Association for Behavior Analysis International

The Association for Behavior Analysis International® (ABAI) is a nonprofit membership organization with the mission to contribute to the well-being of society by developing, enhancing, and supporting the growth and vitality of the science of behavior analysis through research, education, and practice.


43rd Annual Convention; Denver, CO; 2017

Event Details

Previous Page


Invited Paper Session #212
CE Offered: PSY/BACB

How Much of Apparent Complex Cognition Can a Purely Behavioral Account Explain?

Sunday, May 28, 2017
10:00 AM–10:50 AM
Hyatt Regency, Centennial Ballroom D
Area: EAB; Domain: Applied Research
CE Instructor: Douglas Elliffe, Ph.D.
Chair: Elizabeth Kyonka (University of New England)
DOUGLAS ELLIFFE (The University of Auckland), Alex Taylor (The University of Auckland), Brenna Knaebe (The University of Auckland), Russell Gray (The University of Auckland; Institute for the Science of Human History)
Douglas Elliffe has been at the University of Auckland, New Zealand, as student and staff, since 1979. He recently finished a term as Head of Psychology, and is now Deputy Dean of the Faculty of Science. He has served as Associate Editor of the Journal of the Experimental Analysis of Behavior and reviewed for a wide variety of other journals, both behavioral and non-behavioral. He has been a member of the Scientific Advisory Panel for the Geneva International Centre for Humanitarian Demining and on the Ecology, Evolution and Behaviour panel for the Royal Society of New Zealand's Marsden Fund, NZ's principal funding body for basic science. His research lab, the Experimental Analysis of Behaviour Research Group, won the 2009 Society for the Advancement of Behavior Analysis international award for Enduring Programmatic Contributions in Behavior Analysis. Doug has supervised or cosupervised over 60 postgraduate research students, often in collaboration with Michael Davison. Doug's main lines of research have been firmly in the tradition of the experimental analysis of behavior, both on quantitative modelling and experimental analyses of choice, and more recently on a reconceptualization of the way in which reinforcement controls behavior. A second line of research, and the topic of this lecture, is offering behavioral/behaviorist accounts of apparent complex cognition in animals, particularly New Caledonian crows.

Lloyd Morgan’s Canon advises that animal behavior should not be interpreted in terms of higher psychological processes if it can be fairly interpreted in terms of processes which stand lower in the scale of psychological evolution and development. Leaving aside how we might define ‘higher’ and ‘lower’, this is encouraging to the behaviorist, except that we might say that interpreting human behavior should be subject to the same strictures. But, whether an explanation appealing to the simplest possible processes works is an empirical matter. In this talk I’ll describe three experiments with New Caledonian crows, two published and one not at the time of writing, on putative behavioral innovation in metatool use, putative causal understanding in the Aesop’s Fable task, and putative flexibility of tool manufacture in response to environmental demands. I’ll explore the role of the behaviorist in contributing interpretations and devising control conditions when collaborating with behavioral ecologists, consider how the word fairly in Morgan’s Canon should be interpreted, and discuss how we should be guided by the principle of parsimony in understanding behavior.

Target Audience:

Graduate Students and Researchers in Behavior Analysis

Learning Objectives: At the conclusion of this presentation, participants will be able to: (1) Understand both the value and limitations of Lloyd Morgan’s Canon as a guide to the interpretation of animal behaviour; (2) Understand how we might test explanations of apparently complex behaviour that are based on simple learning principles; (3) Have a greater appreciation of the similarities between animal and human behaviour.



Back to Top
Modifed by Eddie Soh