Metabolic considerations for cognitive modeling.

Artificial computing systems are a pervasive phenomenon in today's life. While traditionally such systems were employed to support humans in tasks that required mere number-crunching, there is an increasing demand for systems that exhibit autonomous, intelligent behavior in complex environments. These complex environments often confront artificial systems with ill-posed problems that have to be solved under constraints of incomplete knowledge and limited resources. Tasks of this kind are typically solved with ease by biological computing systems, as these cannot afford the luxury to dismiss any problem that happens to cross their path as “ill-posed.” Consequently, biological systems have evolved algorithms to approximately solve such problems – algorithms that are adapted to their limited resources and that just yield “good enough” solutions quickly. Algorithms from biological systems may, therefore, serve as an inspiration for artificial information processing systems to solve similar problems under tight constraints of computational power, data availability, and time.

Artificial neural networks are composed of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons). Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The real, biological nervous system is highly complex: artificial neural network algorithms attempt to abstract this complexity and focus on what may hypothetically matter most from an information processing point of view. Good performance (e.g. as measured by good predictive ability, low generalization error), or performance mimicking animal or human error patterns, can then be used as one source of evidence towards supporting the hypothesis that the abstraction really captured something important from the point of view of information processing in the brain. Another incentive for these abstractions is to reduce the amount of computation required to simulate artificial neural networks, so as to allow one to experiment with larger networks and train them on larger data sets. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. This is true of ANNs as well

Responsiveness of critically ill patients is affected by disease and by therapy. Subjective tests of responsiveness (e.g., modified Ramsay score) reflect global integrity of a response arc that includes transduction, perception, classification, and an overt response. The performance of individual components is usually not assessed. To determine whether an association exists among sedative medications and the brain's component information processing and adaptive abilities. Initial observational study of a convenience sample. A surgical intensive care unit of a university hospital. A total of 22 endotracheally intubated, mechanically ventilated patients chemically sedated with narcotics and a benzodiazepine (n = 12), or narcotics and propofol (n = 10) using sedation protocols. None. Patients were presented flashes of light and pulses of sound at fixed or random intervals. Patients receiving low doses of fentanyl and propofol adapted to stimuli presented at fixed intervals and retained the response to novelty. Patients receiving higher doses of those medications did not respond to novelty and responded to stimuli presented at fixed intervals as if they were novel. Patients receiving fentanyl and benzodiazepines had generally weaker responses to all stimuli. Moreover, their responses to fixed and random stimuli were similar. At low doses, the patients retained responses to novelty and adapted to fixed, whereas at higher doses the opposite was observed: the response to novelty was lost and the patients responded to fixed stimuli as if they were novel. These observations suggest that the sedative medications generally accelerate the physiologic decay of stimuli as they engage the brain's information processing and adaptive abilities and further suggest that different sedative medications may have different effects on the brain. Commonly administered sedative medications may alter the brain's biophysical state and thereby modulate specific aspects of the brain's information processing and adaptive functions. These functions can be interrogated even when the patient is seemingly unresponsive. If this observation is confirmed in subsequent prospective controlled randomized trials, electrophysiologic interrogation of the brain's information processing and adaptive capacities could serve as an adjunct to clinical assessment of responsiveness and management of sedative medications.

A cognitive control model for space assembly/maintenance task is proposed based on a double-arm robot in this paper. The two arms are designed to implement fixing assemble component and to execute assembling operations respectively. There is one hand and connecting rods articulated by three knots for each arm. The connecting knot retains the rod rotating in one plane. The arm is installed on fixed device which allow arm move with a 2 degree of freedom. By analyzing specific task, the procedural knowledge for cognitive modeling is constructed. Based on ACT-R architecture, a cognitive model for space assembly/maintenance task is constructed, and cognitive control simulation for space assembly is implemented. As an example, to replace solar panel maintenance task is chosen to investigate model’s validation and cognitive procedure’s rationality. The result shows that both the model’s simulating cognitive process and model’s running result satisfy human’s cognitive characteristic.

Cooperation, the process through which individuals work together to achieve common goals, is fundamental to human and animal societies and increasingly critical in artificial intelligence (AI). In this study, we investigated cooperation in mice and AI systems, examining how they learn to actively coordinate their actions to obtain shared rewards. We identified key social behavioral strategies and decision-making processes in mice that facilitate successful cooperation. These processes are represented in the anterior cingulate cortex (ACC), and ACC activity causally contributes to cooperative behavior. We extended our findings to AI systems by training artificial agents in a similar cooperation task. The agents developed behavioral strategies and neural representations reminiscent of those observed in the biological brain, revealing parallels between cooperative behavior in biological and artificial systems.

One of the fundamental human cognitive processes is problem solving. Most of the decisions we make relate to some kind of problems we try to solve no matter how trivial and critical the problem may be. The problem solving process entails performing in a new situation with information acquired and knowledge learned from past situations. As a higher level cognitive process, problem solving involves the correlation process effort to connect newly encounter problem object(s) with the object-attribute-relation (OAR) model representation of knowledge in the brain. The goal of problem solving is to search along various solution paths within the problem solver's knowledge base in the memory. When a problem object is identified, problem solving can be perceived as a search process in the memory space for finding a relationship between a set of problem-solving goals and a set of alternative paths. This paper presents a mathematical and cognitive model that describes problem solving as a cognitive process. The cognitive structures of the brain and the mechanisms of internal knowledge representation behind the cognitive process of problem solving are explained. The cognitive process is then formally and rigorously described using real-time process algebra (RTPA) base on the aforementioned models. Extended discussions are presented on applications of the cognitive process model of problem solving in software engineering and psychology.

In investigating human mental processes and mental representations, a cognitive model represents a theoretical view, provides explanations to the observed phenomena and makes predictions about an unknown future. When evaluating how well a theory can account for the phenomenon of interest, modeling is a powerful research tool. However, local (Taiwanese) psychology students have limited exposure to what cognitive modeling is, how to do implement cognitive models, and why cognitive modelling is important. This is partly due to a lack of university courses that teach cognitive modelling and partly due to the demands that modelling places on one's skills. The purpose of this article is to provide a conceptual guideline of how to do modeling, by fitting two neural network models-ALCOVE and ATRIUM to the data from the study of Yang and Lewandowsky (2003), which tested the theoretical concept of knowledge partitioning in categorization. The modeling results show that ATRIUM outperforms ALCOVE in accounting for the knowledge partitioning results. Some relevant theoretical-level discussions, such as the heterogeneity of categorization, are also included.

: Cognitive coordination is the timely and adaptive sharing of information. Command and control, particularly on the network-centric battle space, requires extensive coordination among a group of cognitive entities (humans and agents). The goal of the proposed research was to better understand cognitive coordination and the impact of mode of communication, presence of synthetic teammates, and training regime on team performance and coordination. We proposed a three-year effort to conduct two team experiments and to develop and test a synthetic agent in the context of UAV (Unmanned Aerial Vehicle) ground control. This work was motivated by theory and empirical research in team cognition and cognitive modeling, much of it attributed to research of the co-PIs on this project. An important objective of synthetic teammate development was to closely match human behavior across several cognitive capacities, such as situation assessment, task behavior, and language comprehension and generation. The initial application for the synthetic teammate research was the creation of an agent capable of functioning as the pilot of an Unmanned Aerial Vehicle (UAV) within a synthetic task environment (STE) which is described in the following section. At the same time, the work was motivated by a theoretical position that team member interaction that includes coordination is central to team cognition and a concomitant question concerning the role of the individual teammate in effective coordination. The ability of the synthetic teammate to participate in coordinated team interaction provides a systematic means to address this question.

Using cognitive models to understand multimodal processes: the case for speech and gesture production

In ergonomics the approach of cognitive modeling is crucial for the design of human-machine systems. With regard to the user interface design of future manufacturing systems eleven requirements for cognitive models are introduced. These requirements are used to evaluate a total of five cognitive models with regard to three ergonomic design paradigms: (1) the design of decision support systems, (2) the design for minimized stress and strain, and (3) the design for wholistic tasks and personal development. The evaluated cognitive models include Caccibue's Cognitive Simulation Model, Card's et al. Model Human Information Processor, and Rasmussen's Model of Human Performance. In addition, two explicatory models are evaluated: Newell's Unified Theories of Cognition and Anderson's ACT-R model. The results demonstrate a superiority of the Rasmussen approach concerning the first and third design paradigm. Concerning the second design paradigm Newell's Unified Theories of Cognition have the best evaluation result.

Spiking Neural Networks (SNNs) are considered as the third generation of artificial neural networks, which are more closely with information processing in biological brains. However, it is still a challenge for how to train the non-differential SNN efficiently and robustly with the form of spikes. Here we give an alternative method to train SNNs by biologically-plausible structural and functional inspirations from the brain. Firstly, inspired by the significant top-down structural connections, a global random feedback alignment is designed to help the SNN propagate the error target from the output layer directly to the previous few layers. Then inspired by the local plasticity of the biological system in which the synapses are more tuned by the neighborhood neurons, a differential STDP is used to optimize local plasticity. Extensive experimental results on the benchmark MNIST (98.62%) and Fashion MNIST (89.05%) have shown that the proposed algorithm performs favorably against several state-of-the-art SNNs trained with backpropagation.

Cognitive vulnerability models offer popular ways of understanding the origins and causal factors that contribute to psychological problems. Cognitive vulnerabilities are typically purported to create liabilities to psychological disorders after individuals encounter stressful events, in a vulnerability-stress interaction, and to maintain the problems after their onset. We present a conceptual framework that describes common features shared by most cognitive models, including schematic biases in information processing, developmental factors, reciprocal feedback loops, and specific vulnerabilities for specific problems or disorders. We also examine issues related to the role of theory in determining the appropriate research design, and discuss actual design options and methods used in cognitive vulnerability research, and their strengths and limitations.

Cognitive scientific approaches to translation investigate the development and workings of the underlying processes that make the complex cognitive behavior of translation possible. They refer to and expand existing cognitive scientific models of the mind to explain the behavior and choices of translators. Cognitive models have been oriented to the metaphors of the computer (translation as information processing, code switching, and symbol manipulation), neural networks (connectionist models of translation), and sociocognitive theory (translation as situated interaction). Cognitive translation research uses a variety of methods to investigate the activity of translating by different groups of participants with a range of text types and in a variety of contexts. The methods used to date have included, for example, introspection, neurological EEG measurements, theoretical analysis, interviews, think‐aloud protocols, participant observation, screen logging, and eye‐tracking. Recent cognitive approaches to translation emphasize the social organization of translation and the ergonomics of the computer‐supported cooperative work in which complex activities are negotiated. Defining professionalism and expertise has also become one of the current themes in cognitive translation studies. In addition, affective and emotional aspects shift into the focus of attention as essential elements of cognition, and their impact on translation performance is being increasingly investigated.

Summary : Internal clock models in the psychology of time. The aim of this theoretical review is to present and discuss models in psychology that support the idea of an internal clock as a basic mechanism involved in temporal judgments. Therefore, first we present the most popular theory conceiving an internal clock as a mechanism composed of a pacemaker and an accumulator. Second, we describe a more elaborated theory about the processing of time information, i.e., scalar timing theory. Finally, we present two others theories, one that proposes behavioral states, and the other a neural oscillatory system as the basis of time judgments. Key words : time, timing, internal clock, model.

Just as there is a huge morphological and functional diversity of neuron types specialized for specific aspects of information processing in the brain, astrocytes have equally distinct morphologies and functions that aid optimal functioning of the circuits in which they are embedded. One type of astrocyte, the Bergmann glial cell (BG) of the cerebellum, is a prime example of a highly diversified astrocyte type, the architecture of which is adapted to the cerebellar circuit and facilitates an impressive range of functions that optimize information processing in the adult brain. In this review we expand on the function of the BG in the cerebellum to highlight the importance of astrocytes not only in housekeeping functions, but also in contributing to plasticity and information processing in the cerebellum.