A Transposed-Word Effect in Chinese Reading

Studies using a grammaticality decision task suggest surprising flexibility in the processing of the relative order of words in sentences when reading alphabetic scripts like French. In these studies, participants made rapid grammaticality decisions for ungrammatical stimuli created by transposing two adjacent words in either a grammatical or an ungrammatical base sentence, which were intermixed with equal numbers of grammatically correct stimuli. The key finding was that participants made more errors and were slower to reject transposed-word stimuli created from grammatical than ungrammatical base sentences. This suggested that flexibility in the processing of word order allowed participants to access representations of the base grammatical sentences, interfering with their decisions to correctly reject transposed-word stimuli. With the present research, we investigated if a similar transposed-word effect is observed for a non-alphabetic script (Chinese) that uses few grammatical markers and primarily conveys grammatical structure via word order. Such scripts may require stricter processing of word order during reading and so provide a strong test of the cross-linguistic generality of the transposed-word effect. We report three experiments using the same design and procedure as previous research, while varying the length of the transposed words across experiments. In all three experiments, participants made more errors and were slower to reject transposed-word stimuli derived from grammatical than ungrammatical base sentences. This replicates previous findings with alphabetic scripts and provides novel evidence for a transposed-word effect in Chinese reading. We consider the implications for models of reading in alphabetic and non-alphabetic scripts.<br

Error rate (%) and correct latency (ms) in each condition for both groups (means ± <i>SD</i>).

<p>Error rate (%) and correct latency (ms) in each condition for both groups (means ± <i>SD</i>).</p

A schematic of the trial sequence for both experimental conditions.

<p>In the blocked condition the symbolic cue display was irrelevant to the task, whereas in the mixed condition each cue symbol signaled the type of saccade to be executed in the upcoming trial.</p

Error rate (%) and correct latency (ms) for switch and repeat trials in the mixed condition (means ± <i>SD</i>).

<p>Error rate (%) and correct latency (ms) for switch and repeat trials in the mixed condition (means ± <i>SD</i>).</p

Error rate (%) and correct latency (ms) under different conditions.

<p>Panel (a). The mean direction error rate for prosaccades and antisaccades for younger and older participants. Panel (b). The mean correct latency for prosaccade and antisaccade tasks in the blocked condition and the mixed condition. Panel (c). The mean correct latency for near and far targets for younger and older participants. Error bars denote 1 standard error from the mean.</p

Semivariograms of L_LFOC (a, f), FR (b, g), SWC (c, h), NCP (d, i), and Q₁₀ (e, j) in 10-m grid squares of OF (left panel) and PP (right panel), respectively.

<p>Model for SWC are exponential models. The SWC were averaged over the 12 (OF) or 13 (PP) measurement campaigns.</p

Seasonal pattern of T₅ (up panel) and SWC (lower panel) for OF (left panel) and PP (right panel) for each subplot, as well as the seasonal pattern of the CV (up triangle) of T₅ and SWC among subplots.

<p>Seasonal pattern of T₅ (up panel) and SWC (lower panel) for OF (left panel) and PP (right panel) for each subplot, as well as the seasonal pattern of the CV (up triangle) of T₅ and SWC among subplots.</p

Isarithmic maps of the Q₁₀ in the 10-m grids of OF and PP are shown in the top and bottom panels respectively, interpolations were done by the inverse distance weighting method.

<p>White areas indicate high values and dark areas indicate low values.</p

Statistical analysis of soil parameters, fine root biomass, soil respiration rate, Q₁₀ values, and carbon pool lability (<i>L</i>_LFOC) for the oak forest and pine plantation.^a

a<p>S.D.: standard deviation; CV: coefficient of variance; R_S: soil respiration; SWC: soil water content; TOC: total organic carbon; TN: total nitrogen; LFOC: light fraction organic carbon; FR: fine root biomass; BD: bulk density; LAI: leaf area index; NCP: non-capillary porosity. <i>n = </i>35. The soil respiration rates R_S and SWC in this table were averaged over the 12 (OF) or 13 (PP) measurement campaigns.</p

Pearson correlation coefficients between Q₁₀ and variables in spatially.

<p>Abbreviations see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0064167#pone-0064167-t001" target="_blank">Table 1</a>. n = 35 for each forest, n = 70 for pooled data of two forest types. The SWC in this table were averaged over the 12 (OF) or 13 (PP) measurement campaigns.</p