The basis of behavioral momentum in the nonlinearity of strength

Killeen and colleagues wrote a theory paper. They asked: why do hard tasks fall apart when you add a disruptor?

The team built math that treats motivation as a curved line, not a straight one. They kept the old term 'behavioral momentum' but gave it a new engine.

The math shows that high-probability responses do not resist change because of mass. They resist because motivation jumps fast at first, then levels off.

When you stack easy wins, the curve is steep. The learner keeps going even when the next ask is tough.

Smith (1996) first said high-p sequences work like momentum. Killeen et al. (2018) keep the label but swap the motor: it is nonlinear drive, not physics.

Bell (1999) found that unsignaled delays weaken resistance. That result pokes a hole in old Pavlovian stories. The new curve model absorbs the hit: signaling keeps the motivation line steep.

Lord et al. (1997) showed better reinforcers boost momentum. The nonlinear view predicts exactly that—better payoff bends the curve higher, so disruption takes longer to flatten it.

You can stop picturing momentum as weight. Picture a ski slope that starts steep and flattens. Stack three easy trials to build the steep part, then slide into the hard one before the hill levels out. If the child stalls, do not add more mass—change the slope with a richer reinforcer or a clearer signal.