In a recent article, I wrote about three types of cognition that are changing how we think about how we think (how meta!). All are interesting, but they also have implications for understanding for supporting us in doing things. I think it’s important to understand these cognitions, and their implications. First, I want to talk about situated cognition.
The psychological models of thinking really started with the behavioral models. The core argument was that we couldn’t look ‘inside the box’, and had to study inputs and outputs. Cognitive psychology was a rebellion from this perspective. The new frameworks started showing that we could posit quite a bit about what went on ‘in the box’. We got concepts like sensory, working, and long-term memory, and processes like attention, rehearsal, encoding, and retrieval. With most of our learning prescriptions. However, both were about the ‘the box’.
However, the observed behavior didn’t match the formal logical reasoning that underpinned the model. We needed new explanations. The computational model fell apart. And, despite rigorous attempts to create logical models that described human behavior, they were awkward at best. The shift came when Rumelhart & McClelland, in their PDP book, described what became known as neural networks. Associated with this was a new model of cognition.
What gets activated in the brain is not a reliably pure representation, and is strongly affected by the context. Thinking is ‘situated’ in the context it arises in. If our thinking is the emergent behavior of patterns across neurons, and those patterns are the result of both internal and external stimuli, then we’re very strongly influenced by what’s happening ‘in the moment’. And that means that we can be captured (and fooled) by elements that may not even be consciously processed.
What this means in practice is that it’s harder than we think to get reliable performance across a range of conditions. That we should ensure that patterns are generated across ‘noise’ so that they’re reliable in the face of the appropriate triggers, despite any accompanying contextual patterns. and recognize that decisions can be biased, and design scaffolding to prevent in appropriate outcomes. Developing mental models that provide reasoning abilities about causes and outcomes are useful here. This flexibility is advantageous (and why machine learning struggles outside it’s range of training), but we want to tap into it in helpful ways.
Our approaches should reflect what’s known, and therefore we need to keep up. Situated cognition is a perspective that’s relevant to more effectively supporting individual and organizational performance and learning. So, what is your thinking about this?