Semantic computations of truth based on associations already learned

This article sets forth a detailed theoretical proposal of how the truth of ordinary empirical statements, often atomic in form, is computed. The method of computation draws on psychological concepts such as those of associative networks and spreading activation, rather that the concepts of philosophical or logical theories of truth. Axioms for a restricted class of cases are given, as well as some detailed examples.     

An evolutionary system for neural logic networks using genetic programming and indirect encoding

Nowadays, intelligent connectionist systems such as artificial neural networks have been proved very powerful in a wide area of applications. Consequently, the ability to interpret their structure was always a desirable feature for experts. In this field, the neural logic networks (NLN) by their definition are able to represent complex human logic and provide knowledge discovery. However, under contemporary methodologies, the training of these networks may often result in non-comprehensible or poorly designed structures. In this work, we propose an evolutionary system that uses current advances in genetic programming that overcome these drawbacks and produces neural logic networks that can be arbitrarily connected and are easily interpretable into expert rules. To accomplish this task, we guide the genetic programming process using a context-free grammar and we encode indirectly the neural logic networks into the genetic programming individuals. We test the proposed system in two problems of medical diagnosis. Our results are examined both in terms of the solution interpretability that can lead in knowledge discovery, and in terms of the achieved accuracy. We draw conclusions about the effectiveness of the system and we propose further research directions.     

Logic programs and connectionist networks

One facet of the question of integration of Logic and Connectionist Systems, and how these can complement each other, concerns the points of contact, in terms of semantics, between neural networks and logic programs. In this paper, we show that certain semantic operators for propositional logic programs can be computed by feedforward connectionist networks, and that the same semantic operators for first-order normal logic programs can be approximated by feedforward connectionist networks. Turning the networks into recurrent ones allows one also to approximate the models associated with the semantic operators. Our methods depend on a well-known theorem of Funahashi, and necessitate the study of when Funahashi's theorem can be applied, and also the study of what means of approximation are appropriate and significant.     

The AHA! experience: creativity through emergent binding in neural networks

Many kinds of creativity result from combination of mental representations. This paper provides a computational account of how creative thinking can arise from combining neural patterns into ones that are potentially novel and useful. We defend the hypothesis that such combinations arise from mechanisms that bind together neural activity by a process of convolution, a mathematical operation that interweaves structures. We describe computer simulations that show the feasibility of using convolution to produce emergent patterns of neural activity that can support cognitive and emotional processes underlying human creativity.     

Impulse processing: a dynamical systems model of incremental eye movements in the visual world paradigm

The Visual World Paradigm (VWP) presents listeners with a challenging problem: They must integrate two disparate signals, the spoken language and the visual context, in support of action (e.g., complex movements of the eyes across a scene). We present Impulse Processing, a dynamical systems approach to incremental eye movements in the visual world that suggests a framework for integrating language, vision, and action generally. Our approach assumes that impulses driven by the language and the visual context impinge minutely on a dynamical landscape of attractors corresponding to the potential eye-movement behaviors of the system. We test three unique predictions of our approach in an empirical study in the VWP, and describe an implementation in an artificial neural network. We discuss the Impulse Processing framework in relation to other models of the VWP.     

A Friend in Creed: Does the Religious Composition of Geographic Areas Affect the Religious Composition of a Person's Close Friends?

Numerous theories of religion rest on the assumption that the religious composition of local populations influences the religious identities of a person's close friends, but there have been few empirical tests of this assumption. Using a combination of data on the religious identity of close friends (from the 1988 and 1998 General Social Survey) and information on the religious composition of counties (from the U.S. Religious Congregations and Membership Study) we find that despite tendencies toward religious homogeneity, the religious composition of the surrounding population has an effect on the proportion of a respondent’s same‐religion friends and on the proportion of friends belonging to specific other religious groups. Local population characteristics are unrelated to the proportion of respondents’ friends known in congregational settings. Results have implications for a broad range of sociological theories of religion as well as research examining the impact of same‐congregation and same‐religion friends (e.g., health and well‐being).     

Religion and suicide acceptability: a cross-national analysis

Four perspectives (moral community thesis, religious integration, religious commitment, and social networks) guide the selection of variables in this study. Data are from the combined World Values/European Values Surveys for 2000 (50,547 individuals nested in 56 nations). The results of a multivariate hierarchical linear model support all four perspectives. Persons residing in nations with relatively high levels of religiosity, who are affiliated with one of four major faiths, are religiously committed, and are engaged with a religious network are found to be lower in suicide acceptability. The religious integration perspective, in particular, is empirically supported; affiliation with Islam is associated with low suicide acceptability. The findings provide strong support for an integrated model and demonstrate the usefulness of the moral community thesis in understanding suicide acceptability.     

Communication,Influence, and Informational Asymmetries among Voters

The costs of political information vary dramatically across individuals, and these costs help explain why some individuals become politically expert while others demonstrate low levels of political knowledge and awareness. An attractive alternative, particularly for those with high information costs, is to rely on information and advice taken from others who are politically expert. This paper focuses on the complications that arise when the informant and the recipient do not share preferences. A series of small group experiments show that subjects tend to weight expertise more heavily than shared preferences in selecting informants, thereby exposing themselves to diverse views and biased information. Experimental subjects employ several heuristic devices in evaluating the reliability of this information, but depending on their own levels of information, these heuristics often lead subjects either to dismiss advice that conflicts with their own prior judgments or to dismiss advice that comes from an informant with divergent preferences. Hence these heuristics produce important consequences for patterns of political influence, as well as reducing the potential for political change.     

Caffeine modulates attention network function

The present work investigated the effects of caffeine (0 mg, 100 mg, 200 mg, 400 mg) on a flanker task designed to test Posner’s three visual attention network functions: alerting, orienting, and executive control [Posner, M. I. (2004). Cognitive neuroscience of attention. New York, NY: Guilford Press]. In a placebo-controlled, double-blind study using a repeated-measures design, we found that the effects of caffeine on visual attention vary as a function of dose and the attention network under examination. Caffeine improved alerting and executive control function in a dose–response manner, asymptoting at 200 mg; this effect is congruent with caffeine’s adenosine-mediated effects on dopamine-rich areas of brain, and the involvement of these areas in alerting and the executive control of visual attention. Higher doses of caffeine also led to a marginally less efficient allocation of visual attention towards cued regions during task performance (i.e., orienting). Taken together, results of this study demonstrate that caffeine has differential effects on visual attention networks as a function of dose, and such effects have implications for hypothesized interactions of caffeine, adenosine and dopamine in brain areas mediating visual attention.     

Acute caffeine consumption enhances the executive control of visual attention in habitual consumers

Recent work suggests that a dose of 200–400 mg caffeine can enhance both vigilance and the executive control of visual attention in individuals with low caffeine consumption profiles. The present study seeks to determine whether individuals with relatively high caffeine consumption profiles would show similar advantages. To this end, we examined the effects of four caffeine doses (0 mg, 100 mg, 200 mg, 400 mg) on low- and high-level visual attention in individuals with high consumption profiles (n = 36), in a double-blind study using a repeated measures design. Results from the Attention Network Test indicated that caffeine enhanced both vigilance and the executive control of visual attention, but only at the highest administered dose (400 mg). We demonstrate that in habitual consumers high doses of caffeine can produce beneficial changes in visual attention. These results carry implications for the theorized interactions between caffeine, adenosine and dopamine in brain regions mediating visual attention.