Optimal temporal differentiation.
When speed pays, adult humans space responses to minimize time-to-next-reinforcer better than Weber-law models predict.
01Research in Context
What this study did
McIntire et al. (1987) asked adults to press a key at steady, self-chosen speeds. Reinforcers only came if the pause between presses stayed inside a narrow time window.
The team compared two math models. One used optimality theory: respond so the next reinforcer arrives as fast as possible. The other used scalar timing: respond with the usual Weber-law spread.
What they found
Average pause times landed almost exactly where optimality theory said they should. The scalar model missed the mark.
People tightened or loosened their pacing to stay within the payoff window. They acted like they were trying to get the most cash per minute.
How this fits with other research
Zeiler (1985) looked like a contradiction at first. That study found Weber-law timing once pauses were measured alone. The difference is task design. Zeiler (1985) removed all payoff for speed; McIntire et al. (1987) kept it. Same people, different rules, different models win.
Cippola et al. (2014) extends the idea. College kids used temporal cues to pick brand-new options by exclusion. The optimality mindset still holds: use time to make the safest, fastest choice.
Animal work backs the lab method. de Villiers (1980) showed pigeons can do free-operant temporal psychophysics. The procedure is solid across species, so the human optimality result is not a one-off.
Why it matters
If you set reinforcement windows in precision teaching, fluency, or DRL programs, think like an economist. Give learners a reason to hit the window and they will shrink variability on their own. State the time boundary clearly, keep the payoff immediate, and let the contingencies do the shaping. No extra feedback required.
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02At a glance
03Original abstract
In order to illuminate a light signaling a correct response, adult humans had to space their button presses according to a range of time requirements. In some conditions, the spacing needed only to exceed a minimum duration; in others, it had to fall between lower and upper bounds. Mean interresponse times always exceeded the lower limit, and decreased the more stringent were the upper bounds. Variability of interresponse times increased with larger lower bounds, but was unaffected by the size of the upper bound. Feedback about the direction of errors in conditions involving both upper and lower bounds did not affect the means, but it did reduce variability. Predictions were derived from optimality theory, based on the assumption that the critical factor was minimization of the time between correct responses. Without upper bounds, the theory overestimated the mean interresponse times by about 10%; with upper bounds, the theoretical predictions corresponded closely to the actual data. The results did not appear to reflect a scalar timing process. Optimality theory, in contrast to Weber's law, correctly predicted the variety of curves relating sensitivity to duration requirements.
Journal of the experimental analysis of behavior, 1987 · doi:10.1901/jeab.1987.47-191