Podcast: Perceptual Invariants: The Hidden Key to Operator Expertise

Experienced operators don't just know what to do — they know what to watch, regardless of how conditions change. That ability hinges on perceptual invariants: the critical relationships and variables that remain meaningful even as everything else shifts. Human factors engineer Dave Strobhar explains how identifying and reinforcing these invariants is the key to effective operator training. Rather than relying on years of trial and error, structured training programs — including targeted simulator use — can accelerate expertise dramatically. The goal is moving operators from simple stimulus-response behavior to true skill-based thinking that transfers across novel situations, closing the experience gap faster than ever before.

Transcript

Traci: Welcome to the Operator Training Edition of Chemical Processing's Distilled Podcast. This podcast and its transcript can be found at chemicalprocessing.com, where you can also download it on your favorite player. I'm Traci Purdum, editor-in-chief of CP, and joining me once again is Dave Strobhar, founder and principal human factors engineer for Bevel Engineering. Dave is also the founder of the Center for Operator Performance and an operator training columnist for Chemical Processing. Hey, Dave.

Dave: Hi, Traci.

Traci: We usually record on Fridays, but today is Monday. I had something going on last week, so I'm curious — did you do anything interesting over the weekend?

Dave: I've been doing a lot of gardening. In New Mexico, that's a bit of a challenge because it's a very arid area, and the major problem people have is overwatering. Strange little factoid, but there you go.

Traci: Interesting. So you have native plants that thrive in that dry climate?

Dave: Yes, and you'll kill them if you give them too much water. You have to be very careful because they're made for dry, well-draining soil. Their roots do not like to sit in water.

Traci: Adaptability at its best. Over the past several episodes, we've been doing a deep dive into operator training, and today we're talking about perceptual invariants. Can you set the stage — what are perceptual invariants and why do they matter specifically for operators?

What are Perceptual Invariants

Dave: Sure. The concept of perceptual invariance emerged when researchers recognized that the human mind can identify features or elements even when the form has changed slightly. If you rotate a glass, you still know it's a glass, even though the image on your retina has changed dramatically. When your dog is soaking wet, you still recognize it. Somehow our brains create perceptual invariance so that even when things change, we still understand certain fundamentals to be true.

That concept has expanded into the realm of complex tasks. The parameters around a task may shift, and the situation may change, but some aspects remain constant. It's those perceptual invariants that enable us to perform at a higher level.

There's a well-known hierarchy from Jens Rasmussen on levels of behavior. At the most basic level, you have what he calls skill- or rule-based behavior — essentially stimulus response. A stimulus occurs and you make a response. That works fine as long as the real-world situation looks exactly like it did in training. But the next level is true skill-based behavior, where you've internalized perceptual invariants from lower-level tasks and can apply them across a broader range of situations. Even when confronted with something that doesn't look exactly like what you trained on, you still know what actions to take. Identifying and reinforcing those perceptual invariants is one of the keys to an effective training program. Operators need to be able to handle circumstances that don't look exactly like they did in training.

Traci: So how do you structure training to help operators recognize perceptual invariants across different process conditions?

Dave: The first step is identifying what those invariants actually are. From there, training can take a couple of different approaches. One is direct emphasis — making clear what operators should be focusing on. In complex simulator environments, there's a lot going on, so part of the job is cutting through the noise and saying, "These are the critical relationships you need to watch." For example, if hydrogen must exceed feed by a ratio of 2-to-1, you focus relentlessly on that ratio. Everything else may be changing, but if that ratio drops, bad things happen. That's a perceptual invariant.

We've also talked in earlier episodes about bringing the real world into training rather than drilling on a single, isolated task. You can present scenarios similar to what was trained on and ask, "We trained at full rates — how do you think things will change at half rates? What would you look at?" You're prompting operators to think about how the invariants hold even as conditions shift, and reinforcing that these are what they should always be focused on.

Examples of Perceptual Invariants

Traci: Are there examples of perceptual invariants that experienced operators just know but newer operators consistently miss? I'm thinking of something like caramelizing onions — I understand when they hit that point, but my sister-in-law doesn't. How does that translate to the chemical industry?

Dave: That's a great analogy. You've developed that skill through repeated experience — you know when the onions are done because you've built that up through trial and error. In the chemical industry, we try to minimize the error portion of that equation.

Most of the critical skills really do come down to perceptual invariants, and new operators are often expected to just pick them up over time. We once were building displays that had stripped out unimportant information and showed less detail. There was concern that newer operators wouldn't be able to use them. But I spoke with an operator in training who said, "No, I like these much better." When asked why, he said, "Because I'm new, I don't know what's important. You stripped out the unimportant, so now I know what to focus on."

That's exactly what you want to do with newer operators — strip away the noise and impress upon them what matters. Right now, in many facilities, it's still trial and error. You put operators through and hope they eventually pick it up. But if you go to your experienced operators, you can find out what those perceptual invariants actually are. I spoke with an operator who had 30 years on a particular unit. He said, "Give me the temperature in the regenerator and I can tell you exactly how this unit is running." He had developed a single invariant that, regardless of what else was happening, let him infer everything he needed to know.

That's what you want to capture and put into the training program — teach newer operators not just what to look at, but why it's always relevant. That's how modern training programs can close the experience gap so much faster. You don't have to learn it through trial and error or wait decades to encounter the right conditions. You can tell them, show them and let them practice in scenarios that highlight exactly what they need to focus on.

Digital Twins and Simulation Training

Traci: Where do these training tools fall short? How well do digital twins and simulation-based training actually replicate perceptual cues — the smells, sounds, visual patterns?

Dave: They do a pretty good job technically, but unfortunately many simulators are divorced from a good training program. It becomes a "build it and they will use it" mentality. Even when a simulator is nearly identical to the actual process, if you don't highlight what operators should be gaining from the experience, you're just hoping they pick up on the right things after enough repetition. That's not a training strategy — that's hope.

A lot of current simulator use simply recreates the control room environment away from the control room. That's useful — it increases how often operators encounter unusual events — but if you're just hoping operators independently identify which variables to focus on, your success rate will be low and progress will be slow.

What you really want is to define clearly what operators need to understand, then drive that home in the simulator. The goal is to move them from rule-based behavior — "when this light comes on, I do this" — to skill-based behavior, where they can apply what they know in new and novel settings.

Assessing Training

Traci: That leads to my next question: What does assessment look like? How do you know when the light bulb has gone off?

Dave: One of the key markers of expertise is not just knowing what to do, but knowing what the expected response should be. Gary Klein has done considerable work in this area. What distinguishes an expert is that after taking an action, they can recognize when something doesn't look right — when the process isn't responding the way it should.

You can test for that with decision-making exercises. You present a scenario, ask what the operator would do, then introduce variations that are atypical or abnormal and see whether they catch them. Because that's what you need: not just "I took this action because I thought this was happening," but "wait — this isn't going the way it should."

The operators at Three Mile Island believed they were in an over-cooling event when they were actually experiencing a loss of cooling. They were taking actions appropriate for over-cooling, and the pressure was dropping as expected — but the temperature was rising. At that point, they should have recognized that something wasn't right and reassessed. That failure to notice the anomaly was critical.

So the real assessment question is: Can they tell you not just what they would do, but what they expect to happen? And can they recognize when reality doesn't match that expectation? When an operator can say, "Some things look fine, but this isn't behaving normally — something's wrong," you know they've got it.

Can You Train Experience?

Traci: Do you think perceptual skill only comes from years on the floor, or can formal training genuinely accelerate it?

Dave: Absolutely, formal training can accelerate it — and it's been proven in aviation. There's a critical relationship between two variables when a plane is on its glide slope, and instructors train pilots specifically on maintaining that relationship. Rather than waiting for numerous real landings to make those two variables intuitive, they drill on them directly in a part-task trainer. Pilots pick it up quickly and become proficient faster.

The same principle applies in process industries. You can highlight the perceptual variables you want operators to focus on, reinforce them in a part-task simulator, and they carry those skills into the real-world environment — saving years of on-the-job learning. With increased plant reliability and fewer upsets, operators may go decades without encountering certain events in the plant. By taking them offline and drilling them on what to look for and how to respond, what might have taken five years of experience can be achieved in under a month.

Traci: Last question: How do you best build transferable skills — the ability to take what's learned in one setting and apply it in another?

Dave: That's really the foundation of everything we've been discussing. Perceptual invariants are what make skill transfer possible. You take a skill from one setting and apply it in a different one, and as humans, we do this remarkably well. You're probably not driving the car you took your driver's test in, but you're managing fine, because certain fundamentals don't change. Going into a corner, you need to decelerate — and how much depends on the angle of that corner. That's a perceptual invariant you developed in driving and apply across every vehicle you get into.

That's the whole notion of perceptual invariance. We don't need things to look exactly the way we learned them, as long as we've extracted what stays constant as we move from one situation to another. Identify those constants, build training around them and the skill transfers.

Traci: Dave, you always help us accelerate learning and apply skills across settings. I appreciate the time and thought you put into this one — it was a bit more cerebral than usual, and you delivered. Folks, if you want to stay on top of operator training and performance, subscribe to this free podcast on your favorite platform for best practices and keen insight. You can also visit chemicalprocessing.com for more tools and resources. On behalf of Dave, I'm Traci, and this is Chemical Processing's Distilled Podcast, Operator Training Edition. Thanks for listening — and thanks again, Dave.

Dave: Thanks, Traci.