Neural bases of rapid word learning
Humans are unique in developing large lexicons as their communication tool; to achieve this, they are able to learn new words rapidly. However, neural bases of this rapid learning, which may be an expression of a more general cognitive mechanism likely rooted in plasticity at cellular and synaptic levels, are not yet understood. In this update, the author highlights a selection of recent studies that attempted to trace word learning in the human brain noninvasively. A number of brain areas, most notably in hippocampus and neocortex, appear to take part in word acquisition. Critically, the currently available data not only demonstrate the hippocampal role in rapid encoding followed by slow-rate consolidation of cortical word memory traces but also suggest immediate neocortical involvement in the word memory trace formation. Echoing early behavioral studies in ultra-rapid word learning, the reviewed neuroimaging experiments can be taken to suggest that our brain may effectively form new cortical circuits online, as it gets exposed to novel linguistic patterns in the sensory input.
Proceedings of the conference "Cognitive Science in Moscow: New Research" (June 19, 2019).
Neuroimaging studies are accumulating fast. A significant number of these studies use functional magnetic resonance imaging (fMRI) and report stereotactic brain coordinates. In the last 15 years meta-analytic software tools have been developed to identify over-arching data agreement across studies (e.g., http://www.brainmap.org/). Meta-analytic studies help establish statistical concordance and quantitatively summarize large amounts of evidence. To date there are 944 papers on fMRI meta-analyses, as indexed by Web of Science (WOS; 28/04/18). Before analyzing coordinates researchers have to compile, systematically review relevant literature and extract stereotaxic coordinates. One process of pooling information from the articles requires manual search of the articles and manual extraction the relevant data, such as coordinates (i.e., foci), contrasts (i.e., experiments) and types of analyses (whole-brain or region of interest). Another available approach is offered by software with pre-extracted information, such as Sleuth (http://brainmap.org/sleuth/), Neurosynth (http://neurosynth.org/) and other open-source programs. Critically, these methods do not have up to date datasets covering only a limited number of studies (e.g., 11406 papers in the Neurosynth and 3294 papers in the Sleuth 2.4 at the 28/04/2018), whereas, a WOS search for the keyword (“fMRI”) yields 61976 papers. To improve the quality of the manual search for area-based meta-analyses and increase the speed of the identification of the foci of interest, we developed CoordsFinder - standalone graphical interface software for addressing the challenge of processing multiple fMRI articles reporting data in coordinate space. The software is written using WPF (C# and XAML), based on .NET Framework 4.5.2, and it supports Microsoft Windows 7 operating system or higher. The CoordsFinder estimates the foci uploaded in the software manually and searches for it inside the specified folder, which contains the pdf files of the papers, as this is the most common file format for articles. Foci coordinates can be found both in tables and in a plain text of the articles. The foci file uploaded could contain MNI or TAL space coordinates, and the software can indicate each type. In the current version, CoordsFinder can explore only files stored at the user’s computer, and process 274 papers per minute for a typical computer. Practically this software provides a solution for automatically extracting coordinates from multiple articles for effectively organizing and further analyzing data already available in the literature.
Connectivity measures are (typically bivariate) statistical measures that may be used to estimate interactions between brain regions from electrophysiological data. We review both formal and informal descriptions of a range of such measures, suitable for the analysis of human brain electrophysiological data, principally electro- and magnetoencephalography. Methods are described in the space–time,space–frequency, and space–time–frequency domains. Signal processing and information theoretic measures are considered, and linear and nonlinear methods are distinguished. A novel set of crosstime–frequency measures is introduced, including a cross-time–frequency phase synchronization measure.
We here investigate whether the well-known laterality of spoken language to the dominant left hemisphere could be explained by the learning of sensorimotor links between a word's articulatory program and its corresponding sound structure. Human-specific asymmetry of acoustic-articulatory connectivity is evident structurally, at the neuroanatomical level, in the arcuate fascicle, which connects superior-temporal and frontal cortices and is more developed in the left hemisphere. Because these left-lateralised fronto-temporal fibres provide a substrate for auditory-motor associations, we hypothesised that learning of acoustic-articulatory coincidences produces laterality, whereas perceptual learning does not. Twenty subjects studied a large (n=48) set of novel meaningless syllable combinations, pseudowords, in a perceptual learning condition, where they carefully listened to repeatedly presented novel items, and, crucially, in an articulatory learning condition, where each item had to be repeated immediately, so that articulatory and auditory speech-evoked cortical activations coincided. In the 14 subjects who successfully passed the learning routine and could recognize the learnt items reliably, both perceptual and articulatory learning were found to lead to an increase of pseudoword-elicited event-related potentials (ERPs), thus reflecting the formation of new memory circuits. Importantly, after articulatory learning, pseudoword-elicited ERPs were more strongly left-lateralised than after perceptual learning. Source localisation confirmed that perceptual learning led to increased activation in superior-temporal cortex bilaterally, whereas items learnt in the articulatory condition activated bilateral superior-temporal auditory in combination with left-pre-central motor areas. These results support a new explanation of the laterality of spoken language based on the neuroanatomy of sensorimotor links and Hebbian learning principles.
Our decisions are affected not only by objective information about the available options but also by other people. Recent brain imaging studies have adopted the cognitive neuroscience approach for studying the neural mechanisms of social influence. A number of studies have shown that social influence is associated with neural activity in the medial prefrontal cortex and ventral striatum, which are two brain areas involved in the fundamental and not exclusively social mechanisms of performance monitoring. Therefore, the neural mechanisms of social influence could be deeply integrated into our general neuronal performance-monitoring mechanisms.
The distractive effects on attentional task performance in different paradigms are analyzed in this paper. I demonstrate how distractors may negatively affect (interference effect), positively (redundancy effect) or neutrally (null effect). Distractor effects described in literature are classified in accordance with their hypothetical source. The general rule of the theory is also introduced. It contains the formal prediction of the particular distractor effect, based on entropy and redundancy measures from the mathematical theory of communication (Shannon, 1948). Single- vs dual-process frameworks are considered for hypothetical mechanisms which underpin the distractor effects. Distractor profiles (DPs) are also introduced for the formalization and simple visualization of experimental data concerning the distractor effects. Typical shapes of DPs and their interpretations are discussed with examples from three frequently cited experiments. Finally, the paper introduces hierarchical hypothesis that states the level-fashion modulating interrelations between distractor effects of different classes.