The science underneath
Attractor states
In shortSome patterns are like valleys a ball keeps rolling into; small changes early can send it elsewhere.
The idea
Picture a ball in a landscape with valleys. The ball tends to roll into the valleys and stay there: these are ‘attractors’. Some patterns of behaviour work like this. A hard morning tends to follow the same path every time, because each step makes the next more likely. A deep valley is hard to leave; but if the landscape changes (routine, relationships, what the person expects), new valleys can form.
What to try
- Find the first step of the usual path and change that, more than the end.
- Build a pleasant alternative ‘valley’ and repeat it until it becomes easy.
- Expect some instability before a new pattern stabilises: it is a sign of change.
The common mistake
Seeing a stable pattern as a fixed trait of the person. It may be a deep valley in a landscape that can change.
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Short, plain pieces by the researchers and good writers.
The science behind itHow strong the evidence is, the model, videos and references.
How strong the evidence is
A way of thinking A lens for interpreting what a child does, not a rule from trials.
Attractors are regions of state space where trajectories converge; the depth and width of the basin describe stability and resilience. In Hopfield networks, memories are attractors; in psychopathology, some propose symptom networks with alternative stable states and early warning signals before transitions (Scheffer). In formulation, ask what deepens the basin (reinforcement, predictions that are confirmed, routines), what perturbations the person has already gone through without changing state, and what widens the repertoire.
More technical books
Self-organisation and attractors applied to brain and behaviour.
See on Amazon ›Amazon links are affiliate links: if you buy, Bounded Uncertainty receives a small commission at no cost to you.
Videos
External videos, chosen because they agree with these ideas. Bounded Uncertainty has no link to the channels.
References
- Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. PNAS, 79(8), 2554–2558.
- Scheffer, M., et al. (2009). Early-warning signals for critical transitions. Nature, 461, 53–59.
- Kelso, J. A. S. (1995). Dynamic Patterns. MIT Press.
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