21st conference of the European Society for Cognitive Psychology, Tenerife, Spain, September 25-28, 2019
Access to word meaning outside of awareness is generally accepted now, at least as indexed by masked priming. Yet, it is not clear what kind of information is grasped subliminally, and whether the mechanisms underlying conscious and unconscious semantic processing are the same. Here, we address this issue by comparing the state-of-art Distributional Semantic Model (DSM), found to successfully predict a wide range of language-related phenomena, with Pointwise Mutual Information (PMI), a measure of association between words based on their mere co-occurrence in language use.
Society for the Neurobiology of Language (SNL) Annual Meeting, Helsinki, August 20-22, 2019
Morphemes constitute the smallest meaning-bearing units of language that are combined to create complex words (e.g., kindness consists of the stem kind and the suffix -ness). Despite considerable behavioral evidence that morphologically complex written words are processed and represented via their constituent morphemes, the neural underpinnings of morphological processing remain poorly understood (Leminen, Smolka, Duñabeitia & Pliatsikas, 2018).
26th Annual Meeting of the Society for the Scientific Study of Reading, July 17-20, 2019, Toronto, Canada
Research shows that we access word internal structure during visual word identification. For example, we see embedded words (e.g., HAT in THAT) and we are sensitive to morphological structure (DEAL–ER, BASKET–BALL). The exact mechanisms that govern this process, however, are not well established yet—what determines access to embedded stem semantics? What is the role of affixes in this process? To address these questions, we tested the activation of meaning of embedded word stems in the presence or absence of morphological structure using two semantic categorization tasks in Italian.
Salzburg Mind-Brain Annual Meeting (SAMBA 2019), July 11-12, 2019
The temporal dynamics of spoken word recognition are highly debated. While some studies suggest serial processing of sublexical and lexico-semantic information (e.g., Kocagoncu et al., 2017), others reported parallel processing since early stages (e.g., Lewis & Poeppel, 2014). The current study employed multiple linear regression to predict MEG-evoked responses in 20 native Italian speakers during the semantic judgment of 438 Italian spoken words.
Interdisciplinary Advances in Statistical Learning, San Sebastian, Spain, June 27-29, 2019
Humans are known to detect probabilistic regularities in learning materials. In language, this ability is particularly well-evidenced for the auditory modality. In two experiments, we tested if learners rely on statistical regularities also when processing text. Italian speakers performed a lexical decision task on a large set of letter strings, distinguishing between nonword strings and strings that were words in a novel language. Participants successfully detected that novel words complied with the statistics of the Italian lexicon, and used this information to perform the task.
Interdisciplinary Advances in Statistical Learning, San Sebastian, Spain, June 27-29, 2019
It has been suggested that the visual word identification system identifies recurrent letter clusters (n–grams) as a bridge between letters and words. Some evidence may suggest, rather indirectly, that n–gram coding is a more general visual mechanism, used beyond reading to extract statistical regularities in smaller visual units to build larger visual units. Here we test this hypothesis directly, and explore the boundaries of the phenomenon—what type of visual object, if any, fails n–gram based statistical learning?
Interdisciplinary Advances in Statistical Learning, San Sebastian, Spain, June 27-29, 2019
Alphabetic writing systems use combinations of individual characters (e.g., letters) to convey information. These combinations exhibit complex statistical regularities whose role in reading is hotly debated in the psycholinguistic community. This theoretical problem could be approached using the framework of Statistical Learning, but most work done so far in visual statistical learning use sequential presentation of stimuli, leaving reading-like material rather unexplored.
