01970nas a2200205 4500008004100000022001400041245009800055210006900153260001200222300001100234490000700245520134700252653001301599653001401612653001101626653002401637653002901661100002601690856004801716 2012 eng d a1744-411X00aA single g factor is not necessary to simulate positive correlations between cognitive tests.0 asingle g factor is not necessary to simulate positive correlatio c01/2012 a378-840 v343 aIn the area of abilities testing, one issue of continued dissent is whether abilities are best conceptualized as manifestations of a single underlying general factor or as reflecting the combination of multiple traits that may be dissociable. The fact that diverse cognitive tests tend to be positively correlated has been taken as evidence for a single general ability or "g" factor. In the present study, simulations of test performance were run to evaluate the hypothesis that multiple independent abilities that affect test performance in a consistent manner will produce a positive manifold. Correlation matrices were simulated from models using either one or eight independent factors. The extent to which these factors operated in a consistent manner across tests (i.e., that a factor that facilitates performance on one test tends to facilitate performance on other tests) was manipulated by varying the mean value of the randomly selected weights. The tendency of both a single factor and eight independent factors to produce positive correlations increased as the randomly selected weights operated in a more consistent fashion. Thus the presence of a positive manifold in the correlations between diverse cognitive tests does not provide differential support for either single factor or multiple factor models of general abilities.10aAptitude10aCognition10aHumans10aModels, Theoretical10aNeuropsychological Tests1 aMcFarland, Dennis, J. uhttp://www.ncbi.nlm.nih.gov/pubmed/2226019002954nas a2200373 4500008004100000022001400041245008200055210006900137260001200206300001100218490000700229520184300236653002802079653002702107653002802134653002702162653002202189653003002211653001102241653001302252653002502265653002402290653001202314653004102326653002802367653001802395653002202413100001902435700001502454700002502469700002002494700001802514856004802532 2011 eng d a1530-888X00aA graphical model framework for decoding in the visual ERP-based BCI speller.0 agraphical model framework for decoding in the visual ERPbased BC c01/2011 a160-820 v233 a

We present a graphical model framework for decoding in the visual ERP-based speller system. The proposed framework allows researchers to build generative models from which the decoding rules are obtained in a straightforward manner. We suggest two models for generating brain signals conditioned on the stimulus events. Both models incorporate letter frequency information but assume different dependencies between brain signals and stimulus events. For both models, we derive decoding rules and perform a discriminative training. We show on real visual speller data how decoding performance improves by incorporating letter frequency information and using a more realistic graphical model for the dependencies between the brain signals and the stimulus events. Furthermore, we discuss how the standard approach to decoding can be seen as a special case of the graphical model framework. The letter also gives more insight into the discriminative approach for decoding in the visual speller system.

10aArtificial Intelligence10aComputer User Training10aDiscrimination Learning10aElectroencephalography10aEvoked Potentials10aEvoked Potentials, Visual10aHumans10aLanguage10aModels, Neurological10aModels, Theoretical10aReading10aSignal Processing, Computer-Assisted10aUser-Computer Interface10aVisual Cortex10aVisual Perception1 aMartens, S M M1 aMooij, J M1 aHill, Jeremy, Jeremy1 aFarquhar, Jason1 aSchölkopf, B uhttp://www.ncbi.nlm.nih.gov/pubmed/20964540