Zara Harmon

Publications

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  • Harmon, Z., & Kapatsinski, V. (2020). The best-laid plan of mice and men: Competition between top-down and preceding-item cues in plan execution. In S. Denison, M. Mack, Y. Xu, & B. C. Armstrong (Eds.), Proceedings of the 42nd Annual Meeting of the Cognitive Science Society (CogSci 2020) (pp. 1674-1680). Montreal, QB: Cognitive Science Society.

    Abstract

    There is evidence that the process of executing a planned utterance involves the use of both preceding-context and top-down cues. Utterance-initial words are cued only by the top-down plan. In contrast, non-initial words are cued both by top-down cues and preceding-context cues. Co-existence of both cue types raises the question of how they interact during learning. We argue that this interaction is competitive: items that tend to be preceded by predictive preceding-context cues are harder to activate from the plan without this predictive context. A novel computational model of this competition is developed. The model is tested on a corpus of repetition disfluencies and shown to account for the influences on patterns of restarts during production. In particular, this model predicts a novel Initiation Effect: following an interruption, speakers re-initiate production from words that tend to occur in utterance-initial position, even when they are not initial in the interrupted utterance.
  • 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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