Abstract

this article are based on the decision boundmodel in Equation 5. Specifically, each model includes one "noise" parameter that represents the sum of perceptual and criterial noise (Ashby, 1992a; Maddox& Ashby, 1993). Each model assumes that the observer has accurate knowledge of the category structures [i.e., l o (x pi )]. To ensure that this was a reasonable assumption, each observer completed a number of baseline trials and was required to meet a stringent performance criterion (see Method section). Finally,each model allows for suboptimal decision criterion placement where the decision criterion is determined from the flat-maxima hypothesis, the COBRA hypothesis, or both, following Equation 6. To determine whether the flat-maxima and COBRA hypothesesare important in accountingfor each observer's data, we developed four models. Each model makes different assumptions about the k r and w values used. The nested structure of the models is represented in Figure 5, with each arrow pointing to a more general model and Figure 4. Decision criterion [ln( b )] predicted from the flat-maxima hypothesisplotted against the decision criterion [ln( b )] predicted from the independence assumption of the optimal classifier for the six simultaneous base-rate/payoff conditions. (A) 2:1B/2:1P condition. (B) 3:1B/3:1P condition

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