|How Machine Implementations of Simple Verbal Operants Demonstrate the Emergence of Complex and Diverse Verbal Behavior
|Sunday, May 25, 2014
|10:00 AM–11:50 AM
|W183a (McCormick Place Convention Center)
|Area: VRB/EAB; Domain: Basic Research
|Chair: Barbara E. Esch (Esch Behavior Consultants, Inc.)
|Discussant: Greg Stikeleather (Palo Alto, CA)
|CE Instructor: Barbara E. Esch, Ph.D.
More traditional computer simulations of human behavior have involved information processing models of the brain, whereby the brain is assumed to be modeled after the way computers are architected: lots of data stored in memory with programs that retrieve the data given certain inputs. Adaptive network systems are elemental computer learning programs that have enabled the simulation of behavior at an operant level, whereby that behavior which is followed by reinforcing consequences is more likely to occur again. This symposium explores how adaptive networks can generate simple verbal operants, and how more diverse and complex behavior can then be generated as a result. Implications for the interpretation of more complex human linguistic behavior and the development of effective teaching programs also are considered.
|Instruction Level: Basic
|Keyword(s): adaptive networks, computer simulations, effective teaching, verbal behavior
Psychologists, behavior analysts, and graduate students interested in exploring how adaptive networks can generate simple verbal operants, and how more diverse and complex behavior can then be generated as a result.
|Learning Objectives: At the conclusion of the presentation, participants should be able to (1) Explain how implementing operant principles in a robot enables analysis of puzzling cases of verbal behavior; (2) Identify one or more examples of verbal behavior whose interpretation can be investigated by adaptive network simulations; and (3) Explain what an ANS is and specify how they differ from a typical computer program.
An Overview of How Adaptive Networks Can Generate Simple Verbal Operants
|WILLIAM F. POTTER (California State University Stanislaus)
Adaptive networks are in essence, computer programs that learn. This very fact places them squarely in the domain of behavior analysis, although few in the field conduct research with them, or develop them for commercial or other purposes. This talk will provide an overview of what Adaptive Network Systems (ANS) are and how they differ from typical computer programs; provide insight into how they work; and show how they can adhere to the behavioral principles that the experimental analysis of behavior has uncovered over the years. The basic components of such a network will be explored including the architecture, some simple learning algorithms, and design features which preclude hard-wiring responses, or using brute computer power to solve problems or to produce more complex behaviors. Finally, some simple examples of ANS will be illustrated, particularly related to the elementary verbal operants.
|After obtaining a bachelor's degree in business administration and a minor in journalism, Dr. Potter worked briefly as a journalist for a small daily newspaper, then left that to work in a small advertising agency in New York City. After 4.5 years of this, his true passion emerged--the pursuit of science. He obtained a spot in the behavior analysis graduate program at Western Michigan University, which eventually resulted in a Ph.D. and much training in behavior analysis under the tutelage of Dr. Jack Michael and Dr. Alan Poling, both of whom he owes much. Throughout the years, he has dabbled in many things (VB, CBT, OBM, ANS, MOs, and a few other obscure acronyms), making him a jack of all trades but a master of none. He currently chairs the Psychology/Child Development Department at California State University, Stanislaus, and is director of the International Dual Behavior Analysis Degree in collaboration with universities in Warsaw, Poland and Bangor, Wales.
How Adaptive Networks Can Aid in the Interpretation of Complex Linguistic Puzzles
|DAVID C. PALMER (Smith College)
Because the experimental analysis of verbal behavior is constrained by practical and ethical considerations, most of our understanding of complex cases arises from verbal interpretations. But such interpretations are limited by the sheer number of relevant variables and our ignorance of subjects' histories. In contrast, adaptive network simulations permit complete control over both complex contextual variables and historical variables. If such simulations are tightly constrained by behavioral principles, they offer powerful demonstrations of the explanatory adequacy of such principles. Dr. Palmer will discuss several examples that seem to defy verbal interpretation, examples such as the problems of novelty, nesting, generalization of neologisms according to apparent grammatical form, conditioning the behavior of the listener, mysterious structural regularities in verbal behavior, and the problem of acquisition of complex forms. He will suggest that adaptive network simulations of verbal behavior may be the best interpretive tool and in some cases the only one.
|With bachelor's degrees in geology and English, Dr. David Palmer was devoting his post-graduate years to avoiding the draft when he chanced to pick up a copy of Walden Two from a friend's bookshelf. It changed the direction of his life. He promptly read the rest of the Skinner canon and spent the next decade trying to start an experimental community and preaching radical behaviorism to anyone who would listen. Eventually, he took some classes with Beth Sulzer-Azaroff, who urged him to apply to graduate school. Thanks to a dyslexic secretary, who entered his undergraduate GPA backward, he was admitted and began working with John Donahoe. He was happy in grad school and would be there still if the University of Massachusetts had not threatened to change the locks. He has spent the past 25 years as the token behaviorist at Smith College. During that time he co-authored, with John W. Donahoe, Learning and Complex Behavior, a book which attempts to integrate adaptive network simulation with experimental analysis and verbal interpretation of complex cases. He continues to puzzle over the interpretation of memory, problem-solving, and, particularly, verbal behavior. He still thinks Skinner was right about nearly everything.
A Demonstration of Teaching Verbal Behavior to an Operant Robot
|WILLIAM R. HUTCHISON (Behavior Systems)
The presentation will describe a robot whose behavior is learned via an adaptive network based on behavior analytic principles, embedded in a body with sensors including vision and hearing and with responses including spatial movements and vocalizations. The demonstration will first show how that robot learns elementary verbal operants, then more complex verbal behaviors based on them. We will examine in detail how some of the puzzling verbal behaviors described in the preceding papers in the symposium are learned, illustrating how using a robot makes it possible to examine moment-to-moment changes in the conditions that control the behavioral sequence.
|William Hutchison earned his bachelor's degree from Kansas University with majors in psychology and mathematics, then entered the Ph.D. program in clinical psychology at State University of New York at Stony Brook, the first purely behavioral clinical psychology program. His major adviser was Leonard Krasner, one of the pioneer generation of researchers in behavior modification, token economies, and verbal conditioning. Equally influential on his career was his work as teaching assistant to Howard Rachlin, a leading figure in quantitative analysis of behavior. He then taught at one of the hotbeds of radical behaviorism, West Virginia University, in its Ph.D. program in behavioral systems analysis. In 1983, he developed a behavioral alternative to cognitive artificial intelligence, a computer system based on the equations from quantitative experimental analysis of behavior. That system became one of the first adaptive ("neural") networks and was the foundation for one of the first companies, BehavHeuristics, applying that methodology to commercial software. The company's focus was on resource allocation in changing environments, but a subsequent company, Applied Behavior Systems, embodied the adaptive network in robots and developed software for computerized training of verbal behavior to the robot and to children. Hutchison continued the robotics direction in a 4-year stint with the government's Intelligence Technology Innovation Center.