Publications

Abstract

Electroencephalography (EEG) provides high temporal resolution cognitive information from non-invasive recordings. However, one of the common practices-using a subset of sensors in ERP analysis is hard to provide a holistic and precise dynamic results. Selecting or grouping subsets of sensors may also be subject to selection bias, multiple comparison, and further complicated by individual differences in the group-level analysis. More importantly, changes in neural generators and variations in response magnitude from the same neural sources are difficult to separate, which limit the capacity of testing different aspects of cognitive hypotheses. We introduce EasyEEG, a toolbox that includes several multivariate analysis methods to directly test cognitive hypotheses based on topographic responses that include data from all sensors. These multivariate methods can investigate effects in the dimensions of response magnitude and topographic patterns separately using data in the sensor space, therefore enable assessing neural response dynamics. The concise workflow and the modular design provide user-friendly and programmer-friendly features. Users of all levels can benefit from the open-sourced, free EasyEEG to obtain a straightforward solution for efficient processing of EEG data and a complete pipeline from raw data to final results for publication.

Abstract

The three Olutec stories from Oluta, Veracruz, werenarrated by Antonio Asistente Maldonado. Roberto Zavala presents amorpheme-by-morpheme analysis of the texts with a sketch of the majorgrammatical and typological features of this language. Olutec is spoken bythree dozen speakers. The grammatical structure of this language has not beendescribed before. The sketch contains information on verb and noun morphology,verb dasses, clause types, inverse/direct patterns, grammaticalizationprocesses, applicatives, incorporation, word order type, and discontinuousexpressions. The stories presented here are the first Olutec texts everpublished. The motifs of the stories are well known throughout Middle America.The story of "the Rabbit who wants to be big" explains why one of the mainprotagonists of Middle American folktales acquired long ears. The story of "theDevil who is inebriated by the people of a village" explains how theinhabitants of a village discover the true identity of a man who likes to dancehuapango and decide to get rid of him. Finally the story of "theshit-sellers" presents two compadres, one who is lazy and the otherone who works hard. The hard-worker asks the lazy compadre how he surviveswithout working. The latter lies to to him that he sells shit in theneighboring village. The hard-working compadre decides to become a shit-sellerand in the process realizes that the lazy compadre deceived him. However, he islucky and meets with the Devil who offers him money in compensation for havingbeen deceived. When the lazy compadre realizes that the hard-working compadrehas become rich, he tries to do the same business but gets beaten in theprocess.

Abstract

Although bilingual speakers are very good at selectively using one language rather than another, sometimes language selection errors occur. To investigate how bilinguals monitor their speech errors and control their languages in use, we recorded event-related potentials (ERPs) in unbalanced Dutch-English bilingual speakers in a cued language-switching task. We tested the conflict-based monitoring model of Nozari and colleagues by investigating the error-related negativity (ERN) and comparing the effects of the two switching directions (i.e., to the first language, L1 vs. to the second language, L2). Results show that the speakers made more language selection errors when switching from their L2 to the L1 than vice versa. In the EEG, we observed a robust ERN effect following language selection errors compared to correct responses, reflecting monitoring of speech errors. Most interestingly, the ERN effect was enlarged when the speakers were switching to their L2 (less conflict) compared to switching to the L1 (more conflict). Our findings do not support the conflict-based monitoring model. We discuss an alternative account in terms of error prediction and reinforcement learning.

Abstract

Although bilingual speakers are very good at selectively using one language rather than another, sometimes language selection errors occur. We examined the relative contribution of top-down cognitive control and bottom-up language priming to these errors. Unbalanced Dutch-English bilinguals named pictures and were cued to switch between languages under time pressure. We also manipulated the number of same-language trials before a switch (long vs. short runs). Results show that speakers made more language selection errors when switching from their second language (L2) to the first language (L1) than vice versa. Furthermore, they made more errors when switching to the L1 after a short compared to a long run of L2 trials. In the reverse switching direction (L1 to L2), run length had no effect. These findings are most compatible with an account of language selection errors that assigns a strong role to top-down processes of cognitive control.

Abstract

It is undisputed that presenting a rhythmic stimulus leads to a measurable brain response that follows the rhythmic structure of this stimulus. What is still debated, however, is the question whether this brain response exclusively reflects a regular repetition of evoked responses, or whether it also includes entrained oscillatory activity. Here we systematically present evidence in favor of an involvement of entrained neural oscillations in the processing of rhythmic input while critically pointing out which questions still need to be addressed before this evidence could be considered conclusive. In this context, we also explicitly discuss the potential functional role of such entrained oscillations, suggesting that these stimulus-aligned oscillations reflect, and serve as, predictive processes, an idea often only implicitly assumed in the literature.

Abstract

The Corpus NGT