🤖 AI Summary
This study addresses a critical limitation in traditional research on base-rate neglect, which has focused exclusively on the underweighting of outcome priors while overlooking the influence of cue frequencies on judgment, thereby yielding an incomplete understanding of reasoning biases. The authors propose that base-rate neglect comprises two distinct dimensions—outcome priors and cue frequencies—and develop a dual-weight Bayesian model to jointly account for both. Using a novel rating-task experimental paradigm, they identify the cue-density effect as a second form of base-rate neglect and achieve a double dissociation between the two types, overcoming the constraints of single-parameter models. This integrative framework not only explains why six standard models appear convergent in conventional experiments despite underlying divergences but also provides a unified interpretation of classic measures such as causal strength and signal detection metrics.
📝 Abstract
Base-rate neglect is usually treated as one mistake: giving the prior too little weight. Turning the co-occurrences you see into a useful judgment, though, means correcting for two base rates, not one. The first is the familiar prior, how common the outcome is. The second is how common the cue itself is. Those are two separate mistakes, and a learner can make either one alone. Under-correcting the prior is classical base-rate neglect; under-correcting the cue is the cue-density effect of contingency learning, long studied but not previously recognised as a kind of base-rate neglect. We write both corrections as two weights in one Bayesian equation. The task decides which weight it can measure: the cue-frequency weight appears only in graded ratings, because a two-choice test cancels it. At their extremes the two weights recover familiar quantities: base-rate neglect, the signal-detection criterion, the contiguity/sensitivity/validity triple, and the "lift" measure of causal strength. The same cue-frequency weight also sits inside six standard learning-and-memory models; they seem to agree, but only because the usual experiments squeeze the data into a form where they cannot disagree. Above all, the two neglects should be separately manipulable: an experimenter can move one without moving the other. That is a double dissociation, and a one-parameter account cannot produce it. That prediction is the framework's centre, and it has not yet been tested. This paper lays out the framework and the rating experiment that would settle it; a companion paper fits the two weights to an existing colour-flavour dataset.