Suggestiveness in photospread line-ups: Similarity induces distinctiveness

The present research examined whether line-ups based on target (‘suspect’) face similarity are biased or suggestive. Four experiments are described in which subjects constructed photographic line-ups by selecting foils similar in appearance to a target. Later, another group of subjects who had not seen the faces before (mock witnesses) were asked to pick out the targets from the line-ups. All four experiments showed that mock witnesses selected the target significantly more often than expected by chance, thereby demonstrating suggestiveness. Three alternative line-up construction methods were also evaluated. In these methods, foil selection was based not only on target similarity but also on similarity with one or more of the other line-up faces. Results showed that alternative line-up targets were not selected significantly more often than chance, suggesting that bias was reduced. An overall analysis showed that the alternative line-ups were significantly less suggestive than target-based line-ups. The results indicate that foil selection procedures that incorporate foil-to-foil similarity produce fairer line-ups than those exclusively based on target similarity.     

Parallel distributed processing and neuropsychology: A neural network model of Wisconsin card sorting and verbal fluency

Neural networks can be used as a tool in the explanation of neuropsychological data. Using the Hebbian Learning Rule and other such principles as competition and modifiable interlevel feedback, researchers have successfully modeled a widely used neuropsychological test, the Wisconsin Card Sorting Test. One of these models is reviewed here and extended to a qualitative analysis of how verbal fluency might be modeled, which demonstrates the importance of accounting for the attentional components of both tests. Difficulties remain in programming sequential cognitive processes within a parallel distributed processing (PDP) framework and integrating exceedingly complex neuropsychological tests such as Proverbs. PDP neural network methodology offers neuropsychologists co-validation procedures within narrowly defined areas of reliability and validity.     

Low-dimensional chaos maps learning in a model neuropil (olfactory bulb).

Quantification of a chaotic system can be made by calculating the correlation dimension (D2) of the data that the system generates (Packard et al., 1980). The D2 algorithm, however, requires stationarity of the generator, a feature that biological data rarely reflect (Mayer-Kress et al., 1988). So we developed the "point correlation dimension" (PD2), an algorithm that accurately tracks D2 in linked data of different dimensions (Carpeggiani et al., 1991). We now present a mathematical argument that, for stationary data, individual PD2s converge to D2 and we demonstrate that the algorithm rejects contributions made by bursts of noise. Data were obtained from the surface of the olfactory bulb of the conscious rabbit (64 electrodes, 640 Hz each, 1.3 sec epochs) before and after presentation of a novel or habituated odor. D2 could be calculated in only 1 of 10 novel-odor trials, whereas PD2 could be calculated in all. Both algorithms indicated that a novel odor evokes a spatially uniform dimensional increase. The PD2 uniquely exhibited the dimensional decreases that occur during inspiration and the gradients of mean dimension present during the nonstimulated control state. These control gradients remained unchanged without odor experience, but showed spatially specific PD2 increases following odor habituation. It is interpreted that, 1) the PD2 is sensitive, accurate, and appropriate for dimensional assessment of biological data, 2) that during analysis of unfamiliar information a single global process is transiently evoked in the neuropil, and 3) after experience multiple spatially specific processes tonically map the sites of learning.     

The design and use of the Bioethics Consultation Form

The emergence of the ethics consultation as a means to resolve moral crises in clinical medicine has revealed the need for a worksheet that would facilitate intake and analysis. The author developed the Bioethics Consultation Form as an attempt to remedy this need. The form is arranged in an outline format and is a useful asset to ethics committee discussions and record keeping. The first section covers basic intake data concerning the patient's medical and personal information, advance directives, and values, as well as the values of the physician and family. After the intake section is completed with the above data, the ethics consultant then turns to the analysis section. This second section allows for (1) the discussion of conflicting values, (2) the identification of priorities, and (3) the elucidation of ethical norms relevant to the case.The Bioethics Consultation Form was adopted by the Patient Care Advisory committee of the Franklin Square Hospital Center in Baltimore, Maryland in 1986. The methodology in the use of the form will be discussed. Further, the potential spectrum of consultative cases that can be analyzed using the form will be highlighted.     

How,in Our Time,Can We Learn about Spirituality?

A rule of thinking which would absolutely prevent me from acknowledging certain kinds of truth, if those kinds of truth were really there, would be an irrational rule.     

Application of chaos theory to biology and medicine

The application of “chaos theory” to the physical and chemical sciences has resolved some long-standing problems, such as how to calculate a turbulent event in fluid dynamics or how to quantify the pathway of a molecule during Brownian motion. Biology and medicine also have unresolved problems, such as how to predict the occurrence of lethal arrhythmias or epileptic seizures. The quantification of a chaotic system, such as the nervous system, can occur by calculating the correlation dimension (D2) of a sample of the data that the system generates. For biological systems, the point correlation dimension (PD2) has an advantage in that it does not presume stationarity of the data, as the D2 algorithm must, and thus can track the transient non-stationarities that occur when the systems changes state. Such non-stationarities arise during normal functioning (e.g., during an event-related potential) or in pathology (e.g., in epilepsy or cardiac arrhythmogenesis). When stochastic analyses, such as the standard deviation or power spectrum, are performed on the same data they often have a reduced sensitivity and specifity compared to the dimensional measures. For example, a reduced standard deviation of heartbeat intervals can predict increased mortality in a group of cardiac subjects, each of which has a reduced standard deviation, but it cannot specify which individuals will or will not manifest lethal arrhythmogenesis; in contrast, the PD2 of the very same data can specify which patients will manifest sudden death. The explanation for the greater sensitivity and specificity of the dimensional measures is that they aredeterministic, and thus are moreaccurate in quantifying the time-series. This accuracy appears to be significant in detecting pathology in biological systems, and thus the use of deterministic measures may lead to breakthroughs in the diagnosis and treatment of some medical disorders.