Everyone agrees the best way to get good at something is to do that thing. Dr. Pete Blair asks why so much police training consists of things that are not the thing.
J. Pete Blair, PhD, is Executive Director of the Advanced Law Enforcement Rapid Response Training (ALERRT) Center and Professor of Criminal Justice at Texas State University. ALERRT trains police officers across the country to respond to active attacks.
He starts with the firearms qualification. An officer shoots a known course of fire from fixed distances, on a timer beep, at a target that never moves. A real encounter has a person who moves, misleads and decides. Pete lays out the four parts of the constraints-led approach (CLA): intention, constrain to afford, representative design, and repetition without repetition. He explains why good training looks messy and why his instructors aim for about a 70 percent success rate.
Boyd’s full sketch
Pete describes how policing learned a simplified OODA loop to explain reaction time. I put Boyd’s full sketch on screen. Pete said its feedback and implicit guidance sit much closer to the perception-action loop of ecological dynamics. Ecological dynamics studies how a person and the environment shape each other in action. Then I laid the CLA constraints triangle (individual, task and environment) over the same sketch.
Start with the role
The second half covers John Vervaeke’s 4P model, the four kinds of knowing: propositional, procedural, perspectival and participatory. Traditional training starts with facts in a classroom and adds context last. Pete argues for the reverse. He starts from the role the learner will occupy, and his example is how you learned to ride a bike.
He closes on why experts cannot fully explain their own skill. He also shows how officers can turn daily work into practice through debriefing, since most get only 20 to 40 training hours a year.
What does your training ask people to do that the job never will?
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Written by Ponch Rivera with AI assistance in production. The full conversation is in the player below.