A computational model of human memory for serial order is described (OSCillator-based Associative Recall [OSCAR]). In the model, successive list items become associated to successive states of a dynamic learning-context signal. Retrieval involves reinstatement of the learning context, successive states of which cue successive recalls. The model provides an integrated account of both item memory and order memory and allows the hierarchical representation of temporal order information. The model accounts for a wide range of serial order memory data, including differential item and order memory, transposition gradients, item similarity effects, the effects of item lag and separation in judgments of relative and absolute recency, probed serial recall data, distinctiveness effects, grouping effects at various temporal resolutions, longer term memory for serial order, list length effects, and the effects of vocabulary size on serial recall. The serial ordering of behavior is central to much, perhaps most, of human cognition (e.g., Lashley, 1951). Studies of memory for serial order have provided rich data on the psychological repre-sentation of serial order information and therefore offer a signifi-cant challenge to any model of serially ordered behavior. In this

The authors investigated the extent to which touch, vision, and audition mediate the processing of statistical regularities within sequential input. Few researchers have conducted rigorous comparisons across sensory modalities; in particular, the sense of touch has been virtually ignored. The current data reveal not only commonalities but also modality constraints affecting statistical learning across the senses. To be specific, the authors found that the auditory modality displayed a quantitative learning advantage compared with vision and touch. In addition, they discovered qualitative learning biases among the senses: Primarily, audition afforded better learning for the final part of input sequences. These findings are discussed in terms of whether statistical learning is likely to consist of a single, unitary mechanism or multiple, modality-constrained ones. The world is temporally bounded: Events do not occur all at once but rather are distributed in time. Therefore, it is crucial for organisms to be able to encode and represent temporal order information. One potential method for encoding temporal order is to learn the statistical relationships of elements within sequential input. This process appears to be important in a diverse set of learning situations, including speech segmentation (Saffran, Newport, & Aslin, 1996), learning orthographic regularities of written words (Pacton, Perruchet, Fayol, & Cleeremans, 2001), visual processing (Fiser & Aslin, 2002), visuomotor learning (e.g., serial reaction time tasks; Cleeremans, 1993) and nonlinguistic, auditory