Quantifying Interpreting Types: Language Sequence Mirrors Cognitive Load Minimization in Interpreting Tasks

Abstract

Most interpreting theories claim that different interpreting types should involve varied processing mechanisms and procedures. However, few studies have examined their underlying differences. Even though some previous results based on quantitative approaches show that different interpreting types yield outputs of varying lexical and syntactic features, the grammatical parsing approach is limited. Language sequences that form without relying on parsing or processing with a specific linguistic approach or grammar excel other quantitative approaches at revealing the sequential behavior of language production. As a non-grammatically-bound unit of language sequences, frequency motif can visualize the local distribution of content and function words, and can also statistically classify languages and identify text types. Thus, the current research investigates the distribution, length and position-dependent properties of frequency motifs across different interpreting outputs in pursuit of the sequential generation behaviors. It is found that the distribution, the length and certain position-dependent properties of the specific language sequences differ significantly across simultaneous interpreting and consecutive interpreting output. The features of frequency motifs manifest that both interpreting output is produced in the manner that abides by the least effort principle. The current research suggests that interpreting types can be differentiated through this type of language sequential unit and offers evidence for how the different task features mediate the sequential organization of interpreting output under different demand to achieve cognitive load minimization.

Differentiating Interpreting Types: Connecting Complex Networks to Cognitive Complexity

“…In exploring the characteristics of central vertices, our result showed that the percentages of function words in the hubs of CI networks were higher than SI networks. This indicates a better use and higher importance of function words in the processes of CI, conforming to previous findings that function words are conceived to be more densely distributed in CI than SI output (Liang et al, 2019). The distinction of the proportion of function words among the central vertices in CI and SI networks may be explicated by different processing mechanisms and cognitive complexity in CI and SI.…”

“…In short, the construction of interpreting networks demonstrates how different types of interpreting tasks mediate the global structural organization of the interpreting outputs to achieve cognitive load minimization. This echoes previous research quantifying interpreting types by language sequence (Liang et al, 2019).…”

“…As a seminal research quantifying different interpreting modes, Liang et al (2017) calculated the dependency distance of output texts and suggested that CI bears heavier cognitive demands than SI. Later on, they found consistent evidence in the lexical features (Lv and Liang, 2019) and language sequences (Liang et al, 2019). A more recent study (Jia and Liang, 2020) probed into the lexical category realm with the activity index and found that CI outputs yield greater activity than SI outputs, indicating "a dynamic adaptive mechanism of language representations to accommodate cognitive constraints.…”

“…According to Cowan (1995), the number of units that can be held in the "focus of attention" in working memory is said to be 3 to 4 chunks. Constrained by the limited cognitive resources, the outputs of SI generally follow the structure of the original chunks without much altering the order of the input text segments (Bacigalupe, 2010;Liang et al, 2017Liang et al, , 2019. To minimize the chunks held in the working memory, the SI production needs to be generated in an efficient way, which renders interpreters to give priority to the content words with specific meanings other than the function words which has limited meaning but occupy the cognitive resources in the process of word selections.…”

“…Interpreting is an extremely intricate language processing task for the cognitive system (Christoffels et al, 2006;Tzou et al, 2012;Pöchhacker, 2016;Defrancq and Plevoets, 2018;Liang et al, 2019), which involves listening and comprehending of the input speech, memory storage, production of target equivalents, and sometimes alternately activating and suppressing production in two languages (Christoffels et al, 2003;Aparicio et al, 2017;Lin et al, 2018). Among different types of interpreting, simultaneous interpreting (SI) and consecutive interpreting (CI) tasks are generally supposed to be highly cognitive demanding and with sheer complexity.…”