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Predictive coding and its generalization to active inference offer a unified theory of brain function. The underlying predictive processing paradigmhas gained significant attention in artificial intelligence research for its representation learning and predictive capacity. Here, we suggest that it is possible to integrate human and artificial generative models with a predictive coding network that processes sensations simultaneously with the signature of predictive coding found in human neuroimaging data. We propose a recurrent hierarchical predictive coding model that predicts low-dimensional representations of stimuli, electroencephalogram and physiological signals with variational inference. We suggest that in a shared environment, such hybrid predictive coding networks learn to incorporate the human predictive model in order to reduce prediction error. We evaluate the model on a publicly available EEG dataset of subjects watching one-minute long video excerpts. Our initial results indicate that the model can be trained to predict visual properties such as the amount, distance and motion of human subjects in videos.
We investigated online electrophysiological components of distributional learning, specifically of tones by listeners of a non tonal language. German listeners were presented with a bimodal distribution of syllables with lexical tones from a synthesized continuum based on Cantonese level tones. Tones were presented in sets of four standards (within-category tokens) followed by a deviant (across-category token). Mismatch negativity (MMN) was measured. Earlier behavioral data showed that exposure to this bimodal distribution improved both categorical perception and perceptual acuity for level tones [I]. In the present study we present analyses of the electrophysiological response recorded during this exposure, i.e., the development of the MMN response during distributional learning. This development over time is analyzed using Generalized Additive Mixed Models and results showed that the MMN amplitude increased for both within and across-category tokens, reflecting higher perceptual acuity accompanying category formation. This is evidence that learners zooming in on phonological categories undergo neural changes associated with more accurate phonetic perception.
Speech scientists have long noted that the qualities of naturally-produced vowels do not remain constant over their durations regardless of being nominally "monophthongs" or "diphthongs". Recent acoustic corpora show that there are consistent patterns of first (F1) and second (F2) formant frequency change across different vowel categories. The three Australian English (AusE) close front vowels /i:, 1, i/ provide a striking example: while their midpoint or mean F1 and F2 frequencies are virtually identical, their spectral change patterns distinctly differ. The results indicate that, despite the distinct patterns of spectral change of AusE /i:, i, la/ in production, its perceptual relevance is not uniform, but rather vowel-category dependent.
Voice onset time (VOT), a primary cue for voicing in many languages including English and German, is known to vary greatly between speakers, but also displays robust within-speaker consistencies, at least in English. The current analysis extends these findings to German. VOT measures were investigated from voiceless alveolar and velar stops in CV syllables cued by a visual prompt in a cue-distractor task. Comparably to English, a considerable portion of German VOT variability can be attributed to the syllable’s vowel length and the stop’s place of articulation. Individual differences in VOT still remain irrespective of speech rate. However, significant correlations across places of articulation and between speaker-specific mean VOTs and standard deviations indicate that talkers employ a relatively unified VOT profile across places of articulation. This could allow listeners to more efficiently adapt to speaker-specific realisations.
Words as social tools
(2019)