Second Language Processing Shows Increased Native-Like Neural Responses after Months of No Exposure

Although learning a second language (L2) as an adult is notoriously difficult, research has shown that adults can indeed attain native language-like brain processing and high proficiency levels. However, it is important to then retain what has been attained, even in the absence of continued exposure to the L2—particularly since periods of minimal or no L2 exposure are common. This event-related potential (ERP) study of an artificial language tested performance and neural processing following a substantial period of no exposure. Adults learned to speak and comprehend the artificial language to high proficiency with either explicit, classroom-like, or implicit, immersion-like training, and then underwent several months of no exposure to the language. Surprisingly, proficiency did not decrease during this delay. Instead, it remained unchanged, and there was an increase in native-like neural processing of syntax, as evidenced by several ERP changes—including earlier, more reliable, and more left-lateralized anterior negativities, and more robust P600s, in response to word-order violations. Moreover, both the explicitly and implicitly trained groups showed increased native-like ERP patterns over the delay, indicating that such changes can hold independently of L2 training type. The results demonstrate that substantial periods with no L2 exposure are not necessarily detrimental. Rather, benefits may ensue from such periods of time even when there is no L2 exposure. Interestingly, both before and after the delay the implicitly trained group showed more native-like processing than the explicitly trained group, indicating that type of training also affects the attainment of native-like processing in the brain. Overall, the findings may be largely explained by a combination of forgetting and consolidation in declarative and procedural memory, on which L2 grammar learning appears to depend. The study has a range of implications, and suggests a research program with potentially important consequences for second language acquisition and related fields

BLISS: an artificial language for learnability studies

To explore neurocognitive mechanisms underlying the human language faculty, cognitive scientists use artificial languages to control more precisely the language learning environment and to study selected aspects of natural languages. Artificial languages applied in cognitive studies are usually designed ad hoc, to only probe a specific hypothesis, and they include a miniature grammar and a very small vocabulary. The aim of the present study is the construction of an artificial language incorporating both syntax and semantics, BLISS. Of intermediate complexity, BLISS mimics natural languages by having a vocabulary, syntax, and some semantics, as defined by a degree of non-syntactic statistical dependence between words. We quantify, using information theoretical measures, dependencies between words in BLISS sentences as well as differences between the distinct models we introduce for semantics. While modeling English syntax in its basic version, BLISS can be easily varied in its internal parametric structure, thus allowing studies of the relative learnability of different parameter sets

The impact of first and second language exposure on learning second language constructions

We study how the learning of argument structure constructions in a second language (L2) is affected by two basic input properties often discussed in literature – the amount of input and the time of L2 onset. To isolate the impact of the two factors on learning, we use a computational model that simulates bilingual construction learning. In the first two experiments we manipulate the sheer amount of L2 exposure, both in absolute and in relative terms (that is, in relation to the amount of L1 exposure). The results show that higher cumulative amount of L2 exposure leads to higher performance. In the third experiment we manipulate the prior amount of L1 input before the L2 onset (that is, the time of L2 onset). Given equal exposure, we find no negative effect of the later onset on learners’ performance. This has implications for theories of order of acquisition and bilingual construction learning

How the Emotional Content of Discourse Affects Language Comprehension

Emotion effects on cognition have often been reported. However, only few studies investigated emotional effects on subsequent language processing, and in most cases these effects were induced by non-linguistic stimuli such as films, faces, or pictures. Here, we investigated how a paragraph of positive, negative, or neutral emotional valence affects the processing of a subsequent emotionally neutral sentence, which contained either semantic, syntactic, or no violation, respectively, by means of event-related brain potentials (ERPs). Behavioral data revealed strong effects of emotion; error rates and reaction times increased significantly in sentences preceded by a positive paragraph relative to negative and neutral ones. In ERPs, the N400 to semantic violations was not affected by emotion. In the syntactic experiment, however, clear emotion effects were observed on ERPs. The left anterior negativity (LAN) to syntactic violations, which was not visible in the neutral condition, was present in the negative and positive conditions. This is interpreted as reflecting modulatory effects of prior emotions on syntactic processing, which is discussed in the light of three alternative or complementary explanations based on emotion-induced cognitive styles, working memory, and arousal models. The present effects of emotion on the LAN are especially remarkable considering that syntactic processing has often been regarded as encapsulated and autonomous

Sequence-Specific Binding of Recombinant Zbed4 to DNA: Insights into Zbed4 Participation in Gene Transcription and Its Association with Other Proteins

Zbed4, a member of the BED subclass of Zinc-finger proteins, is expressed in cone photoreceptors and glial Müller cells of human retina whereas it is only present in Müller cells of mouse retina. To characterize structural and functional properties of Zbed4, enough amounts of purified protein were needed. Thus, recombinant Zbed4 was expressed in E. coli and its refolding conditions optimized for the production of homogenous and functionally active protein. Zbed4’s secondary structure, determined by circular dichroism spectroscopy, showed that this protein contains 32% α-helices, 18% β-sheets, 20% turns and 30% unordered structures. CASTing was used to identify the target sites of Zbed4 in DNA. The majority of the DNA fragments obtained contained poly-Gs and some of them had, in addition, the core signature of GC boxes; a few clones had only GC-boxes. With electrophoretic mobility shift assays we demonstrated that Zbed4 binds both not only to DNA and but also to RNA oligonucleotides with very high affinity, interacting with poly-G tracts that have a minimum of 5 Gs; its binding to and GC-box consensus sequences. However, the latter binding depends on the GC-box flanking nucleotides. We also found that Zbed4 interacts in Y79 retinoblastoma cells with nuclear and cytoplasmic proteins Scaffold Attachment Factor B1 (SAFB1), estrogen receptor alpha (ERα), and cellular myosin 9 (MYH9), as shown with immunoprecipitation and mass spectrometry studies as well as gel overlay assays. In addition, immunostaining corroborated the co-localization of Zbed4 with these proteins. Most importantly, in vitro experiments using constructs containing promoters of genes directing expression of the luciferase gene, showed that Zbed4 transactivates the transcription of those promoters with poly-G tracts

Model Cortical Association Fields Account for the Time Course and Dependence on Target Complexity of Human Contour Perception

Can lateral connectivity in the primary visual cortex account for the time dependence and intrinsic task difficulty of human contour detection? To answer this question, we created a synthetic image set that prevents sole reliance on either low-level visual features or high-level context for the detection of target objects. Rendered images consist of smoothly varying, globally aligned contour fragments (amoebas) distributed among groups of randomly rotated fragments (clutter). The time course and accuracy of amoeba detection by humans was measured using a two-alternative forced choice protocol with self-reported confidence and variable image presentation time (20-200 ms), followed by an image mask optimized so as to interrupt visual processing. Measured psychometric functions were well fit by sigmoidal functions with exponential time constants of 30-91 ms, depending on amoeba complexity. Key aspects of the psychophysical experiments were accounted for by a computational network model, in which simulated responses across retinotopic arrays of orientation-selective elements were modulated by cortical association fields, represented as multiplicative kernels computed from the differences in pairwise edge statistics between target and distractor images. Comparing the experimental and the computational results suggests that each iteration of the lateral interactions takes at least ms of cortical processing time. Our results provide evidence that cortical association fields between orientation selective elements in early visual areas can account for important temporal and task-dependent aspects of the psychometric curves characterizing human contour perception, with the remaining discrepancies postulated to arise from the influence of higher cortical areas

Developmental malformation of the corpus callosum: a review of typical callosal development and examples of developmental disorders with callosal involvement

This review provides an overview of the involvement of the corpus callosum (CC) in a variety of developmental disorders that are currently defined exclusively by genetics, developmental insult, and/or behavior. I begin with a general review of CC development, connectivity, and function, followed by discussion of the research methods typically utilized to study the callosum. The bulk of the review concentrates on specific developmental disorders, beginning with agenesis of the corpus callosum (AgCC)—the only condition diagnosed exclusively by callosal anatomy. This is followed by a review of several genetic disorders that commonly result in social impairments and/or psychopathology similar to AgCC (neurofibromatosis-1, Turner syndrome, 22q11.2 deletion syndrome, Williams yndrome, and fragile X) and two forms of prenatal injury (premature birth, fetal alcohol syndrome) known to impact callosal development. Finally, I examine callosal involvement in several common developmental disorders defined exclusively by behavioral patterns (developmental language delay, dyslexia, attention-deficit hyperactive disorder, autism spectrum disorders, and Tourette syndrome)

The Brain's Router: A Cortical Network Model of Serial Processing in the Primate Brain

The human brain efficiently solves certain operations such as object recognition and categorization through a massively parallel network of dedicated processors. However, human cognition also relies on the ability to perform an arbitrarily large set of tasks by flexibly recombining different processors into a novel chain. This flexibility comes at the cost of a severe slowing down and a seriality of operations (100–500 ms per step). A limit on parallel processing is demonstrated in experimental setups such as the psychological refractory period (PRP) and the attentional blink (AB) in which the processing of an element either significantly delays (PRP) or impedes conscious access (AB) of a second, rapidly presented element. Here we present a spiking-neuron implementation of a cognitive architecture where a large number of local parallel processors assemble together to produce goal-driven behavior. The precise mapping of incoming sensory stimuli onto motor representations relies on a “router” network capable of flexibly interconnecting processors and rapidly changing its configuration from one task to another. Simulations show that, when presented with dual-task stimuli, the network exhibits parallel processing at peripheral sensory levels, a memory buffer capable of keeping the result of sensory processing on hold, and a slow serial performance at the router stage, resulting in a performance bottleneck. The network captures the detailed dynamics of human behavior during dual-task-performance, including both mean RTs and RT distributions, and establishes concrete predictions on neuronal dynamics during dual-task experiments in humans and non-human primates