What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Cognition
His scientific interests lie mostly in Reinforcement, Cognitive psychology, Artificial intelligence, Reinforcement schedules and Statistics.
His Reinforcement research is included under the broader classification of Social psychology.
His work carried out in the field of Cognitive psychology brings together such families of science as Adaptive behavior, Control and Action.
His Artificial intelligence research includes themes of Matching, Algorithm, Data science and Set.
John Staddon combines subjects such as Developmental psychology and Operant conditioning with his study of Reinforcement schedules.
Within one scientific family, he focuses on topics pertaining to Hill climbing under Statistics, and may sometimes address concerns connected to Operations research.
His most cited work include:
- The "supersitition" experiment: A reexamination of its implications for the principles of adaptive behavior. (985 citations)
- Handbook of operant behavior (845 citations)
- Adaptive behavior and learning (568 citations)
What are the main themes of his work throughout his whole career to date?
John Staddon focuses on Reinforcement, Statistics, Social psychology, Cognitive psychology and Cognitive science.
His Reinforcement study deals with Artificial intelligence intersecting with Action.
His Statistics study typically links adjacent topics like Duration.
His Duration research focuses on subjects like Time perception, which are linked to Interval.
His Cognitive psychology research incorporates elements of Cognition and Operant conditioning.
His research investigates the link between Cognitive science and topics such as Behaviorism that cross with problems in Psychoanalysis.
He most often published in these fields:
- Reinforcement (25.79%)
- Statistics (15.84%)
- Social psychology (13.12%)
What were the highlights of his more recent work (between 2008-2021)?
- Behaviorism (8.14%)
- Cognitive psychology (11.76%)
- Social psychology (13.12%)
In recent papers he was focusing on the following fields of study:
John Staddon mainly investigates Behaviorism, Cognitive psychology, Social psychology, Epistemology and Cognitive science.
His work deals with themes such as Metacognition, Cognition, Comparative cognition and Reinforcement, Operant conditioning, which intersect with Cognitive psychology.
His Cognition research focuses on Fear conditioning and how it connects with Stimulus control.
His work investigates the relationship between Operant conditioning and topics such as Law of effect that intersect with problems in Past history, Regression and Statistics.
His specific area of interest is Social psychology, where he studies Punishment.
His studies in Cognitive science integrate themes in fields like Object, Teleological behaviorism, Behavioural sciences and Developmental psychology.
Between 2008 and 2021, his most popular works were:
- Metacognition in animals: how do we know that they know? (74 citations)
- Neural Network Models of Conditioning and Action (70 citations)
- The behavioral economics of choice and interval timing. (44 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Cognition
His primary scientific interests are in Behaviorism, Cognitive psychology, Time perception, Social psychology and Epistemology.
His study explores the link between Behaviorism and topics such as Cognitive science that cross with problems in Mentalism and Cognition.
The Cognitive psychology study combines topics in areas such as Metacognition, Comparative cognition and Operant response.
John Staddon combines subjects such as Metamemory, Animal cognition, Reinforcement and Principles of learning with his study of Comparative cognition.
John Staddon has included themes like Classical conditioning, Communication and Standard deviation in his Time perception study.
His work on Operant conditioning and Law of effect as part of general Social psychology study is frequently linked to Variable, bridging the gap between disciplines.
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