The ghost of Christmas future: didn't Scrooge learn to be good? Commentary on Magnuson,McMurray, Tanenhaus,and Aslin (2003)

Magnuson, McMurray, Tanenhaus, and Aslin [Cogn. Sci. 27 (2003) 285] suggest that they have evidence of lexical feedback in speech perception, and that this evidence thus challenges the purely feedforward Merge model [Behav. Brain Sci. 23 (2000) 299]. This evidence is open to an alternative explanation, however, one which preserves the assumption in Merge that there is no lexical–prelexical feedback during on‐line speech processing. This explanation invokes the distinction between perceptual processing that occurs in the short term, as an utterance is heard, and processing that occurs over the longer term, for perceptual learning.     

A visual auditory model based on Growing Self-Organizing Maps to analyze the taxonomic response in early childhood

In this paper we present an extension of a visual auditory neural network model previously proposed by Mayor and Plunkett (2010) in order to explain the emergence of the taxonomic response in early childhood. The original model consists of two self-organizing maps (respectively, visual and acoustic) connected with Hebbian connections. With respect to the original model, our proposal adds two major features. First, our model follows a dynamic training regime, learning categories and word-object associations that evolve through time. Second, the visual and acoustic maps are Growing self-organizing maps that grow during training, when they are no longer able to consistently represent categories. With these two new characterizing features, our model replicates the performance of the original Mayor and Plunkett (2010)’s model, acquires psychological plausibility in the training regime, and avoids the risk of catastrophic interference.     

Neoclassical Economics and the Last Dogma of Positivism: Is the Normative-Positive Distinction Justified?

Neoclassical economic theory in its pretensions to scientific status is founded on one of the variants of a now discredited positivism. Neoclassical economic theory claims that there are two distinct areas of economic research: positive economics and normative economics. The former is assumed to deal with the cognitive as scientific content of economics while the later focuses on welfare or equity issues. I argue that the reliance of the whole theoretical structure of economics on the normative postulate of rationality renders neoclassical economics a normative discipline. I also argue that neoclassical economics should thus be viewed as an instance of applied ethics rather than as applied mathematics, say. Finally, it is suggested that neoclassical economics, if its scientific pretensions are to be taken seriously, should be absorbed theoretically into general anthropology.     

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.     

Developmental stuttering and the role of the supplementary motor cortex

Developmental stuttering is a frequent neurodevelopmental disorder with a complex neurobiological basis. Robust neural markers of stuttering include imbalanced activity of speech and motor related brain regions, and their impaired structural connectivity. The dynamic interaction of cortical regions is regulated by the cortico-basal ganglia-thalamo-cortical system with the supplementary motor area constituting a crucial cortical site. The SMA integrates information from different neural circuits, and manages information about motor programs such as self-initiated movements, motor sequences, and motor learning. Abnormal functioning of SMA is increasingly reported in stuttering, and has been recently indicated as an additional “neural marker” of DS: anatomical and functional data have documented abnormal structure and activity of the SMA, especially in motor and speech networks. Its connectivity is often impaired, especially when considering networks of the left hemisphere. Compatibly, recent data suggest that, in DS, SMA is part of a poorly synchronized neural network, thus resulting in a likely substrate for the appearance of DS symptoms. However, as evident when considering neural models of stuttering, the role of SMA has not been fully clarified. Herein, the available evidence is reviewed, which highlights the role of the SMA in DS as a neural “hub”, receiving and conveying altered information, thus “gating” the release of correct or abnormal motor plans.     

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.     

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.     

Identifying neural correlates of consciousness: the state space approach

This article sketches an idealized strategy for the identification of neural correlates of consciousness. The proposed strategy is based on a state space approach originating from the analysis of dynamical systems. The article then focuses on one constituent of consciousness, phenomenal awareness. Several rudimentary requirements for the identification of neural correlates of phenomenal awareness are suggested. These requirements are related to empirical data on selective attention, on completely intrinsic selection and on globally unconscious states. As an example, neuroscientific findings on synchronized gamma activity are categorized according to these requirements.     

Edward L. Thorndike: the selectionist connectionist

From the very outset of his work, Thorndike allied himself with the Darwinian proposition that complex phenomena can arise as the cumulative effects of a selection process, here the process envisioned by the law of effect. Thorndike's selectionist approach, when combined with his connectionism, laid the foundation for a synthesis of behavior analysis and neuroscience.