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Considering the media elements received from the application, the available devices and the available content transformers, the Modality Function is able to determine possible matches A match is a combination of a media element and a device on which it can be displayed, possibly using content transformers These matches are then ranked using the recommendations coming from the Personal Context Function The major factors for ranking the matches are modality and device recommendations Depending on the context and user preferences, the Personal Context Function can suggest the best modalities or devices to use For instance, when the user is in her car, audio modality is preferred When the user is at home, the Home Theatre could be a recommended device Other criteria are also used: for instance, if a video media element is proposed in several resolutions, the one giving the best quality will be ranked higher.

likelihood (L) can be calculated to index the mis t of the model expectations to the observed data. Assuming we have one or more alternative models (see next section), we can compare these models using the differences in log-likelihood ( L = L 1 L 2 ) and the difference in the numbers of parameters estimated ( NP = NP1 NP2 ). Under standard normal theory assumptions about the distribution of the errors, we can compare model differences NP df ) and to a chi-square distribution ( L 2 , determine the accuracy (i.e., signi cance) of our comparison. To index the multivariate effect sizes, we can calculate a noncentrality index and provide the statistical power (P = 1 ) for all likelihood-based comparisons (e.g., based on = .01 test size). These likelihood-based calculations can answer basic questions phrased as To what degree do the data conform to the model expectations We can also use this same likelihood approach to answer more complex questions, such as Which is the best model for our data and Do the t statistics indicate that the same dynamic patterns exist in different sub-groups Although we do not need to make a rigorous use of probability tests, we do provide information to calculate alternative indices of t, including test statistics for perfect or close t (e.g., Browne & Cudeck, 1993; Burnham & Anderson, 1998; McArdle, Prescott, Hamagami, & Horn, 1998). The resulting parameter estimates allow us to form expected group growth curves for both the observed and true scores (for details, see McArdle, 1986, 2001; McArdle & Woodcock, 1997). We can also characterize the relative size of these parameters by calculating time-speci c ratios of the estimated variances

Then, depending on the user preferences and context, either the top-ranked match is automatically selected, or the user is asked to choose among the possible matches Figure 510 shows an example of the modality selection dialogue The rst tab shows the modalities that are recommended by the Personal Context Function in the current context It shows a list of modalities on speci c devices The second tab shows all possible matches, ordered by device and by modality Through this tab, the user can select her preferred modality even if it is not actually recommended The third tab gives some information as to why speci c modalities were selected These explanations are derived from the process where recommendations are used to rank the matches After this stage, an output modality has been selected.

, Yang, J, Langer, R, Freed, LE (1999) Mammalian chondrocytes expanded in the presence of broblast growth factor 2 maintain the ability to differentiate and regenerate three-dimensional cartilaginous tissue Exp Cell Res 253: 681 688 Mason, JM, Grande, DA, Barcia, M, Grant, R, Pergolizzi, RG, Breitbart, AS (1999) Expression of human bone morphogenic protein 7 in primary rabbit periosteal cells: potential utility in gene therapy for osteochondral repair Gene Ther 5: 1098 1104 Masuda, K, Sah, RL, Hejna, MJ, Thonar, EJM-A (2003) A novel two-step method for the formation of tissue-engineered cartilage by mature bovine chondrocytes: the alginaterecovered-chondrocyte (ARC) method J Orthop Res 21: 139 148 Merker, HJ, Zimmermann, B, Barrach, HJ (1984) The signi cance of cell contacts for the differentiation of the skeletal blastema.

A model circuit for three resistors in series. A voltmeter measures the IR drop across re-

The corresponding media element is then sent to the content transformers, if needed (actually only a URL pointing to the content is sent), and nally the media element is sent to the output device 517 Multimodal Input Multimodal fusion or input integration aims at allowing the user to more naturally interact with computer systems through various modalities (speech, gesture, keyboard, etc) The user experience can be enhanced by integrating several user input modalities into one consistent input model The input model then would contain not only the knowledge about single modalities, but also extracted meaning from the user interaction Although there is some advanced work.

1. Prefrontal cortex (executive control)