Sentence examples for subject bias from inspiring English sources

Experiment 1 first identifies a regular subject bias on the location of antecedents for pronouns in connected discourse.

This paper summarizes the newest development on this subject (bias grading and metal doping) and presents findings in adhesion and tribological studies.

The use of pronouns in the film script thus provides additional, independent support for the conclusion from Experiment 1 that the interpretation of pronouns in Chinese exhibits a clear subject bias.

The results showed differences in patterns of ratings, which may be culturally linked and could help determine aspects of symbol design and usage that may be more helpful in designing instructions, learning aids, etc. Awareness of such subject bias and their implications are important on how one interprets the test results.

Remarking that the common subject bias for pronouns is heavily depressed in these conditions, we also asked why the frequency of reference to Goals was actually not considerably higher when Occasion/Result relations occurred with realized TOP events, as this combination of features might be expected to cause an even higher rate of pronoun use to link to preceding Goals.

This is revealed both directly in the high frequency of anaphoric relations between pronouns and elements in subject positions, and in the way that this subject bias can be deliberately manipulated and affected by certain specific changes made to aspects of the discourse structure.

These sensations can nonspecifically interfere with task performance via distraction or subject biasing, contaminating the results.

Both measures revealed a continued-subject bias in the interpretation of pronouns: Children tended to interpret a pronoun as coreferential with the subject (and first-mentioned character) in the preceding context.

The intra-subject bias and precision of the predictions were assessed by quantifying the MDPE and MDAPE, respectively.

The intra-subject bias (inaccuracy) and precision of the predictions were assessed by quantifying the median prediction error (MDPE) (median of all MDPEi) and median absolute weighted residual (MDAPE) (median of all MDAPEi), respectively.

(2) T1 images from both time points were rigidly registered by maximizing the normalized mutual information of the joint intensity histograms (Maes et al. 1997) and corrected for intra-subject bias differences using the VBM8 toolbox (http://dbm.neuro.uni-jena.de/vbm/).