The mind readily learns cue-outcome associations where an object predicts a specific outcome. Previous work suggested that when multiple objects associated with different outcomes were jointly presented , the mind made conjunctive predictions that represented the common property of the associated outcomes. Using attentional tracking measures, we provided more evidence for the weighted summation framework when the conjunctive predictions involved spatial locations (Experiment 1) or conceptual categories (Experiment 2). Then, we examined the reverse of conjunction, where participants were presented with a single object, which is a part of an object pair that was previously associated with an outcome (Experiment 3). Rather than making predictions based on mental operations such as subtraction, we found that participants’ predictions were purely based on previous associations. These results together demonstrated the robust tendency to make conjunctive predictions based on knowledge of cue-outcome associations.

The cognitive system readily detects statistical relationships where the presence of an object predicts a specific outcome. What is less known is how the mind generates predictions when multiple objects predicting different outcomes are present simultaneously. Here we examine the rules with which predictions are made in the presence of two objects that are associated with two distinct outcomes. In three experiments, participants first implicitly learned that an object predicted a specific target location in a visual search task. When two objects predicting two different target locations were present simultaneously, participants were reliably faster to find the target when it appeared in the conjunctive location than in disjunctive locations. This was true even if participants were not consciously aware of the association between the objects and target locations. The results suggest that in the presence of multiple predictors, statistical learning generates implicit expectations about the outcomes in a conjunctive fashion.

An adaptive function of the visual system is that it flexibly updates existing representations of objects upon changes.Such updating can also alter the representations of associated objects that are not directly observable. What mechanism supportsthis process? We propose that statistical learning provides a channel through which changes in one object are automaticallytransferred to related objects. Observers viewed a temporal sequence of paired circles. One circle in each pair then changed insize, and observers recalled the size of the other circle. When the first circle enlarged (or shrank), the second circle was judgedto be larger (or smaller), suggesting that the change was automatically transferred to the predicted object (Experiment 1). Thesame, however, was not true if the second circle changed in size (Experiment 2). No observer was explicitly aware of the circlepairs. Thus, statistical learning enables the implicit updating of representations through predictive associations.

Binary information is prevalent in the environment. In this study, we examined how people process repetition and alternation in binary sequences. Across four paradigms involving estimation, working memory, change detection, and visual search, we found that the number of alternations is under-estimated compared to repetitions (Experiment 1). Moreover, recall for binary sequences deteriorates as the sequence alternates more (Experiment 2). Changes in bits are also harder to detect as the sequence alternates more (Experiment 3). Finally, visual targets superimposed on bits of a binary sequence take longer to process as alternation increases (Experiment 4). Overall, our results indicate that compared to repetition, alternation in a binary sequence is less salient in the sense of requiring more attention for successful encoding. The current study thus reveals the cognitive constraints in the representation of alternation and provides a new explanation for the over-alternation bias in randomness perception.