Neural correlates of processing sentences and compound words in Chinese

FMRI 2nd-level analysis file

Neural correlates of processing sentences and compound words in Chinese - Fig 2

<p><b>Significant activations elicited by the main conditions of sentence (A), word list (B) and character list (C) against baseline (fixations & blanks).</b> Voxel-wise: uncorrected <i>ps</i> = 0.001; Cluster-wise: <i>ps</i> = FDR_{<i>0</i>.<i>001</i>}.</p

FMRI procedure.

<p>FMRI procedure.</p

Significant activations elicited by each comparison.

<p>Panel A, B, D: uncorrected <i>ps</i> = .001; Panel C: Voxel-wise: uncorrected <i>p</i> = 0.001; Cluster-wise: <i>p</i> = FDR_{<i>0</i>.<i>05</i>}.</p

Experimental conditions and example stimuli.

<p>Experimental conditions and example stimuli.</p

Regions of interest with their proposed functions.

<p>Yellow: Sentence-level integration (Sentence vs. Word/Character List); Red: Word-level integration/Compounding (Word List vs. Character List); Blue: Restoration of sentence structure (Word List vs. Sentence).</p

Antisaccade Cost Is Modulated by Contextual Experience of Location Probability

It is well known that pro- and antisaccades may deploy different cognitive processes. However, the specific reason why antisaccades have longer latencies than prosaccades is still under debate. In three experiments, we studied the factors contributing to the antisaccade cost by taking attentional orienting and target location probabilities into account. In experiment 1, using a new antisaccade paradigm, we directly tested Olk and Kingstone's hypothesis, which attributes longer antisaccade latency to the time it takes to reorient from the visual target to the opposite saccadic target. By eliminating the reorienting component in our paradigm, we found no significant difference between the latencies of the two saccade types. In experiment 2, we varied the proportion of prosaccades made to certain locations and found that latencies in the high location-probability (75%) condition were faster than those in the low location-probability condition. Moreover, antisaccade latencies were significantly longer when location probability was high. This pattern can be explained by the notion of competing pathways for pro- and antisaccades in findings of others. In experiment 3, we further explored the degrees of modulation of location probability by decreasing the magnitude of high probability from 75 to 65%. We again observed a pattern similar to that seen in experiment 2 but with smaller modulation effects. Together, these experiments indicate that the reorienting process is a critical factor in producing the antisaccade cost. Furthermore, the antisaccade cost can be modulated by probabilistic contextual information such as location probabilities

Neural correlates of merging number words

International audienceComplex number words (e.g., "twenty two") are formed by merging together several simple number words (e.g., "twenty" and "two"). In the present study, we explored the neural correlates of this operation and investigated to what extent it engages brain areas involved processing numerical quantity and linguistic syntactic structure. Participants speaking two typologically distinct languages, French and Chinese, were required to read aloud sequences of simple number words while their cerebral activity was recorded by functional magnetic resonance imaging. Each number word could either be merged with the previous ones (e.g., 'twenty three') or not (e.g., 'three twenty'), thus forming four levels ranging from lists of number words to complex numerals. When a number word could be merged with the preceding ones, it was named faster than when it could not. Neuroimaging results showed that the number of merges correlated with activation in the left inferior frontal gyrus and in the left inferior parietal lobule. Consistent findings across Chinese and French participants suggest that these regions serve as the neural bases for forming complex number words in different languages

The Dorsal Attentional System in Oculomotor Learning of Predictive Information

The dorsal attentional network is known for its role in directing top-down visual attention towards task-relevant stimuli. This goal-directed nature of the dorsal network makes it a suitable candidate for processing and extracting predictive information from the visual environment. In this mini review we briefly summarize some of the findings that delineate the neural substrates that contribute to predictive learning at both levels within the dorsal attentional system: including the frontal eye field and posterior parietal cortex. We also discuss the similarities and differences between these two regions when it comes to learning predictive information. The current findings from the literature suggest that the frontal eye fields may be more involved in top-down spatial attention, whereas the parietal cortex is involved in processing task-relevant attentional influences driven by stimulus salience, both contribute to the processing of predictive cues at different time points