Animals perform flexible goal-directed behaviours to satisfy their basic physiological needs1,2,3,4,5,6,7,8,9,10,11,12. However, little is known about how unitary behaviours are chosen under conflicting needs. Here we reveal principles by which the brain resolves such conflicts between needs across time. We developed an experimental paradigm in which a hungry and thirsty mouse is given free choices between equidistant food and water. We found that mice collect need-appropriate rewards by structuring their choices into persistent bouts with stochastic transitions. High-density electrophysiological recordings during this behaviour revealed distributed single neuron and neuronal population correlates of a persistent internal goal state guiding future choices of the mouse. We captured these phenomena with a mathematical model describing a global need state that noisily diffuses across a shifting energy landscape. Model simulations successfully predicted behavioural and neural data, including population neural dynamics before choice transitions and in response to optogenetic thirst stimulation. These results provide a general framework for resolving conflicts between needs across time, rooted in the emergent properties of need-dependent state persistence and noise-driven shifts between behavioural goals.
Trying to read through the technobabble, the discovery here reads like shard theory to me. Pinging @TurnTrout.
Perhaps the methodologies they use here can be used to speed up the locating of shards, if they exist, inside current ML models.
If the alignment field ever gets confident enough in itself to spend a whole bunch of money, and look weirder than its ever looked before, perhaps we’ll want to hire some surgeons and patients, and see whether we can replicate these results in humans rather than just mice (though you’d probably want to get progressively more cerebral animals & build your way up, and hopefully not starve or water-deprive the humans, aiming for higher-level values).
Progress in neuromorphic value theory
Trying to read through the technobabble, the discovery here reads like shard theory to me. Pinging @TurnTrout.
Seems also of use to @Quintin Pope
h/t Daniel Murfet via
twitter retweetX repostPerhaps the methodologies they use here can be used to speed up the locating of shards, if they exist, inside current ML models.
If the alignment field ever gets confident enough in itself to spend a whole bunch of money, and look weirder than its ever looked before, perhaps we’ll want to hire some surgeons and patients, and see whether we can replicate these results in humans rather than just mice (though you’d probably want to get progressively more cerebral animals & build your way up, and hopefully not starve or water-deprive the humans, aiming for higher-level values).