#oeb_midsummit Cognitive neuroscience and learning by @BekkeringHarold

Harold Bekkering reminds me of some of my Dutch family, funny feeling. Once he took the stage, it really was like listening to cousin Folkert.

Everything is connected in the brain, and the brain is a predictive machine.

A neuron at work is incoming data is output in just one unit. So in neuroscience we see that multiple inputs triggers a process into one output.

Hebbian learning: everything is connected (Hebb’ theory).

A human can adapt to one specific tone of voice and timbre. Our brain adjusts.

The brain is a prediction machine. Read Karl Friston and Andy Clark.

Distributed knowledge in the brain. Exteroceptive, autonomic (interoceptive), motoric (proprioceptive) given the conceptual multimodel representations. The brain tries to make the best things for you. It is a multimodal representations, which makes it of interest.

The whole brain is summed up in error correction.

E.g. you walk to a door (prediction is going through the door), but if a door is closed you adjust your action based upon the perception after the prediction.

Amal and Giraud, TICS 2012, beta and gamma power. The brain is only active when you make errors. Only after making an error your model is updated [interesting].

We learn more from negative feedback. Oh no! 😃

Creating a safe environment to learn from errors.

Social learning: humans are by nature social, homo imitans.

Dunbar (1998) The social Cortex, it is big because we have huge networks. The Dunbar’s number.

Social learning is needed due to the brain needing to be matured over time (baby to adult).

Good tip for learning: saying: hi (insert name) and then say the action. The brain does not ignore the calling of a name to become active.

Mirror neurons: have revolutionised neuroscience. Motorcortex was investigated, and in the lab, monkeys mimicked humans picking up peanuts. There are cells that only get active if we see someone else moving. Your brain cannot help to observe and react.

Learning analytics: extracting data, predicting data, … data driven education.

It is not useful to only learn in one learning style is the real point of interest, if you want to really learn the best thing to do is to use all styles for learning.  

Lessons for online learning

Hebbian learning: be careful with what you offer

Predictive learning: structure and provoking errors (job aids similar)

Social learning