ACT-R submissions for comment

[ERIK M. ALTMANN]

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Altmann & Gray.  The Anatomy of Serial Attention: An integrated model
of set shifting and maintenance.  Submitted to Psychological Science.

        Serial attention is the process of adopting a task set (or intention
        to do a task), maintaining that set for a time, then shifting
        set. When set must be shifted often, serial attention is difficult
        because old sets cannot be forgotten instantly and will interfere with
        the current one. This memory perspective on set shifting is the basis
        for a computational cognitive model that explains a variety of effects
        in serial attention, some well-documented (e.g., RT switch cost) and
        some novel (e.g., prolonged error switch cost and within-run
        performance decline). The mechanisms producing these effects are
        tightly integrated with each other and with memory theory. The model
        absorbs extant accounts of set shifting, including failure to prepare
        (De Jong, Berendsen, & Cools, 1999) and exogenous cueing (Rogers &
        Monsell, 1995), and promises to unify the effects presented here with
        other set-shifting phenomena.

Altmann.  Memory in Chains: A dual-code associative model of
positional uncertainty.  Submitted to ICCM.

        A persistent problem in modeling human memory has been how to explain
        positional uncertainty (or "near-miss" error) in memory for serial
        order. Models like perturbation (Estes, 1997), primacy (Henson,
        Norris, Page, & Baddeley, 1996), and partial matching (Anderson &
        Matessa, 1997) can fit uncertainty gradients but fail to address how
        cognition might encode the underlying memory representation in the
        first place. This paper introduces a model of uncertainty gradients
        that explains the encoding as well as the reconstruction of order
        information. The model is based on an integrated and computational
        cognitive theory (ACT-R/PM) that provides the central mechanisms: a
        dual-code representation of attended items, associative learning, and
        noise in activation levels. The model addresses the main structural
        objection to chaining models (over- predicting relative or "shift"
        error), fits experimental data better than the perturbation and
        partial matching models, and makes principled predictions concerning
        primacy and recency effects in memory for order.





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