Learning Tech Labs – Cognitive Science

The Learning Tech Labs sessions have proven to be a unique forum providing an opportunity for a dialogue between education and emerging technology. This month’s was something different with two talks in and around cognitive science – one on learning and another on perception.


When we encounter a term we’re unsure of, chances are we’ll just Google it – a common diagnostic response that produces a surface understanding but, as WIT clinical psychology lecturer Dr David Delany suggested, if we reverse engineer an expert’s definition we can develop a more sophisticated and deeper understanding of a concept by inference. In that way we are thinking like the expert – it’s here that the we see the distinction between the ‘knowing how’ and ‘knowing that’ that the philosopher Gilbert Ryle talked about when he wrote that “it requires intelligence not only to discover truths, but also to apply them”.
It’s this Deep Structure of knowledge that forms the basis of Superintelligence which, as you can see from the graph above, sits opposite from rote learning (which is just up from illiteracy).

David discussed the qualitative differences in intelligence between literate and illiterate learners. Illiterate cultures have less capacity for abstraction and as a result form a more fixed, concrete impression of the world. There’s an inflection point somewhere around 700BC when literacy became widespread in Greece.
Literacy lets us externalise knowledge – a keystone in the spread of abstract concepts since externalising knowledge lets us record and transfer knowledge so the ideas mooted by the pre-Socratics are passed to and built upon by Plato which, in turn gives way to Aristotelian metaphysics.
It’s this propensity for deep, abstract thought that sits at the other end of the spectrum from shallow, rote learning. An ability to make connections between concepts and draw inferences rather than an expertise that’s narrow in its application is the hallmark of ‘far transfer’ of learning spanning contexts and disciplines.
In the future learning techniques might draw us toward methods which amplify intelligence by cultivating these connections

Attention Orientation

DCU’s Dr Graham Healytook us through his work with EEG tests which measure the brain’s electrical signals. There are a number of commercial products available, like the Emotiv EEG, though their accuracy would fall well short of the medical grade equipment.
There is one signal in particular, the P300, that’s signifiant since it can detect our attention orientation. In the lab a subject is asked to look out for a can of coke that will feature among a quick succession of images that will be shown (this is a technique known as Rapid Serial Visual Presentation). When the coke can image is shown the P300 signal increases telling us that the image is identified and recognised.

Both talks provided fascinating insight into the future of learning and our understanding of it.

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