Vocal theories of the origin of language rarely make a case for the precursor functions that underlay the evolution of speech. The vocal expression of emotion is unquestionably the best candidate for such a precursor, although most evolutionary models of both language and speech ignore emotion and prosody altogether. I present here a model for a joint prosodic precursor of language and music in which ritualized group-level vocalizations served as the ancestral state. This precursor combined not only affective and intonational (...) aspects of prosody, but also holistic and combinatorial mechanisms of phrase generation. From this common stage, there was a bifurcation to form language and music as separate, though homologous, specializations. This separation of language and music was accompanied by their (re)unification in songs with words. (shrink)

Humans typically combine linguistic and nonlinguistic information to comprehend emotions. We adopted an emotion identification Stroop task to investigate how different channels interact in emotion communication. In experiment 1, synonyms of “happy” and “sad” were spoken with happy and sad prosody. Participants had more difficulty ignoring prosody than ignoring verbal content. In experiment 2, synonyms of “happy” and “sad” were spoken with happy and sad prosody, while happy or sad faces were displayed. Accuracy was lower when two channels expressed an (...) emotion that was incongruent with the channel participants had to focus on, compared with the cross-channel congruence condition. When participants were required to focus on verbal content, accuracy was significantly lower also when prosody was incongruent with verbal content and face. This suggests that prosody biases emotional verbal content processing, even when conflicting with verbal content and face simultaneously. Implications for multimodal communication and language evolution studies are discussed. (shrink)

Temporal regularities in speech, such as interdependencies in the timing of speech events, are thought to scaffold early acquisition of the building blocks in speech. By providing on-line clues to the location and duration of upcoming syllables, temporal structure may aid segmentation and clustering of continuous speech into separable units. This hypothesis tacitly assumes that learners exploit predictability in the temporal structure of speech. Existing measures of speech timing tend to focus on first-order regularities among adjacent units, and are overly (...) sensitive to idiosyncrasies in the data they describe. Here, we compare several statistical methods on a sample of 18 languages, testing whether syllable occurrence is predictable over time. Rather than looking for differences between languages, we aim to find across languages (using clearly defined acoustic, rather than orthographic, measures), temporal predictability in the speech signal which could be exploited by a language learner. First, we analyse distributional regularities using two novel techniques: a Bayesian ideal learner analysis, and a simple distributional measure. Second, we model higher-order temporal structure—regularities arising in an ordered series of syllable timings—testing the hypothesis that non-adjacent temporal structures may explain the gap between subjectively-perceived temporal regularities, and the absence of universally-accepted lower-order objective measures. Together, our analyses provide limited evidence for predictability at different time scales, though higher-order predictability is difficult to reliably infer. We conclude that temporal predictability in speech may well arise from a combination of individually weak perceptual cues at multiple structural levels, but is challenging to pinpoint. (shrink)