Zara Harmon

Publications

Displaying 1 - 2 of 2
  • Sander, J., Çetinçelik, M., Zhang, Y., Rowland, C. F., & Harmon, Z. (2024). Why does joint attention predict vocabulary acquisition? The answer depends on what coding scheme you use. In L. K. Samuelson, S. L. Frank, M. Toneva, A. Mackey, & E. Hazeltine (Eds.), Proceedings of the 46th Annual Meeting of the Cognitive Science Society (CogSci 2024) (pp. 1607-1613).

    Abstract

    Despite decades of study, we still know less than we would like about the association between joint attention (JA) and language acquisition. This is partly because of disagreements on how to operationalise JA. In this study, we examine the impact of applying two different, influential JA operationalisation schemes to the same dataset of child-caregiver interactions, to determine which yields a better fit to children's later vocabulary size. Two coding schemes— one defining JA in terms of gaze overlap and one in terms of social aspects of shared attention—were applied to video-recordings of dyadic naturalistic toy-play interactions (N=45). We found that JA was predictive of later production vocabulary when operationalised as shared focus (study 1), but also that its operationalisation as shared social awareness increased its predictive power (study 2). Our results emphasise the critical role of methodological choices in understanding how and why JA is associated with vocabulary size.
  • Harmon, Z., Idemaru, K., & Kapatsinski, V. (2019). Learning mechanisms in cue reweighting. Cognition, 189, 76-88. doi:10.1016/j.cognition.2019.03.011.

    Abstract

    Feedback has been shown to be effective in shifting attention across perceptual cues to a phonological contrast in speech perception (Francis, Baldwin & Nusbaum, 2000). However, the learning mechanisms behind this process remain obscure. We compare the predictions of supervised error-driven learning (Rescorla & Wagner, 1972) and reinforcement learning (Sutton & Barto, 1998) using computational simulations. Supervised learning predicts downweighting of an informative cue when the learner receives evidence that it is no longer informative. In contrast, reinforcement learning suggests that a reduction in cue weight requires positive evidence for the informativeness of an alternative cue. Experimental evidence supports the latter prediction, implicating reinforcement learning as the mechanism behind the effect of feedback on cue weighting in speech perception. Native English listeners were exposed to either bimodal or unimodal VOT distributions spanning the unaspirated/aspirated boundary (bear/pear). VOT is the primary cue to initial stop voicing in English. However, lexical feedback in training indicated that VOT was no longer predictive of voicing. Reduction in the weight of VOT was observed only when participants could use an alternative cue, F0, to predict voicing. Frequency distributions had no effect on learning. Overall, the results suggest that attention shifting in learning the phonetic cues to phonological categories is accomplished using simple reinforcement learning principles that also guide the choice of actions in other domains.

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