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.     

Developmental changes in associations between auditory-visual events

The goal of the present study was twofold: to examine the influence of two amodal properties, co-location and temporal synchrony, on infants' associating a sight with a sound, and to determine if the relative influence of these properties on crossmodal learning changes with age. During familiarization 2-, 4-, 6- and 8-month-olds were presented two toys and a sound, with sights and sounds varying with respect to co-location and temporal synchrony. Following each familiarization phase infants were given a paired preference test to assess their learning of sight-sound associations. Measures of preferential looking revealed age-related changes in the influence of co-location and temporal synchrony on infants' learning sight-sound associations. At all ages, infants could use temporal synchrony and co-location as a basis for associating an auditory with a visual event and, in the absence of temporal synchrony, co-location was sufficient to support crossmodal learning. However, when these cues conflicted there were developmental changes in the influence of these cues on infants' learning auditory-visual associations. At 2 and 4 months infants associated the sounds with the toy that moved in synchrony with the sound's rhythm despite extreme violation of co-location of this sight and sound. In contrast, 6- and 8-month-olds did not associate a specific toy with the sound when co-location and synchrony information conflicted. The findings highlight the unique and interactive effects of distinct amodal properties on infants' learning arbitrary crossmodal relations. Possible explanations for the age shift in performance are discussed.     

Place navigation in mammals: a configuration-based model

Recent water maze experiments suggest that rats performing place navigation primarily use the geometric information provided by a set of landmarks, and neglect the featural information provided by the identities of the landmarks. Here, I develop a model that explains how an animal may perform place navigation by relying only on geometric information. The core of the model is the representation of places as panoramas defined by circular bar-codes embodying the relative bearings and apparent sizes of the landmarks, irrespective of their identities. There are two stages in the model. During the first stage, the animal freely explores its environment in order to acquire spatial information at the local level. During the second stage, the animal uses the information previously memorized to perform place navigation towards the goal it intends to reach. The possible role of two brain areas in place navigation is discussed within this framework. Beyond their primary role in landmark-based representations of places, hippocampal place cells may be involved in computing the current distances to the landmarks. Beyond their primary role in landmark-based representations of headings, post-subicular head-direction cells may be involved in computing the “compass bearings” of the landmarks. Received: 17 January 1998 / Accepted after revision: 19 April 1998