Language learning in infancy

Although immensely complex, speech is also a very efficient means of communication between humans. Understanding how we acquire the skills necessary for perceiving and producing speech remains an intriguing goal for research. However, while learning is likely to begin as soon as we start hearing speech, the tools for studying the language acquisition strategies in the earliest stages of development remain scarce. One prospective strategy is statistical learning. In order to investigate its role in language development, we designed a new research method. The method was tested in adults using magnetoencephalography (MEG) as a measure of cortical activity. Neonatal brain activity was measured with electroencephalography (EEG). Additionally, we developed a method for assessing the integration of seen and heard syllables in the developing brain as well as a method for assessing the role of visual speech when learning phoneme categories. The MEG study showed that adults learn statistical properties of speech during passive listening of syllables. The amplitude of the N400m component of the event-related magnetic fields (ERFs) reflected the location of syllables within pseudowords. The amplitude was also enhanced for syllables in a statistically unexpected position. The results suggest a role for the N400m component in statistical learning studies in adults. Using the same research design with sleeping newborn infants, the auditory event-related potentials (ERPs) measured with EEG reflected the location of syllables within pseudowords. The results were successfully replicated in another group of infants. The results show that even newborn infants have a powerful mechanism for automatic extraction of statistical characteristics from speech. We also found that 5-month-old infants integrate some auditory and visual syllables into a fused percept, whereas other syllable combinations are not fully integrated. Auditory syllables were paired with visual syllables possessing a different phonetic identity, and the ERPs for these artificial syllable combinations were compared with the ERPs for normal syllables. For congruent auditory-visual syllable combinations, the ERPs did not differ from those for normal syllables. However, for incongruent auditory-visual syllable combinations, we observed a mismatch response in the ERPs. The results show an early ability to perceive speech cross-modally. Finally, we exposed two groups of 6-month-old infants to artificially created auditory syllables located between two stereotypical English syllables in the formant space. The auditory syllables followed, equally for both groups, a unimodal statistical distribution, suggestive of a single phoneme category. The visual syllables combined with the auditory syllables, however, were different for the two groups, one group receiving visual stimuli suggestive of two separate phoneme categories, the other receiving visual stimuli suggestive of only one phoneme category. After a short exposure, we observed different learning outcomes for the two groups of infants. The results thus show that visual speech can influence learning of phoneme categories. Altogether, the results demonstrate that complex language learning skills exist from birth. They also suggest a role for the visual component of speech in the learning of phoneme categories.

Bayesian statistical analysis of bacterial diversity

Bacteria play an important role in many ecological systems. The molecular characterization of bacteria using either cultivation-dependent or cultivation-independent methods reveals the large scale of bacterial diversity in natural communities, and the vastness of subpopulations within a species or genus. Understanding how bacterial diversity varies across different environments and also within populations should provide insights into many important questions of bacterial evolution and population dynamics. This thesis presents novel statistical methods for analyzing bacterial diversity using widely employed molecular fingerprinting techniques. The first objective of this thesis was to develop Bayesian clustering models to identify bacterial population structures. Bacterial isolates were identified using multilous sequence typing (MLST), and Bayesian clustering models were used to explore the evolutionary relationships among isolates. Our method involves the inference of genetic population structures via an unsupervised clustering framework where the dependence between loci is represented using graphical models. The population dynamics that generate such a population stratification were investigated using a stochastic model, in which homologous recombination between subpopulations can be quantified within a gene flow network. The second part of the thesis focuses on cluster analysis of community compositional data produced by two different cultivation-independent analyses: terminal restriction fragment length polymorphism (T-RFLP) analysis, and fatty acid methyl ester (FAME) analysis. The cluster analysis aims to group bacterial communities that are similar in composition, which is an important step for understanding the overall influences of environmental and ecological perturbations on bacterial diversity. A common feature of T-RFLP and FAME data is zero-inflation, which indicates that the observation of a zero value is much more frequent than would be expected, for example, from a Poisson distribution in the discrete case, or a Gaussian distribution in the continuous case. We provided two strategies for modeling zero-inflation in the clustering framework, which were validated by both synthetic and empirical complex data sets. We show in the thesis that our model that takes into account dependencies between loci in MLST data can produce better clustering results than those methods which assume independent loci. Furthermore, computer algorithms that are efficient in analyzing large scale data were adopted for meeting the increasing computational need. Our method that detects homologous recombination in subpopulations may provide a theoretical criterion for defining bacterial species. The clustering of bacterial community data include T-RFLP and FAME provides an initial effort for discovering the evolutionary dynamics that structure and maintain bacterial diversity in the natural environment.