Despite burgeoning evidence that listeners are highly sensitive to statistical distributions of speech cues, the mechanism underlying learning may not be purely statistical tracking. Decades of research in animal learning suggest that learning results from prediction and prediction error. Two artificial language learning experiments test two predictions that distinguish error-driven from purely statistical models; namely, cue competition – specifically, Kamin’s (1968) ‘blocking’ effect (Experiment 1) – and the predictive structure of learning events (Experiment 2).

In Experiment 1, prior knowledge of an informative cue blocked learning of a second cue. This finding may help explain second language learners’ difficulty in acquiring native-level perception of non-native speech cues. In Experiment 2, learning was better with a discriminative (cue–outcome) order compared to a non-dis- criminative (outcome–cue) order. Experiment 2 suggests that learning speech cues, including reversing effects of blocking, depends on (un)learning from prediction error and depends on the temporal order of auditory cues versus semantic outcomes.<\p>

Together, these results show that (a) existing knowledge of acoustic cues can block later learning of new cues, and (b) speech sound acquisition depends on the predictive structure of learning events. When feedback from prediction error is available, this drives learners to ignore salient non-discriminative cues and effectively learn to use target cue dimensions. These findings may have considerable implications for the field of speech acquisition.

Since its introduction in the late 1990s, the ‘visual world’ eyetracking paradigm (VWP) has come to be highly valued in linguistic and related research as a means of investigating moment-by-moment online processing. However, the very aspect that makes the visual world paradigm so exciting as a research tool – tracking looking behaviour over time – also creates challenges for statistical analysis. Several papers in the late 2000s raised issues with the analysis methods typically used for analysing VWP data (e.g. Barr, 2008; Mirman et al., 2008). In this paper, we present a new solution to these issues. We introduce the use of Generalised Additive Mixed Models (GAMMs) for the analysis of VWP data. As GAMMs have not previously been used to analyse VWP data, we provide a brief introduction to the use and benefits of GAMMs for analysing time series data. In addition, we also introduce a new measure of uncertainty (i.e. a new response variable), Euclidean Distance from the target and competitor.

Two visual world eyetracking experiments investigated how acoustic cue value and statistical variance affect perceptual uncertainty during Cantonese consonant (Experiment 1) and tone perception (Experiment 2). Participants heard low- or high-variance acoustic stimuli. Euclidean distance of fixations from target and competitor pictures over time was analysed using Generalised Additive Mixed Modelling. Distance of fixations from target and competitor pictures varied as a function of acoustic cue, providing evidence for gradient, nonlinear sensitivity to cue values. Moreover, cue value effects significantly interacted with statistical variance, indicating that the cue distribution directly affects perceptual uncertainty. Interestingly, the time course of effects differed between target distance and competitor distance models. The pattern of effects over time suggests a global strategy in response to the level of uncertainty: as uncertainty increases, verification looks increase accordingly. Low variance generally creates less uncertainty, but can lead to greater uncertainty in the face of unexpected speech tokens.

Listeners are able to very approximately estimate speakers’ ages, with a mean estimation error of around ten years. Interestingly, accuracy varies considerably, depending on a number of social aspects of both speaker and listener, including age, gender and native language or language variety. The present study considers the effects of four factors on age perception. It investigates whether there is a main effect of speakers’ native language (Arabic, Korean and Mandarin) even when speaking a second language, English. It also investigates a particular speaker-listener relationship, namely the degree of linguistic familiarity. Linguistic familiarity was expected to be greater between Mandarin and Korean than between Mandarin or Korean and Arabic. In addition, it considers the effect of the acoustic cues of mean fundamental frequency (F0) and speech rate on age estimates.

Fifteen Arabic-accented, fifteen Korean-accented and twenty Mandarin-accented English speakers participated as listeners. They heard audio stimuli produced by forty-eight speakers, equally distributed between native Arabic, Korean and Mandarin speakers, reading a short passage in English. Listeners were instructed to estimate speakers’ ages in years. Listeners’ age estimates and reaction times were recorded.

Results indicate a significant main effect of speaker native language on perceived age such that Mandarin speakers were estimated to be younger than Arabic speakers. There was also a significant effect of linguistic familiarity on age estimation accuracy. Age estimates were more accurate with greater linguistic familiarity, i.e., native Korean and Mandarin listeners estimated ages of speakers of their own native languages more accurately than native Arabic speakers’ ages and vice versa. In terms of acoustic cues, mean F0 and speech rate were significant predictors of age estimation. These effects suggest that in perception, age may be marked not only by biological changes that occur over the lifetime, but also by language-specific socio-cultural features.