Interventionist Explanation and the Problem of Single Variable Boundary Constraints

According to Interventionism , explanations cite invariant relations which hold among multiple variables. Interventionism incorrectly implies, however, that many common scientific explanations—which cite single-variable boundary constraints—are not actually explanatory. So I propose a different account of explanation, similar in spirit to Interventionism , which gets those cases of scientific explanation right.     

Conditioned taste aversions support drug discrimination learning at low dosages of morphine.

The present experiment shows that a conditioned taste aversion procedure can support discrimination learning at dosages of morphine comparable to those required to produce motivational effects. Sprague-Dawley rats were injected with 4.0 mg/kg morphine sulfate prior to a saccharin-lithium chloride pairing, and physiological saline prior to a saccharin-saline pairing. The rats avoided the saccharin solution following the administration of morphine and consumed significantly more saccharin following saline administration after four discrimination cycles. After this initial discrimination the subjects were trained with progressively lower doses of morphine. Discrimination learning was apparent at doses of 2.0, 1.5, 1.0, 0.75 and 0.5 mg/kg. Animals initially trained with 1.0 mg/kg morphine also learned the discrimination but required 10 training cycles. After this initial discrimination the subjects were trained with progressively lower dosages of morphine and showed a discrimination at a dosage of 0.5 mg/kg.     

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.     

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.     

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 issensitive, accurate, and appropriate for dimensional assessment of biological data, 2) that during analysis of unfamiliar information a singleglobal process is transiently evoked in the neuropil, and 3) after experience multiplespatially specific processes tonically map the sites of learning. Grant Support: National Institutes of Health, HL 31164 and NS27745     

Counselling issues in the fostering and adoption of children at risk of HIV infection

The heterosexual nature of the HIV epidemic in Lothian is such that a number (50 to date) of babies have been born to women who have HIV. Some have needed social work services such as Children Centre placement, foster parents or adopters. Since this work began in 1986, our understanding of the transmission and manifestation of HIV infection in children has grown. However, it is still not possible until children reach the age of two to be certain whether or not they have been infected, or what the outlook is for them. Even then there are some further areas of uncertainty. We have developed a programme of preparation, training and support which has enabled a number of families to live with this uncertainly in offering placements to children at risk of HIV infection.     

#sustainablefashion on Instagram: A content and network analysis of user-generated posts

The social and environmental impacts of the global fashion industry face increasing scrutiny. As such, consumers and brands look towards ‘sustainable fashion’ for low-impact alternatives. Simultaneously, visual social media platforms such as Instagram have become prevalent places for fashion advertising and discourse. To investigate this under-researched area, the current study utilises visual content and social network analysis to explore how sustainable fashion is presented on Instagram, specifically: (a) what visual characteristics are present within sustainable fashion content, and (b) in what broader contexts and communities is discussion of sustainable fashion taking place? Posts included under the hashtag #sustainablefashion (N = 650) were inspected for visual characteristics, including type of clothing, presence of person, post setting, presence of nature, and post format. Most posts were photographs located indoors with no person and no nature-based elements. The hashtags used alongside #sustainablefashion were analysed with Gephi software to establish a social network and community groups of related topics. Network analysis revealed four key community groups: online-promotional, artisan-traditional, eco-ethical, and Malay-online-selling. Secondhand fashion also appeared to be the most prominent sustainable fashion alternative on Instagram. This study is one of the first to investigate the characteristics of user-generated #sustainablefashion content on Instagram. Whilst social media is a burgeoning avenue for research, existing studies primarily investigate how these platforms can be harnessed for advertising, rather than what users themselves are already posting. Implications for brands who use this platform and opportunities for future research are discussed.