Super-optimality and relative distance coding in location memory

The prevailing model of landmark integration in location memory is Maximum Likelihood Estimation, which assumes that each landmark implies a target location distribution that is narrower for more reliable landmarks. This model assumes weighted linear combination of landmarks and predicts that, given optimal integration, the reliability with multiple landmarks is the sum of the reliabilities with the individual landmarks. Super-optimality is reliability with multiple landmarks exceeding optimal reliability given the reliability with each landmark alone; this is shown when performance exceeds predicted optimal performance, found by aggregating reliability values with single landmarks. Past studies claiming super-optimality have provided arguably impure measures of performance with single landmarks given that multiple landmarks were presented at study in conditions with a single landmark at test, disrupting encoding specificity and thereby leading to underestimation in predicted optimal performance. This study, unlike those prior studies, only presented a single landmark at study and the same landmark at test in single landmark trials, showing super-optimality conclusively. Given that super-optimal information integration occurs, emergent information, that is, information only available with multiple landmarks, must be used. With the target and landmarks all in a line, as throughout this study, relative distance is the only emergent information available. Use of relative distance was confirmed here by finding that, when both landmarks are left of the target at study, the target is remembered further right of its true location the further left the left landmark is moved from study to test.