[Discrimination learning of rotated faces: a Markov analysis of coding and association processes].

30 subjects participated in a discrimination experiment learning face-letter associations under four rotation conditions (45 degrees, 90 degrees, 135 degrees, 180 degrees). Under each condition two thirds of the faces were presented twice, upright and rotated away from the vertical; the remaining faces were presented once, upright or rotated. Learning is described by a joint Markov model: For faces that are presented twice it assumes a separate association and encoding process (two-stage-model), for faces that are presented once it assumes an association process (all-or-none-model). The Markov model fits the data for all four rotation conditions. The angle of rotation does not affect learning for faces that are presented once. For faces that are presented twice it influences both the association and the encoding process. For the angles employed, the effect of rotation can be approximated linearly. The results suggest that the encoding of a rotated face differs increasingly from an upright face as a function of these angles of rotation. This confirms analogous conclusions from mental rotation experiments.