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With a p-value at. Students who watched any of the 4 videos did better than those who watched no video at all, but no particular video was more effective than any other in improving test performance. Maybe the difference between the videos was too subtle to make an impact. Perhaps it was sufficient for the students to read a few sample sentences which appeared in all the videos in order to improve performance. What does this mean for Spanish at USC? Rather than impacting the way the program is delivered to students, this experiment showed faculty that it was possible to evaluate learning in a critical and rigorous way.

Now that the director has put systems in place for running experiments, she can run other experiments in future semesters. The results from those experiments can be used to improve the program and folded into self-assessment and accreditation exercises. In short, this was a critical first step in making a great program even better.

As with all things learning, creating a process for continual improvement is more important that any one product. For reference, here are the column names.

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F-value: 2. All the 28 questions were presented in a pseudo-random order. In the last testing phase, apart from the measures to the normal testing questions, we also recorded the accuracies and average reaction times to the additional questions about the novel items. We grouped the accuracy and average reaction time data in the experimental conditions 1, 3, and 5 the artificial languages therein had congruent orders as the congruent set, and those in the conditions 2, 4, and 6 as the incongruent set. In each set, the accuracies and average reaction times were grouped according to the three testing phases.

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The measures to the additional questions formed the fourth phase. To meet the assumption of normality, we used the log-transformed base e reaction times in the analyses. Following the general procedure in assessing experimental data Osborne and Overbay, , we removed the outliers from the accuracy and reaction time data before the analyses. Outliers were values exceeding 2. For accuracies, outliers were accuracies that were too low; for reaction times, they were times either too long or too short.

Another way to handle outliers is to replace them with the group means, the results following this procedure were similar see Supplementary Table S1. We conducted two ANOVAs, respectively, on accuracy and average reaction time to test our working hypothesis that the PSH affects the learning of regulating orders between segments encoding the involved visual features.

In the ANOVAs, we treated the congruency of artificial languages as a between-subjects factor two levels: the congruent languages, those used in conditions 1, 3, and 5, and incongruent languages, those used in conditions 2, 4, and 6 , and the experimental phase as a within-subjects factor four levels: the testing phases 1, 2, 3, and 4, the latter of which consists of the measures to the additional testing questions involving the unseen items. The ANOVA tests also took into account the question type two levels: meaning selection or utterance selection and interaction between congruency and experimental phase.

In addition to the ANOVA tests, we conducted group t-tests to compare the accuracies and average reaction times between the conditions differing in regulating orders conditions 1 vs. Following the Bonferroni correction, we set the critical p value to identify significant effects as 0. All the analyses were carried out in R 3.

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  6. In both tests, question type showed no significant effect, which matched the post hoc surveys; participants felt invariant to both types of questions. This indicated that the way of recording the individual learning performance in our study had no obvious effect on the recorded results.

    Both congruency and experimental phase showed significant main effects, but there was no significant interaction between the two. The significant effect of congruency confirmed our working hypothesis that the perceptual saliency hierarchy could affect individual learning of congruent or incongruent orders. The significant effect of experimental phase indicated that learning occurred at different experimental phases of instance exposure. The non-significant interaction between congruency and experimental phase suggested that the learning patterns across phases for the congruent and incongruent orders were largely the same.

    Across the three phases, there were a general increase in accuracy and a general decrease in average reaction time, which echoed the significant effect of experimental phase in the ANOVAs. In our experiment, individual learning started at the first phase, and the improvement after the third phase was smaller than that after the second phase.

    After three phases, the participants had largely grasped the compositional languages in different conditions. These results also matched the post hoc surveys; participants claimed that they had confidently learned most of the meaning-form mappings after the second phase, and the other four said that they had learned the language right after the first phase.

    Accuracies ACC at the four experimental phases. A CS vs. SC; B CT vs. TC; C ST vs. Error bars denote standard errors.

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    Average reaction times RT at the four experimental phases. Although the participants claimed to have learned the artificial languages, their performances on the unseen items at the last phase revealed some biases for the congruent orders. As for the CS and SC orders, the participants showed similar accuracies, but their reaction times to the CS orders were shorter than those to the SC orders. Significant difference in accuracy was also shown at the first and third testing phases concerning the seen instances.

    In this paper, we evaluated whether the perceptual constraint regarding the saliency hierarchy of the basic visual features affects the learnability of ordering structures between the segments encoding such features in an artificial language.

    After repeated exposure to the tokens of the artificial languages with different orderings, the participants gradually learned the segments encoding color, shape, or textural patterns and the orders between these segments. Their judgements on the unseen instances indicated that they could generalize their learned knowledge and apply it to novel items.

    Moreover, they exhibited biases for the orders that were congruent with the perceptual saliency hierarchy regarding color, shape, and textural patterns. To be specific, they showed strong biases for the CT color before textural pattern and ST shape before textural pattern orders over the TC and TS orders, in terms of judging accuracy and average reaction time. Such biases started to exhibit during the learning process. They also showed a weak bias for the CS over the SC order, which only manifested in average reaction time when judging the unseen items.

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    In simple phrases of Mandarin or Cantonese, information of textural pattern often appears before that of shape, and color before shape e. In simple phrases of English, adjectives of shapes often appear in front of those of colors e. In addition, the participants had no previous experience of the segments used in our experiment, and had no chance to apply their prior linguistic knowledge to change the artificial languages or develop one from scratch.

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    These ensure that the observed patterns can be safely ascribed to the perceptual saliency hierarchy. This is an inevitable limitation of ALL experiments recruiting alphabetic language speakers to learn alphabetic languages. Recruiting participants with no alphabetic experiences e. However, many of such studies focus on the emergence of a language-like communication system out of random signals, and participants therein are allowed to introduce signals that they prefer during the recall tasks.

    Our fact that the perceptual saliency hierarchy affects the learning and processing of relevant language structures reveals a close relation between perceptual constraints in humans and structural configuration in language. In a linguistic form, if the ordering of segments encoding the visual features follows naturally the perceptual saliency of those features, production and comprehension of the form would be more straightforward.

    Then, compared with the forms having an incongruent order between those features, the accuracies of answering questions about the forms having congruent orders tend to be higher, and the reaction times shorter.


    In addition, compared with color and shape, textural pattern is much less salient, and it is shown as contrast of color or shape. Detection of such pattern occurs after detection of color or shape, and relies on detection of color or shape Treisman, ; Healey and Enns, This also explains the strong bias for the CT and ST orders. By contrast, color appears to be slightly more salient than shape, which resulted in the weak bias for the CS over SC order.

    All these results are in line with the perspective that perceptual constraints affect the learning and use of related language structures Heine, ; Pullum and Scholz, ; Mesoudi and Whiten, ; Christiansen and Chater, They also suggest that difference in saliency levels of the visual features could affect the degree of bias for the congruent orders regulating those features.

    Given the bias for the congruent orders, a follow-up question arises: If the perceptual saliency hierarchy affects the regulating orders of the segments encoding the involved visual features, is this structure in all languages the same, favoring the congruent orders? The answer to this question is NO. Although in many English phases, the shape adjectives should appear in front of the color adjectives, most people have a relatively free order between the two.

    Considering these, apart from the perceptual saliency hierarchy, the structural configuration of language is also subject to other constraints. One candidate of such constraints comes from the socio-cultural environment of language. In addition, our experiment showed that despite the fact that the participants exhibited biases towards the congruent orders, after a small number of learning rounds, they could largely grasp both the biased and unbiased orders, reaching high over 0. Following the dynamics in the three experimental phases, we can reasonably expect that given more rounds of learning the participants would learn each artificial language equally well, no matter whether its order was congruent or incongruent.

    This suggests that the structures distinct from the biased ones can be equally acquired by speakers.