Author
Molly Babel
Other affiliations: University of California, Berkeley, University of Minnesota
Bio: Molly Babel is an academic researcher from University of British Columbia. The author has contributed to research in topics: Speech perception & Speech production. The author has an hindex of 18, co-authored 65 publications receiving 5991 citations. Previous affiliations of Molly Babel include University of California, Berkeley & University of Minnesota.
Papers published on a yearly basis
Papers
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Alexander A. Aarts, Joanna E. Anderson1, Christopher J. Anderson2, Peter Raymond Attridge3 +287 more•Institutions (116)
TL;DR: A large-scale assessment suggests that experimental reproducibility in psychology leaves a lot to be desired, and correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams.
Abstract: Reproducibility is a defining feature of science, but the extent to which it characterizes current research is unknown. We conducted replications of 100 experimental and correlational studies published in three psychology journals using high-powered designs and original materials when available. Replication effects were half the magnitude of original effects, representing a substantial decline. Ninety-seven percent of original studies had statistically significant results. Thirty-six percent of replications had statistically significant results; 47% of original effect sizes were in the 95% confidence interval of the replication effect size; 39% of effects were subjectively rated to have replicated the original result; and if no bias in original results is assumed, combining original and replication results left 68% with statistically significant effects. Correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams.
5,532 citations
TL;DR: The degree to which vowels were imitated was subtly affected by attractiveness ratings and this also interacted with the experimental condition, demonstrating the labile nature of linguistic segments with respect to both their perceptual encoding and their variation in production.
289 citations
TL;DR: The authors investigated phonetic accommodation in New Zealand English when speakers of NZE are responding to an Australian talker in a speech production task and found that participants who scored with a pro-Australia bias were more likely to accommodate to the speech of the AuE speaker.
Abstract: Recent research has been concerned with whether speech accommodation is an automatic process or determined by social factors (e.g. Trudgill 2008). This paper investigates phonetic accommodation in New Zealand English when speakers of NZE are responding to an Australian talker in a speech production task. NZ participants were randomly assigned to either a Positive or Negative group, where they were either flattered or insulted by the Australian. Overall, the NZE speakers accommodated to the speech of the AuE speaker. The flattery/insult manipulation did not influence degree of accommodation, but accommodation was predicted by participants' scores on an Implicit Association Task that measured Australia and New Zealand biases. Participants who scored with a pro-Australia bias were more likely to accommodate to the speech of the AuE speaker. Social biases about how a participant feels about a speaker predicted the extent of accommodation. These biases are, crucially, simultaneously automatic and social. (Speech accommodation, phonetic convergence, New Zealand English, dialect contact)*
188 citations
01 Jan 2009
TL;DR: The authors investigated phonetic accommodation in New Zealand English when speakers of NZE are responding to an Australian talker in a speech production task and found that participants who scored with a pro-Australia bias were more likely to accommodate to the speech of the AuE speaker.
Abstract: Recent research has been concerned with whether speech accommodation is an automatic process or determined by social factors (e.g. Trudgill 2008). This paper investigates phonetic accommodation in New Zealand English when speakers of NZE are responding to an Australian talker in a speech production task. NZ participants were randomly assigned to either a Positive or Negative group, where they were either flattered or insulted by the Australian. Overall, the NZE speakers accommodated to the speech of the AuE speaker. The flattery/insult manipulation did not influence degree of accommodation, but accommodation was predicted by participants' scores on an Implicit Association Task that measured Australia and New Zealand biases. Participants who scored with a pro-Australia bias were more likely to accommodate to the speech of the AuE speaker. Social biases about how a participant feels about a speaker predicted the extent of accommodation. These biases are, crucially, simultaneously automatic and social. (Speech accommodation, phonetic convergence, New Zealand English, dialect contact)*
180 citations
TL;DR: An auditory naming task with two conditions tested the hypothesis that fundamental frequency is a critical component of phonetic accommodation, and acoustic measurements and listener judgments of accommodation were not significantly correlated, enforcing the intuitive concept that accommodation and listeners’ judgments of similarity are holistic and do not hone on singular features in the acoustic signal.
Abstract: Previous research has argued that fundamental frequency is a critical component of phonetic accommodation. We tested this hypothesis in an auditory naming task with two conditions. Participants in an Unfiltered Condition completed an auditory naming task with a single male model talker. A second group of participants was assigned to a Filtered Condition where the same stimuli had been high-pass filtered at 300 Hz, thereby eliminating the fundamental frequency. Acoustic analysis of f0 revealed that participants assigned to the Unfiltered Condition imitated the pitch of the model talker more than those assigned to the Filtered Condition. Although accommodation was statistically significant, the effect was small, so we followed with a perception study to examine listeners’ abilities to detect differences in accommodation across conditions. Shadowed tokens from participants in the Unfiltered Condition were indeed judged by listeners to be more similar to the model talker’s productions that those from particip...
124 citations
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01 Jan 2016
TL;DR: The using multivariate statistics is universally compatible with any devices to read, allowing you to get the most less latency time to download any of the authors' books like this one.
Abstract: Thank you for downloading using multivariate statistics. As you may know, people have look hundreds times for their favorite novels like this using multivariate statistics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their laptop. using multivariate statistics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the using multivariate statistics is universally compatible with any devices to read.
14,604 citations
2,609 citations
University of Southern California1, Duke University2, Stockholm School of Economics3, Center for Open Science4, University of Virginia5, University of Amsterdam6, University of Pennsylvania7, University of North Carolina at Chapel Hill8, University of Regensburg9, California Institute of Technology10, New York University11, Research Institute of Industrial Economics12, Cardiff University13, Mathematica Policy Research14, Northwestern University15, Ohio State University16, University of Sussex17, Texas A&M University18, Royal Holloway, University of London19, University of Zurich20, University of Melbourne21, University of Wisconsin-Madison22, University of Michigan23, Stanford University24, Rutgers University25, Columbia University26, University of Washington27, University of Edinburgh28, National University of Singapore29, Utrecht University30, Arizona State University31, Princeton University32, University of California, Los Angeles33, Imperial College London34, University of Innsbruck35, Harvard University36, University of Chicago37, University of Pittsburgh38, University of Notre Dame39, University of California, Berkeley40, Johns Hopkins University41, University of Bristol42, University of New South Wales43, Dartmouth College44, Whitman College45, University of Puerto Rico46, University of Milan47, University of California, Irvine48, Paris Dauphine University49, University of British Columbia50, Ludwig Maximilian University of Munich51, Purdue University52, Washington University in St. Louis53, University of California, Davis54, Microsoft55
TL;DR: The default P-value threshold for statistical significance is proposed to be changed from 0.05 to 0.005 for claims of new discoveries in order to reduce uncertainty in the number of discoveries.
Abstract: We propose to change the default P-value threshold for statistical significance from 0.05 to 0.005 for claims of new discoveries.
1,586 citations
TL;DR: In this article, the authors introduce the current state-of-the-art of network estimation and propose two novel statistical methods: the correlation stability coefficient and the bootstrapped difference test for edge-weights and centrality indices.
Abstract: The usage of psychological networks that conceptualize behavior as a complex interplay of psychological and other components has gained increasing popularity in various research fields. While prior publications have tackled the topics of estimating and interpreting such networks, little work has been conducted to check how accurate (i.e., prone to sampling variation) networks are estimated, and how stable (i.e., interpretation remains similar with less observations) inferences from the network structure (such as centrality indices) are. In this tutorial paper, we aim to introduce the reader to this field and tackle the problem of accuracy under sampling variation. We first introduce the current state-of-the-art of network estimation. Second, we provide a rationale why researchers should investigate the accuracy of psychological networks. Third, we describe how bootstrap routines can be used to (A) assess the accuracy of estimated network connections, (B) investigate the stability of centrality indices, and (C) test whether network connections and centrality estimates for different variables differ from each other. We introduce two novel statistical methods: for (B) the correlation stability coefficient, and for (C) the bootstrapped difference test for edge-weights and centrality indices. We conducted and present simulation studies to assess the performance of both methods. Finally, we developed the free R-package bootnet that allows for estimating psychological networks in a generalized framework in addition to the proposed bootstrap methods. We showcase bootnet in a tutorial, accompanied by R syntax, in which we analyze a dataset of 359 women with posttraumatic stress disorder available online.
1,584 citations