Previous studies have shown congruency effects between specific speech articulations and manual grasping actions. For example, uttering the syllable [kα] facilitates power grip responses in terms of reaction time and response accuracy. A similar association of the syllable [ti] with precision grip has also been observed. As these congruency effects have been to date shown only for Finnish native speakers, this study explored whether the congruency effects generalize to native speakers of another language. The original experiments were therefore replicated with English participants (N=16). Several previous findings were reproduced, namely the association of syllables [kα] and [ke] with power grip and of [ti] and [te] with precision grip. However, the association of vowels [α] and [i] with power and precision grip, respectively, previously found for Finnish participants, was not significant for English speakers. This difference could be related to ambiguities of English orthography and pronunciation variations. It is possible that for English speakers seeing a certain written vowel activates several different phonological representations associated with that letter. If the congruency effects are based on interactions between specific phonological representations and grasp actions, this ambiguity might lead to weakening of the effects in the manner demonstrated here.

Face recognition algorithms perform very unreliably when the pose of the probe face is different from the gallery face: typical feature vectors vary more with pose than with identity. We propose a generative model that creates a one-to-many mapping from an idealized "identity" space to the observed data space. In identity space, the representation for each individual does not vary with pose. We model the measured feature vector as being generated by a pose-contingent linear transformation of the identity variable in the presence of Gaussian noise. We term this model "tied" factor analysis. The choice of linear transformation (factors) depends on the pose, but the loadings are constant (tied) for a given individual. We use the EM algorithm to estimate the linear transformations and the noise parameters from training data. We propose a probabilistic distance metric which allows a full posterior over possible matches to be established. We introduce a novel feature extraction process and investigate recognition performance using the FERET, XM2VTS and PIE databases. Recognition performance compares favourably to contemporary approaches.