Cognitive Systems Research最新文献

Schizophrenia is a chronic psychiatric disorder that is highly debilitating. One of the most frequent symptoms is the presence of auditory hallucinations (AH), which could be related to alterations in brain electrical activity measurable with electroencephalography (EEG). Although many previous works have recorded EEG signals of schizophrenia patients with medical EEG devices, the study of AH has never been developed by means of portable EEG measuring instruments. Therefore, the aim of this study is to detect AH in schizophrenia patients with a wireless EEG device. For that purpose, the spectral power from EEG recordings of periods with and without AH has been evaluated in a group of nine schizophrenia patients. Results reported that the main activation during hallucinations was found in right frontal locations, whereas the left hemisphere presented a stronger activation in hallucination-free periods. Furthermore, a generalized decrease of spectral power in hallucination with respect to hallucination-free episodes has been observed. Hence, this work demonstrates the possibility of detecting AH episodes with a wearable EEG device. In addition, the results obtained were compatible with the default model network, reporting a greater activation during no hallucination periods compared to hallucination moments.

In this paper, a fifth-order adaptive self-modelling network model is introduced to describe epigenetic involvement in the development of anxiety disorders and its regulation by a possible epigenetics-based therapeutic method. Multiple orders of adaptivity are used in the model to depict the development process, where a higher pathway of any order of adaptivity adapts characteristics of pathways in lower orders and acts as a form of control. These orders of adaptivity and their interlevel interaction were modelled as a higher-order adaptive dynamical system according to the self-modelling network modelling principle. The model was inspired by the structure of the relevant human biological and neurological processes. In addition to modelling the development of an anxiety disorder, also the possibility of an epigenetics-based therapy is suggested and computationally analyzed in this paper.

Traditionally, neuroimaging studies of fluid intelligence have focused on brain activation in frontal-parietal regions. In the past decade there has been accumulating evidence regarding the involvement of the cerebellum in higher cognitive function. In the current study we aimed to further investigate the role of the cerebellum in processing of fluid intelligence. We therefore scanned thirty-nine healthy participants (13 females and 26 males), recruited from the general population. Participant performed a novel abstract reasoning functional Magnetic Resonance Imaging task, modeled after stimuli from the advanced Raven's Progressive Matrices test. Analyses of both brain function and network architecture focusing on hubness were performed. We demonstrate activation in frontal and parietal well-known regions, together with an extensive activation in several cerebellar sub-regions. Moreover, four cerebellar regions served as crucial hub regions. Therefore, we provide evidence of the role of the cerebellum in fluid intelligence both by means of task brain activation and graph theory topology. Future studies should further assess in-depth the cerebellar contribution to cognitive processing in different brain disorders involving neural network alterations, allowing a better understanding of cognitive deficits.

A computational approach to cognitive modeling is proposed. The computational model is a parametric construction that takes into account cognitive stages and transitions between them. The cognitive model enables the idea of information processes, from their birth and appearance in a scope, evolution and canceling out their existence and disappearing from the scope. Process habitats are Lawvere’s variable domains; inter-transition is based on the notion of channeled spreading of processes.

Over the last decade, AI models of language and word meaning have been dominated by what we might call a statistics-of-occurrence, strategy: these models are deep neural net structures that have been trained on a large amount of unlabeled text with the aim of producing a model that exploits statistical information about word and phrase co-occurrence in order to generate behavior that is similar to what a human might produce, or representations that can be probed to exhibit behavior similar to what a human might produce (meaning-semblant behavior). Examples of what we can call Statistics-of-Occurrence Models (SOMs) include: Word2Vec (CBOW and Skip-Gram), BERT, GPT-3, and, most recently, ChatGPT. Increasingly, there have been suggestions that such systems have semantic understanding, or at least a proto-version of it. This paper argues against such claims. I argue that a necessary condition for a system to possess semantic understanding is that it function in ways that are causally explainable by appeal to its semantic properties. I then argue that SOMs do not plausibly satisfy this Functioning Criterion. Rather, the best explanation of their meaning-semblant behavior is what I call the Statistical Hypothesis: SOMs do not themselves function to represent or produce meaningful text; they just reflect the semantic information that exists in the aggregate given strong correlations between word placement and meaningful use. I consider and rebut three main responses to the claim that SOMs fail to meet the Functioning Criterion. The result, I hope, is increased clarity about why and how one should make claims about AI systems having semantic understanding.

Emotion modeling has always been intriguing to researchers, where detecting emotion is highly focused and generating emotion is much less focused to date. Therefore, in this paper, we aim to exploring emotion generation, particularly for general-purpose conversations. Based on the Cognitive Appraisal Theory and focusing on audio and textual inputs, we propose a novel method to calculate informative variables to evaluate a particular emotion-generating event and six primary emotions. Incorporating such a method of artificial emotion generation, we implement an emotional chatbot, namely EmoBot. Accordingly, EmoBot analyzes continuous audio and textual inputs, calculates the informative variables to evaluate the current situation, generates appropriate emotions, and responds accordingly. An objective evaluation indicates that EmoBot could generate more accurate emotional and semantic responses than a traditional chatbot that does not consider emotion. Additionally, a subjective evaluation of EmoBot demonstrates the appreciation of users for EmoBot over a traditional chatbot that does not consider emotion.

Autonomous vehicles promise to revolutionize society and improve the daily life of many, making them a coveted aim for a vast research community. To enable complex reasoning in autonomous vehicles, researchers are exploring new methods beyond traditional engineering approaches, in particular the idea of drawing inspiration from the only existing being able to drive: the human. The mental processes behind the human ability to drive can inspire new approaches with the potential to bridge the gap between artificial drivers and human drivers. In this review, we categorize and evaluate existing work on autonomous driving influenced by cognitive science, neuroscience, and psychology. We propose a taxonomy of the various sources of inspiration and identify the potential advantages with respect to traditional approaches. Although these human-inspired methods have not yet reached widespread adoption, we believe they are critical to the future of fully autonomous vehicles.

Integrating robots into our daily lives, once a distant dream, is gradually becoming a reality, surpassing our initial expectations. Today, we aspire for these robots to not only perform rudimentary tasks but to emulate human behavior, and in some aspects, even exceed it. The realm of research dedicated to achieving human-like competencies in robots has given rise to the fields of Developmental and Cognitive Robotics. These domains find their foundation in cognitive architectures and insights from human development. Despite the substantial progress in these fields, a conspicuous gap exists in the literature related to the evaluation of cognitive architectures and the advanced capabilities exhibited by robots. Recognizing this void, we aim at establishing a bridge between the insights gleaned from human developmental theories and the potential applications in robotics. Central to our investigation is the notion that learning follows a cumulative trajectory of escalating complexity. Consequently, our focus centers on the early stages of human development, particularly within the realm of children aged 0 to 2 years. Drawing inspiration from Piaget’s constructivist theory aligned with empirical studies in the Developmental Robotics domain, we unveil a framework that facilitates the classification of these studies. In light of this, we curate a series of progressive experiments, mirroring the motor and cognitive growth exhibited by children from birth to two years of age, to be conducted with robots. We also described a methodology for designing these experiments considering the robotics aspects.

Many existing approaches to model and compute trust in a quantitative way rely on ranking, rating or assessments of agents by other agents. Even though reputation is related with trust, it does not capture all its characteristics. In parallel, many works in neuroscience shows evidence about interpersonal trust being an associative learning process encoded in the human brain. Inspired by other subjects such as Cognitive Processing/Dopamine, where Reinforcement Learning algorithms have served to model those phenomena, we propose a model for trust dynamics based on a multi-agent RL algorithm. We corroborate some trust concepts developed in social sciences within a quantitative framework. We do also propose and assess some metrics for a better understanding about the relation between the trust behaviour and the performance of the agents. Finally, we show that Trust, as described by our proposal, can serve to accelerate learning.

This paper presents a framework based on natural language processing and first-order logic aiming at instantiating cognitive chatbots. The proposed framework leverages two types of knowledge bases interacting with each other in a meta-reasoning process. The first one is devoted to the reactive interactions within the environment, while the second one to conceptual reasoning. The latter exploits a combination of axioms represented with rich semantics and abduction as pre-stage of deduction, dealing also with some of the state-of-the-art issues in the natural language ontology domain. As a case study, a Telegram chatbot system has been implemented, supported by a module which automatically transforms polar and wh-questions into one or more likely assertions, so as to infer Boolean values or snippets with variable length as factoid answer. The conceptual knowledge base is organized in two layers, representing both long- and short-term memory. The knowledge transition between the two layers is achieved by leveraging both a greedy algorithm and the engine’s features of a NoSQL database, with promising timing performance if compared with the adoption of a single layer. Furthermore, the implemented chatbot only requires the knowledge base in natural language sentences, avoiding any script updates or code refactoring when new knowledge has to income.

The framework has been also evaluated as cognitive system by taking into account the state-of-the art criteria: the results show that AD-Caspar is an interesting starting point for the design of psychologically inspired cognitive systems, endowed of functional features and integrating different types of perception.