Continuing commentary : challenges or misunderstandings? A defence of the two-factor theory against the challenges to its logic

Corlett, P. R. (2019. Factor one, familiarity and frontal cortex: A challenge to the two-factor theory of delusions. Cognitive Neuropsychiatry, 2 4(3), 165–177. doi:10.1080/13546805.2019.1606706) raises two groups of challenges against the two-factor theory of delusions: One focuses on weighing “the evidence for … the two-factor theory”; the other aims to question “the logic of the two-factor theory” (ibid., p. 166). McKay, R. (2019. Measles, magic and misidentifications: A defence of the two-factor theory of delusions. Cognitive Neuropsychiatry, 24(3), 183–190. doi:10.1080/13546805.2019.1607273) has robustly defended the two-factor theory against the first group. But the second group, which Corlett believes is in many aspects independent of the first group and Darby, R. R. (2019. A network-based response to the two-factor theory of delusion formation. Cognitive Neuropsychiatry, 24(3), 178–182. doi:10.1080/13546805.2019.1606709, p. 180) takes as “[t]he most important challenge to the two-factor theory raised by Dr. Corlett”, has by large remained. Here I offer my two cents in response to the second group. More specifically, I argue that Corlett’s challenges to the logic of the two-factor theory, concerning modularity, double dissociation and cognitive penetration, seem to be based on some misunderstandings of the two-factor theory

Can a bodily theorist of pain speak Mandarin?

According to a bodily view of pain, pains are objects which are located in body parts. This bodily view is supported by the locative locutions for pain in English, such as that “I have a pain in my back.” Recently, Liu and Klein (Analysis, 80(2), 262–272, 2020) carry out a cross-linguistic analysis, and they claim that (1) Mandarin has no locative locutions for pain and (2) the absence of locative locutions for pain puts the bodily view at risk. This paper rejects both claims. Regarding the philosophical claim, I argue that a language without locative locutions for pain only poses a limited challenge to the bodily view. Regarding the empirical claim, I identify the possible factors which might have misled Liu and Klein about the locative locutions for pain in Mandarin, and argue that Mandarin has a wide range of locative locutions for pain by conducting a corpus analysis. I conclude that compared to English, Mandarin lends no less, if not more, support to the bodily view of pain

Can a Bodily Theorist of Pain Speak Mandarin?

According to a bodily view of pain, pains are objects which are located in body parts. This bodily view is supported by the locative locutions for pain in English, such as that “I have a pain in my back.” Recently, Liu and Klein (Analysis, 80(2), 262–272, 2020) carry out a cross-linguistic analysis, and they claim that (1) Mandarin has no locative locutions for pain and (2) the absence of locative locutions for pain puts the bodily view at risk. This paper rejects both claims. Regarding the philosophical claim, I argue that a language without locative locutions for pain only poses a limited challenge to the bodily view. Regarding the empirical claim, I identify the possible factors which might have misled Liu and Klein about the locative locutions for pain in Mandarin, and argue that Mandarin has a wide range of locative locutions for pain by conducting a corpus analysis. I conclude that compared to English, Mandarin lends no less, if not more, support to the bodily view of pain

Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image

Powdery mildew, caused by the fungus Blumeria graminis, is a major winter wheat disease in China. Accurate delineation of powdery mildew infestations is necessary for site-specific disease management. In this study, high-resolution multispectral imagery of a 25 km2 typical outbreak site in Shaanxi, China, taken by a newly-launched satellite, SPOT-6, was analyzed for mapping powdery mildew disease. Two regions with high representation were selected for conducting a field survey of powdery mildew. Three supervised classification methods—artificial neural network, mahalanobis distance, and maximum likelihood classifier—were implemented and compared for their performance on disease detection. The accuracy assessment showed that the ANN has the highest overall accuracy of 89%, following by MD and MLC with overall accuracies of 84% and 79%, respectively. These results indicated that the high-resolution multispectral imagery with proper classification techniques incorporated with the field investigation can be a useful tool for mapping powdery mildew in winter wheat

Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects

Yellow rust (Puccinia striiformis f. sp. Tritici), powdery mildew (Blumeria graminis) and wheat aphid (Sitobion avenae F.) infestation are three serious conditions that have a severe impact on yield and grain quality of winter wheat worldwide. Discrimination among these three stressors is of practical importance, given that specific procedures (i.e. adoption of fungicide and insecticide) are needed to treat different diseases and insects. This study examines the potential of hyperspectral sensor systems in discriminating these three stressors at leaf level. Reflectance spectra of leaves infected with yellow rust, powdery mildew and aphids were measured at the early grain filling stage. Normalization was performed prior to spectral analysis on all three groups of samples for removing differences in the spectral baseline among different cultivars. To obtain appropriate bands and spectral features (SFs) for stressor discrimination and damage intensity estimation, a correlation analysis and an independent t-test were used jointly. Based on the most efficient bands/SFs, models for discriminating stressors and estimating stressor intensity were established by Fisher’s linear discriminant analysis (FLDA) and partial least square regression (PLSR), respectively. The results showed that the performance of the discrimination model was satisfactory in general, with an overall accuracy of 0.75. However, the discrimination model produced varied classification accuracies among different types of diseases and insects. The regression model produced reasonable estimates of stress intensity, with an R2 of 0.73 and a RMSE of 0.148. This study illustrates the potential use of hyperspectral information in discriminating yellow rust, powdery mildew and wheat aphid infestation in winter wheat. In practice, it is important to extend the discriminative analysis from leaf level to canopy level

Evaluating how lodging affects maize yield estimation based on UAV observations

Timely and accurate pre-harvest estimates of maize yield are vital for agricultural management. Although many remote sensing approaches have been developed to estimate maize yields, few have been tested under lodging conditions. Thus, the feasibility of existing approaches under lodging conditions and the influence of lodging on maize yield estimates both remain unclear. To address this situation, this study develops a lodging index to quantify the degree of lodging. The index is based on RGB and multispectral images obtained from a low-altitude unmanned aerial vehicle and proves to be an important predictor variable in a random forest regression (RFR) model for accurately estimating maize yield after lodging. The results show that (1) the lodging index accurately describes the degree of lodging of each maize plot, (2) the yield-estimation model that incorporates the lodging index provides slightly more accurate yield estimates than without the lodging index at three important growth stages of maize (tasseling, milking, denting), and (3) the RFR model with lodging index applied at the denting (R5) stage yields the best performance of the three growth stages, with R2 = 0.859, a root mean square error (RMSE) of 1086.412 kg/ha, and a relative RMSE of 13.1%. This study thus provides valuable insight into the precise estimation of crop yield and demonstra\tes that incorporating a lodging stress-related variable into the model leads to accurate and robust estimates of crop grain yield

A novel method for maize leaf disease classification using the RGB-D post-segmentation image data

Maize (Zea mays L.) is one of the most important crops, influencing food production and even the whole industry. In recent years, global crop production has been facing great challenges from diseases. However, most of the traditional methods make it difficult to efficiently identify disease-related phenotypes in germplasm resources, especially in actual field environments. To overcome this limitation, our study aims to evaluate the potential of the multi-sensor synchronized RGB-D camera with depth information for maize leaf disease classification. We distinguished maize leaves from the background based on the RGB-D depth information to eliminate interference from complex field environments. Four deep learning models (i.e., Resnet50, MobilenetV2, Vgg16, and Efficientnet-B3) were used to classify three main types of maize diseases, i.e., the curvularia leaf spot [Curvularia lunata (Wakker) Boedijn], the small spot [Bipolaris maydis (Nishik.) Shoemaker], and the mixed spot diseases. We finally compared the pre-segmentation and post-segmentation results to test the robustness of the above models. Our main findings are: 1) The maize disease classification models based on the pre-segmentation image data performed slightly better than the ones based on the post-segmentation image data. 2) The pre-segmentation models overestimated the accuracy of disease classification due to the complexity of the background, but post-segmentation models focusing on leaf disease features provided more practical results with shorter prediction times. 3) Among the post-segmentation models, the Resnet50 and MobilenetV2 models showed similar accuracy and were better than the Vgg16 and Efficientnet-B3 models, and the MobilenetV2 model performed better than the other three models in terms of the size and the single image prediction time. Overall, this study provides a novel method for maize leaf disease classification using the post-segmentation image data from a multi-sensor synchronized RGB-D camera and offers the possibility of developing relevant portable devices

Delusional Beliefs, Two-Factor Theories, and Bizarreness

In order to explain delusional beliefs, one must first consider what factors should be included in a theory of delusion. Unlike a one-factor theory, a two-factor theory of delusion argues that not only anomalous experience (the first factor) but also an impairment of the belief-evaluation system (the second factor) is required. Recently, two-factor theorists have adopted various Bayesian approaches in order to give a more accurate description of delusion formation. By reviewing the progression from a one-factor theory to a two-factor theory, I argue that in light of the second factor’s requirements, different proposed impairments can be unified within a consistent belief-evaluation system. Under this interpretation of the second factor, I further argue that the role of a mechanism responsible for detecting bizarreness is wrongly neglected. I conclude that the second factor is a compound system which consists of differing functional parts, one of which functions to detect bizarreness in different stages of delusion; moreover, I hold that the impairment can be one or several of these functional parts

Akratic beliefs and seemings

How does it come about that a person akratically believes that P, while at the same time believing that the available evidence speaks against that P? Among the current accounts, Scanlon offers an intuitive suggestion that one’s seeming experience that P may play an important role in the aetiology of their akratic belief that P. However, it turns out to be quite challenging to articulate what the role of seeming experience is. This paper will offer a novel development of Scanlon’s intuitive suggestion, with a focus on clear-eyed epistemic akrasia. I will argue that the primary role of seeming experience is unlikely to act as the subject’s reason or to provide the subject with prima facie justification; instead, based on the recent work in dogmatism and Cartesian clarity, I will propose a causal account, according to which, when it seems clear to the subject that P, the seeming experience may exert a brute causal force to persistently compel the subject to believe that P. This causal account also has the advantage of helping some existing accounts to explain clear-eyed epistemic akrasia