Marr and Reductionism

David Marr's three-level method for completely understanding a cognitive system and the importance he attaches to the computational level are so familiar as to scarcely need repeating. Fewer seem to recognize that Marr defends his famous method by criticizing the “reductionistic approach.” This sets up a more interesting relationship between Marr and reductionism than is usually acknowledged. I argue that Marr was correct in his criticism of the reductionists of his time—they were only describing (cellular activity), not explaining (cognitive functions). But a careful metascientific account of reductionistic neuroscience over the past two decades reveals that Marr's criticisms no longer have force. Contemporary neuroscience now explains cognition directly, although in a fashion—causal-mechanistically—quite different than Marr recommended. So while Marr was correct to reject the reductionism of his day and offer an alternative method for genuinely explaining cognition, contemporary cognitive scientists now owe us a new defense of Marr's famous method and the advantages of its explanations over the type now pursued successfully in current reductionist neuroscience. There are familiar reasons for thinking that this debt will not be paid easily.     

经后颅窝乙状窦后入路三叉神经微血管减压术的快速康复外科护理

目的 探讨快速康复外科 （FTS）理念在经后颅窝乙状窦后入路三叉神经微血管减压术围术期患者护理中的应用效果。方法 选择2018年1月~2019年1月我院收治并行经后颅窝乙状窦后入路三叉神经微血管减压患者100例，随机分成观察组和对照组，每组50例。对照组应用常规方法进行围术期护理，观察组应用FTS理念进行围术期护理。比较两组焦虑情况、术后并发症发生率、平均住院日及平均住院费用。结果 观察组轻度焦虑多于对照组，中、重度焦虑少于对照组（P＜0.05）；观察组术后并发症发生率低于对照组[恶心呕吐（4.00% vs 16.00%）、尿路感染（4.00% vs 18.00%）、颅内感染（0 vs 8.00%）、颅内血肿（2.00% vs 14.00%）和术后应激性疼痛（16.00% vs 34.00%）]，差异有统计学意义（P＜0.05）；观察组平均住院日和平均住院费用均低于对照组[（9.13±1.14）d vs（12.44±0.89）d]；[（2.15±0.66）万元 vs （3.05±0.61）万元]，差异有统计学意义（P＜0.05）。结论 应用FTS理念能缓解经后颅窝乙状窦后入路三叉神经微血管减压术患者术前紧张恐惧的心理，减少术后并发症，缩短患者住院日，降低住院费用，护理效果较好。     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.