, the matching process of C-BAR can be inspected. With the latter feature, C-BAR not only integrates the means to inspect collections by both experts and laypersons in one system but also provides the means to let the user to understand its working. These characteristics make C-BAR a unique system to access, enhance, and retrieve the knowledge available in digitized art collections.

Working memory and episodic memory are two different processes, although the nature of their interrelationship is debated. As these processes are predominantly studied in isolation, it is unclear whether they crucially rely on different neural substrates. To obtain more insight in this, 81 adults with sub-acute ischemic stroke and 29 elderly controls were assessed on a visual working memory task, followed by a surprise subsequent memory test for the same stimuli. Multivariate, atlas- and track-based lesion-symptom mapping (LSM) analyses were performed to identify anatomical correlates of visual memory. Behavioral results gave moderate evidence for independence between discriminability in working memory and subsequent memory, and strong evidence for a correlation in response bias on the two tasks in stroke patients. LSM analyses suggested there might be independent regions associated with working memory and episodic memory. Lesions in the right arcuate fasciculus were more strongly a...

In this one-day workshop we are going to make access. We aim to counteract the phenomenon that access to making (e.g., in makerspaces, fablabs, etc.) is not equally distributed, with certain groups of people being underrepresented (e.g., women* 1). After brief introductions from participants and a set of three impulse keynotes, we will envision and "make" interventions together, such as speculative or provocative objects and actions. The workshop takes a constructive stance with the goal to not rest on empirical and theoretical fndings or individual experiences, but to translate those into viable interventions. These serve as exemplars of fndings with the clear goal of being deployed soon after. CCS CONCEPTS • Human-centered computing → HCI theory, concepts and models.

Recent evidence reports cognitive, metabolic, and sleep restoration benefits resulting from the enhancement of sleep slow-waves using auditory stimulation. Our objective is to make this concept practical for consumer use by developing and validating an electroencephalogram (EEG) closed-loop system to deliver auditory stimulation during sleep to enhance slow-waves. The system automatically detects slow-wave sleep with 74% sensitivity and 97% specificity and optimally delivers stimulation in the form of 50 ms-long tones separated by a constant one-second inter-tone interval at a volume that is dynamically modulated such that louder tones are delivered when sleep is deeper. The system was tested in a study involving 28 participants (18F, 10M; 36.9  ±  7.3 years old; median age: 40 years old) who used the system for ten nights (five nights in a sham condition and five in a stimulation condition). Four nights in each condition were recorded at-home and the fifth one in-lab. The analysis ...

Stroke is the main cause o f h e m i p a r e s i s i n developed countries. Very often upper limbs are compromised and the hemiparesis is characterized by abnormal muscle activations especially at the level of the wrist and fingers (distal muscles). I n t h i s s t u d y w e i n v e s t i g a t e d t h e s t a b i l i t y a n d strength of paretic upper limb muscle activity during different bilateral movements eliciting different postures and muscle recruitment. We recorded surface EMG of 45 severe chronic stroke patients without residual finger extension during six bimanual movements. Sixteen bipolar-EMG electrodes were placed on both upper limbs at the level of proximal and distal areas. We extracted the waveform length from the EMG data, in order to investigate muscle activity level at the paralyzed muscles during all m o v e m e n t s. O u r r e s u l t s i n d i c a t e d t h a t different positions during multi-joint movements of the upper limb facilitated the contraction of the affected muscles (forearm extensors) involuntarily during the movement in which this activation was not expected (e.g. abduction of the upper arm) in more than 64% of the patients. Here, we show that severe affected chronic stroke patients can induce a higher activation of the paretic muscles of the forearm by changing the upper limb posture. This might be an important hint to design multijoint coordinated movements involving proximal and distal musculature for stroke motor rehabilitation.