Pub Date : 2019-05-16DOI: 10.1017/9781108290876.013
Cathy L Faye
{"title":"Social Psychology","authors":"Cathy L Faye","doi":"10.1017/9781108290876.013","DOIUrl":"https://doi.org/10.1017/9781108290876.013","url":null,"abstract":"","PeriodicalId":414110,"journal":{"name":"The Cambridge Handbook of the Intellectual History of Psychology","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130605812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-16DOI: 10.1017/9781108290876.006
M. Posner
{"title":"Attention: Awareness and Control","authors":"M. Posner","doi":"10.1017/9781108290876.006","DOIUrl":"https://doi.org/10.1017/9781108290876.006","url":null,"abstract":"","PeriodicalId":414110,"journal":{"name":"The Cambridge Handbook of the Intellectual History of Psychology","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122227627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-16DOI: 10.1017/9781108290876.016
Richard M. Ryan, E. Bradshaw, E. L. Deci
{"title":"Motivation","authors":"Richard M. Ryan, E. Bradshaw, E. L. Deci","doi":"10.1017/9781108290876.016","DOIUrl":"https://doi.org/10.1017/9781108290876.016","url":null,"abstract":"","PeriodicalId":414110,"journal":{"name":"The Cambridge Handbook of the Intellectual History of Psychology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131343829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-16DOI: 10.1017/9781108290876.009
Y. Erez, Valerie F. Reyna
{"title":"Decision-Making","authors":"Y. Erez, Valerie F. Reyna","doi":"10.1017/9781108290876.009","DOIUrl":"https://doi.org/10.1017/9781108290876.009","url":null,"abstract":"","PeriodicalId":414110,"journal":{"name":"The Cambridge Handbook of the Intellectual History of Psychology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128945653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-16DOI: 10.1017/9781108290876.014
A. Rutherford
{"title":"Gender","authors":"A. Rutherford","doi":"10.1017/9781108290876.014","DOIUrl":"https://doi.org/10.1017/9781108290876.014","url":null,"abstract":"","PeriodicalId":414110,"journal":{"name":"The Cambridge Handbook of the Intellectual History of Psychology","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132085953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-16DOI: 10.1017/9781108290876.012
J. Burman
CD72 is a B lymphocyte surface protein expressed from early stages of B cell development through to the mature B cell stage, but its expression is turned off as B cells differentiate into plasma cells. To elucidate the function of CD72 we have used gene targeting to generate homozygous mutant mice that totally lack CD72 expression. The B cells in these mice are hyper-responsive to stimulation through the B cell receptor. These data indicate that CD72 plays a negative regulatory role on B cell responsiveness. In accord with our findings, an ITIM motif in the cytoplasmic tail of CD72 has been shown to bind to the tyrosine phosphatase SHP-1, a feature characteristic of other surface proteins that negatively regulate lymphocyte responses. We postulate that CD72 is involved in setting the threshold for B cell responsiveness and that it therefore plays an important role in B cell repertoire selection. Our current studies are examining how CD72 regulates the balance between B cell tolerance and autoimmunity in several model systems. We have evidence that the CD72-deficient mice are more susceptible to the induced autoimmune disease experimental allergic encephalomyelitis, a mouse model of multiple sclerosis. We are studying the mechanisms responsible for the increased severity of disease in CD72-deficient mice. CD72-deficient mice also develop spontaneous autoimmune disease as they age, characterized by production of antinuclear antibodies including anti-single-stranded-and anti-double-stranded-DNA antibodies and eventual development of glomerulonephritis. Current studies are aimed at further characterizing the autoantibodies produced, determining the regulatory changes responsible for their production, as well as their pathogenicity. We are additionally studying the mechanisms by which CD72-deficiency leads to a partial abrogation of B cell anergic tolerance in mice in which all B cells express a transgenic B cell receptor specific for hen-egg lysozyme (HEL) and in which the antigen HEL is expressed in the serum. Finally, the lab is examining the biochemistry of signaling through CD72 to determine the molecular mechanisms by which CD72 regulates B cell responsiveness
{"title":"Development","authors":"J. Burman","doi":"10.1017/9781108290876.012","DOIUrl":"https://doi.org/10.1017/9781108290876.012","url":null,"abstract":"CD72 is a B lymphocyte surface protein expressed from early stages of B cell development through to the mature B cell stage, but its expression is turned off as B cells differentiate into plasma cells. To elucidate the function of CD72 we have used gene targeting to generate homozygous mutant mice that totally lack CD72 expression. The B cells in these mice are hyper-responsive to stimulation through the B cell receptor. These data indicate that CD72 plays a negative regulatory role on B cell responsiveness. In accord with our findings, an ITIM motif in the cytoplasmic tail of CD72 has been shown to bind to the tyrosine phosphatase SHP-1, a feature characteristic of other surface proteins that negatively regulate lymphocyte responses. We postulate that CD72 is involved in setting the threshold for B cell responsiveness and that it therefore plays an important role in B cell repertoire selection. Our current studies are examining how CD72 regulates the balance between B cell tolerance and autoimmunity in several model systems. We have evidence that the CD72-deficient mice are more susceptible to the induced autoimmune disease experimental allergic encephalomyelitis, a mouse model of multiple sclerosis. We are studying the mechanisms responsible for the increased severity of disease in CD72-deficient mice. CD72-deficient mice also develop spontaneous autoimmune disease as they age, characterized by production of antinuclear antibodies including anti-single-stranded-and anti-double-stranded-DNA antibodies and eventual development of glomerulonephritis. Current studies are aimed at further characterizing the autoantibodies produced, determining the regulatory changes responsible for their production, as well as their pathogenicity. We are additionally studying the mechanisms by which CD72-deficiency leads to a partial abrogation of B cell anergic tolerance in mice in which all B cells express a transgenic B cell receptor specific for hen-egg lysozyme (HEL) and in which the antigen HEL is expressed in the serum. Finally, the lab is examining the biochemistry of signaling through CD72 to determine the molecular mechanisms by which CD72 regulates B cell responsiveness","PeriodicalId":414110,"journal":{"name":"The Cambridge Handbook of the Intellectual History of Psychology","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125928993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-16DOI: 10.1017/9781108290876.021
{"title":"Index","authors":"","doi":"10.1017/9781108290876.021","DOIUrl":"https://doi.org/10.1017/9781108290876.021","url":null,"abstract":"","PeriodicalId":414110,"journal":{"name":"The Cambridge Handbook of the Intellectual History of Psychology","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132482354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-16DOI: 10.1017/9781108290876.007
M. Bouton, R. Boakes
—A crucial part of the crop protection system in the early and accurate identification of healthy and unhealthy plants. The orthodox methods of identification involve either visual inspection or laboratory testing. Visual inspection involves experience and can vary depending on the individual, which could lead to an error, but laboratory testing takes time and might not be able to give the results quickly. Therefore, in this paper, we propose an image-based machine learning technique to recognize and classify healthy and unhealthy plants. In this work, we have focused solely on the rice plant (Oryza Sativa). The original dataset is available on Kaggle, which includes images of both healthy and unhealthy rice plants. Dataset consists of 501 healthy rice plants and 505 unhealthy rice plants. After validation, we obtained a total of 900 images, including both healthy and unhealthy rice plants. There are 4 models that we use in this experiment: VGG16, VGG19, ResNet50, and InceptionV3. In this project, we tried data augmentation and regularization to improve the performance of our program. After regularization, the results that were obtained improved. However, the results we got when we included data augmentation were worse, so we opted to solely apply regularization. The model that provides the best accuracy for the loss model is VGG19 with 84.4% accuracy and 55.1% loss. The early identification of healthy and unhealthy rice plants using this model could serve as a preventative measure as well as an early warning system. It might also be expanded to create a model for identifying rice plants' health in the actual agricultural fields.
{"title":"Learning","authors":"M. Bouton, R. Boakes","doi":"10.1017/9781108290876.007","DOIUrl":"https://doi.org/10.1017/9781108290876.007","url":null,"abstract":"—A crucial part of the crop protection system in the early and accurate identification of healthy and unhealthy plants. The orthodox methods of identification involve either visual inspection or laboratory testing. Visual inspection involves experience and can vary depending on the individual, which could lead to an error, but laboratory testing takes time and might not be able to give the results quickly. Therefore, in this paper, we propose an image-based machine learning technique to recognize and classify healthy and unhealthy plants. In this work, we have focused solely on the rice plant (Oryza Sativa). The original dataset is available on Kaggle, which includes images of both healthy and unhealthy rice plants. Dataset consists of 501 healthy rice plants and 505 unhealthy rice plants. After validation, we obtained a total of 900 images, including both healthy and unhealthy rice plants. There are 4 models that we use in this experiment: VGG16, VGG19, ResNet50, and InceptionV3. In this project, we tried data augmentation and regularization to improve the performance of our program. After regularization, the results that were obtained improved. However, the results we got when we included data augmentation were worse, so we opted to solely apply regularization. The model that provides the best accuracy for the loss model is VGG19 with 84.4% accuracy and 55.1% loss. The early identification of healthy and unhealthy rice plants using this model could serve as a preventative measure as well as an early warning system. It might also be expanded to create a model for identifying rice plants' health in the actual agricultural fields.","PeriodicalId":414110,"journal":{"name":"The Cambridge Handbook of the Intellectual History of Psychology","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133925632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-16DOI: 10.1017/9781108290876.015
David C Devonis
{"title":"Emotion","authors":"David C Devonis","doi":"10.1017/9781108290876.015","DOIUrl":"https://doi.org/10.1017/9781108290876.015","url":null,"abstract":"","PeriodicalId":414110,"journal":{"name":"The Cambridge Handbook of the Intellectual History of Psychology","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124955361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-16DOI: 10.1017/9781108290876.004
G. Berntson, D. Hothersall
{"title":"Neuroscience in Psychology","authors":"G. Berntson, D. Hothersall","doi":"10.1017/9781108290876.004","DOIUrl":"https://doi.org/10.1017/9781108290876.004","url":null,"abstract":"","PeriodicalId":414110,"journal":{"name":"The Cambridge Handbook of the Intellectual History of Psychology","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114257559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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