references.bib

@article{Werker2005,
abstract = {Over the past few years, there has been an increasing emphasis on studying the link between infant speech perception and later language acquisition. This research has yielded some seemingly contradictory findings: In some studies infants appear to use phonetic and indexical detail that they fail to use in other studies. In this article we present a new, unified framework for accounting for these divergent findings. PRIMIR (a developmental framework for Processing Rich Information from Multidimensional Interactive Representations) assumes there is rich information available in the speech input and that the child picks up and organizes this information along a number of multidimensional interactive planes. Use of this rich information depends on the joint activity of 3 dynamic filters. These filters-the initial biases, the developmental level of the child, and requirements of the specific language task the child is facing-work together to differentially direct attention to 1 (or more) plane. In this article we outline the contradictory data that need to be explained, elucidate PRIMIR, including its underlying assumptions and overall architecture, and compare it to existing frameworks. We conclude by presenting core predictions of PRIMIR.},
author = {Werker, Janet and Curtin, Suzanne},
doi = {10.1207/s15473341lld0102_4},
file = {:Users/rodrigo/Library/Application Support/Mendeley Desktop/Downloaded/Werker, Curtin - 2005 - PRIMIR A Developmental Framework of Infant Speech Processing.pdf:pdf},
isbn = {1547-5441},
issn = {1547-5441},
journal = {Language Learning and Development},
mendeley-groups = {02 Doutorado/Tese/Statistical Language Learning/Others,Post-doc},
number = {2},
pages = {197--234},
title = {{PRIMIR: A Developmental Framework of Infant Speech Processing}},
volume = {1},
year = {2005}
}

@book{frank2021,
  title={Variability and consistency in early language learning: The Wordbank project},
  author={Frank, Michael C and Braginsky, Mika and Yurovsky, Daniel and Marchman, Virginia A},
  year={2021},
  publisher={MIT Press}
}

@book{fenson2007,
  title={MacArthur-Bates communicative development inventories},
  author={Fenson, Larry and others},
  year={2007},
  publisher={Paul H. Brookes Publishing Company Baltimore, MD}
}

@article{Frank2017,
abstract = {The MacArthur-Bates Communicative Development Inventories (CDIs) are a widely used family of parent-report instruments for easy and inexpensive data-gathering about early language acquisition. CDI data have been used to explore a variety of theoretically important topics, but, with few exceptions, researchers have had to rely on data collected in their own lab. In this paper, we remedy this issue by presenting Wordbank, a structured database of CDI data combined with a browsable web interface. Wordbank archives CDI data across languages and labs, providing a resource for researchers interested in early language, as well as a platform for novel analyses. The site allows interactive exploration of patterns of vocabulary growth at the level of both individual children and particular words. We also introduce wordbankr, a software package for connecting to the database directly. Together, these tools extend the abilities of students and researchers to explore quantitative trends in vocabulary development.},
author = {Frank, Michael C. and Braginsky, Mika and Yurovsky, Daniel and Marchman, Virginia A.},
doi = {10.1017/S0305000916000209},
file = {:Users/rodrigo/Downloads/frank-2016-jcl.pdf:pdf},
issn = {14697602},
journal = {Journal of Child Language},
mendeley-groups = {edx_ml},
number = {3},
pages = {677--694},
pmid = {27189114},
title = {{Wordbank: An open repository for developmental vocabulary data}},
volume = {44},
year = {2017}
}

@article{Kachergis2017a,
abstract = {Prior research has shown that people can learn many nouns (i.e., word-object mappings) from a short series of ambiguous situations containing multiple words and objects. For successful cross-situational learning, people must approximately track which words and referents co-occur most frequently. This study investigates the effects of allowing some word-referent pairs to appear more frequently than others, as is true in real-world learning environments. Surprisingly, high-frequency pairs are not always learned better, but can also boost learning of other pairs. Using a recent associative model (Kachergis, Yu, & Shiffrin, 2012), we explain how mixing pairs of different frequencies can bootstrap late learning of the low-frequency pairs based on early learning of higher frequency pairs. We also manipulate contextual diversity, the number of pairs a given pair appears with across training, since it is naturalistically confounded with frequency. The associative model has competing familiarity and uncertainty biases, and their interaction is able to capture the individual and combined effects of frequency and contextual diversity on human learning. Two other recent word-learning models do not account for the behavioral findings.},
author = {Kachergis, George and Yu, Chen and Shiffrin, Richard M},
doi = {10.1111/cogs.12353},
file = {:Users/rodrigo/Library/Application Support/Mendeley Desktop/Downloaded/Kachergis, Yu, Shiffrin - 2017 - A Bootstrapping Model of Frequency and Context Effects in Word Learning.pdf:pdf},
isbn = {1212998790},
issn = {03640213},
journal = {Cognitive Science},
keywords = {contextual,cross-situational learning,diversity,language acquisition,statistical learning,word frequency},
mendeley-groups = {02 Doutorado/Tese/AtualBiblio/Bases 2017/a_Lidos,02 Doutorado/Tese/AtualBiblio/Bases (07-16)},
month = {apr},
number = {3},
pages = {590--622},
pmid = {26988198},
title = {{A Bootstrapping Model of Frequency and Context Effects in Word Learning}},
url = {http://doi.wiley.com/10.1111/cogs.12353},
volume = {41},
year = {2017}
}

@book{Werker2013,
abstract = {Key Points 1. Newborns have sophisticated speech perception abilities. ey prefer language over complex speech analogs, discriminate rhythmically di erent languages, prefer to listen to their native language, detect word boundaries, and discriminate most phonemes of the world's languages. ese perceptual abilities lay the foundations for subsequent learning. 2. Perceptual biases present at birth prepare the infant to learn the sound system of any of the world's languages, and perceptual learning, using the statistical properties of the input speech, helps establish the repertoire of native speech sound categories. 3. Infants start to segment the continuous speech stream into constituent words during the second half of the rst year. Initially, they seem to rely primarily on statistical information such as the co-occurrence patterns of phonemes and syllables, 31 Abstract We discuss the development of speech perception and its contribution to the acquisition of the native language(s) during the first year of life, reviewing recent empirical evidence as well as current theoretical debates. We situate the discussion in an epigenetic framework in an attempt to transcend the traditional nature/nurture controversy. As we illustrate, some perceptual and learning mechanisms are best described as experience-expectant processes, embedded in our biology and awaiting minimal environmental input, while others are experience-dependent, emerging as a function of sufficient exposure and learning. We argue for a cascading model of development, whereby the initial biases guide learning and constrain the influence of the environmental input. To illustrate this, we first review the perceptual abilities of newborn infants, then discuss how these broad-based abilities are attuned to the native language at different levels (phonology, syntax, lexicon etc.). which they can extract using general-purpose, universal learning mechanisms. 4. Toward the end of the rst year, as they gain more experience with the native language, infants increasingly rely on language-speci c cues to word segmentation, such as stress, phonotactics, or phoneme allophony. 5. As they extract an increasing number of potential word forms from the input, infants start to associate these with concrete, perceptually available objects in the environment. is initial associative labeling is the rst step toward word learning and the lexicon. Soon after the rst birthday, the main cognitive focus of the infant changes from phoneme to word learning. is functional reorganization allows infants to use phoneme-level representations, ignoring other acoustic details that are not relevant for lexical distinctions. 6. During this process, the two levels (i.e., the presentations of word forms and of objects) C H A P T E R OUP UNCORRECTED PROOF – FIRSTPROOFS, Mon Sep 10 2012, NEWGEN 31_Zelazo-V1_Ch31.indd 909 31_Zelazo-V1_Ch31.indd 909 9/12/2012 8:42:54 PM 9/12/2012 8:42:54 PM interact. Naming objects acts as an invitation to form conceptual categories for these objects, while evidence of a meaning contrast can strengthen the establishment of language-speci c phoneme categories. 7. Sensitivity to language structure appears early on. At birth, infants are able to detect identity relations, and they show evidence of rule learning and structural generalizations in the second half of the rst year. 8. e perception of the acoustic and phonological properties of speech also plays a role in acquiring language structure, as certain morphosyntactic constructions have phonological and prosodic correlates. Infants are able to exploit these correlations to bootstrap abstract grammatical structure from perceptually available cues. 9. As an example of prosodic bootstrapping, infants are able to use cues such as intensity, pitch, and duration to establish an initial, rudimentary representation of the basic word order of their native language(s), which has well-de ned prosodic correlates. 10. Overall, early language acquisition can be characterized by an epigenetically determined set of initial biases, on which speci c language experience can build.},
author = {Werker, Janet F. and Gervain, Judit},
booktitle = {Human Auditory Development},
doi = {10.1093/oxfordhb/9780199958450.013.0031},
editor = {Zelazo, Philip David},
file = {:Users/rodrigo/Library/Application Support/Mendeley Desktop/Downloaded/Werker, Gervain - 2013 - Speech Perception in Infancy.pdf:pdf},
isbn = {978-1-4614-1420-9},
issn = {00014966},
keywords = {epigenetics,experience-expectant and experience-dependent,linguistic rhythm,mechanisms,neonates,perceptual attunement,phoneme perception,phonological,prosodic bootstrapping,rule learning,word learning,word order},
mendeley-groups = {02 Doutorado/Tese/Statistical Language Learning/Reviews},
month = {mar},
pmid = {23398218},
publisher = {Oxford University Press},
title = {{Speech Perception in Infancy}},
url = {http://www.springerlink.com/index/10.1007/978-1-4614-1421-6 http://oxfordhandbooks.com/view/10.1093/oxfordhb/9780199958450.001.0001/oxfordhb-9780199958450-e-31},
year = {2013}
}

@article{Yarkoni2017,
abstract = {Psychology has historically been concerned, first and foremost, with explaining the causal mechanisms that give rise to behavior. Randomized, tightly controlled experiments are enshrined as the gold standard of psychological research, and there are endless investigations of the various mediating and moderating variables that govern various behaviors. We argue that psychology's near-total focus on explaining the causes of behavior has led much of the field to be populated by research programs that provide intricate theories of psychological mechanism but that have little (or unknown) ability to predict future behaviors with any appreciable accuracy. We propose that principles and techniques from the field of machine learning can help psychology become a more predictive science. We review some of the fundamental concepts and tools of machine learning and point out examples where these concepts have been used to conduct interesting and important psychological research that focuses on predictive research questions. We suggest that an increased focus on prediction, rather than explanation, can ultimately lead us to greater understanding of behavior.},
author = {Yarkoni, Tal and Westfall, Jacob},
doi = {10.1177/1745691617693393},
file = {:Users/rodrigo/Library/Application Support/Mendeley Desktop/Downloaded/Yarkoni, Westfall - 2017 - Choosing Prediction Over Explanation in Psychology Lessons From Machine Learning(3).pdf:pdf},
issn = {17456924},
journal = {Perspectives on Psychological Science},
keywords = {explanation,machine learning,prediction},
mendeley-groups = {edx_ml},
number = {6},
pages = {1100--1122},
pmid = {28841086},
title = {{Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning}},
volume = {12},
year = {2017}
}

@article{Jacobucci2020,
abstract = {Machine learning (i.e., data mining, artificial intelligence, big data) has been increasingly applied in psychological science. Although some areas of research have benefited tremendously from a new set of statistical tools, most often in the use of biological or genetic variables, the hype has not been substantiated in more traditional areas of research. We argue that this phenomenon results from measurement errors that prevent machine-learning algorithms from accurately modeling nonlinear relationships, if indeed they exist. This shortcoming is showcased across a set of simulated examples, demonstrating that model selection between a machine-learning algorithm and regression depends on the measurement quality, regardless of sample size. We conclude with a set of recommendations and a discussion of ways to better integrate machine learning with statistics as traditionally practiced in psychological science.},
author = {Jacobucci, Ross and Grimm, Kevin J.},
doi = {10.1177/1745691620902467},
file = {:Users/rodrigo/Library/Application Support/Mendeley Desktop/Downloaded/Jacobucci, Grimm - 2020 - Machine Learning and Psychological Research The Unexplored Effect of Measurement.pdf:pdf},
issn = {17456924},
journal = {Perspectives on Psychological Science},
keywords = {data mining,machine learning,measurement error,psychometrics,structural-equation modeling},
mendeley-groups = {edx_ml},
number = {3},
pages = {809--816},
pmid = {32348703},
title = {{Machine Learning and Psychological Research: The Unexplored Effect of Measurement}},
volume = {15},
year = {2020}
}