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The Structure of the Turkish Economy
No full textOzden, Oktay/0000-0001-5739-8783This essay presents a critical analysis of ozgur Orhangazi's Turkiye ekonomisinin yapisi (The structure of the Turkish economy). The book examines the last four decades of the Turkish economy, accounting for its place in the global capitalist system and focusing on the radical effects of the liberalization of capital mobility in 1989. Moreover, the book describes the growth model of the Turkish economy over the last two decades, consisting of three components: high dependency on foreign capital inflow, debt accumulation, and construction-oriented growth. This review finds that the book lacks in its examination of the internal dynamics of capitalism in Turkey, especially concerning the evolution of the two branches of the Turkish bourgeoisie: construction capital, which is the extension of commercial capital; and industrial capital, which has made its mark on the history of capitalism in Turkey. Class struggle inside the bourgeoisie has in fact shaped the last century of economic and political history in Turkey
A New Approach for Classifying Maize Crop Diseases Using Iot-Based Deep Learning Convolutional Networks
No full textMahsul zararlıları ve hastalıkları, mahsul veriminin azalması ve üretkenliğin azalmasıyla ilişkilendirilmiştir. Sürdürülebilir tarım uygulamalarına geçme çabalarını ciddi şekilde engelledi. Mahsullerdeki zararlıları ve hastalıkları tespit etmek, derin öğrenme evrişimli sinir ağlarından (DLCN) ve IoT gömülü sistemlerden yararlanılarak gerçekleştirilebilir. Bu çalışmada, modeli barındırmak için kullanılan gömülü bir cihaz daha sonra TinyML kısıtlamalarına göre çalışacak şekilde tasarlanmıştır. Tasarım sürecinde TinyML ile birlikte 4 katmanlı IoT çerçevesi kullanıldı. Öncelikle her katmandaki hem donanım hem de yazılım gereksinimlerine ilişkin özellikler tanımlandı. Daha sonra gereksinimleri en iyi karşılayan bileşenler seçildi. Daha sonra IoT TinyML gömülü sistemiyle kullanılmak üzere bir DLCN modeli oluşturulur. MobileNet-V3 mimarisinden yararlanan bir transfer öğrenme yaklaşımı kullanıldı. Model, 3 sınıfta en az 18.000 görüntüden oluşan bir veri seti ile eğitildi: sağlıklı mısır mahsulü, Mısır Çizgi Virüsünden (MSV) etkilenen mahsul ve Sonbahar Ordu Solucanından (FAW) etkilenen mahsul. Modeli kategorik bir çapraz entropi kaybı fonksiyonu ve Uyarlanabilir Moment Tahmini (ADAM) optimize edici kullanarak eğitmek için elli dönem kullanıldı. Model, eğitim ve doğrulama bölümlerinde sırasıyla %85 ve %88 doğruluk elde etti. Daha sonra model test bölümünde test edildi ve %88'den az olmayan bir doğruluk elde edildi. Hassasiyet, geri çağırma ve F1 puanı gibi diğer ölçümler, elde edilen doğruluğu yansıtıyordu. Son olarak gömülü cihazın çalışması kavramsal bir model olarak tartışılmıştır.Crop pests and diseases have been associated with reduced crop yield and lower productivity. It has severely hindered efforts to move to sustainable farming practices. Detecting pests and diseases in crops can be achieved by leveraging deep learning convolutional neural networks (DLCN) and IoT embedded systems. In this study, an embedded device used for hosting the model was then designed to operate based on the constraints of TinyML. A 4-layer IoT framework was used in conjunction with TinyML in the design process. First, the specifications for both hardware and software requirements in each layer were defined. Then, the components that best satisfied the requirements were selected. A DLCN model is then built for use with an IoT TinyML embedded system. A transfer learning approach was used, capitalizing on the MobileNet-V3 architecture. The model was trained a dataset of not less than 18,000 images across 3 classes: healthy maize crop, Maize Streak Virus (MSV) affected crop, and Fall Army Worm (FAW) affected crop. Fifty epochs were used to train the model using a categorical cross entropy loss function and Adaptive Moment Estimation (ADAM) optimizer. The model achieved an accuracy of 85% and 88% on the training and validation splits respectively. Then, the model was tested on the test split, achieving an accuracy of not less than 88%. Other metrics such as precision, recall and F1-score reflected the accuracy achieved. Finally, the operation of the embedded device was discussed as a conceptual model
Telescopic forklift selection through a novel interval-valued Fermatean fuzzy PIPRECIA-WISP approach
No full textUlutas, Alptekin/0000-0002-8130-1301; ECER, FATIH/0000-0002-6174-3241; TOPAL, AYSE/0000-0003-1882-4545Telescopic forklifts stand apart from other forklift types, boasting numerous benefits. They offer notable advantages, such as enhanced manoeuvrability, accessibility to elevated areas, versatility, and the capacity to operate at high speeds. Choosing appropriate telescopic forklifts can substantially enhance operational efficiency and efficacy within the industry. Concurrently, it can bolster business competitiveness by expediting logistical processes, yielding notable cost reductions. Nonetheless, the intricacy and specificity inherent in these machines complicate the decision-making process for stakeholders. Furthermore, conflicting criteria, the continuous evolution of manufacturers' models, and the industry's intricate nature compound the selection challenges. Hence, there is a pressing need for a resilient, dependable, and practical decision-making framework capable of adeptly navigating uncertainties to yield reliable and coherent outcomes. This study aimed to develop an integrated decision-making model based on interval-valued Fermatean fuzzy (IVFF) sets to respond to these requirements and address this critical decision-making problem in the relevant industry, which is also significantly affected by complex uncertainties. This work, therefore, introduces an integrated methodology for decisionmaking, IVFF-PIPRECIA and IVFF-WISP techniques. IVFF-PIPRECIA determines criteria importance weights, whereas IVFF-WISP identifies optimal alternatives. A case study in the textile industry validates the framework's practicality. Purchase price emerges as the primary criterion, exceeding 500 thousand euros for telescopic forklifts, followed closely by load-carrying capacity. The second alternative proves to be the best option. Comparative and sensitivity analyses confirm the model's credibility. The approach effectively handles decisionmaking uncertainties, yielding competitive outcomes. It can be applied to diverse engineering problems. Insights from this study assist users in selecting optimal equipment and may inform forklift manufacturers in improving machinery. Future research could focus on establishing real-time operational data collection frameworks for these vehicles.Science Citation Index Expande
How Do Intuitive and Reflective Thinking Shape Cooperation Under Resource Scarcity?
No full textİş birliği insan sosyal davranışının önemli bir unsurudur. Bu tez kapsamında yürütülen çalışmalar sezgisel ve derin düşünmenin iş birliğini şekillendirmedeki rolünü ve bu ilişkide kaynak kıtlığının düzenleyici rolünü araştırmaktadır. Çalışma 1a'da derin düşünme ve kaynak kıtlığının iş birliği davranışı ile ilişkisini araştırmak amacıyla Türkiye örneklemi üzerinde korelasyonel bir çalışma yürütülmüş, Çalışma 1b'de ise aynı örneklem yeniden davet edilerek düşünme tarzlarının manipüle edildiği deneysel bir çalışma yürütülmüştür. Çalışma 2'de Çalışma 1b bir ABD örnekleminde tekrarlanmıştır. Çalışma 1a'da iş birliğinin derin düşünmeyle arttığı gözlemlenerek öz-kontrol hipotezi (ÖKH) için korelasyonel destek bulunmuş ve iş birliğinin kaynak kıtlığıyla azaldığı gözlemlenmiştir. Çalışma 1b'de ÖKH için destek bulunamazken kıtlığın daha düşük iş birliğiyle ilişkili olduğu yine kısmen gözlemlenmiştir. Çalışma 2'de ise derin düşünmenin iş birliğini azalttığı gözlemlenerek sosyal kısayollar hipotezi (SKH) için kısmi deneysel destek bulunmuş, ancak kıtlık ile iş birliği arasında herhangi bir ilişki bulunamamıştır. Genele bakıldığında, sezgisel/derin düşünmenin iş birliğini mi yoksa bencilliği mi teşvik ettiği ve iş birliğinin kaynak kıtlığından nasıl etkilendiği hakkında net bir sonuca varılamamıştır. Ancak Türkiye ve ABD örneklemleri arasında gözlemlenen bulgu farklılıkları kültürün iş birliğini şekillendirmedeki rolünü vurgulamaktadır.Cooperation is an essential element of human sociality. The current set of studies investigates the role of intuitive and reflective thinking in shaping cooperation and the moderating role of resource scarcity in this relationship. In Study 1a, we conducted a correlational analysis to investigate the associations between reflective thinking, resource scarcity, and cooperation in a Turkish sample. In Study 1b, we invited the same set of participants and experimentally manipulated thinking styles to observe the causal effect of intuitive/analytical thinking on cooperation, while still exploring the moderating role of resource scarcity on this relationship. In Study 2, we replicated Study 1b in a US sample. In Study 1a, we found correlational support for the self-control account (SCA) by showing that cooperation increases with reflective thinking, and found partial evidence that resource scarcity impairs cooperation. In Study 1b, we did not find support for the SCA, while still partially demonstrating that scarcity is associated with lower cooperation. In Study 2, we found partial experimental support for the social heuristics hypothesis (SHH) by demonstrating that reflection decreases cooperation, however found no relationship between scarcity and cooperation. Taken together, while whether intuition promotes cooperation or selfishness, and how cooperation is affected by resource scarcity remains inconclusive, the difference in findings between the Turkish and US samples emphasizes the role of culture in shaping cooperation
Resource-Efficient Ensemble Learning for Edge Iiot Network Security Against Osint-Based Attacks
No full textThe rise of Edge IIoT networks has transformed industries by enabling real-time data processing, but these networks face significant c ybersecurity risks, particularly from OSINT-based attacks. This paper presents a resource-efficient ensemble learning framework designed to detect such attacks in Edge IIoT environments. The framework integrates machine learning models, including RandomForest, K-Nearest Neighbors, and Logistic Regression, optimized with Principal Component Analysis (PCA) to reduce data dimensionality and computational overhead. GridSearchCV and StratifiedKFold cross-validation were employed to fine-tune the models, resulting in high detection accuracy. This approach ensures robust and efficient security for resource-constrained Edge IIoT networks. © 2024 IEEE
Becoming-In Fant in the Zone of Indistinction: Sound Poetry
No full textBu çalışmanın ana konusu, tarihsel, kavramsal ve önermesel düzeyde şiir ve performans arasında bir belirsizlik bölgesinde kendini gösteren ses şiiri türünü incelemektir. Tez, ses şiiri türünün kavramsal saptamalara ve kategorileştirme çabalarına direnen paradoksal yapısını ortaya koymakta ve türün açtığı dilsel ve bedensel imkânları sorgulamaktadır. Tez, bu dilsel/bedensel sürekliliğin ana bileşeni olarak sesin, ses şiirinin dilde ve bedende bir bebek/ konuşamayan-oluşa olanak tanıdığını savunuyor. Bir belirsizlik bölgesinde bebek/konuşamayan oluş olarak kavramsallaştırılan ses şiiri, dilin ve bedenin sınırları aracılığıyla dilin kendisinde zaten var olan bir potansiyele doğru radikal bir deney alanı açıyor.The main subject of this study is to examine the genre of sound poetry, which presents itself in the zone of indistinction between poetry and performance on a historical, conceptual, and propositional level. The thesis reveals the paradoxical structure of the sound poetry genre, which resists conceptual determinations and categorization efforts and questions the linguistic and bodily possibilities opened by the genre. The thesis argues that voice, as the main component of this linguistic/bodily continuum, allows sound poetry becoming-in/fant in language and body. Conceptualized as a becoming-in/fant in the zone of indistinction, sound poetry opens up a radical field of experimentation through the limits of language and the body to a potential already inherent in language itself
Customer Purchase Intent Prediction Using Feature Aggregation on E-Commerce Clickstream Data
No full textThis paper presents a machine learning model for predicting customer purchase intent using e-commerce clickstream data. The model is built using the LightGBM framework, chosen for its efficiency in handling large-scale datasets and complex feature interactions. Key challenges addressed include the high dimensionality of clickstream data, the inherent class imbalance between purchase and non-purchase sessions, and the temporal variability of user behavior. The feature engineering process involved creating and selecting features that capture relevant user behaviors, such as session duration, event counts, and interaction diversity. The model was evaluated using ROC-AUC, F1-score, precision, and recall metrics, demonstrating strong performance in identifying sessions likely to result in a purchase. This study contributes to the field of e-commerce analytics by providing a robust framework for conversion prediction, enabling more effective customer engagement strategies. Our findings underscore the potential of machine learning to enhance e-commerce conversion rates, thereby optimizing customer engagement. © 2024 IEEE
An Ethical Review on Psychologists' Use of Social Media in Turkey
No full textKitle iletişim araçları ile olan ilişkisinde geçmişe kıyasla çok daha aktif bir konumda yer alan birey, sosyalleşme, bilgi alma, zaman geçirme gibi ihtiyaçlarını doyuma ulaştırmak için televizyon izleme, radyo dinleme, sosyal medyada içerik üretip tüketme şeklinde çeşitli kullanımlar gerçekleştirmektedir. Gazeteden günümüz internet dünyasına evirilen bu kullanımlar, bireysel motivasyonların yanı sıra hedef kitlelere ulaşmak için profesyonel amaçlarla da gerçekleşebilmektedir. Çeşitli meslek gruplarınca varlık gösterilen bu kullanımlar Türkiye'de Hukuk ve Tıp gibi alanlarda anayasal ve etik yönetmeliklerle sınırlandırılmış olsa da Psikoloji özelinde herhangi bir düzenleme bulunmamaktadır. Sosyal medya ekseninde sunulan bir etik yönetmelik de mevcut değildir. Düzenlemelere sistematik bilimsel arka plan oluşturma ve literatüre katkı sağlama girişimi olan bu çalışma (1) psikoloji profesyonellerinin sosyal medya kullanımının ardındaki motivasyonları araştırmakta, (2) psikoloji profesyonelleri tarafından gerçekleştirilen sosyal medya kullanımı var olan geleneksel etik ilkelerle analiz etmekte ve (3) sosyal medyanın mesleğin kendisine ve teröpatik ilişkiye dair potansiyellerini incelemektedir. Bu noktada niteliksel metodolojik yaklaşım benimsenerek terapi veren 21 psikoloji profesyoneliyle derinlemesine görüşme gerçekleştirilmiştir. Araştırma sonuçları profesyonellerin görünür olma, toplumsal fayda oluşturma, kendini ifade etme ve tatmin olmayla birlikte topluluklara aidiyet hissetme motivasyonlarıyla sosyal medya kullandıklarını ortaya çıkarmıştır. Psikolojinin sosyal medyadaki yansıması mevcut etik ilkelerle örneklendirilebilmiştir. Bu ilkelere uyumlu hareket edildiğinde psikolojiye dair farkındalığın artması, terapinin normalleşmesi, psikologların daha fazla danışana erişmesi ve teröpatik ilişkilerin desteklenmesi yönünde potansiyel faydalar görülmüştür. Ancak ilkelerle uyumlanmayan kullanımlar, gizliliğin ihlal edilmesi, mesleğin yanlış tanıtımı, etiketlemelerin oluşması, danışan-terapist arasındaki teröpatik ilişkinin farklılaşması noktalarında tehlikeler ortaya çıkarmaktadır. Anahtar Sözcükler: Psikoloji, Sosyal Medya, Meslek Etiği, Kendini Açma, InstagramIn comparison to the past, individuals are now in a much more active position in their relationship with mass media, engaging in various activities such as watching television, listening to radio, and creating/consuming content on social media to fulfill their needs for socialization, information acquisition, and leisure. These media practices, evolving from newspapers to today's internet-dominated world, serve not only individual motivations but also professional purposes, aiming to reach target audiences as per their desires. While fields like Law and Medicine are governed by constitutional and ethical regulations in Turkey, professions such as Psychology lack specific regulations. There is also no ethical guideline for social media usage from a psychological perspective. As an attempt to provide systematic scientific knowledge to serve as the background for these regulations, and contribute to the research literature, this study (1) examines the motivations behind social media usage of psychology professionals, (2) scrutinizing the use of social media by psychology professionals according to the traditional ethical principles and (3) examining the potential of social media in enhancing the profession and therapeutic relationships. To do so, a qualitative methodological approach adopted, and online in-depth interviews were conducted with 21 psychology professionals providing therapy services. Findings reveal that professionals utilize social media for visibility, social benefit creation, self-expression, satisfaction, and a sense of community belonging. The reflection of psychology on social media can be exemplified in terms of existing ethical principles. When these principles are followed, various potential benefits have been observed, including increased mental health awareness, therapy normalization, improved psychologist-client access, and support for therapeutic relationships. However, uses that do not comply with ethics pose dangers such as confidentiality violation, professional misrepresentation, labels, and altering the dynamics of the therapist-client relationship. Keywords: Psychology, Social media, Professional Ethics, Self-disclosure, Instagra
The International Climate Psychology Collaboration: Climate Change-Related Data Collected From 63 Countries
No full textKrouwel, Andre/0000-0003-0952-6028; Alfano, Mark/0000-0001-5879-8033; SUKO, Yasushi/0000-0001-6224-659X; Butalia, Radhika/0000-0001-7288-3103; Elbaek, Christian/0000-0002-7039-4565; Lopez Ortega, Alberto/0000-0003-2765-7232; Palumbo, Helena/0000-0003-1978-3386; Kankaanpaa, Reeta/0000-0001-9111-7076; Pfattheicher, Stefan/0000-0002-0161-1570; Lees, Jeffrey/0000-0001-6030-4207; /0000-0002-9495-7369; Levy, Neil/0000-0002-5679-1986; Huang, Guanxiong/0000-0002-8588-1454; Chow, Dawn/0000-0002-7544-4859; Fang, Ke/0000-0003-0374-9706; Pronizius, Ekaterina/0000-0003-1446-196X; Rhoads, Shawn/0000-0003-1350-9458; Brick, Cameron/0000-0002-7174-8193; Ross, Robert/0000-0001-8711-1675; Palacios Haugestad, Christian/0000-0001-6787-1992; Dubey, Shreya/0000-0002-8882-3356; Berkebile-Weinberg, Michael/0000-0001-8935-5737; Vanags, Edmunds/0000-0003-1932-936X; Todorova, Boryana/0000-0003-4840-498X; , Madalina Vlasceanu/0000-0003-2138-1968; Schulreich, Stefan/0000-0001-9708-1545; Jia, Fanli/0000-0002-7149-455X; Doell, Kimberly/0000-0002-0043-9609; Gjoneska, Biljana/0000-0003-1200-6672; Azevedo, Flavio/0000-0001-9000-8513; Huaman, Enma Tereza/0000-0001-6596-6234; Spampatti, Tobia/0000-0003-0714-6494; Gaudencio Rego, Gabriel/0000-0003-3304-4723; Lagomarsino, Maria/0000-0002-2238-8726; van Stekelenburg, Aart/0000-0002-9978-0224; Guilaran, Johnrev/0000-0001-6607-8001Climate change is currently one of humanity's greatest threats. To help scholars understand the psychology of climate change, we conducted an online quasi-experimental survey on 59,508 participants from 63 countries (collected between July 2022 and July 2023). In a between-subjects design, we tested 11 interventions designed to promote climate change mitigation across four outcomes: climate change belief, support for climate policies, willingness to share information on social media, and performance on an effortful pro-environmental behavioural task. Participants also reported their demographic information (e.g., age, gender) and several other independent variables (e.g., political orientation, perceptions about the scientific consensus). In the no-intervention control group, we also measured important additional variables, such as environmentalist identity and trust in climate science. We report the collaboration procedure, study design, raw and cleaned data, all survey materials, relevant analysis scripts, and data visualisations. This dataset can be used to further the understanding of psychological, demographic, and national-level factors related to individual-level climate action and how these differ across countries.Google Jigsaw grant; Swiss National Science Foundation [203283]; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy -EXC [2117 -422037984]; Dutch Research Council [7934]; European Union [776608]; John Templeton Foundation [62631]; National Council for Scientific and Technological Development; Christ Church College Research Centre grant [BB/R010668/2]; Jacobs Foundation Fellowship [390683824]; NYUAD research funds; Swiss Federal Office of Energy through the ""Energy, Economy, and Society"" program [SI/502093-01]; Belgian National Fund for Scientific Research (FRS-FNRS) [PDR 0253.19]; Fund for scientific development at the Faculty of Psychology at SWPS University; Radboud University Behavioural Science Institute; "Leuphana University Luneburg research fund; University of Birmingham Start up Seed Grant; University of Birmingham; University of Geneva Faculty Seed Funding; Center for Social Conflict and Cohesion Studies grant ANID/FONDAP [15130009]; Center for Intercultural and Indigenous Research grant ANID/FONDAP [15110006]; National Research Foundation of Korea [NRF-2020S1A3A2A02097375]; Darden School of Business; National Agency of Research and Development, National Doctoral Scholarship [24210087]; Dutch Science Foundation (NWO) [VI.Veni.191 G.034]; Netherlands Organization for Scientific Research (NWO) Vici grant [453-15-005]; Foundation for Science and Technology -FCT (Portuguese Ministry of Science, Technology and Higher Education) [UIDB/05380/2020]; Slovak Research and Development Agency (APVV) [APVV-21-0114]; James McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition-Scholar Award [220020334]; Fundacion Universidad Torcuato Di Tella grant [INB2376941]; Thammasat University Fast Track Research Fund (TUFT) [12/2566]; HSE University Basic Research Program; ARU Centre for Societies and Groups Research Centre Development Funds; University of Stavanger faculty of Social Science research activities grant; University of Colorado Boulder Faculty research fund; Kochi University of Technology Research Funds; Dean's Office, College of Arts and Sciences at Seton Hall University; Nicolaus Copernicus University (NCU) budget; Sectorplan Social Sciences and Humanities; Erasmus Centre of Empirical Legal Studies (ECELS); Erasmus School of Law, Erasmus University Rotterdam; American University of Sharjah Faculty Research Grant [2020 FRG20-M-B134]; ANU Futures Grant (Colin Klein); Research Council of Norway through Centres of Excellence Scheme, FAIR project [262675]; Aarhus University Research Foundation [AUFF-E-2018-7-13]; Intergroup Inequality Lab at Cornell University; COVID-19 Rapid Response grant, University of Vienna; Austrian Science Fund FWF [W1262-B29]; FWO Postdoctoral Fellowship [12U1221N]; National Geographic Society; University of Michigan Ross School of Business Faculty Research Funds; Norwegian Retailers' Environment Fund, Poster Competition Grant [2022]; ARC [DP180102384]; Medical Research Council [MR/P014097/2]; Jacobs Foundation; Wellcome Trust; Royal Society Sir Henry Dale Fellowship [223264/Z/21/Z]; Social Sciences and Humanities Research Council (SSHRC) Doctoral Fellowship; Simon Fraser University Psychology Department Research Grant; FAPESP [2020/15230-5]; Shell Brasil; Brazil's National Oil; Natural Gas and Biofuels Agency (ANP) through the R&D levy regulation; ANR [ANR-21-CE28-0016-01]; NOMIS Foundation; Universidad Peruana Cayetano Heredia Project [209465]; Riksbankens Jubileumsfond grant [P21-0384]; European Research Council [EP/X02170X/1]; Statutory Funding of Institute of Psychology, University of Silesia in Katowice; Sao Paulo Research Foundation (FAPESP) [2019/26665-5]; National Science Foundation GRFP Award [1937959]; Japan Society for the Promotion of Science [21J01224]; Institute of Psychology & the Faculty of Social and Political Sciences, University of Lausanne; Universitat Ramon Llull, Esade Business School (Katharina Schmid); University of St Andrews; Faculty of Health PhD fellowship; Aarhus University; School of Medicine and Psychology, Australian National University; Swedish Research Council; Russian Federation Government [075-15-2021-611]; Cooperatio Program MCOM; Stanford Center on Philanthropy and Civil Society; Canada Research Chairs program (Jiaying Zhao)We would like to acknowledge the following funding contributions: Google Jigsaw grant (Kimberly C. Doell; Madalina Vlasceanu; Jay J. Van Bavel). Swiss National Science Foundation P400PS_190997 (Kimberly C. Doell). Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy -EXC 2117 -422037984 (Kimberly C. Doell). Dutch Research Council grant 7934 (Karlijn L. van den Broek). European Union Grant No. ID 776608 (Karlijn L. van den Broek). John Templeton Foundation grant 61378 (Mark Alfano). The National Council for Scientific and Technological Development grant (Angelica Andersen). Christ Church College Research Centre grant (Matthew A. J. Apps). David Phillips Fellowship grant BB/R010668/2 (Matthew A. J. Apps). Jacobs Foundation Fellowship (Matthew A. J. Apps). "DFG grant project no. 390683824 (Moritz A. Drupp; Piero Basaglia; Bjorn Bos)". NYUAD research funds (Jocelyn J. Belanger). "The Swiss Federal Office of Energy through the ""Energy, Economy, and Society"" program grant number: SI/502093-01 (Sebastian Berger)". The Belgian National Fund for Scientific Research (FRS-FNRS) PDR 0253.19 (Paul Bertin). Fund for scientific development at the Faculty of Psychology at SWPS University in Warsaw (Olga Bialobrzeska). Radboud University Behavioural Science Institute (Danielle N. M. Bleize). "Leuphana University Luneburg research fund (David D. Loschelder; Lea Boecker; Yannik A. Escher; Hannes M. Petrowsky; Meikel Soliman)". University of Birmingham Start up Seed Grant (Ayoub Bouguettaya). Prime-Pump Fund from University of Birmingham (Ayoub Bouguettaya; Mahmoud Elsherif). University of Geneva Faculty Seed Funding (Tobias Brosch). "Pomona College Hirsch Research Initiation Grant (Adam R. Pearson)". Center for Social Conflict and Cohesion Studies grant ANID/FONDAP #15130009 (Hector Carvacho; Silvana D'Ottone). Center for Intercultural and Indigenous Research grant ANID/FONDAP #15110006 (Hector Carvacho; Silvana D'Ottone). National Research Foundation of Korea NRF-2020S1A3A2A02097375 (Dongil Chung; Sunhae Sul). Darden School of Business (Luca Cian). Kieskompas -Election Compass (Tom W. Etienne; Andre P. M. Krouwel; Vladimir Cristea; Alberto Lopez Ortega). The National Agency of Research and Development, National Doctoral Scholarship 24210087 (Silvana D'Ottone). Dutch Science Foundation (NWO) grant VI.Veni.201S.075 (Marijn H.C. Meijers). The Netherlands Organization for Scientific Research (NWO) Vici grant 453-15-005 (Iris Engelhard). Foundation for Science and Technology -FCT (Portuguese Ministry of Science, Technology and Higher Education) grant UIDB/05380/2020 (Ana Rita Farias). The Slovak Research and Development Agency (APVV) contract no. APVV-21-0114 (Andrej Findor). The James McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition-Scholar Award grant 220020334 (Lucia Freira; Joaquin Navajas). Sponsored Research Agreement between Meta and Fundacion Universidad Torcuato Di Tella grant INB2376941 (Lucia Freira; Joaquin Navajas). Thammasat University Fast Track Research Fund (TUFT) 12/2566 (Neil Philip Gains). HSE University Basic Research Program (Dmitry Grigoryev; Albina Gallyamova). ARU Centre for Societies and Groups Research Centre Development Funds (Sarah Gradidge; Annelie J. Harvey; Magdalena Zawisza). University of Stavanger faculty of Social Science research activities grant (Simone Grassini). Center for the Science of Moral Understanding (Kurt Gray). University of Colorado Boulder Faculty research fund (June Gruber). Swiss National Science Foundation grant 203283 (Ulf J. J. Hahnel). Kochi University of Technology Research Funds (Toshiyuki Himichi). RUB appointment funds (Wilhelm Hofmann). Dean's Office, College of Arts and Sciences at Seton Hall University (Fanli Jia). Nicolaus Copernicus University (NCU) budget (Dominika Jurgiel; Adrian Dominik Wojcik). Sectorplan Social Sciences and Humanities, The Netherlands (Elena Kantorowicz-Reznichenko). Erasmus Centre of Empirical Legal Studies (ECELS), Erasmus School of Law, Erasmus University Rotterdam, The Netherlands (Elena KantorowiczReznichenko). American University of Sharjah Faculty Research Grant 2020 FRG20-M-B134 (Ozgur Kaya; Ilker Kaya). Centre for Social and Early Emotional Development SEED grant (Anna Klas; Emily J. Kothe). ANU Futures Grant (Colin Klein). Research Council of Norway through Centres of Excellence Scheme, FAIR project No 262675 (Hallgeir Sjastad and Simen Bo). Aarhus University Research Foundation grant AUFF-E-2021-7-16 (Ruth Krebs; Laila Nockur). Social Perception and Intergroup Inequality Lab at Cornell University (Amy R. Krosch). COVID-19 Rapid Response grant, University of Vienna (Claus Lamm). Austrian Science Fund FWF I3381 (Claus Lamm). Austrian Science Fund FWF: W1262-B29 (Boryana Todorova). FWO Postdoctoral Fellowship 12U1221N (Florian Lange). National Geographic Society (Julia Lee Cunningham). University of Michigan Ross School of Business Faculty Research Funds (Julia Lee Cunningham). The Clemson University Media Forensics Hub (Jeffrey Lees). Norwegian Retailers' Environment Fund, Poster Competition Grant 2022 (Isabel Richter). John Templeton Foundation grant 62631 (Neil Levy; Robert M. Ross). ARC Discovery Project DP180102384 (Neil Levy). Medical Research Council Fellowship grant MR/P014097/1 (Patricia L. Lockwood). Medical Research Council Fellowship grant MR/P014097/2 (Patricia L. Lockwood). Jacobs Foundation (Patricia L. Lockwood). Wellcome Trust and the Royal Society Sir Henry Dale Fellowship grant 223264/Z/21/Z (Patricia L. Lockwood). JFRAP grant (Jackson G. Lu). Social Sciences and Humanities Research Council (SSHRC) Doctoral Fellowship (Yu Luo). Simon Fraser University Psychology Department Research Grant (Annika E. Lutz; Michael T. Schmitt). GU internal funding (Abigail A. Marsh; Shawn A. Rhoads). FAPESP 2014/50279-4 (Karen Louise Mascarenhas). FAPESP 2020/15230-5 (Karen Louise Mascarenhas). Shell Brasil (Karen Louise Mascarenhas). Brazil's National Oil, Natural Gas and Biofuels Agency (ANP) through the R&D levy regulation (Karen Louise Mascarenhas). ANR grant SCALUP, ANR-21-CE28-0016-01 (Hugo Mercier). NOMIS Foundation grant for the Centre for the Politics of Feelings (Katerina Michalaki; Manos Tsakiris). "Applied Moral Psychology Lab at Cornell University (Sarah Milliron; Laura Niemi; Magdalena Zawisza)". Universidad Peruana Cayetano Heredia Project 209465 (Fredy S. MongeRodriguez). Belgian National Fund for Scientific Research (FRS-FNRS) grant PDR 0253.19 (Youri L. Mora). Riksbankens Jubileumsfond grant P21-0384 (Gustav Nilsonne). European Research Council funded by the UKRI Grant EP/X02170X/1 (Maria Serena Panasiti; Giovanni Antonio Travaglino). Statutory Funding of Institute of Psychology, University of Silesia in Katowice (Mariola Paruzel-Czachura). Aarhus University Research Foundation AUFF-E-2018-7-13 (Stefan Pfattheicher). Sao Paulo Research Foundation (FAPESP) grant 2019/26665-5 (Gabriel G. Rego). Mistletoe Unfettered Research Grant, National Science Foundation GRFP Award 1937959 (Shawn A. Rhoads). Japan Society for the Promotion of Science grant 21J01224 (Toshiki Saito). Institute of Psychology & the Faculty of Social and Political Sciences, University of Lausanne (Oriane Sarrasin). Universitat Ramon Llull, Esade Business School (Katharina Schmid). University of St Andrews (Philipp Schoenegger). Dutch Science Foundation (NWO) VI.Veni.191 G.034 (Christin Scholz). Universitat Hamburg (Stefan Schulreich). Faculty of Health PhD fellowship, Aarhus University (Katia Soud). School of Medicine and Psychology, Australian National University (Samantha K. Stanley). Swedish Research Council grant 2018-01755 (Gustav Tinghog). Russian Federation Government grant project 075-15-2021-611 (Danila Valko). Swedish Research Council (Daniel Vastfjall). Cooperatio Program MCOM (Marek Vranka). Stanford Center on Philanthropy and Civil Society (Robb Willer). Canada Research Chairs program (Jiaying Zhao). For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.Science Citation Index Expande
Do Autistic Adults Spontaneously Reason About Belief? a Detailed Exploration of Alternative Explanations
No full textWhite, Sarah J/0000-0001-6946-9155Southgate et al.'s (Southgate 2007 Psychol. Sci. 18, 587-92 (doi:10.1111/j.1467-9280.2007.01944.x)) anticipatory-looking paradigm has presented exciting yet inconclusive evidence surrounding spontaneous mentalizing in autism. The present study aimed to develop this paradigm to address alternative explanations for the lack of predictive eye movements on false-belief tasks by autistic adults. This was achieved through implementing a multi-trial design with matched true-belief conditions, and both high and low inhibitory demand false-belief conditions. We also sought to inspect if any group differences were related to group-specific patterns of attention on key events. Autistic adults were compared with non-autistic adults on this adapted implicit mentalizing task and an established explicit task. The two groups performed equally well in the explicit task; however, autistic adults did not show anticipatory-looking behaviour in the false-belief trials of the implicit task. Critically, both groups showed the same attentional distribution in the implicit task prior to action prediction, indicating that autistic adults process information from social cues in the same way as non-autistic adults, but this information is not then used to update mental representations. Our findings further document that many autistic people struggle to spontaneously mentalize others' beliefs, and this non-verbal paradigm holds promise for use with a wide range of ages and abilities.University College London; London Autism Group CharityWe sincerely thank all participants for their participation and the London Autism Group Charity for supporting the recruitment. We are genuinely grateful to Prof Antonia F. de C. Hamilton for helping with producing graphs and Hannah Partington for making insightful comments on an earlier draft of the article.Science Citation Index Expande