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Is there such a thing as an 'objective truth' that is independent of an individual's experience? For many, the point of science is to discover such a truth that is invariant across observers.  On the other hand, one might ask whether the unique subjective experiences of individuals are valid?

Consider the question of whether a frozen pond will support an individual. We can 'objectively' measure the thickness of the ice. We can do experiments to objectively determine what weight the ice will support.  But we can't say 'objectively' whether the ice will afford walking, because the experience of 'support' depends both on the thickness of the ice and the weight of an individual. The same frozen pond can mean different things to different individuals. So, the affordance of support or the functional meaning of the ice cannot be specified independently from an observer's weight.

Thus, individuals of different weights might experience the frozen pond differently. The lighter individual might validly see the pond as a safe field of travel, and the heavier individual might validly see the pond as an unsafe field of travel. The subjective experiences are different, but both might be valid.

On the other hand, the experience for both individuals might be ambiguous. The lighter individual might experience the pond as potentially dangerous, and decide to walk around the pond, rather than take a more direct path across the pond. Or the heavier individual might perceive the pond as a safe path, but then discover his error when the ice cracks and he falls through.

If the pair walks together, the lighter individual might convince the heavier individual that the pond is safe (which it is for her) and they might then both walk together across the ice, and both fall into the lake when the ice fails to support their combined weight. 

Thus, different experiences might be valid - though different. And common experiences/interpretations might be valid for one individual, but not for another or not for the group - though the interpretations are identical.

Note in an attempt to achieve objectivity, science establishes measures/rulers like pounds and inches that are observer independent. But these measures are not invariantly related to peoples' experiences.  Do people experience the world relative to these observer independent metrics/standards? Or do people experience the world pragmatically? Does a surface support locomotion? Is a gap pass-through-able? Is an object reachable or graspable?  We don't directly experience the world in pounds or inches. Rather we experience the world directly in functional terms - we directly experience what the world affords. The measures of the physicist are abstractions - not direct experiences!

The observer independent metrics serve the physical sciences well, but are they appropriate for the social sciences?  Does it make sense to gauge whether people's experiences or perceptions are 'right' or 'wrong' using the metrics of the physical sciences? This is the question that James Gibson asked? And he concluded that perception is not based in "the world of physics, but the world at the level of ecology." Similarly, Jacob Johann von Uexkull introduced the construct of 'umwelt' to illustrate how functional ecologies can be describe in relation to the unique capabilities of different animals.

The implication of this is that the truth, validity or meaning of our experiences is not observer independent. Yet, they also are not arbitrary. The fact of whether the frozen pond will support us is a pragmatic truth, and the ultimate proof is in the doing. The ultimate tests of our perceptions (i.e., the meanings that are most significant) are the potential consequences of our actions!

 

I recently listened to a talk by Jamer Hunt on "The Anxious Space between Design and Ethnography" In his talk, he creates a two-dimensional space (four quadrants) to reflect the famous quote from Donald Rumsfeld on "Unknown-Unknowns." He uses this space to illustrate the overlapping territory explored by anthropology and design.

The talk stimulated me to think more generally about relations between basic laboratory research, field research, and design. I was trained in human experimental psychology in a way that emphasized experimental design and controlled laboratory research. This was motivated by a clear bias that doing controlled experiments was the way to do "real" science. However, I was also involved in the development of the aviation psychology laboratory at Ohio State University and was exposed to applied Engineering Psychology in the tradition of Paul Fitts. And much of my career has been focused toward applying cognitive psychology in applied domains such as aviation, driving, and healthcare.

In light of these experiences, I have come to see most laboratory research as situated in the domain of Known-Knowns. Much of the published experimental literature are demonstrations of what we already know. For example, consider all the replications of Fitts' Law or all the variations on visual and memory search or more recently the replications of 'blind sight' experiments. Laboratory research does sometime open up insights into and fill gaps in the Known-Unknown territory, but surprise is extremely rare!

In my experience field research pushes us further into the region of Unknown-Unknowns - increasing the potential for surprise. Increasing the potential to learn something knew.

And design pushes us still further into the region of Unknown-Unknowns, increasing the potential for surprise - increasing the potential for learning and discovery. Also note that as we move deeper into the region of Unknown-Unknown, we also move deeper into the region of Unknown-Knowns. This is the region for reflection, meta-analysis, and metaphysics.  As we move into the Unknown it becomes more important to reconsider and reflect on foundational assumptions and to build theory to connect the dots and integrate across empirical experiments.

I have come to the conclusion that a mature science depends on a healthy coupling between laboratory research, field research, and design.  I believe that the ultimate test of any hypothesis or theory is its ability to motivate solutions to practical problems. I believe that paradigm shifts emerge from the coupling of research and design.

Certainly, experimental research serves a valuable function. However, if you are serious about learning and discovery, then it is important to explore beyond the laboratory, to get out of the territory of the known - to test your hypotheses and theories in practice, and to increase the potential for surprise.

Cognitive Systems Engineering (CSE) emerged from Human Factors as researchers began to realize that in order to fully understand human-computer interaction it was necessary to understand the 'work to be done' on the other side of the computer. They began to realize that for an interface to be effective, it had to map into both a 'mind' and onto a 'problem domain.'  They began to realize that a representation only leads to productive thinking if it makes the 'deep structure' of the work domain salient.  Thus, the design of the representation had to be motivated by a deep understanding of the domain (as well as a deep understanding of the mind).

User-eXperience Design (UXD) emerged from Product Design as designers began to realize that they were not simply creating 'objects.' They were creating experiences. They began to realize that products were embedded in a larger context, and that the ultimate measure of the quality of their design was the impact on this larger context - on the user experience. They began to realize that the quality of their designs did not simply lie in the object, but rather in the impact that the object had on the larger experience that it engendered. Designers began to realize that they were not simply shaping objects, but they were shaping experiences. Thus, the design of the object had to be motivated by a deep understanding of the context of use (as well as a deep understanding of the materials or technologies).

The common ground is the user-experience.  CSE and UXD are both about designing experiences. They both require that designers deal with minds, objects, and contexts or ecologies. The motivating contexts have been different, with CSE emerging largely from experiences in safety critical systems (e.g., aviation, nuclear power); and UXD emerging largely from experiences with consumer products (e.g., tooth brushes, doors). However, the common realization is that 'context matters.' The common realization is that the constraints of the 'mind' and the constraints of the 'object' can only be fully understood in relation to a 'context of use.'  The common realization is that 'functions matter.' And that 'functions' are relations between agents, tools, and ecologies.

The CSE and UXD communities have both come to realize that the qualities that matter are not in either the mind or the object, but rather in the experience. They have discovered that the proof of the pudding is in the eating.

Cognitive Systems Engineering (CSE) emerged from Human Factors as researchers began to realize that in order to fully understand human-computer interaction it was necessary to understand the 'work to be done' on the other side of the computer. They began to realize that for an interface to be effective, it had to map into both a 'mind' and onto a 'problem domain.'  They began to realize that a representation only leads to productive thinking if it makes the 'deep structure' of the work domain salient.  Thus, the design of the representation had to be motivated by a deep understanding of the domain (as well as a deep understanding of the mind).

User-eXperience Design (UXD) emerged from Product Design as designers began to realize that they were not simply creating 'objects.' They were creating experiences. They began to realize that products were embedded in a larger context, and that the ultimate measure of the quality of their design was the impact on this larger context - on the user experience. They began to realize that the quality of their designs did not simply lie in the object, but rather in the impact that the object had on the larger experience that it engendered. Designers began to realize that they were not simply shaping objects, but they were shaping experiences. Thus, the design of the object had to be motivated by a deep understanding of the context of use (as well as a deep understanding of the materials or technologies).

The common ground is the user-experience.  CSE and UXD are both about designing experiences. They both require that designers deal with minds, objects, and contexts or ecologies. The motivating contexts have been different, with CSE emerging largely from experiences in safety critical systems (e.g., aviation, nuclear power); and UXD emerging largely from experiences with consumer products (e.g., tooth brushes, doors). However, the common realization is that 'context matters.' The common realization is that the constraints of the 'mind' and the constraints of the 'object' can only be fully understood in relation to a 'context of use.'  The common realization is that 'functions matter.' And that 'functions' are relations between agents, tools, and ecologies.

The CSE and UXD communities have both come to realize that the qualities that matter are not in either the mind or the object, but rather in the experience. They have discovered that the proof of the pudding is in the eating.

Over the last 20 years or so, the vision of how to help organizations improve safety has been changing from a focus on 'stamping out errors' to a focus on 'managing the quality of work.'

This change reflects a similar evolution in how the Forestry service manages fire safety. There was a period when the focus was on 'stamping out forrest fires,' and the poster child for these efforts was Smokey the Bear (Only you can prevent forrest fires). However, the forestry service has learned that a side-effect of an approach that focusses exclusively on preventing fires is the build up of fuel on the forest floors. Because of this build up, when a fire inevitably occurs it can burn at levels that can be catastrophic for forest health. The forest will not naturally recover from the burn.

Smokey the Bear Effect

The forestry service now understands that low intensity fires can be integral to the long term health of a forrest. These low intensity fires help to prevent the build up of fuel and also can promote germination of seeds and new growth.

The alternative to 'stamping out fires' is to manage forrest health. This sometimes involves introducing controlled burns or letting low intensity fires burn themselves out.

The general implication of this is that safety programs should be guided by a vision of health or quality, rather than be simply a reaction to errors. With respect to improving safety, programs focused on health/quality will have greater impacts, than programs designed to 'stamp out errors.' Programs designed to stamp out errors, tend to also end up stamping out the information (e.g., feedback) that is essential for systems to learn from mistakes and to tune to complex, dynamic situations. Like low intensity fires, learning from mistakes and near misses actually contributes to the overall health of a high reliability organization.

This new perspective is beautifully illustrated in Sidney Dekker's new movie that can be viewed on YouTube:

Safety Differently

The CVDi display for evaluating heart health has been updated. The new version includes an option for SI units.  Also, some of the interaction dynamics have been updated. This is still a work in progress, so we welcome feedback and suggestions for how to improve and expand this interface.

https://mile-two.gitlab.io/CVDI/

 

 

A new edition of our What Matters? book is now available online.

In the new version an acknowledgment section, endorsements, indexes, and a back cover have been added. Also, a number of typos have been corrected.

The paperback edition is now available for purchase through What Matters on Lulu

A Triadic Semiotics

Inspired by the computer metaphor and the developing field of linguistics (e.g., Chomsky), the main stream of cognitive science was framed as a science where mind was considered to be a computational, symbol-processing device that was evaluated relative to the norms of logic and mathematics. However, there were a few, such as James Gibson, who followed a different path.

Gibson followed a path that was originally blazed by functional psychology (e.g., James, Dewey) and pragmatist philosophers (e.g., Peirce). Along this path, psychology was framed in the context of natural selection and the central question was to understand the capacity for humans to intelligently adapt to the demands of survival. Thus, the question was framed in terms of the pragmatic consequences of human thinking (e.g., beliefs) for successful adaptation to the demands of their ecology.

An important foundation for Gibson's ecological approach was Peirce's Triadic Semiotics. In contrast with Saussure's Dyadic approach - Peirce framed the problem of semiotics as a pragmatic problem - rather than as a symbol processing problem. Saussure was impressed by the arbitrariness of signs (e.g., C - A - T) and the ultimate interpretation of an observer (e.g., kind of house pet). In contrast, Peirce was curious about how our interpretation of a sign (e.g., pattern of optical flow) provides the basis for beliefs that support successful action in the world (e.g., braking in time to avoid a collision). In addition to considering the observer's interpretation of the sign, this required a consideration of the relation of the sign to the functional ecology (e.g., how well the pattern specifies relative motion of the observer to obstacles - the field of safe travel), and the ultimate pragmatic consequences of the belief or interpretation relative to adaptations to the ecology (e.g., how skillfully the person controls locomotion).

The figure below illustrates the two views of the semiotic system. In comparing these two systems it is important to keep in mind Peirce's admonition that the triadic system has emergent properties that can never be discover from analyses of any of the component dyads. For Peirce the triad was a fundamental primitive with respect to human experience. Thus, arguing that the whole of human experience is more than the sum of the component dyads.

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Mace's Contrast

William 'Bill' Mace provided a clever way to contrast the dyadic framework of conventional approaches to cognition with the triadic framework of ecological approaches to cognition.

The conventional (dyadic) approach frames the question in terms of computational constraints, asking:

                            How do we see the world the way we do?

The ecological (triadic) approach frames the question in terms of the pragmatic constraints, asking:

                            How do we see the world the way it is?

What Matters?

For a laboratory science of mind, either framing of the question might lead to interesting discoveries and eventually some of the discoveries may lead to valuable applications. However, for those with a practical bent, who are interested in a cognitive science that provides a foundation for designing quality human experiences, the second question provides a far more productive path. For example, if the goal is to increase safety and efficiency and to support problem solving in complex domains such as healthcare or transportation, then the ecological framing of the question will be preferred! You can't design either training programs or interfaces to improve piloting without some understanding of the dynamics of flight.

If the goal is to discover what matters in terms of skillful adaptations to the demands of complex ecologies, then a triadic semiotic frame is necessary. To understand skill, it is not enough to know what people think (i.e., awareness), it is also necessary to know how that thinking 'fits' relative to success in the ecological context (i.e., the functional demands of situations).

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Perspicacity:

Keenness of mental perception and understanding; discernment; penetration.

Knowing versus Seeing

In studying human performance, I have been most curious about expertise or skill; and my original intuitions came from my own experiences in sports. My initial motivation was to discover the 'magical' attribute that separated me from the really excellent athletes. At the start I tended to frame the questions as "What do they know that I don't know?"  But as I began to explore deeper, I quickly reframed the question to "What do they see that I don't see?" Or more generally, "what do they sense; or what are they attuned to that I am not sensitive to?"  This change of perspective was strongly influenced by Eleanor Gibson's work on perceptual learning and de Groot's work on expertise in chess.

I don't think it is necessarily an either/or proposition with respect to knowing versus seeing. I expect that both knowing and seeing are involved, but there is an important difference between these two ways of framing the research question. Approaches focused on knowing tend to see expertise as a result of accrual of knowledge that can be 'added to' the information available through perception that allows better mental computations.  The general implication is that experts have a more extensive data base to tap into.

However, approaches based on seeing, tend to see expertise as reflecting something akin to a coordinate transformation in mathematics (for example a log transform). The benefits of coordinate transformations are that they can make certain patterns easier to pick-up.  A good example is work on visual skill involved in avoiding collisions, landing aircraft, or catching baseballs. This work illustrates that when you look at visual perception in terms of angular coordinates (angles and expansion rates), rather than Euclidean (x,y,z) coordinates then the computations needed to brake, land or catch a ball become relatively simple.

This is why I have chosen to title this blog Perspicacity. As a scientist, the focus of my work is to discover how the underlying coordinate systems or representations that experts use are different from those of non-experts. As a designer, the focus of my work is to create representations (i.e., interfaces) that help people to see phenomena in ways that are more similar to what the experts are seeing. The design goal is to create perspicacious systems.

The other reason that I like the term is that perspicacity suggests an intimacy between perception and cognition (between seeing and knowing) that I think has been lost in a reductionist cognitive science where perception and cognition are seen as independent or at least loosely coupled modules in an information processing system. I believe that a parsing that treats perception and cognition as different phenomena breaks the system in such a way that it will not be possible to put the pieces back together again to achieve a complete understanding of human experience.