Individual differences in automatic semantic priming

This research investigated whether automatic semantic priming is modulated by individual differences in lexical proficiency. A sample of 89 skilled readers, assessed on reading comprehension, vocabulary and spelling ability, were tested in a semantic categorisation task that required classification of words as animals or non-animals. Target words were preceded by brief (50 ms) masked semantic primes that were either congruent or incongruent with the category of the target. Congruent primes were also selected to be either high (e.g., hawk EAGLE, pistol RIFLE) or low (e.g., mole EAGLE, boots RIFLE) in feature overlap with the target. ‘Overall proficiency’, indexed by high performance on both a ‘semantic composite’ measure of reading comprehension and vocabulary and a ‘spelling composite’, predicted stronger congruence priming from both high and low feature overlap primes for animal exemplars, but only predicted priming from low overlap primes for non-exemplars. Classification of high frequency non-exemplars was also significantly modulated by an independent ‘spelling-meaning’ factor, indexed by differences between the semantic and spelling composites, which appeared to tap sensitivity to semantic relative to orthographic feature overlap between the prime and target. These findings show that higher lexical proficiency predicts stronger automatic semantic priming and suggest that individual differences in lexical quality modulate the division of labor between orthographic and semantic processing in early lexical retrieval.Australian Research Counci

Conceptual organisation of the Chinese-English bilingual mental lexicon: investigations of cross-language priming

The majority of research on the organisation of bilinguals’ lexical memory has focused on alphabetic languages with shared etymological roots and scripts. Theories based on such evidence may not generalise to noncognate languages with different scripts, such as Chinese and English. This thesis reports a systematic series of experiments designed to investigate the organisation of lexical and conceptual knowledge for bilinguals’ first (L1) and second (L2) language in late L1-dominant Chinese-English bilinguals using the classical cross-language priming paradigm. It aims to investigate how such bilinguals store the meanings of Chinese and English words. It also aims to identify the similarities and discrepancies in the conceptual organisation between noncognate languages with different scripts, i.e., Chinese and English, and to investigate how the lexical representations of a bilingual’s two languages interact with each other and with the conceptual representation. The introductory chapter reviews early theoretical formulations of bilingualism, and evaluates more recent models of bilingual memory. The empirical chapters present three series comprising eight experiments which directly compared cross-language translation priming and semantic priming in both L1-L2 and L2-L1 language directions under conditions designed to tap automatic semantic processes using the same relatively short stimulus onset asynchrony (SOA) of 200 ms but different priming paradigms and task contexts. Series 1 (Experiments 1A and 1B) compared repetition/translation priming and semantic priming within and between languages for various semantic relations using an unmasked priming paradigm in lexical decision and word naming tasks. Both tasks produced similar patterns of unmasked translation priming in both L1-L2 and L2-L1 directions, although the priming effects in naming were of a smaller magnitude. Both tasks also showed significant unmasked semantic priming effects for English word targets in the L1-L2 and L2-L2 conditions, but there was little evidence of semantic priming for L1 word targets in the L1-L1 and L2-L1 conditions. Neither task yielded any semantic priming in the within-language L1-L1 condition. Series 2 (Experiments 2A, 2B and 3A, 3B) reported two pairs of semantic categorisation and lexical decision tasks designed to test the predictions of the Sense Model (Finkbeiner, Forster, Nicol, & Nakamura, 2004). The experiments replicated Finkbeiner et al.’s finding that L2-L1 priming is somewhat stronger in semantic categorisation than lexical decision, selectively for category exemplars. However, the direct comparison of L1-L2 and L2-L1 translation priming failed to confirm the Sense Model’s central prediction that translation priming asymmetry is significantly reduced in semantic categorisation. The findings therefore did not support the category filtering account of translation priming asymmetry proposed by the Sense Model but were consistent with semantic feedback (e.g., Hoshino, Midgley, Holcomb, & Grainger, 2010; Midgley, Holcomb, & Grainger, 2009) accounts of cross-script L2-L1 translation priming and suggested that pre-activation of relevant semantic features by a category cue compensates for the weak connections between L2 lexical forms and their conceptual referents. Series 3 (Experiments 4A and 4B) directly compared masked translation and cross-language semantic priming for moderately semantically related pairs with no associative relationships, in semantic categorisation and lexical decision tasks. Both tasks showed similar asymmetrical patterns of masked translation and cross-language semantic priming, characterised by larger priming effects from L1 to L2 than from the reverse. The masked translation priming data fully replicated the findings obtained in Series 2. Masked semantic priming was significant in the L1-L2 but not in the L2-L1 direction, and of smaller magnitude than masked translation priming in both directions. Neither experiment found masked L2-L1 semantic priming. These data can be accommodated by a modified version of the Revised Hierarchical Model (RHM, Kroll & Stewart, 1994) based on Duyck and Brysbaert’s (2004) proposal for alphabetic languages in combination with the semantic feedback account. The data are also consistent with the DevLex-II model (Li & Zhao, 2013; Li, Zhao, & MacWhinney, 2007; Zhao & Li, 2010, 2013) regarding the graded relationships between translation and cross-language semantic priming. The findings of this research clearly demonstrated both shared and independent aspects of L1 and L2 semantic representations in unbalanced Chinese-English bilinguals. They are compatible with the cognitive architecture of the RHM combined with the representational assumptions of the Distributed Conceptual Feature Model (De Groot, 1992a, 1992b, 1995; Van Hell & De Groot, 1998)

Geospatial distribution of <i>Mycobacterium tuberculosis</i> genotypes in Africa

<div><p>Objective</p><p>To investigate the distribution of <i>Mycobacterium tuberculosis</i> genotypes across Africa.</p><p>Methods</p><p>The SITVIT2 global repository and PUBMED were searched for spoligotype and published genotype data respectively, of <i>M</i>. <i>tuberculosis</i> from Africa. <i>M</i>. <i>tuberculosis</i> lineages in Africa were described and compared across regions and with those from 7 European and 6 South-Asian countries. Further analysis of the major lineages and sub-lineages using Principal Component analysis (PCA) and hierarchical cluster analysis were done to describe clustering by geographical regions. Evolutionary relationships were assessed using phylogenetic tree analysis.</p><p>Results</p><p>A total of 14727 isolates from 35 African countries were included in the analysis and of these 13607 were assigned to one of 10 major lineages, whilst 1120 were unknown. There were differences in geographical distribution of major lineages and their sub-lineages with regional clustering. Southern African countries were grouped based on high prevalence of LAM11-ZWE strains; strains which have an origin in Portugal. The grouping of North African countries was due to the high percentage of LAM9 strains, which have an origin in the Eastern Mediterranean region. East African countries were grouped based on Central Asian (CAS) and East-African Indian (EAI) strain lineage possibly reflecting historic sea trade with Asia, while West African Countries were grouped based on Cameroon lineage of unknown origin. A high percentage of the Haarlem lineage isolates were observed in the Central African Republic, Guinea, Gambia and Tunisia, however, a mixed distribution prevented close clustering.</p><p>Conclusions</p><p>This study highlighted that the TB epidemic in Africa is driven by regional epidemics characterized by genetically distinct lineages of <i>M</i>. <i>tuberculosis</i>. <i>M</i>. <i>tuberculosis</i> in these regions may have been introduced from either Europe or Asia and has spread through pastoralism, mining and war. The vast array of genotypes and their associated phenotypes should be considered when designing future vaccines, diagnostics and anti-TB drugs.</p></div

Clustering of countries according the proportion of <i>M</i>. <i>tuberculosis</i> isolates present in a specific lineage.

<p>Only data from the Beijing, Cameroon, CAS, EAI, H, LAM, Manu, and S lineages was included. Country codes according to (<a href="http://www.worldatlas.com/aatlas/ctycodes.htm" target="_blank">http://www.worldatlas.com/aatlas/ctycodes.htm</a>). (A) Principle component analysis: African countries in the PCA plot are coloured based on their most dominant lineage: CAS (red), Cameroon (green), H (purple), LAM (brown), Manu (blue), and EAI (yellow). European and Asian countries are shown in black. Overlapping country codes in the PCA plot indicate a similar distribution of <i>M</i>. <i>tuberculosis</i> lineages in the respective countries. (B) pvclust analysis: The clusters edges are numbered in grey and the AU p-values are shown in black. Strongly supported clusters with AU greater than 95% are highlighted with a dotted line.</p

Clustering oof countries according the proportion of <i>M</i>. <i>tuberculosis</i> isolates belonging to different LAM sub-lineages.

<p>(A) Principle component analysis: African countries in the PCA plot are coloured based on their most dominant LAM sub-lineage: LAM1 (blue), LAM3 (blown), LAM9 (purple), LAM11-ZIM (red). PCA plot axes have been labelled with an “L” to indicate LAM followed by the sub-lineage number. European and Asian countries are shown in black. Overlapping country codes in the PCA plot (Morocco and Italy, Tunisia and France) indicate a similar distribution of LAM sub-lineages in the respective countries. (B) pvclust analysis: The clusters edges are numbered in grey and the AU p-values are shown in black. Strongly supported clusters with AU greater than 95% are highlighted with a dotted line. Country codes (<a href="http://www.worldatlas.com/aatlas/ctycodes.htm" target="_blank">http://www.worldatlas.com/aatlas/ctycodes.htm</a>).</p

Geospatial distribution of <i>M</i>. <i>tuberculosis</i> isolates belonging to the LAM sub-lineage.

<p>Country specific spoligotype data was only included if the country had >100 <i>M</i>. <i>tuberculosis</i> isolates and ≥15% of these isolates were from the LAM lineage. The sizes of the pie chart segments depict the proportion of isolates belonging to the different LAM sub-lineages (see colour chart for the respective sub-lineages). Each country has been shaded according to the proportion of LAM lineages isolates present in that country (see colour intensity chart). Country codes (<a href="http://www.worldatlas.com/aatlas/ctycodes.htm" target="_blank">http://www.worldatlas.com/aatlas/ctycodes.htm</a>).</p

Geospatial distribution of <i>M</i>. <i>tuberculosis</i> isolates belonging to the T sub-lineages.

<p>Country specific spoligotype data was only included if the country had >100 <i>M</i>. <i>tuberculosis</i> isolates and ≥15% of these isolates were from the T lineage. The sizes of the pie chart segments depict the proportion of isolates belonging to the different T sub-lineages (see colour chart for the respective sub-lineages). Each country has been shaded according to the proportion of T sub-lineages isolates present in that country (see colour intensity chart). Country codes (<a href="http://www.worldatlas.com/aatlas/ctycodes.htm" target="_blank">http://www.worldatlas.com/aatlas/ctycodes.htm</a>).</p

Geospatial distribution of <i>M</i>. <i>tuberculosis</i> lineages in Africa.

<p>Each pie chart segment reflects the relative proportion of <i>M</i>. <i>tuberculosis</i> isolates belonging to respective major lineages for each country (see colour chart for the respective major lineages). Each country has been shaded according to the number of isolates contributed to the analysis (see colour intensity chart). Country codes (<a href="http://www.worldatlas.com/aatlas/ctycodes.htm" target="_blank">http://www.worldatlas.com/aatlas/ctycodes.htm</a>).</p