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Abstract: Studies on masked and unmasked priming have long shown reliable positive effects of the congruent prime on target processing. Paradoxically, a negative effect has also been found, showing faster and more accurate responses in the incongruent compared to the congruent trials. Positive effects have been found with a short time between the prime and the target, while negative effects have been found with a long time between the prime and the target. This has been modeled by assuming that the prime initiates a motor self-inhibitory process that causes these effects (Bowman, H., Schlaghecken, F., Eimer, M., 2006. A neural network model of inhibitory processes and cognitive control. Vis. Cogn. 13, 401–480). We have developed an alternative explanation based on attentional neuro-modulation. In this paper we show that attentional neuro-modulation can be used to model a wide range of findings in this area. [Copyright &y& Elsevier]

Abstract: Evaluating variations in the structure of computational models of cognition is as important as evaluating variations in the numerical parameters of such models. However, computational models tend not to be organized in such a way as to directly support such research. To address this need, we have taken the well-known cognitive architecture ACT-R, reduced it to its fundamental components, and reconstructed it. Our new system, Python ACT-R, facilitates exploration of the space of possible models and architectures based on the core ACT-R theory. The result has enabled us to examine the possibility of using basic ACT-R components such as the declarative memory system in new ways; for example, as the basis for a new visual attention system. Python ACT-R allows the same model definition syntax to be used to define both ACT-R models and new ACT-R components, as well as making explicit the processes specified by the ACT-R theory. [Copyright &y& Elsevier]

Abstract: A series of computational models are presented which address the question of how peer relations change over time. We examine data from a standardized metric (CDC) that places school children in one of five categories: Popular, Rejected, Neglected, Controversial, and Average, and how such classifications change over time. A simple random model is shown to not match the empirical data, while a computational model and an ACT-R model are shown to match equally well, even though they are highly architecturally distinct. To test these models’ ability to give useful predictions in other domains, we introduced variation among the individuals in the models. For both models, we observed equivalent behavior that was consistent with the empirically known effects of Hostile Attribution Bias, variations in social skill, and shyness among others. This indicates that both models are capturing inherent underlying regularities of the social dynamics of peer relations in children. The relationship between these models and its implications are discussed. [Copyright &y& Elsevier]

This paper argues for the relevance of cognitive modeling and cognitive architectures to cyberpsychology. From a human-computer interaction point of view, cognitive modeling can have benefits both for theory and model building, and for the design and evaluation of sociotechnical systems usability. Cognitive modeling research applied to human-computer interaction has two complimentary objectives: (1) to develop theories and computational models of human interactive behavior with information and collaborative technologies, and (2) to use the computational models as building blocks for the design, implementation, and evaluation of interactive technologies. From the perspective of building theories and models, cognitive modeling offers the possibility to anchor cyberpsychology theories and models into cognitive architectures. From the perspective of the design and evaluation of socio-technical systems, cognitive models can provide the basis for simulated users, which can play an important role in usability testing. As an example of application of cognitive modeling to technology design, the paper presents a simulation of interactive behavior with five different adaptive menu algorithms: random, fixed, stacked, frequency based, and activation based. Results of the simulation indicate that fixed menu positions seem to offer the best support for classification like tasks such as filing e-mails. This research is part of the Human–Computer Interaction, and the Broadband Visual Communication research programs at the National Research Council of Canada, in collaboration with the Carleton Cognitive Modeling Lab at Carleton University. [ABSTRACT FROM AUTHOR]

Extends the existing application of the frontal lobe hypothesis of cognitive aging beyond the limited work on inhibitory control to include memory process supported by the prefrontal cortex. Structural and functional changes in the aging brain; Description of the prefrontal cognitive deficit; Theory of cognitive aging.

Examines the features of chaotic sequences generated by iterating the logistic difference equation. Prevalence of chaos; Deterministic view of the world; Model of subject's behavior.

El artículo discute detalles sobre un curso de administración económica internacional que se lleva a cabo en la Universidad de Yale. El programa ha entrado su sexto año de existencia, proveyendo cursos intensivos sobre aspectos económicos para funcionarios bancarios y otros becarios. Los becarios se dedican un semestre para estudiar temas como el análisis económico, la política monetaria, y problemas económicos internacionales.

We demonstrate that the key components of cognitive architectures (declarative and procedural memory) and their key capabilities (learning, memory retrieval, probability judgment, and utility estimation) can be implemented as algebraic operations on vectors and tensors in a high‐dimensional space using a distributional semantics model. High‐dimensional vector spaces underlie the success of modern machine learning techniques based on deep learning. However, while neural networks have an impressive ability to process data to find patterns, they do not typically model high‐level cognition, and it is often unclear how they work. Symbolic cognitive architectures can capture the complexities of high‐level cognition and provide human‐readable, explainable models, but scale poorly to naturalistic, non‐symbolic, or big data. Vector‐symbolic architectures, where symbols are represented as vectors, bridge the gap between the two approaches. We posit that cognitive architectures, if implemented in a vector‐space model, represent a useful, explanatory model of the internal representations of otherwise opaque neural architectures. Our proposed model, Holographic Declarative Memory (HDM), is a vector‐space model based on distributional semantics. HDM accounts for primacy and recency effects in free recall, the fan effect in recognition, probability judgments, and human performance on an iterated decision task. HDM provides a flexible, scalable alternative to symbolic cognitive architectures at a level of description that bridges symbolic, quantum, and neural models of cognition. [ABSTRACT FROM AUTHOR]

Introduction: The current study aimed to elucidate the role of preparatory cognitive control in decision making and its neural correlates using functional Magnetic Resonance Imaging (fMRI). To this effect, by employing a series of new cognitive tasks, we assessed the role of preparatory cognitive control in monetary (risky) decision making. Methods: The participants had to decide between a risky and a safe gamble based on their chance of winning (high or low). In the 2-phase gambling task (similar to Cambridge gambling task), the chance and the gamble were presented at the same time (i.e. in a single phase), but in a new 3-phase gambling task, the chance is presented before the gamble. The tasks ended with a feedback phase. Results: In the 3-phase task, holding the chance in memory to guide their decision enabled the participants to have more control on their risk taking behaviors as shown by activation in a network of brain areas involved in the control and conflict, including dorsal Anterior Cingulate Cortex (dACC), indexed by faster reaction times and better performance in the gambling task, and the temporal lobe, which has a role in holding contextual information. Discussion: Holding information in memory to guide decision presumably enables the participants to have more control on their risk taking behaviors. The conflict and uncertainty resulting from this risky decision was indexed by the activation of dACC, known to be activated in conflict and cognitive control. [ABSTRACT FROM AUTHOR]

The mechanical properties in shear of unfilled and nanoparticle-filled polydimethylsiloxane (PDMS) networks are reported. The effect of silicate-based nanoparticles on the mechanical response was studied as functions of rate and temperature using the time-temperature superposition principle. An apparent yielding phenomenon was observed in the filled polymer in spite of the more typical elastomeric behavior exhibited by the pure PDMS network. The time-temperature superposition principle was applied to capture the shear strain rate (10−4-10−1 s−1) and temperature (−40 to 60°C) dependence of the stress response of the filled PDMS at different strains and at the yield point. A power-law relationship was found to adequately describe the resulting master curves for yield stress in shear. Using a triangular shear displacement profile at 10−2 s−1, the effect of temperature (−20 to 80°C) on the recovery from a particularly pronounced Mullins effect was investigated as a function of rest time. Given adequate rest time (between 10 and 102 min), recovery was observed for the temperature range studied. © 2012 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys, 2012 [ABSTRACT FROM AUTHOR]

A framework for linear viscoelastic analysis of sealants is presented for analyzing stresses resulting from thermally driven deformations. Assuming that the strains induced within the sealant are proportional to the change in temperature from the strain-free state, the nominal stress state within the sealant can be estimated. The analysis method is used to estimate the stress states resulting from assumed diurnal temperature profiles for two representative Dow Corning silicone glazing sealants: a conventional elastomer and a crosslinked hot melt adhesive containing a high volume fraction of a silicate-based nanoparticle filler. The latter exhibits considerably more rate- and temperature-dependence than conventional silicones. The viscoelastic analysis allows for comparisons of stresses resulting in these two sealant systems, which are presented for several sinusoidal thermal profiles. However, the pronounced yielding behavior exhibited by the hot melt appears to limit the stress buildup, resulting in stress states that are significantly below those predicted using the linear viscoelastic model. Estimates of the yielding envelope for a representative thermal cycle profile are provided, based on experimental results reported elsewhere for the time and rate dependent yielding of the hot melt. [ABSTRACT FROM AUTHOR]

• Words in the lexicon have varying degrees of separation on a graph of co-occurrences. • Words frequently at 4 degrees of separation are likely to share part-of-speech. • 4–6 degrees of separation correlate with Combinatory Categorical Grammar type. • Relations at 4 degrees are useful for ordering words into grammatical sentences. • Higher degrees of separation are more useful for larger n-gram models. Computational models of distributional semantics (a.k.a. word embeddings) represent a word's meaning in terms of its relationships with all other words. We examine what grammatical information is encoded in distributional models and investigate the role of indirect associations. Distributional models are sensitive to associations between words at one degree of separation, such as 'tiger' and 'stripes', or two degrees of separation, such as 'soar' and 'fly'. By recursively adding higher levels of representations to a computational, holographic model of semantic memory, we construct a distributional model sensitive to associations between words at arbitrary degrees of separation. We find that word associations at four degrees of separation increase the similarity assigned by the model to English words that share part-of-speech or syntactic type. Word associations at four degrees of separation also improve the ability of the model to construct grammatical English sentences. Our model proposes that human memory uses indirect associations to learn part-of-speech and that the basic associative mechanisms of memory and learning support knowledge of both semantics and grammatical structure. [ABSTRACT FROM AUTHOR]