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this article. We also thank Lofilei Carderhas and Robert F. zwin for help with data collection

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

Abstract not found