Writing, building, and consulting on complex systems across behavior
analysis, artificial intelligence, experimental and research methods,
and executive leadership.
AI progress keeps changing what work is worth, which moves still matter, and who gets to keep playing.
Every week, some piece of AI competence gets demoted from skill to default. A prompt trick becomes a button. A careful context routine matters less when the window expands. A five-step workflow becomes native agent behavior. The work was real; it saved time, sharpened judgment, and made projects possible. It also aged in public.
That churn is only the personal version. Everyone else around the table is also getting new powers: competitors, employers, customers, frontier labs, platforms, and states. Advantage starts to mean position: what you can still route, what you can verify, what you can power, and which decisions others still trust you to make after the next release.
The arms-race frame captures the speed. The stranger pressure is that people are building positions around scoring rules that have not yet been revealed.
The board is already in motion; the scoring rule is still changing.
Heavy Eurogames make that pressure easier to see. Their drama is the slow construction of a position: a cube becomes a resource; a resource buys a card; a card changes the value of a city, a route, a worker, or a province. Early moves matter because they commit you to a theory of the later scoring rule.
AI work has acquired that shape. Immediate payoffs matter, but so do the moves that buy future turns. Some work feels productive until the rulebook changes and exposes what it was really worth. Speed is one score. The harder test is whether the position still leaves a player on the board after the multiplier is drawn.
AI in practice · ContinuedThree from the desk · Behavior, strategy, persistence
Modern AI inherited reinforcement learning from a line running through
Thorndike, Skinner, and a century of studying behavior as a function
of environment. Now AI is rebuilding measurement in clinical settings.
A chatbot greets, apologizes, remembers, and waits as if someone is
there. The practical question is not whether users believe the machine
is conscious. It is whether users treat the machine like a human. The
social surface invites one repertoire. The machine underneath is more
sensitive to another.
AI decision debates usually ask who clicked approve. The direction
was shaped earlier — when a system turned a messy file into a
frame, a ranking, a draft reason.
From the FieldTalks · Project · AI in applied work
A web-app refactor of Formative Grapher is in early development.
The new version drops the Excel dependency, carries the
time-series graphing primitives to the browser, and keeps the
original’s bias toward accuracy and speed.
Clinical behavior analysts are expected not only to graph data
continuously but also to follow particular conventions that are
laborious to implement with commonly available software. The 2015
original, developed with Dr. Benjamin Witts for my master’s thesis, addressed that gap with a free APA-Style Excel template. While it still finds users a decade later, it is no longer maintained.
Talk
Inevitable: Opportunities and ethical challenges of artificial intelligence in ABA
D. M. Cole & S. Carstens · 2024
Cagnes-sur-Mer — As ChatGPT
was still percolating into public discourse, the talk surveyed
where AI tools open up everyday behavior-analytic work, and where
the new ethical hazards land: supervision, documentation, and
clinical judgment among them. Originally given at the Best of ABA
Conference, with a follow-up panel scheduled at the European
Association for Behaviour Analysis in Brno.
From the LabScience, decision-making, neuroscience
Symposium
Adding genetically modified mice to the armamentarium of behavior analysis
D. M. Cole · 2018
San Diego — Rats and pigeons
still dominate as animal models in the experimental analysis of
behavior. In this symposium on alternative model organisms —
from alcoholic bees to robotic zebrafish — I discussed
tradeoffs of mice, which learn more slowly than rats but open wide
the genetic toolkit.
Motor preparation for compensatory reach-to-grasp responses
D. A. E. Bolton, D. M. Cole, B. Butler, et al. · 2019
A handle on a wall is more than background scenery. We unexpectedly
released a cable holding people in a forward lean. Using transcranial
magnetic stimulation, we demonstrated that merely seeing the handle
was sufficient to prepare their motor system, such that participants
later reached for the handle with greater specificity than pure
reflex explains and with greater speed than pure volition explains.
Falls are the leading cause of accidental death among older adults.
The usual suspect is frailty, but greater culpability lies with the
nervous system. Specifically and paradoxically, the culprit may be
less the failure to rapidly fire a recovery action and more the
failure to inhibit competing, incompatible actions in time.
Assessing susceptibility of a temporal discounting task to faking
D. M. Cole, J. M. Rung, & G. J. Madden · 2019
Delay discounting describes how people choose between smaller sooner
rewards and larger delayed ones. It can also be faked. Given a
motivational prompt and no other insight into common laboratory
assessments, participants systematically manipulated their results.
Translational researchers and test designers should take note.
Neuronal response variability as a product of divisive normalization
K. L. Ruddy, D. M. Cole, C. Simon, & M. Bächinger · 2020
Some brain waves are illusory, artifacts of averaging punctuated
bursts of brain activity across hundreds of trials. Buried in the
smoothly undulating waves is trial-by-trial variability that can
predict behavior with trial-by-trial resolution.
D. Steinhauff, J. H. Ellwanger, … D. M. Cole, et al. · 2019
Managing people is an unavoidable part of laboratory work. And it
deserves the same rigor: Identify manipulatable variables,
systematically change them, and keep the PI informed.
