Congratulations to recent lab alumni and first year doctoral students Ariel James (U. Illinois), Justine Kao (Stanford) and Melody Dye (Indiana). This year, all three won highly prestigious National Science Foundation graduate fellowships in psychology. Melody and Justine were awarded fellowships in the cognitive area, while Ariel won in psycholinguistics.
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In 2011, “Learning language from the input” was one of Cognitive Psychology‘s twenty most read papers. It was in the top five for papers published that year. Read it here, or take a glance at the companion article, now in press at Language and Cognitive Processes.
Do the production and interpretation of patterns of plural forms in noun-noun compounds reveal the workings of innate constraints that govern morphological processing? The results of previous studies on compounding have been taken to support a number of important theoretical claims: first, that there are fundamental differences in the way that children and adults learn and process regular and irregular plurals, second, that these differences reflect formal constraints that govern the way the way regular and irregular plurals are processed in language, and third, that these constraints are unlikely to be the product of learning. In a series of seven experiments, we critically assess the evidence that is cited in support of these arguments. The results of our experiments provide little support for the idea that substantively different factors govern the patterns of acquisition, production and interpretation patterns of regular and irregular plural forms in compounds. Once frequency differences between regular and irregular plurals are accounted for, we find no evidence of any qualitative difference in the patterns of interpretation and production of regular and irregular plural nouns in compounds, in either adults or children. Accordingly, we suggest that the pattern of acquisition of both regular and irregular plurals in compounds is consistent with a simple account, in which children learn the conventions that govern plural compounding using evidence that is readily available in the distribution patterns of adult speech.
Friday Morning Session, 11:00 a.m.: Psycholinguistics: Syntax/Discourse
Richard Futrell (Stanford University), Michael Ramscar (University of Tübingen): German grammatical gender contributes to communicative efficiency
Sunday Morning Session, 10:30 a.m.: Psycholinguistics Across Level
Inbal Arnon (University of Haifa), Michael Ramscar (University of Tübingen): Granularity and the acquisition of grammatical gender: How order-of-acquisition affects what gets learned (Cognition article available online)
Looking for our award-winning PLoS One paper on how children learn numbers? In the last month, roughly half of Google searchers that stumbled onto this page were doing just that. We’ll save you the trouble of browsing further: find it here!
Although number words are common in everyday speech, learning their meanings is an arduous, drawn-out process for most children, and the source of this delay has long been the subject of inquiry. Children begin by identifying the few small numerosities that can be named without counting, and this has prompted further debate over whether there is a specific, capacity-limited system for representing these small sets, or whether smaller and larger sets are both represented by the same system. Here we present a formal, computational analysis of number learning that offers a possible solution to both puzzles. This analysis indicates that once the environment and the representational demands of the task of learning to identify sets are taken into consideration, a continuous system for learning, representing and discriminating set-sizes can give rise to effective discontinuities in processing. At the same time, our simulations illustrate how typical prenominal linguistic constructions (“there are three balls”) structure information in a way that is largely unhelpful for discrimination learning, while suggesting that postnominal constructions (“balls, there are three”) will facilitate such learning. A training-experiment with three-year olds confirms these predictions, demonstrating that rapid, significant gains in numerical understanding and competence are possible given appropriately structured postnominal input. Our simulations and results reveal how discrimination learning tunes children’s systems for representing small sets, and how its capacity-limits result naturally out of a mixture of the learning environment and the increasingly complex task of discriminating and representing ever-larger number sets. They also explain why children benefit so little from the training that parents and educators usually provide. Given the efficacy of our intervention, the ease with which it can be implemented, and the large body of research showing how early numerical ability predicts later educational outcomes, this simple discovery may have far-reaching consequences.
We are very excited to announce that the lab’s research into how children learn number has just taken home this year’s CaSL prize in Cognitive Science, which is sponsored by the Institute of Education Sciences, a part of the US Dept of Education. The prize is awarded to the best research “on a topic directly related to cognitive science, in the areas of educational practice, or subject-matter learning.” A final report of the findings has just been published in PLoS One. A press release is available in English and Spanish. Congratulations to co-authors Michael Ramscar, Melody Dye, Hanna Popick and Fiona O’Donnell-McCarthy!
This year, five of the lab’s papers were accepted for presentation at the 33rd Annual Meeting of the Cognitive Science Society in Boston. These include:
1. How children learn to value numbers: Information structure & the acquisition of numerical understanding
2. How pitch category learning comes at a cost to absolute frequency representations
3. Informativity versus logic: Children and adults take different approaches to word learning
4. Investigating how infants learn to search in the A-not-B task
5. Breaking the World into Symbols
In addition, two abstracts were accepted for presentation at the LSA Workshop “Information-theoretic Approaches to Linguistics.”
Thanks to all the hard work from contributing lab members – Richard Futrell, Hanna Popick, Adam November, Joseph Klein, Nikki Aguirre, Linda Diane Ruiz, Edward Suh, Lily Sadaat and Melody Dye!
The May/June issue of Scientific American Mind features an article about the lab’s findings on how children learn to name colors, along with a quick trick for speeding their acquisition. The original “Mind Matters” post on the subject is also available at Scientific American.com, with a helpful comment-and-reply section by the author.
“Learning Language from the Input: Why Innate Constraints Can’t Explain Noun Compounding” is now ‘trending’ on SciVerse. The paper, which was published in the February edition of Cognitive Psychology, was one of the journal’s top 5 most downloaded articles in the last three months. The 7-experiment paper gives a detailed account of why children’s learning reflects the statistical patterns seen in the input and not, as has been frequently claimed, the working of a native rule-based constraint. If you are interested in our corpus-based research, you may wish to skip to Exp. 7 and our subsequent analyses (pp. 28-35).
One of our favorite quotes from the paper?
“Thought-experiments, by their very nature, run into serious problems when it comes to making hypothesis blind observations, and because of this, it seems reasonable to suggest that their results should be afforded less credence in considering the phenomena themselves.” (p. 35)
This morning, Mark Liberman over at Language Log wrote an intriguing post about how people use the word “snuck” in conversation, but then sneakily use the word “sneaked” in writing. At the end of his post, Mark recommends everyone read Donald Davidson’s brilliant article A Nice Derangement of Epitaphs on the puzzles posed by malapropisms, and ‘passing theories’ of language.
We were also very much inspired by Davidson’s paper, which motivated two recent research efforts in our lab:
On how readers of fiction and non-fiction differ in their sensitivity to distributions of words in literary and non-literary texts.
On how listeners parse (and remember) malapropisms in speech, depending on the predictability of the preceding context.
*Earlier readers may have noticed that we managed to mangle the spelling of “Liberman” in the original post. We offer our sincere apologies to Mark, whose name — all things considered — shouldn’t be that difficult to spell. All we can say by way of excuse is — we’re not the first?
In April, Profs Michael Ramscar and Sam McClure will be presenting at the 18th Annual Cognitive Neuroscience meeting in San Francisco. They will report the first neurobiological evidence for Feature-Label-Order Effects in learning.
Manipulating information structure as a method of localizing information processing in the brain
When formalized in terms of prediction and cue competition, symbolic learning takes two forms: learning to predict labels from the features of objects and events (Feature-to-Label learning), or learning to predict features from labels (Label-to-Feature learning). When the information available in training is structured in one or another of these formats, qualitative differences in symbolic learning occur. Discrimination learning is facilitated when objects precede labels (FL), because the structure of information promotes cue competition between individual features. However, this competition is inhibited when labels predict objects (LF; Ramscar et al, 2010). We report an fMRI investigation of these Feature-Label-Ordering effects in learning. Participants were trained and tested on a category-learning task while the frequency of confusable categories was manipulated so that successful discrimination was essential to successful categorization. Participants trained to predict labels from features (FL) showed higher levels of dorsal striatal activity (caudate and putamen), which correlated with overall performance at test. The opposite pattern was observed with ventrolateral prefrontal cortex (VLPFC) activation, which was greater in participants trained to predict features from labels (LF), and which correlated negatively with performance on the difficult to discriminate low frequency items. The increased striatal activity we observed in the FL-trained participants is consistent with evidence linking this area to discrimination learning, while the correlation between VLPFC activity and poorer discrimination in the LF-trained participants supports the idea that the structure of information in training forced participants to rely on working memory, fixating on cues that were frequent, salient, and yet ultimately uninformative.