Busting a myth with the Bayes Factor: Effects of letter bigram frequency in visual lexical decision do not reflect reading processes

Psycholinguistic researchers identify linguistic variables and assess if they affect cognitive processes. One such variable is letter bigram frequency, or the frequency with which a given letter pair co-occurs in an orthography. While early studies reported that bigram frequency affects visual lexical decision, subsequent, well-controlled studies not shown this effect. Still, researchers continue to use it as a control variable in psycholinguistic experiments. We propose two reasons for the persistence of this variable: (1) Reporting no significant effect of bigram frequency cannot provide evidence for no effect. (2) Despite empirical work, theoretical implications of bigram frequency are largely neglected. We perform Bayes Factor analyses to address the first issue. In analyses of existing large-scale databases, we find no effect of bigram frequency in lexical decision in the British Lexicon Project, and some evidence for an inhibitory effect in the English Lexicon Project. We find strong evidence for an effect in reading aloud. This suggests that, for lexical decision, the effect is unstable, and may depend on item characteristics and task demands rather than reflecting cognitive processes underlying visual word recognition. We call for more consideration of theoretical implications of the presence or absence of a bigram frequency effect

Recognizing Speech in a Novel Accent: The Motor Theory of Speech Perception Reframed

The motor theory of speech perception holds that we perceive the speech of another in terms of a motor representation of that speech. However, when we have learned to recognize a foreign accent, it seems plausible that recognition of a word rarely involves reconstruction of the speech gestures of the speaker rather than the listener. To better assess the motor theory and this observation, we proceed in three stages. Part 1 places the motor theory of speech perception in a larger framework based on our earlier models of the adaptive formation of mirror neurons for grasping, and for viewing extensions of that mirror system as part of a larger system for neuro-linguistic processing, augmented by the present consideration of recognizing speech in a novel accent. Part 2 then offers a novel computational model of how a listener comes to understand the speech of someone speaking the listener's native language with a foreign accent. The core tenet of the model is that the listener uses hypotheses about the word the speaker is currently uttering to update probabilities linking the sound produced by the speaker to phonemes in the native language repertoire of the listener. This, on average, improves the recognition of later words. This model is neutral regarding the nature of the representations it uses (motor vs. auditory). It serve as a reference point for the discussion in Part 3, which proposes a dual-stream neuro-linguistic architecture to revisits claims for and against the motor theory of speech perception and the relevance of mirror neurons, and extracts some implications for the reframing of the motor theory

The unexplained nature of reading.

The effects of properties of words on their reading aloud response times (RTs) are 1 major source of evidence about the reading process. The precision with which such RTs could potentially be predicted by word properties is critical to evaluate our understanding of reading but is often underestimated due to contamination from individual differences. We estimated this precision without such contamination individually for 4 people who each read 2,820 words 50 times each. These estimates were compared to the precision achieved by a 31-variable regression model that outperforms current cognitive models on variance-explained criteria. Most (around 2/3) of the meaningful (non-first-phoneme, non-noise) word-level variance remained unexplained by this model. Considerable empirical and theoretical-computational effort has been expended on this area of psychology, but the high level of systematic variance remaining unexplained suggests doubts regarding contemporary accounts of the details of the mechanisms of reading at the level of the word. Future assessment of models can take advantage of the availability of our precise participant-level database

Presenting GECO : an eyetracking corpus of monolingual and bilingual sentence reading

This paper introduces GECO, the Ghent Eye-tracking Corpus, a monolingual and bilingual corpus of eye-tracking data of participants reading a complete novel. English monolinguals and Dutch-English bilinguals read an entire novel, which was presented in paragraphs on the screen. The bilinguals read half of the novel in their first language, and the other half in their second language. In this paper we describe the distributions and descriptive statistics of the most important reading time measures for the two groups of participants. This large eye-tracking corpus is perfectly suited for both exploratory purposes as well as more directed hypothesis testing, and it can guide the formulation of ideas and theories about naturalistic reading processes in a meaningful context. Most importantly, this corpus has the potential to evaluate the generalizability of monolingual and bilingual language theories and models to reading of long texts and narratives

Why computational models are better than verbal theories: the case of nonword repetition

Tests of nonword repetition (NWR) have often been used to examine children’s phonological knowledge and word learning abilities. However, theories of NWR primarily explain performance either in terms of phonological working memory or long-term knowledge, with little consideration of how these processes interact. One theoretical account that focuses specifically on the interaction between short-term and long-term memory is the chunking hypothesis. Chunking occurs because of repeated exposure to meaningful stimulus items, resulting in the items becoming grouped (or chunked); once chunked, the items can be represented in short-term memory using one chunk rather than one chunk per item. We tested several predictions of the chunking hypothesis by presenting 5-6 year-old children with three tests of NWR that were either high, medium, or low in wordlikeness. The results did not show strong support for the chunking hypothesis, suggesting that chunking fails to fully explain children’s NWR behavior. However, simulations using a computational implementation of chunking (namely CLASSIC, or Chunking Lexical And Sublexical Sequences In Children) show that, when the linguistic input to 5-6 year old children is estimated in a reasonable way, the children’s data is matched across all three NWR tests. These results have three implications for the field: (a) a chunking account can explain key NWR phenomena in 5-6 year old children; (b) tests of chunking accounts require a detailed specification both of the chunking mechanism itself and of the input on which the chunking mechanism operates; and (c) verbal theories emphasizing the role of long-term knowledge (such as chunking) are not precise enough to make detailed predictions about experimental data, but computational implementations of the theories can bridge the gap