Andrew running workshop on ecological information 1/7/16

Andrew is travelling down to run a workshop demystifying ecological information at the Third International Conference on Interactivity, Language and Cognition. The workshop begins 9am on Friday 01/07/16 Room TK402. (I’ll only be down for the Friday session, but I’m looking forward to meeting you all!)

Slides: Ecological Information Workshop (Kingston June 2016) (note, I’m not going to deliver this as a lecture, it’s a little more hands on with some Matlab demos and discussion; these slides are just things to use to make it all a little more concrete)



Sabrina at Mechanism conference, Warsaw, June 23-25

Sabrina is attending the Mechanistic Integration and Unification in Cognitive Science from June 23 to June 25 presenting our latest work on mechanistic models using ecological information as the correct place to define the models.

Over on the other blog, we have a week of posts detailing what mechanistic models are and how we think we can get some – enjoy!

Slides: 2016-06-23 Ecological Behavioral Science

Agnes presenting for CeASR on Real Time Visual Feedback in Speech Training

Agnes will be presenting some of her PhD data to the Centre for Applied Social Research (CeASR) seminar series tomorrow

City Campus Calverley Building, CL213, Leeds, LS1 3HE (Full details)

Our first language can impact on the acquisition of second language speech sounds due to perception biased by the L1.  Augmented visual feedback designed to compensate for these influences typically involve visual representation of acoustics or visual representation of the vocal tract e.g. ultrasound. These methods suffer from difficulties in interpretation, either due to their abstract nature or by invoking an internal locus of attention.  An ecologically-inspired feedback will be presented which tracks the appropriate articulatory degrees of freedom in vowel production, without drawing attention to the actions of the vocal tract.

Lab Meeting 6/6/16: Jonas & Kording, Could a neuroscientist understand a microprocessor?

Jonas & Kording, Could a neuroscientist understand a microprocessor? (Preprint on


There is a popular belief in neuroscience that we are primarily data limited, that producing large, multimodal, and complex datasets will, enabled by data analysis algorithms, lead to fundamental insights into the way the brain processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. Here we take a simulated classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the processor. This suggests that current approaches in neuroscience may fall short of producing meaningful models of the brain.