The River Tiger Podcast from Dynamics Coaching
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The River Tiger Podcast from Dynamics Coaching
Why the concept of affordances is so important to our understanding of becoming skilful. A conversation with Dr Andrew Wilson
This is a conversation that I have been looking forward to for a long time. We covered a lot of ground especially defining affordances and linking affordances not only to individual movement, but to multi organism and multi species interactions. On the way we connected affordance perception, calibration, flow, and many other importance concepts.
Andrew Wilson, an ecological psychologist at Leeds Beckett University, discusses the concept of affordances, emphasising their central role in skill acquisition and movement. He explains that affordances are properties of the environment that enable or constrain action, and that perception of these affordances is crucial for successful interaction.
Andrew contrasts affordances with traditional stimulus-response models, highlighting their intrinsic meaningfulness and the importance of complementarity between organisms and their environments. He also addresses the application of affordances in human-horse interactions, stressing the need for mutual calibration and adaptability.
The conversation then touched on the potential connections between Gibson's concept of affordances and other emerging frameworks like the free energy principle and predictive processing.
My guest on this episode was Dr Andrew Wilson
Email: a.d.wilson@leedsbeckett.ac.uk
Bluesky: https://bsky.app/profile/adw.bsky.social
Blog: https://psychsciencenotes.blogspot.com/
Full episode notes:
Where next
- Explore the concept of affordances further, particularly in the context of skill development and human-animal interactions.
- Consider how the language of affordances can provide a more nuanced and ecologically-valid way of understanding behaviour, compared to traditional approaches.
- Discuss the potential connections between affordance theory and other emerging frameworks like the free energy principle and predictive processing.