What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Cognition
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 most cited work include:
- Toward a method of selecting among computational models of cognition. (412 citations)
- Advances in Minimum Description Length: Theory and Applications (363 citations)
- Applying Occam’s razor in modeling cognition: A Bayesian approach (327 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (36.73%)
- Speech recognition (27.89%)
- Machine learning (18.37%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (36.73%)
- Machine learning (18.37%)
- Bayesian probability (10.20%)
In recent papers he was focusing on the following fields of study:
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.
Between 2017 and 2021, his most popular works were:
- Mechanisms underlying the spacing effect in learning: A comparison of three computational models. (9 citations)
- Neurophysiology underlying influence of stimulus reliability on audiovisual integration. (8 citations)
- Anxiety Modulates Preference for Immediate Rewards Among Trait-Impulsive Individuals: A Hierarchical Bayesian Analysis (3 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Cognition
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.
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