Cognitive Models on the Efficiency Frontier

In my last post, The Efficiency Frontier, I talked about how products span an efficiency frontier that is always moving. In that movement, an application moves away from the observable into an imagined future. This requires customer followship, as well as customer leadership.

When I talked about the figure in the last post, I mentioned the cognitive load, but I drew the delivered functionality as being linear. Our cognitive limits force cognitive models into a log rather than linearly scaled measure. I’ve redrawn the figure to highlight this log scale. Drawing this figure presented a problem, because each of us has a different cognitive limit.

Our cognitive limit shows up in the length of a the lists that we routinely handle. The rule is 7+/-2 list items. Powerpoint experts might tell you 3 list items. We might be smart enough to handle a list that’s 12 items long, but we are not our customers. Shorter lists cause no harm where longer lists might cause chunking and the need to store short-term memory into long-term memory. The following figure illustrates the effort differences when people with different cognitive limits: 3 and 7, are confronted by a cognitive model encompassing 9 concepts.

Different users experience your application on different log scales.

Log scales express bases or positional notation by doubling in length for each additional position. The gray numbers express the base arithmetic. For a list of 3 items, use base 4. For a list of 7 items, use base 8.

The scales shift to yellow and later to red to indicate the need to move the content of short-term memory into long-term memory. The cognitive models are exactly the same, except their horizontal locations move with the differences in the log scales.

In the next figure, I’ve gone back to the Triangle Model and Efficiency Frontier diagram from the first post and updated it with a log scale for a user with a cognitive limit of 3 items.

The efficiency frontier of an application across a log scale for a user with a cognitive limit of 3.

When we partition functionality into minimal marketable functionality packages, we usually do so in terms of iterations, releases, and cash flows on the vendor side, and value delivery and value proofs for project continuation. Cognitive limits gives us a user-centered way to partition functionality. Packaging to cognitive limits ensures that a learner installs functionality that is learned quickly, so each package achieves its ROI quicker. Such packaging also sequences the delivery of minimal marketable functionality in a user, rather than developer, facing manner.

An application’s features are used in a zero-sum way. If I am using feature A, I’m not using feature B. A minimal marketable function is a network. Each such network exhibits a power-law distribution, or long tail. The features packaged in that minimal marketable function serve as the basis of the user conceptual model.

Developers express a conceptual model captured in the requirements (carried) in terms of design artifacts like UML and code artifacts like frameworks and APIs (carrier) before expressing a conceptual model in the GUI. UML is a long way from the user conceptual model. The conceptual model in the GUI is what we count when applying cognitive limits, but users bring their internal conceptual model to the conceptual model expressed by the GUI. These conceptual models may conflict. Those conflicts may insert additional cognitive efforts into the experience.

The application’s feature networks exhibit a frequency of use. That frequency of use is expressed in the power-law distribution. The frequency of use of a feature, expresses the frequency at which a concept is dealt with, which in turn indicates how quickly it is learned. In these figures the concept at the far right would be infrequently used, and it would be the least know or understood concept in the conceptual model. Adding features always adds to the conceptual model and the learning required to achieve full value.

The following figure shows the conceptual model organized along a power-law distribution (orange) for a user with a cognitive limit of 3.

A cognitive model for a user with a cognitive limit of 3 laid out along a power-law distribution

The challenge involved in dealing with cognitive limits include discovering a user’s particular cognitive limit, discovering the terrain of a population’s cognitive limits in aggregate, and using cognitive limits as an architectural element or aspect of the code itself via Aspect-Oriented Programming (AOP).

Meeting this challenge will enable us to deliver applications that meet our economic buyer’s need for a quick realization of ROI.



One Response to “Cognitive Models on the Efficiency Frontier”

  1. More on Innovation Visualization « Product Strategist Says:

    […] More on Innovation Visualization By davidwlocke Revisiting the exponential-polar representation, discussed in Innovation Visualization, I’ve expanded the representation, and found surprising extension to the long tail/power law version, discussed in Cognitive Models on the Efficiency Frontier. […]

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