Controlled information integration and bayesian inference

One of the oldest hypotheses in cognitive psychology is that controlled information integration1 is a serial, capacityconstrained process that is delimited by our working memory resources, and this seems to be the most uncontroversial aspect also of present-day dual-systems theories (Evans, 2008). The process is typically conceived of as a sequential adjustment of an estimate of a criterion (e.g., a probability), in view of successive consideration of inputs to the judgment (i.e., cues or evidence). The “cognitive default” seems to be to consider each attended cue in isolation, taking its impact on the criterion into account by adjusting a previous estimate into a new estimate, until a stopping rule applies (e.g., Juslin et al., 2008). Considering each input in isolation, without modifying the adjustments contingently on other inputs to the judgment, invites additive integration. The limits on working memory moreover contribute to an illusion of linearity. If people, when pondering the relationship between variables X and Y, are constrained by working memory to consider only two X–Y pairs, the function induced can take no other form than a line. As illustrated by many scientific models, with computational aids people can capture also non-additive and non-linear relations. But without support, this is rather taxing on working memory and additive integration, typically as a