Theory of Mind, Religiosity, and Autistic Spectrum Disorder: a Review of Empirical Evidence Bearing on Three Hypotheses

2019 ◽  
Vol 19 (5) ◽  
pp. 411-431
Author(s):  
Robert N. McCauley ◽  
George Graham ◽  
A. C. Reid
Keyword(s):  
Theory Of Mind ◽  
Empirical Evidence ◽  
Language Processing ◽  
Autistic Spectrum Disorder ◽  
Empirical Studies ◽  
Spectrum Disorder ◽  
Cognitive Measures ◽  
Autistic Spectrum ◽  
Intelligence Theory ◽  
And Behavior

AbstractThe cognitive science of religions’ By-Product Theory contends that much religious thought and behavior can be explained in terms of the cultural activation of maturationally natural cognitive systems. Those systems address fundamental problems of human survival, encompassing such capacities as hazard precautions, agency detection, language processing, and theory of mind. Across cultures they typically arise effortlessly and unconsciously during early childhood. They are not taught and appear independent of general intelligence. Theory of mind (mentalizing) undergirds an instantaneous and automatic intuitive understanding of minds, mental representations, and their implications for agents’ actions. By-Product theorists hypothesize about a social cognition content bias, holding that mentalizing capacities inform participants’ implicit understanding of religious representations of agents with counter-intuitive properties. That hypothesis, in combination with Baron-Cohen’s account of Autistic Spectrum Disorder (ASD) in terms of diminished theory of mind capacities (what he calls “mind-blindness”), suggests an impaired religious understanding hypothesis. It proposes that people with ASD have substantial limitations in intuitive understanding of and creative inferences from such representations. Norenzayan argues for a mind-blind atheism hypothesis, which asserts that the truth of these first two hypotheses suggests that people with ASD have an increased probability, compared to the general population, of being atheists. Numerous empirical studies have explored these three hypotheses’ merits. After carefully pondering distinctions between intuitive versus reflective mentalizing and between explicit versus implicit measures and affective versus cognitive measures of mentalizing, the available empirical evidence provides substantial support for the first two hypotheses and non-trivial support for the third.

Classifying Autism Spectrum Disorder using Machine Learning Models

2021 ◽  
Author(s):  
Tingyan Deng
Keyword(s):  
Logistic Regression ◽  
Autistic Spectrum Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Autistic Spectrum ◽  
Time Treatment ◽  
Proposed Model ◽  
Related Feature ◽  
And Behavior ◽  
Logistic Regression Classifier

Autistic Spectrum Disorder (ASD) is a developmental disability, which can affect communication and behavior, causing significant social, communication, and behavior challenge. From a rare childhood disorder, ASD has evolved into a disorder that is found, according to the National Institute of Health, in 1% to 2% of the population in high income countries. A potential early and accurate diagnosis can not only help doctors to find the disease early, leading to a more on time treatment to the patient, but also can save significant healthcare costs for the patients. With the rapid growth of ASD cases, many open-source ASD related datasets were created for scientists and doctors to investigate this disease. Autistic Spectrum Disorder Screening Data for Adult is a well-known dataset, which contains 20 features to be utilized for further analysis on the potential cause and prediction of ASD. In this paper, we developed an Autism classification algorithm based on logistic regression model. Our model starts with featuring engineering to extract deep information from the dataset and then applied a modified logistic regression classifier to the data. The model can predict the ASD in an average F1 score of 0.97, which displays the superiority and feasibility of the proposed model. Besides, the data visualization technique was used to displays several feature distributions images for people to better understand the data and related feature engineering.

Hearing Voices and Other Matters of the Mind

2020 ◽  
Author(s):  
Robert N. McCauley ◽  
George Graham
Keyword(s):  
Theory Of Mind ◽  
Mental Disorders ◽  
Language Processing ◽  
Autistic Spectrum Disorder ◽  
Religious Thought ◽  
Moral Thought ◽  
Hearing Voices ◽  
The Mind ◽  
And Behavior ◽  
Thought Action Fusion

This book endorses an ecumenical naturalism toward all cognition, which will illuminate the long-recognized and striking similarities between features of mental disorders and features of religions. The authors emphasize underlying cognitive continuities between familiar features of religiosity, of mental disorders, and of everyday thinking and action. They contend that much religious thought and behavior can be explained in terms of the cultural activation of maturationally natural cognitive systems, which address fundamental problems of human survival, encompassing such capacities as hazard precautions, agency detection, language processing, and theory of mind. The associated skills are not taught and appear independent of general intelligence. Religions’ representations cue such systems’ operations. The authors hypothesize that in doing so they sometimes elicit responses that mimic features of cognition and conduct associated with mental disorders. Both in schizophrenia and in religions some people hear alien voices. The inability of depressed participants to communicate with or sense their religions’ powerful, caring gods can exacerbate their depression. Often religions can domesticate the concerns and compulsions of people with OCD. Religions’ rituals and pronouncements about moral thought-action fusion can temporarily evoke similar obsessions and compulsions in the general population. A chapter is devoted to each of these and to the exception that proves the rule. The authors argue that if autistic spectrum disorder involves theory-of mind-deficits, then people with ASD will lack intuitive insight and find inferences with many religious representations challenging. Ecumenical naturalism’s approach to mental abnormalities and religiosity promises both explanatory and therapeutic understanding.

Searching for the Gods’ Minds the Hard Way

2020 ◽  
pp. 157-210
Author(s):  
Robert N. McCauley ◽  
George Graham
Keyword(s):  
Theory Of Mind ◽  
General Population ◽  
Empirical Research ◽  
Autistic Spectrum Disorder ◽  
Spectrum Disorder ◽  
Autistic Spectrum ◽  
Considerable Evidence ◽  
High Functioning ◽  
Over Time

The only exceptions that prove principles are those whose exceptional status those principles explain. People with autistic spectrum disorder (ASD) tend to be more attentive to details, more likely to apprehend events mechanistically, and more inclined to systemize about both than does the general population. Considerable evidence suggests that they are also far less likely to possess ready intuitions about the workings of people’s minds and that even high-functioning people with ASD are mindblind. If the by-product theory of religious representations is correct, then people with ASD will lack intuitive insight about religious representations of gods as minded agents and find creative inferences with them challenging. Theorists differ about how extensive such limitations will be, especially in light of the ability of some people with ASD to laboriously piece together a partial, ersatz theory of mind over time. Overall the available empirical research mostly corroborates these proposals about these limitations.

PERMODELAN PREDIKTIF AUTISTIC SPECTRUM DISORDER DENGAN ALGORITMA C.45

2018 ◽  
Vol 15 (2) ◽  
pp. 99
Author(s):  
Oktafian Farhan ◽  
Agus Subekti
Keyword(s):  
Autistic Spectrum Disorder ◽  
Spectrum Disorder ◽  
Data Set ◽  
Autistic Spectrum

Autisme merupakan disabilitas perkembangan yang dialami sepanjang hidup penderita Autistic Spectrum Disorder (ASD). Semakin cepat ditangani, semakin besar kemungkinan anak akan kembali normal. Untuk alasan ini, diperlukan metode baru yang dapat membantu orang tua dengan cepat mengenali gejala autisme pada anak-anak mereka. Dalam studi sebelumnya yang dilakukan oleh Fadi Fayez Tabhtah, suatu data set dihasilkan untuk mendeteksi apakah seorang anak memiliki autisme atau tidak. Tetapi penelitiannya hanya menghasilkan data set, ia tidak memeriksa lebih lanjut dimana algoritma cocok untuk data set yang telah dihasilkan. Atribut data set ternyata memiliki nilai yang salah, yang mengundang pertanyaan tentang keakurasian data. Dalam penelitian ini peneliti menggunakan metode CRISP-DM dan menguji keakuratan data set penelitian sebelumnya menggunakan algoritma C.45. Selanjutnya, aplikasi WEKA menggunakan pemilihan fitur dan pengaruh dari nilai yang salah untuk setiap atribut dan menemukan atribut yang paling signifikan. Atribut-atribut ini kemudian diuji dengan algoritma C.45 sehingga model prediksi dari data set diperoleh. Atribut A6 dari perhitungan pohon keputusan tidak muncul sama sekali sebagai cabang. Sebuah model baru diperoleh di mana atribut A6 dihilangkan, sehingga ketika diukur oleh algoritma C.45, nilai akurasi yang lebih baik diperoleh. Hasil model baru kemudian diuji pada data kuesioner baru, yang menghasilkan prediksi yang tepat.

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