Short-Circuiting the “Ladder of Inference”
A LinkedIn discussion came across my laptop the other day, asking me to look at a video and offer my explanation to a condition described as “change blindness“. Being a practitioner of Change Management, I felt compelled to opine, which I did.
The set-up is this: two very different-looking students “staff” an experiment to which other students are directed; the respondents know only that they are being asked to participate in “an experiment” with no knowledge of what it entails; Student A greets them at the counter and secures identification data; Student A then ducks down behind the desk to retrieve the participant’s packet; Student B, hiding behind the desk, then raises up and presents the participant with their packet. Pretty simple. Think you would notice the “change”?
Seventy-five percent of the participants did not notice that the “study leader” was not the person they had been interacting with only a few seconds prior. Seventy-five percent!! Are we that inattentive? What might explain this phenomenon?
I was doing some work the other day that required me to refresh my familiarity with Chris Argyris’s “Ladder of Inference”. During that Internet search, I came across an interesting hypothesis that might serve to explain how three-quarters of us would not notice that the person serving us was no longer the person serving us. This “short-circuit” was posited by Gene Bellinger in a Wiki article.
Argyris’s original concept indicated a ladder that started with real data and experience, leading to the next rung consisting of selected data and experience. From these selected data, Argyris maintained, we affix meanings which lead us to formulate assumptions. These assumptions will then inform our conclusions and decisions, which will slowly build into our most closely-held beliefs, which will drive our actions. What Mr. Bellinger did, as a result of “systems thinking”, was to close the loop, indicating that these actions would then result in new real data and experience, from which we would select data to consider, repeating the cycle.
Bellinger’s short-circuit, however, compared to closing the loop, indicates that once we have entered this cycle, we truly no longer even consider the full inventory of real data and experience, choosing instead to go directly to the filtering step, wherein we select the data we will consider, not even aware of the real data that we are overlooking. To the point of this video/experiment, we have become so accustomed to the experience that the person originally serving us would not be changed, we often times overlook very obvious “real data” in front of us, as we select which data we will consider.
To the point of this posting, there are two factors to consider:
- If 75% of the participants failed to notice substantial changes right before their eyes, how can employees be expected to detect [and respond to] incremental changes taking place over extended periods? Practices that once demonstrated world leadership slowly become less effective and more counterproductive, but due to the pace of change, these deteriorations may not be noticed [or considered].
- The value of an outside observer/change agent that does not suffer the same experiential bias that the participants have learned over time is incalculable. The outside observer detects and points out data that, in reality, the involved employee literally does not even “see”.
Understanding this potential shortcoming of the “learning loop” can help to explain why today’s change management experience is that fully 70% of all change initiatives will not accomplish their intended goals. For some reason, a 30% success rate seems to be acceptable. If this rankles you, here is a recommendation to improve that performance: use an outside consultant for your change initiatives. They will literally see things that you will not.