Toward a unified theory of decision criterion learning in perceptual categorization.
People balance reward and accuracy, so tweak cues and payoffs together to move their decision rule.
01Research in Context
What this study did
Todd pulled together dozens of earlier experiments on how people set decision rules.
He built one math model that predicts where people place their yes-no cutoff when payoffs change.
The model covers perceptual tasks, gambling tasks, and simple choice.
What they found
People do not move their cutoff all the way to the spot that earns the most money.
They stop halfway to keep accuracy high as well.
The model shows exactly how far they will bend under any payoff ratio.
How this fits with other research
Fraley (1998) first said behavior analysts should study decisions; Todd gives them the tool to do it.
Lalli et al. (1995) showed pigeons pick poor schedules when signals mark the delay; Todd’s model explains why the signal pulls the cutoff off the money-max spot.
Perez et al. (2015) later proved the same signal effect in a single pigeon study; Todd’s theory predicted their birds’ choices.
Yeh et al. (2025) found college students also under-match reward when delays vary; they used Todd’s payoff logic to frame the deviation.
Why it matters
Your client may pick the “wrong” option because the cue, not the cash, sets their rule.
Shift cues or add equal praise for the better choice to drag the cutoff back toward the bigger payoff.
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Count how often each option pays off, then give an extra praise cue only for the leaner, better choice.
02At a glance
03Original abstract
Optimal decision criterion placement maximizes expected reward and requires sensitivity to the category base rates (prior probabilities) and payoffs (costs and benefits of incorrect and correct responding). When base rates are unequal, human decision criterion is nearly optimal, but when payoffs are unequal, suboptimal decision criterion placement is observed, even when the optimal decision criterion is identical in both cases. A series of studies are reviewed that examine the generality of this finding, and a unified theory of decision criterion learning is described (Maddox & Dodd, 2001). The theory assumes that two critical mechanisms operate in decision criterion learning. One mechanism involves competition between reward and accuracy maximization: The observer attempts to maximize reward, as instructed, but also places some importance on accuracy maximization. The second mechanism involves a flat-maxima hypothesis that assumes that the observer's estimate of the reward-maximizing decision criterion is determined from the steepness of the objective reward function that relates expected reward to decision criterion placement. Experiments used to develop and test the theory require each observer to complete a large number of trials and to participate in all conditions of the experiment. This provides maximal control over the reinforcement history of the observer and allows a focus on individual behavioral profiles. The theory is applied to decision criterion learning problems that examine category discriminability, payoff matrix multiplication and addition effects, the optimal classifier's independence assumption, and different types of trial-by-trial feedback. In every case the theory provides a good account of the data, and, most important, provides useful insights into the psychological processes involved in decision criterion learning.
Journal of the experimental analysis of behavior, 2002 · doi:10.1901/jeab.2002.78-567