The scientist’s investigation covers issues in Artificial intelligence, Model selection, Minimum description length, Machine learning and Speech perception. Mark A. Pitt works on Artificial intelligence which deals in particular with Statistical model. In his study, Probability distribution and Model complexity is inextricably linked to Algorithm, which falls within the broad field of Model selection.
His Minimum description length research is multidisciplinary, incorporating elements of Inductive reasoning, Set, Generalizability theory and Computational model. Machine learning connects with themes related to Categorization in his study. Mark A. Pitt combines subjects such as Context, Consonant, Speech recognition, Vowel and Syllabification with his study of Speech perception.
His scientific interests lie mostly in Artificial intelligence, Speech recognition, Machine learning, Cognition and Speech perception. His Artificial intelligence research includes themes of Pattern recognition and Natural language processing. His Speech recognition research is multidisciplinary, incorporating perspectives in Word recognition, Context, Timbre and Communication.
His biological study spans a wide range of topics, including Categorization, Information integration, Cognitive model and Measure. His Cognition research focuses on Cognitive psychology and how it connects with Lexico, Lexicon and Phonology. His Model selection research incorporates elements of Minimum description length, Selection and Generalizability theory.
Mark A. Pitt spends much of his time researching Artificial intelligence, Machine learning, Bayesian probability, Delay discounting and Cognition. His Artificial intelligence research incorporates themes from Context, Generalization and Reading aloud. His study in Machine learning is interdisciplinary in nature, drawing from both Estimation, Robustness and Numerosity adaptation effect.
His work deals with themes such as Trait, Preference, Econometrics and Impulsivity, which intersect with Bayesian probability. His work on Functional neuroimaging as part of general Cognition research is frequently linked to Mathematical psychology, thereby connecting diverse disciplines of science. His studies deal with areas such as Inference and Cognitive model as well as Parametric statistics.
His primary areas of study are Bayesian probability, Bayesian statistics, Cognitive psychology, Alpha band and Auditory system. His Bayesian probability study results in a more complete grasp of Artificial intelligence. His Bayesian statistics study combines topics from a wide range of disciplines, such as Python, Programming language, Formal methods, Design of experiments and Data collection.
He has included themes like Experimental psychology and Computational model in his Cognitive psychology study. Mark A. Pitt regularly ties together related areas like Learning theory in his Computational model studies. Mark A. Pitt interconnects Stimulus, Neurophysiology, Event-related potential and Audiology in the investigation of issues within Alpha band.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Toward a method of selecting among computational models of cognition.
Mark A. Pitt;In Jae Myung;Shaobo Zhang.
Psychological Review (2002)
Applying Occam’s razor in modeling cognition: A Bayesian approach
In Jae Myung;Mark A. Pitt.
Psychonomic Bulletin & Review (1997)
When a good fit can be bad.
Mark A. Pitt;In Jae Myung.
Trends in Cognitive Sciences (2002)
Advances in Minimum Description Length: Theory and Applications
Peter D. Grünwald;In Jae Myung;Mark A. Pitt.
The Buckeye corpus of conversational speech: labeling conventions and a test of transcriber reliability
Mark A. Pitt;Keith Johnson;Elizabeth Hume;Scott F. Kiesling.
Speech Communication (2005)
Is Compensation for Coarticulation Mediated by the Lexicon
Mark A. Pitt;James M. McQueen.
Journal of Memory and Language (1998)
Counting probability distributions: Differential geometry and model selection
In Jae Myung;Vijay Balasubramanian;Mark A. Pitt.
Proceedings of the National Academy of Sciences of the United States of America (2000)
Altering Context Speech Rate Can Cause Words to Appear or Disappear
Laura C. Dilley;Mark A. Pitt.
Psychological Science (2010)
The use of rhythm in attending to speech.
Mark A. Pitt;Arthur G. Samuel.
Journal of Experimental Psychology: Human Perception and Performance (1990)
Phonological processes and the perception of phonotactically illegal consonant clusters
Mark A. Pitt.
Attention Perception & Psychophysics (1998)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: