Moral Accountancy and Moral Worth

People do good or bad things, and get or do not get good or bad credit for their actions, depending (in part) on knowledge of their actions. I attempt to unfold some of the interconnections between these matters, and between them and the achievement of moral worth. The main conclusion is that the heights of moral worth seem to appear in the oddest places.     

Network Analysis in Community Psychology: Looking Back,Looking Forward

Network analysis holds promise for community psychology given the field's aim to understand the interplay between individuals and their social contexts. Indeed, because network analysis focuses explicitly on patterns of relationships between actors, its theories and methods are inherently extra‐individual in nature and particularly well suited to characterizing social contexts. But, to what extent has community psychology taken advantage of this network analysis as a tool for capturing context? To answer these questions, this study provides a review of the use network analysis in articles published in American Journal of Community Psychology . Looking back, we describe and summarize the ways that network analysis has been employed in community psychology research to understand the range of ways community psychologists have found the technique helpful. Looking forward and paying particular attention to analytic issues identified in past applications, we provide some recommendations drawn from the network analysis literature to facilitate future applications of network analysis in community psychology.     

Enterprise credit risk evaluation based on neural network algorithm

To explore the enterprise credit risk evaluation, the application effect of several common neural network models in Chinese small and medium-sized enterprise data sets was compared and the optimal parameters for each model were determined. In addition, the classification accuracy and the applicability of the model were compared, and finally the common problem of optimization neural network algorithm based on population was solved: need to determine the dimensions in advance. The experimental results showed that the probabilistic neural network (PNN) had the minimum error rate and second types of errors, while the PNN model had the highest AUC value and was robust. To sum up, the algorithm makes some contributions to solve the financing problem of small and medium-sized enterprises in China.     

Accuracy and performance of the state-based Φ and liveliness measures of information integration

A number of people have suggested that there is a link between information integration and consciousness, and a number of algorithms for calculating information integration have been put forward. The most recent of these is Balduzzi and Tononi’s state-based Φ algorithm, which has factorial dependencies that severely limit the number of neurons that can be analyzed. To address this issue an alternative state-based measure known as liveliness has been developed, which uses the causal relationships between neurons to identify the areas of maximum information integration. This paper outlines the state-based Φ and liveliness algorithms and sets out a number of test networks that were used to compare their accuracy and performance. The results show that liveliness is a reasonable approximation to state-based Φ for some network topologies, and it has a much more scalable performance than state-based Φ.     

Extracting Low-Dimensional Psychological Representations from Convolutional Neural Networks

Convolutional neural networks (CNNs) are increasingly widely used in psychology and neuroscience to predict how human minds and brains respond to visual images. Typically, CNNs represent these images using thousands of features that are learned through extensive training on image datasets. This raises a question: How many of these features are really needed to model human behavior? Here, we attempt to estimate the number of dimensions in CNN representations that are required to capture human psychological representations in two ways: (1) directly, using human similarity judgments and (2) indirectly, in the context of categorization. In both cases, we find that low-dimensional projections of CNN representations are sufficient to predict human behavior. We show that these low-dimensional representations can be easily interpreted, providing further insight into how people represent visual information. A series of control studies indicate that these findings are not due to the size of the dataset we used and may be due to a high level of redundancy in the features appearing in CNN representations.     

On the Generalization of Simple Alternating Category Structures

A fundamental question in the study of human cognition is how people learn to predict the category membership of an example from its properties. Leading approaches account for a wide range of data in terms of comparison to stored examples, abstractions capturing statistical regularities, or logical rules. Across three experiments, participants learned a category structure in a low-dimension, continuous-valued space consisting of regularly alternating regions of class membership (A B A B). The dependent measure was generalization performance for novel items outside the range of the training space. Human learners often extended the alternation pattern––a finding of critical interest given that leading theories of categorization based on similarity or dimensional rules fail to predict this behavior. In addition, we provide novel theoretical interpretations of the observed phenomenon.     

An Evolutionary Analysis of Learned Attention

Humans and many other species selectively attend to stimuli or stimulus dimensions—but why should an animal constrain information input in this way? To investigate the adaptive functions of attention, we used a genetic algorithm to evolve simple connectionist networks that had to make categorization decisions in a variety of environmental structures. The results of these simulations show that while learned attention is not universally adaptive, its benefit is not restricted to the reduction of input complexity in order to keep it within an organism's processing capacity limitations. Instead, being able to shift attention provides adaptive benefit by allowing faster learning with fewer errors in a range of ecologically plausible environments.     

Neural circuitry of the bilingual mental lexicon: Effect of age of second language acquisition

Numerous studies have proposed that changes of the human language faculty caused by neural maturation can explain the substantial differences in ultimate attainment of grammatical competences between first language (L1) acquirers and second language (L2) learners. However, little evidence on the effect of neural maturation on the attainment of lexical knowledge in L2 is available. The present functional magnetic resonance study addresses this question via a cross-linguistic neural adaptation paradigm. Age of acquisition (AoA) of L2 was systematically manipulated. Concrete nouns were repeated across language (e.g., French–German, valise^suitcase–Koffer^suitcase). Whereas early bilinguals (AoA of L2 < 3 years) showed larger repetition enhancement (RE) effects in the left superior temporal gyrus, the bilateral superior frontal gyrus and the right posterior insula, late bilinguals (AoA of L2 > 10 years) showed larger RE effects in the middle portion of the left insula and in the right middle frontal gyrus (MFG). We suggest that, as for grammatical knowledge, the attainment of lexical knowledge in L2 is affected by neural maturation. The present findings lend support to neurocognitive models of bilingual word recognition postulating that, for both early and late bilinguals, the two languages are interconnected at the conceptual level.     

Food Systems Change and the Environment: Local and Global Connections

Making changes to the way food is produced, distributed, and processed is one strategy for addressing global climate change. In this case study, we examine the "forming" stage of an emergent and locally-based coalition that is both participatory and focused on promoting food security by creating food systems change. Social network analysis is used to compare network density, centrality, and centralization among coalition partners before the formation of the coalition and at its one-year anniversary. Findings reveal that the coalition facilitated information seeking, assistance seeking, and collaborative efforts related to food security among a group of organizational stakeholders that were relatively disconnected pre-coalition. Results also illuminate tensions related to increased centralization of the network, coalition efficiency, and the goals of democratic decision-making. This study highlights the utility of social network analysis as a tool for evaluating the aims and trajectory of locally-based coalitions focused on global concerns.     

The Isolation, Primacy, and Recency Effects Predicted by an Adaptive LTD/LTP Threshold in Postsynaptic Cells

An item that stands out (is isolated) from its context is better remembered than an item consistent with the context. This isolation effect cannot be accounted for by increased attention, because it occurs when the isolated item is presented as the first item, or by impoverished memory of nonisolated items, because the isolated item is better remembered than a control list consisting of equally different items. The isolation effect is seldom experimentally or theoretically related to the primacy or the recency effects—that is, the improved performance on the first few and last items, respectively, on the serial position curve. The primacy effect cannot easily be accounted for by rehearsal in short-term memory because it occurs when rehearsal is eliminated. This article suggests that the primacy, the recency, and the isolation effects can be accounted for by experience-dependent synaptic plasticity in neural cells. Neurological empirical data suggest that the threshold that determines whether cells will show long-term potentiation (LTP) or long-term depression (LTD) varies as a function of recent postsynaptic activity and that synaptic plasticity is bounded. By implementing an adaptive LTP-LTD threshold in an artificial neural network, the various aspects of the isolation, the primacy, and the recency effects are accounted for, whereas none of these phenomena are accounted for if the threshold is constant. This theory suggests a possible link between the cognitive and the neurological levels.