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      On the learnability of level-based and unit-based tonal OCP generalizations: An artificial grammar learning study

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      Glossa: a journal of general linguistics
      Open Library of the Humanities

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          Abstract

          In previous research of tonal phonology, contour tones were assumed to be phonologically presented not only as a sequence of level tones but also as a single unit. Obligatory Contour Principle (OCP) constraints, by extension, were thought to prohibit identical level tones associated to adjacent terminal nodes (OCP-Terminal) and identical adjacent tonal units (OCP-Unit). This proposal has nevertheless been challenged theoretically and empirically. This study seeks to offer new experimental evidence to help solve the debate by comparing the learnability of the two OCP generalizations in an artificial grammar learning paradigm. In Exp I, we exposed disyllabic tonal patterns conforming either to OCP-Terminal or to OCP-Unit to two target groups of learners, who were then tested if they could extend the hidden generalization to their auditory acceptability judgment of novel disyllabic items. Exp II had the same exposure phase but required learners to produce novel disyllabic items with tonal combinations of their choice. In both experiments, the two target groups demonstrated signs of learning target OCP generalizations from different channels. Crucially, learners implicitly acquired the OCP-Terminal generalization but only learned the OCP-Unit pattern as explicit knowledge. These findings led us to conclude that OCP-Unit may not be involved in implicit and automatized phonological computation.

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          Random effects structure for confirmatory hypothesis testing: Keep it maximal.

          Linear mixed-effects models (LMEMs) have become increasingly prominent in psycholinguistics and related areas. However, many researchers do not seem to appreciate how random effects structures affect the generalizability of an analysis. Here, we argue that researchers using LMEMs for confirmatory hypothesis testing should minimally adhere to the standards that have been in place for many decades. Through theoretical arguments and Monte Carlo simulation, we show that LMEMs generalize best when they include the maximal random effects structure justified by the design. The generalization performance of LMEMs including data-driven random effects structures strongly depends upon modeling criteria and sample size, yielding reasonable results on moderately-sized samples when conservative criteria are used, but with little or no power advantage over maximal models. Finally, random-intercepts-only LMEMs used on within-subjects and/or within-items data from populations where subjects and/or items vary in their sensitivity to experimental manipulations always generalize worse than separate F 1 and F 2 tests, and in many cases, even worse than F 1 alone. Maximal LMEMs should be the 'gold standard' for confirmatory hypothesis testing in psycholinguistics and beyond.
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            Is Open Access

            PsychoPy2: Experiments in behavior made easy

            PsychoPy is an application for the creation of experiments in behavioral science (psychology, neuroscience, linguistics, etc.) with precise spatial control and timing of stimuli. It now provides a choice of interface; users can write scripts in Python if they choose, while those who prefer to construct experiments graphically can use the new Builder interface. Here we describe the features that have been added over the last 10 years of its development. The most notable addition has been that Builder interface, allowing users to create studies with minimal or no programming, while also allowing the insertion of Python code for maximal flexibility. We also present some of the other new features, including further stimulus options, asynchronous time-stamped hardware polling, and better support for open science and reproducibility. Tens of thousands of users now launch PsychoPy every month, and more than 90 people have contributed to the code. We discuss the current state of the project, as well as plans for the future.
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              Can sequence learning be implicit? New evidence with the process dissociation procedure.

              Can we learn without awareness? Although this issue has been extensively explored through studies of implicit learning, there is currently no agreement about the extent to which knowledge can be acquired and projected onto performance in an unconscious way. The controversy, like that surrounding implicit memory, seems to be at least in part attributable to unquestioned acceptance of the unrealistic assumption that tasks are process-pure--that is, that a given task exclusively involves either implicit or explicit knowledge. Methods such as the process dissociation procedure (PDP, Jacoby, 1991) have been developed to overcome the conceptual limitations of the process purity assumption but have seldom been used in the context of implicit learning research. In this paper, we show how the PDP can be applied to a free generation task so as to disentangle explicit and implicit sequence learning. Our results indicate that subjects who are denied preparation to the next stimulus nevertheless exhibit knowledge of the sequence through their reaction time performance despite remaining unable (1) to project this knowledge in a recognition task and (2) to refrain from expressing their knowledge when specifically instructed to do so. These findings provide strong evidence that sequence learning can be unconscious.

                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Glossa: a journal of general linguistics
                Open Library of the Humanities
                2397-1835
                January 14 2022
                March 25 2022
                : 7
                : 1
                Affiliations
                [1 ]National Tsing Hua University, Taiwan
                Article
                10.16995/glossa.5795
                0a9d4108-22f1-4d07-8a2c-b34d6939c067
                © 2022

                https://creativecommons.org/licenses/by/4.0

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