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THE NATURAL SELECTION: BEHAVIOR ANALYSIS AMONG THE NATURAL SCIENCES

This presentation examines the connection between behavior analysis and other natural sciences, focusing on the fundamental principles of natural science. It discusses essential themes such as the ontology, epistemology, and mechanisms in behavior analysis as a biological science. Key topics include contingency, biological variation, developmental dynamics, and computational modeling, highlighting the complexity of behavioral variation and interaction with evolution. The goal is to establish a deeper understanding of behavioral analysis in the broader context of natural science.

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THE NATURAL SELECTION: BEHAVIOR ANALYSIS AMONG THE NATURAL SCIENCES

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  1. THE NATURAL SELECTION:BEHAVIOR ANALYSIS AMONG THE NATURAL SCIENCES M. Jackson Marr School of Psychology Georgia Tech Atlanta, GA 30332-0170 USA mm27@prism.gatech.edu

  2. In understanding behavior analysis as a natural science, we need to examine ties, conceptual and otherwise, between behavior analysis and other natural sciences—this is my overall theme.

  3. WHAT IS A NATURAL SCIENCE?

  4. SOME ISSUES TO CONSIDER • *1. What is a Natural Science? • *2. Ontology, Epistemology, and Patterns of Explanation. • *3. Behavior Analysis as a Biological Science • 4. Contingency: The Fundamental Explanatory Concept • 5. The Problem of Behavioral Units • 6. The Role of Symmetry • 7. Dynamical Systems • 8. Mathematical Models • 9. Problems of Reductionism • 10. Scientific and Mathematical Verbal Behavior • 11. Creativity in the Sciences and Mathematics • *I plan to discuss these—a bit—today.

  5. ONTOLOGY, EPISTEMOLOGY, AND PATTERNS OF EXPLANATION: Realism vs. Pragmatism, and Contextualism vs. Mechanism

  6. ELEMENTS OF CONTEXTUALISM • 1. The ongoing act in context as the unit of analysis. • 2. Focus on the whole event. • 3. Sensitivity to the role of context in understanding the event. • 4. “Successful working” as a pragmatic truth criterion.

  7. WHAT KINDS OF MECHANISMS: BEHAVIOR ANALYSIS AS A BIOLOGICAL SCIENCE

  8. GENERAL FUND OF BIOLOGICAL EXPLANATION • 1. Molecular (biochemical, biophysical) • 2. Cellular functions • 3. Tissue/organ functions • 4. Morphogenic/developmental • 5. Behavioral/environmental • 6. Species adaptation/evolution

  9. SOME SOURCES OF BIOLOGICAL VARIATION • MEIOSIS PROCESSES (e.g., recombination, linkage distance) • SEGREGATION (e.g., independent assortment, dominance, incomplete dominance, epistasis, pleiotropy) • NON-MENDELIAN PROCESSES (e.g., cytoplasmic inheritance, dependent assortment) • CHROMOSOMAL VARIATIONS (e.g., polyploidy, deletions, duplications, inversions, translocations) • MUTATIONS (e.g., transitions, transversions, tautometric, regulatory effects) • ALTERNATIVE SPLICING • QUANTITATIVE (e.g., polygenic expression, genetic drift, gene-environment interaction)

  10. MORE SOURCES OF BIOLOGICAL VARIATION • DEVELOPMENTAL DYNAMICS (e.g., “evo-devo”) • ALLOPATRIC, PARAPATRIC, AND SYMPATRIC ISOLATION • IN UTERO HISTORY • STOCHASTIC / CHAOTIC PHYSIOLOGICAL PROCESSES

  11. SOME SOURCES OF BEHAVIORAL VARIATION • 1. REFLEX PATTERNS AND THRESHOLDS • 2. SPECIES-SPECIFIC SENSORY / MOTOR PROGRAMS • 3. CONTINGENCIES AND DIFFERENTIAL SENSITIVITY TO THEM • 4. SHAPING: VARIATION AS A RESPONSE CLASS • 5. SELF-ORGANIZATION PROCESSES: “EMERGENCE” • 6. SOCIAL/CULTURAL DYNAMICS • 7. A HOST OF INDIVIDUAL DIFFERENCES RELATED TO ALL THE ABOVE AND MORE

  12. COMPUTATIONAL MODELING • NEURAL NETWORKS • CELLULAR AUTOMATA • DYNAMIC PROGRAMING • DYNAMIC STATE VARIABLE MODELS • GENETIC ALGORITHMS • SIMULATED ANNEALING • MONTE CARLO METHODS • STATISTICAL MECHANICS OF LEARNING

  13. PHYSICS Deterministic Reductive Mechanistic +Immediate Causation SIMPLICITY BIOLOGY Stochastic Emergent Selectionistic +Historical Causation COMPLEXITY PHYSICS VS. BIOLOGY/BEHAVIOR Mayr’s Distinctions

  14. CODA: So What?

  15. CONSEQUENCE-DRIVEN SYSTEMS Stevo Bozinovski (1995) REINFORCEMENT LEARNING R.S. Sutton & A.G. Barto (1998)

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