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References


Knowledge Acquisition


Gurrie, E., Tickle, A.B., Diederich, J., Anderson, A., Knowledge Acquisition and the Re-Use of Security Knowledge, Gappa, U., Voss, H. (Eds.), Knowledge Engineering Forum, St Augustin: GMD (March 1995, Pre-publication).

Diederich, J., Ruhmann, I., May, M. (1987). KRITON: A knowledge acquisition tool for expert systems, International Journal of Man-Machine Studies, 26, 29-40.

Diederich, J., Linster, M., Ruhmann, I., Uthmann, T., A Methodology for Integrating Different Knowledge Elicitation Techniques. European Workshop for Knowledge Acquisition for Knowledge-Based Systems (EKAW-87), London, UK, (September, 1987).

Diederich, J., Knowledge-Based Knowledge Elicitation, International Joint Conference on Artificial Intelligence (IJCAI-87), Los Altos, USA: Morgan Kaufman Publ. 1 (1987) 201-204.

Diederich, J., Ruhmann, I., May, M., KRITON: A Knowledge Acquisition Tool For Expert Systems, in Boose, J.H., Gaines, B. (Eds.), Proceedings of the "Knowledge Acquisition for Knowledge-Based Systems Workshop", Banff, Canada (3-7 November 1986).

Diederich, J., May, M., Ruhmann, I., Hybrid Knowledge Acquisition, in Rollinger, C.R.., Horn, W. (Eds.) GWAI-86 und 2. Oesterreichische Artificial-Intelligence-Tagung, Berlin: Springer Verlag (1986) 343-348 = Springer Informatik Fachberichte 12.

Lenat, D.B., The Nature of Heuristics. Artificial Intelligence, 19 (1982) 189-249.

Simon, H. A. (1971) Designing Organizations for an Information-Rich World. In: Martin Greenberger, Computers, Communication and the Public Interest, Baltimore. MD: The Johns Hopkins Press. pp40-41.

Machine Learning and Mental Health


Abu Salem, F.K, Al-Abbas, K., Dhaini, A., Diederich, J., El Bassuoni, S., El Hajj, W., Exploring Refugee Mobility due to Large Scale Events using Mobile Phone Records. Report to D4R Turkish Telekom. Under review for the D4R Workshop in Istanbul, January 2019 (and possible Springer Book Chapter).

Diederich, J., The Mental State Tracker - Language Anlysis for Mental Health. The Australian & New Zealand Mental Health Association, January 2017.

Diederich, J., AI psychologists are ready now. Science meets Business, 1 December 2016.

Choo, C., Diederich, J., Song, I., Ho, R., Cluster analysis reveals risk factors for repeated suicide attempts in a multi-ethnic Asian population. Asian Journal of Psychiatry (2014) 8, 38-42.

Song, I., Vong, J., Nguwi, Y.Y., Diederich, J., Yellowlees, P., Profiling Bell's Palsy Based on House-Brackman Score. Journal of Artificial Intelligence and Soft Computing Research. 3 (2014) 1, 41-50.

Diederich, J., Song, I., Mental Health Informatics: Current approaches. In: Lech, M., Song, I., Yellowlees, P., Diederich, J. (Eds.), Mental Health Informatics. Berlin, Heidelberg, New York: Springer Verlag, 2014, 1-16.

Song, I., Diederich, J., Speech Analysis for Mental Health Assessment using Support Vector Machines. In: Lech, M., Song, I., Yellowlees, P., Diederich, J. (Eds.), Mental Health Informatics. Berlin, Heidelberg, New York: Springer Verlag, 2014, 79-106.

Song, I., Diederich, J., Generating Explanations from Support Vector Machines for Psychological Classifications. In: Lech, M., Song, I., Yellowlees, P., Diederich, J. (Eds.), Mental Health Informatics. Berlin, Heidelberg, New York: Springer Verlag, 2014, 125-150.

Felin, Diederich, J., Song, I., An Alternative Method of Analysis in the Absence of a Control Group. In: Lech, M., Song, I., Yellowlees, P., Diederich, J. (Eds.), Mental Health Informatics. Berlin, Heidelberg, New York: Springer Verlag, 2014, 151-162.

Tilaka, A.D., Diederich, J., Song, I., Teoh, A.N. Automated Method for Diagnosing Speech and Language Dysfunction in Schizophrenia. In: Lech, M., Song, I., Yellowlees, P., Diederich, J. (Eds.), Mental Health Informatics. Berlin, Heidelberg, New York: Springer Verlag, 2014, 201-216.

Choo, C., Diederich, J., Song, I., Suicide Risk Analysis. In: Lech, M., Song, I., Yellowlees, P., Diederich, J. (Eds.), Mental Health Informatics. Berlin, Heidelberg, New York: Springer Verlag, 2014, 217-228.

Goh, T.J., Diederich, J., Song, I., Sung, M. Using Diagnostic Information to Develop a Machine Learning Application for the Effective Screening of Autism Spectrum Disorders. In: Lech, M., Song, I., Yellowlees, P., Diederich, J. (Eds.), Mental Health Informatics. Berlin, Heidelberg, New York: Springer Verlag, 2014, 229-246.

Diederich, J., Tickle, A.B., Geva, S., Rule Extraction from Neural Networks: Recent Developments. In: J. Koronacki, S. T. Wirzchon, Z. Ras, J. Kacprzyk, Recent Advances in Machine Learning. Dedicated to the memory of Ryszard S. Michalski. Berlin, Heidelberg, New York: Springer Verlag, 2009.

Diederich, J., Rule Extraction from Support Vector Machines - An Introduction. In: Diederich, J. (Ed.), Rule-Extraction from Support Vector Machines. Berlin, Heidelberg, New York: Springer Verlag, 2008.

Pedersen, C., Diederich, J., Accent in Speech Samples: Support Vector Machines for Classification and Rule Extraction. In: Diederich, J. (Ed.), Rule-Extraction from Support Vector Machines. Berlin, Heidelberg, New York: Springer Verlag, 2008.

Mitsdorffer, R., Diederich, J., Prediction of first-day returns of initial public offerings in the US stock market using rule-extraction from support vector machines. In: Diederich, J. (Ed.), Rule-Extraction from Support Vector Machines. Berlin, Heidelberg, New York: Springer Verlag, 2008.

Diederich, J., Al-Ajmi, A., Yellowlees, P., Ex-Ray: Data Mining and Mental Health. Applied Softcomputing 7 (2007) 923-928.

Afifi, N., Diederich, J., Shanableh, T., Computational methods for the detection of facial palsy. Journal of Telemedicine and Telecare 12 (2006) S3:3-7. Based on: Successes and Failures in Telehealth. Brisbane, Australia, 24-25 August 2006.

Taji, M.H., Al-Khouri, H., Misto, W., Abu-Salah, A.K., Wootton, R., Diederich, J., Kawash, J., Tele-Medica: Data Mining and Web Services for Online Health. Journal of Telemedicine and Telecare 12 (2006) S3:85-87. Based on: Successes and Failures in Telehealth. Brisbane, Australia, 24-25 August 2006.

Diederich, J., A Classification System for Rule-Extraction from Support Vector Machines. Accepted for: Nonlinear Analysis: Theory, Methods & Applications, Elsevier (2006). Based on: New Paradigms for Hybrid Learning System (Workshop), International Conference on Hybrid Systems and Applications (ICHSA 2006), University of Louisiana, Lafayette, LA, USA, 24 May 2006.

Barakat, M.N., Barakat, N., Diederich, J., Al-Lawati, J., Diagnosis of Diabetes Mellitus: A data mining approach. International Journal of Diabetes and Metabolism. Vol. 13, No 1 (April 2005) 42.

Barakat, N., Diederich, J., Eclectic Rule-Extraction from Support Vector Machines. International Journal of Computational Intelligence, Vol. 2, No. 1, 2005, 59-62.

Diederich, J., Brugman, C., Towsey, M., Neural Networks and Machine Learning for Natural Language Processing. Applied Intelligence, Vol. 19, No 1. (July/Aug, 2003).

Diederich, J., Kindermann, J., Leopold, E., Paass, G., Authorship Attribution with Support Vector Machines. Applied Intelligence, Vol. 19, No. 1 (July/Aug, 2003) 109-123.

Towsey, M., Brown, A., Wright, S., Diederich, J., Towards Melodic Extension Using Genetic Algorithms. Educational Technology and Society, Vol. 4 (2) (April, 2001).

Hogan, J.M., Diederich, J., Recruitment Learning of Boolean Functions in Sparse Random Networks. International Journal of Neural Systems, 11(6), 537-559, 2001.

Diederich, J., Towsey, M., Brown, A., Wright, S., Genetic Algorithm as an Aid to Learning the Art of Melodic Composition (CD-ROM). Proceedings of the International Conference ISSEI 2000, Approaching a New Millenium: Lessons from the Past - Prospects for the Future (University of Bergen, Norway, 14-18 August 2000), the First International Workshop, Developing Creativity and Large Mental Outlook in the Computer Age.

Hogan, J.M., Norris, M., Diederich, J., Classification of Facial Expressions with Domain Gaussian RBF Networks, Howlett, R.J. and Jain, L.C. (Eds.). Radial Basis Function Theory and Applications, Vol. 2, Applications, Berlin: Physica Verlag (2000).

Tickle, A.B., Maire, F., Bologna, G., Andrews, R., Diederich, J., Lessons from Past, Current Issues and Future Research Directions in Extracting the Knowledge Embedded in Artificial Neural Networks, Wermter, S., Sun, R. (Eds.). Hybrid Neural Systems. Berlin: Springer Verlag (2000), ISBN 3-540-67305-9.

Andrews, R., Tickle A.B., Diederich J., A Review of Techniques for Extracting Rules From Trained Artificial Neural Networks, in print, Dybowski, R. (Ed.). Clinical Applications of Artificial Neural Networks. Cambridge, UK: Cambridge University Press (1999).

Diederich, J., Connectionist Symbol Processing: Is there an Efficient Mapping of Feedforward Networks to a Set of Rules? Neural Computing Surveys (1998).

Tickle, A.B., Andrews, R., Golea, M., Diederich, J., The Truth Will Come to Light: Directions and Challenges in Extracting the Knowledge Embedded within Trained Artificial Neural Networks. IEEE Transaction on Neural Networks 9 (1998) 6, 1057-1068.

Hayward, R., Diederich, J., Realising Connectionist Explanation Based Generalisation, in Maire, F., Hayward, R., Diederich, J. (Eds.), Connectionist Systems for Knowledge Representation and Deduction. Brisbane, Australia.: QUT Publication (1997) 65-86.

Nayak, R., Hayward, R., Diederich, J., Connectionist Knowledge Base Representation by Generic Rules from Trained Feedforward Networks, in Maire, F., Hayward, R., Diederich, J. (Eds.), Connectionist Systems for Knowledge Representation and Deduction. Brisbane, Australia: QUT Publication (1997) 87-98.

Hayward, R., Ho-Stuart, C., Diederich, J., Neural Networks as Oracles for Rule Extraction, in Maire, F., Hayward, R., Diederich, J. (Eds.), Connectionist Systems for Knowledge Representation and Deduction. Brisbane, Australia: QUT Publication (1997) 105-116.

Hayward, R., Diederich, J., SHRUTI: A Model for Reflexive Reasoning. Artificial Intelligence and the Simulation of Behaviour, Quarterly Journal (1997).

Tickle, A.B., Hayward, R., Diederich, J., Techniques for Extracting Rules from Trained Artificial Neural Networks, in Herrmann, C.S., Reine F., and Strohmaier, A. (Eds.), Knowledge Representation in Neural Networks (Workshop Proceedings), Logos-Verlag, Berlin, Germany (1997).

Diederich, J., Hogan, J.M., Constructive Learning in Connectionist Semantic Networks, in Dorffner, G. (Ed.), Neural Networks and a New Artificial Intelligence. London, UK: International Thomson Computer Press (1997) 233-255, ISBN 1-85032-172-8.

Diederich, J., Regelextraktion aus Trainierten Neuronalen Netzen, (Rule Extraction from Trained Neural Networks), in Thielscher, M., Bornscheuer, S.E. (Eds.), Fortschritte der Kuenstlichen Intelligenz.Dresden, Germany: Dresden University Press (1996) 29, ISBN 3-931828-45-X

Tickle, A., Andrews, R., Golea, M., Diederich, J., Rule Extraction from Trained Artificial Neural Networks, in Browne, A. (Ed.), Current Perspectives in Neural Computing. Bristol, UK: Institute of Physics Publishing (1996).

Tickle, A., Hayward, R., Diederich, J., Recent Developments in Techniques for Extracting Rules from Trained Artificial Neural Networks, in Dorffner, G., Möller, K., Paass, G., Rojas, R., Vogel, S. (Eds.), Konnektionismus und Neuronale Netze. St Augustin: GMD - Forschungszentrum Informationstechnik GmbH, 231-247, ISBN 3-88457-300-4, ISSN 0170-8120.

Hayward, R., Tickle, A., Diederich, J., Extracting Rules for Grammar Recognition from Cascade-2 Networks, in Wermter, S., Riloff, A., Scheller, G. (Eds.), Symbolic, Connectionist and Statistical Approaches to Learning for Natural Language Processing. Berlin, Germany: Springer Verlag (1996) 48-60.

Andrews, R., Diederich, J., Tickle, A.B., A Survey and Critique of Techniques for Extracting Rules from Trained Artificial Neural Networks. Knowledge-Based Systems 8 (1995) 373-389.

Andrews, R., Diederich, J., Tickle, A.B., A Survey and Critique of Techniques for Extracting Rules from Trained Artificial Neural Networks. Herbstschule Konnektionismus (HeKonn '95), Muenster (October 1995).

Diederich, J., Tickle, A.B., Explanation and Collective Computation. Complexity International 2 (1995).

Hogan, J.M., Diederich, J., Probabilistic Relations in Randomly Connected Neural Networks. Complexity International 2 (1995).

Bateson, S., Hogan, J.M., Diederich, J., Learning Declarative Relations in an Oscillatory Neural Network. Complexity International 2 (1995)

Diederich, J., Tickle, A.B., Andrews, R., Erklaerung, Transparenz und Regelextraktion (Explanation, Transparency and Rule-Extraction). Herbstschule Konnektionismus (HeKonn '94), Muenster (October 1994).

Diederich, J., Kuenstliche Neuronale Netze zur Erklaerung der menschlichen Intelligenz (Artificial Neural Networks for the Explanation of Human Intelligence). Kuenstliche Intelligenz (April 1994).

Diederich, J., Neurocomputing and Modularity, a comment on Farah, S.: Neuropsychological Inference with an Interactive Brain: A Critique of the Locality Assumption. Behavioral and Brain Sciences (1994).

Diederich, J., Reasoning, Learning and Neuropsychological Plausibility, a comment on: Shastri, L. & Ajjanagadde, V.: From Simple Associations to Systematic Reasoning. Behavioral and Brain Sciences 3 (1993) 16, 417-494.

Diederich, J., Explanation and Artificial Neural Networks. International Journal of Man-Machine Studies (1992) 37, 335-355.

Diederich, J., Neue Trends im Konnektionismus (New Trends in Connectionism). Kuenstliche Intelligenz (1991) 2, 6-11.

Diederich J., “Simulated Annealing” as a Cognitive Process. Cognitive Systems 2/4 (1990) 321-328.

Diederich, J., Spreading Activation and Connectionist Systems for Natural Language Processing. Theoretical Linguistics 16 (1990) 1, 25-64.

Diederich, J., Instruction and High-Level Learning in Connectionist Networks. Connection Science. Journal of Neural Computing, Artificial Intelligence and Cognitive Research 1 (1989) 2, 163-182. Reprinted in: Hendler, J.A., Sharkey, N.E. (Eds.), Connectionist AI: Readings from Connection Science, Intellect Publ. (1992).

Diederich, J., Steps Toward Knowledge-Intensive Connectionist Learning, in Pollack, J., Barnden J. (Eds.), Advances in Connectionist and Neural Computation Theory. Norwood, N.J., Ablex Publ. (1989) 284-304.

Diederich, J., Linster, M., Knowledge-Based Knowledge Elicitation, in Guida, G., Tasso, C. (Eds.), Topics in Expert System Design. Amsterdam, North Holland: (1989) 323-350.

Diederich, J., Techniken des Wissenserwerbs (Knowledge Acquisition Techniques), in Christaller, Th. (Ed.), 5. Kuenstliche Intelligenz Fruehjahrsschule (1987). Berlin: Springer Verlag 1988 = Springer Informatik Fachberichte 202, 295-335.

Lischka, C., Diederich, J., Von Symbolen zu lernfaehigen Systemen (From Symbols to Adaptive Systems), Chip Soft Tech (April/May 1988) 2, 22-28.

Diederich, J., Ruhmann, I., May, M., KRITON: A Knowledge Acquisition Tool for Expert Systems, in Boose, J.H., Gaines, B. (Eds.), Knowledge Acquisition Tools for Expert Systems. Knowledge-Based Systems 2. London etc: Academic Press (1988) 83-94.

Diederich, J., Trends im Konnektionismus (Trends in Connectionism). Kuenstliche Intelligenz (1988) 1, 28-32.

Diederich, J., Wissensakquisition fuer Expertensysteme. Eine Arbeitstagung in der GMD (Knowledge Acquisition for Expert Systems). GMD-Spiegel (1987) 1.

Lischka, C., Diederich, J., Gegenstand und Methode der Kognitionswissenschaft (Contents and Methods in Cognitive Science), GMD-Spiegel (1987) 2/3, 21-32.

Diederich, J., Ruhmann, I., May, M., KRITON: A Knowledge Acquisition Tool For Expert Systems. International Journal of Man-Machine Studies 26 (1987) 29-40.

Diederich, J., Lischka, C., SPREAD-3: Ein Werkzeug zur Simulation konnektionistischer Modelle auf Lisp-Maschinen (SPREAD-3: A Simulation Tool for Connectionist Systems on Lisp Machines). KI-Rundbrief 46 (1987).

Diederich, J., Verfahren des automatischen Wissenserwerbs (Automatic Knowledge Acquisition Techniques). Jahresbericht der GMD (1986), Gesellschaft fuer Mathematik und Datenverarbeitung mbH (Hrsg.), Sankt Augustin: GMD (1987).

Diederich, J., Wissenserwerb und Wissensrepraesentation in KRITON (Knowledge Elicitation and Knowledge Representation in KRITON), in Heyer, G., Krems, J. (Eds.), Wissensarten und ihre Darstellung. Berlin, Germany: Springer Verlag (1987).

Diederich, J., May, M., Ruhmann, I., KRITON: Wissensakquisition fuer Expertensysteme (KRITON: Knowledge Acquisition for Expert Systems), in Balzert, G., Heyer, G., Lutze, R. (Eds.), Expertensysteme '87, Konzepte und Werkzeuge. Stuttgart: Teubner Verlag (1987) 210-221.

Diederich, J., Tagung: "Knowledge Acquisition for Knowledge-Based Systems" (Conference Report: Knowledge Acquisition for Knowledge-Based Systems). KI-Rundbrief 45 (1987).

Clinical Psychology


How to prevent a Relapse. Anxiety BC. Retrieved: 26 January 2019.

Barrett, P. M., Duffy, A. L., Dadds, M. R., & Rapee, R. M. (2001). Cognitive-behavioral treatment of anxiety disorders in children: long-term (6-year) follow-up. Journal of Consulting and Clinical Psychology, 69, 135-141.

Bartling, G. u.a. Problemanalyse im therapeutischen Prozess. Leitfaden fuer die Praxis. Kohlhammer Urban-Tachenbuecher, Band 307. Stuttgart 1980.

Cacioppo, S., Grippo, A.J., London, S., Goossens, L., Cacioppo, J.T. (2015). Loneliness: Clinical Import and Interventions. Perspect Psychol Sci., 10(2): 238–249.

Strachey, J. Analysis of a Phobia in a Five-Year-Old Boy. The Standard Edition of the Complete Psychological Works of Sigmund Freud, Volume X (1909): Two Case Histories (Little Hans and the Rat Man), 1-150.

Sijbrandija, M., Acarturkb, C., Birdc, M., Bryantd, R.A., Burcherte, S.... . Strengthening mental health care systems for Syrian refugees in Europe and the Middle East: integrating scalable psychological interventions in eight countries. European Journal of Psychotraumatology, 2017, VOL. 8, 1388102

Session 3, Cognitive Restructuring. UC Davis Retrieved: 26 January 2019.

Trull, T.J., Prinstein, M.J. Clinical Psychology (Eighth Edition). Wadsworth CENGAGE Learning, Belmond CA, 2013.

What is Problem-Solving Therapy? American Psychological Association | Division 12 Retrieved 22 March 2018.

Wolpe, J., Rachman, S. Psychoanalytic "evidence": A critique based on Freud's case of little Hans. Journal of Nervous and Mental Disease, 131 (1960) 135-148.

Autism Spectrum Disorder


Aitchison, L., & Lengyel, M. (2017). With or without you: predictive coding and Bayesian inference in the brain. Current Opinion in Neurobiology, 46, 219-227.

Aviezer, H., Trope, Y., & Todorov, A. (2012). Body cues, not facial expressions, discriminate between intense positive and negative emotions. Science, 338(6111), 1225-1229.

Bishop-Fitzpatrick, L., Minshew, N. J., & Eack, S. M. (2013). A systematic review of psychosocial interventions for adults with autism spectrum disorders. Journal of Autism and Developmental Disorders, 43(3), 687-694.

Chamberlain, P., Rodgers, J., Crowley, M.J., White, S.E., Freeston, M.H., & South, M. (2013). A potentiated startle study of uncertainty and contextual anxiety in adolescents diagnosed with autism spectrum disorder. Molecular Autism, 4(31).

Crespi, B. (2013). Developmental heterochrony and the evolution of autistic perception, cognition and behavior. BMC Medicine, 11, Article 119. Doi:10.1186/1741-7015-11-119.

Daunizeau, J., Den Ouden, H., Pessiglione, M., Kiebel, S., Stephan, K., & Friston, K. (2010). Observing the observer (I): Meta-Bayesian models of learning and decision-making. PLoS ONE, 5(12).

Dillenburger, K., & Keenan, M. (2009). None of the As in ABA stand for autism: Dispelling the myths. Journal of Intellectual and Developmental Disability, 34(2), 193-195. Doi:10.1080/13668250902845244

Donaldson, A. L., & Stahmer, A. C. (2014). Team collaboration: The use of behavior principles for serving students with ASD. Language, Speech, and Hearing Services in Schools, 45(4), 261-276. Doi:10.1044/2014_LSHSS-14-0038

Friston, K., Lawson, R., & Frith, C. (2012). On hyperpriors and hypopriors: Comment on Pellicano and Burr. Trends in Cognitive Sciences, 17(1), 1. Doi:10.1016/j.tics.2012.11.003.

Froese, T., & Ikegami, T. (2013). The brain is not an isolated “black box,” nor is its goal to become one. Behavioral and Brain Sciences, 36, 213-214. Doi:10.1017/S0140525X12002348.

Gensler, D. (2012). Autism spectrum disorder in DSM-V: Differential diagnosis and boundary conditions. Journal of Infant, Child & Adolescent Psychotherapy, 11(2), 86-95. Doi:10.1080/15289168.2012.676339

Heaton, P. (2003). Pitch memory, labelling and disembedding in autism. Journal of Child Psychology and Psychiatry, 44 (4), 543-551.

Hill, A. L. (1978). Savants: Mentally retarded individuals with special skills. International Review of Research in Mental Retardation, 9, 277- 298.

Hodgson, A, Freeston, M., Honey, E., &Rodgers, J. (2017). Facing the unknown: Intolerance of in children with autism spectrum disorder. Journal of Applied Research in Intellectual Disabilities, 30, 336-344.

Hohwy, J., Paton, B., & Palmer, C. (2015). Distrusting the present. Phenomenology and the Cognitive Sciences, 15(3), 315-335.

Kite, D. M., Gullifer, J., & Tyson, G. A. (2013). Views on the diagnostic labels of autism and Asperger’s disorder and the proposed changes in the DSM. Journal of Autism and Developmental Disorders, 43(7), 1692-1700.

Knill, D., & Pouget, A. (2004)). The Bayesian brain: The role of uncertainty in neural coding and computation. TRENDS in Neurosciences, 27(12), 712-719. Doi: 10.1016/j.tins.2004.10.007.

Kulage, K. M., Smaldone, A. M., & Cohn, E. G. (2014). How will DSM-5 affect autism diagnosis? A systematic literature review and meta-analysis. Journal of Autism and Developmental Disorders, 44(8), 1918-1932.

Lawson, R., Rees, G., & Friston, K. (2014). An aberrant precision account of autism. Front Hum Neurosci., 8, 302. Doi:10.3389/fnhum.2014.00302

Matson, J. L., Hattier, M. A., & Belva, B. (2012). Treating adaptive living skills of persons with autism using applied behavior analysis: A review. Research in Autism Spectrum Disorders, 6(1), 271-276.

Mehling, M. H., & Tassé, M. J. (2016). Severity of autism spectrum disorders: Current conceptualization, and transition to DSM-5. Journal of Autism and Developmental Disorders, 46(6), 2000-2016. Doi:10.1007/s10803-016-2731-7

Mohammadzaheri, F., Koegel, L. K., Rezaee, M., & Rafiee, S. M. (2014). A randomized clinical trial comparison between pivotal response treatment (PRT) and structured applied behavior analysis (ABA) intervention for children with autism. Journal of Autism and Developmental Disorders, 44(11), 2769-2777. Doi:10.1007/s10803-014-2137-3

Neil, L., Olsson, N.C., & Pellicano, E. (2016). The relationship between intolerance of uncertainty, sensory sensitivities, and anxiety in autistic and typically developing children. Journal of Autism and Developmental Disorders, 46(6), 1962-1973.

Ohan, J. L., Ellefson, S. E., & Corrigan, P. W. (2015). Brief report: The impact of changing from DSM-IV ‘Asperger’s’ to DSM-5 ‘autistic spectrum disorder’ diagnostic labels on stigma and treatment attitudes. Journal of Autism and Developmental Disorders, 45(10), 3384-3389.

Palmen, A., Didden, R., & Lang, R. (2012). A systematic review of behavioral intervention research on adaptive skill building in high-functioning young adults with autism spectrum disorder. Research in Autism Spectrum Disorders, 6(2), 602-617.

Palmer, C., Lawson, R., & Hohwy, J. (2017). Bayesian approaches to autism: Towards volatility, action, and behavior. Psychological Bulletin, 143(5), 521-542.

Parsloe, S. M., & Babrow, A. S. (2016). Removal of Asperger’s syndrome from the DSM V: Community response to uncertainty. Health Communication, 31(4), 485-494.

Pellicano, E., & Burr, D. (2012). When the world becomes ‘too real’: A Bayesian explanation of autistic perception. Trends in Cognitive Sciences,16(10), 504-510.

Robic, S., Sonié, S., Fonlupt, P., Henaff, M.A., Touil, N., Coricelli,G., …& Schmitz, C. (2015). Decision-making in a changing world: A study in autism spectrum disorders. Journal of Autism and Developmental Disorders, 45(6), 1603-1613.

Roth, M. E., Gillis, J. M., & DiGennaro Reed, F. D. (2014). A meta-analysis of behavioral interventions for adolescents and adults with autism spectrum disorders. Journal of Behavioral Education, 23(2), 258-286.

Smith, T., & Eikeseth, S. (2011). Ivar Lovaas: Pioneer of applied behavior analysis and intervention for children with autism. Journal of Autism and Developmental Disorders, 41(3), 375-378.

Steege, M. W., Mace, F. C., Perry, L., & Longenecker, H. (2007). Applied behavior analysis: Beyond discrete trial teaching. Psychology in The Schools, 44(1), 91-99.

Treffert, D. (2009). The savant syndrome: An extraordinary condition. A synopsis: past, present, future. Philos Trans R Soc Lond B Biol Sci., 364(1522), 1351-1357. Doi:10.1098/rstb.2008.0326

Van de Cruys, S., De-Wit, L., Evers, K., Boets, B., &Wagemans, J. (2013). Weak priors versus overfitting of predictions in autism: Reply to Pellicano and Burr. I-Perception, 4(2). Doi:10.1068/i0580ic

Van de Cruys, S., Evers, K., Van der Hallen, R., Van Eylen, L., Boets, B., De-Wit, L., & Wagemans, J. (2014). Precise minds in uncertain worlds: predictive coding in autism. Psychol Rev., 121(4),649-675.

Wigham, S., Rodgers, J., South, M., McConachie, H., & Freeston, M. (2015). The interplay between sensory processing abnormalities, intolerance of uncertainty, anxiety and restricted and repetitive behaviors in autism spectrum disorder. Journal of Autism and Developmental Disorders, 45(4), 943-952.

Zuddas, A. (2013). Autism assessment tools in the transition from DSM-IV to DSM-5. European Child & Adolescent Psychiatry, 22(6), 325-327. Doi:10.1007/s00787-013-0424-8