Working memory and prediction

Further evidence that training may influence predictive processing in aspiring interpreters comes from behavioural and neuroimaging studies showing training-mediated cognitive change. Cognitive enhancements could lead to greater prediction, as there is evidence that cognitive resources are necessary for prediction (Ito et al., 2017), and that working memory is linked to predictive processing (Hintz et al., 2017; Huettig & Janse, 2016;

Huettig, Olivers, et al., 2011). Seeber and Kerzel (2012) also suppose that prediction during interpreting requires cognitive resources (see Section 1.7) .

A number of behavioural studies have been carried out to establish whether 1.

interpreters have a working memory advantage as compared to control groups and 2.

whether this advantage is linked to experience in interpreting. Findings have been mixed (Köpke & Signorelli, 2012), and, as discussed in Chapter 1, Section 1.8.2, sample sizes tend

to be small and between-group studies do not always control for key variables. However, there is both some evidence of a working memory advantage in interpreters, and some indications that this advantage relates to interpreting training or experience.

First let us consider evidence of a working memory advantage in interpreters.

Timarová et al. (2014) correlated measures of simultaneous interpreting performance and measures of working memory, and found support for the theory that attentional control (as a component of working memory) plays an important role in simultaneous interpreting (although, when age was controlled for, working memory advantages in this study were not linked to experience in simultaneous interpreting). However, the ability to resist

interference was related to experience, suggesting that some measures of working memory do improve due to experience in interpreting. Babcock and Vallesi (2015) also found verbal memory was enhanced in interpreters compared to non-interpreter controls. Meanwhile, Christoffels, De Groot and Kroll (2006) found a working memory advantage in a group of interpreters compared to a group of English teachers matched with the interpreters for language proficiency, suggesting that the working memory advantage was linked to

simultaneous interpreting, rather than language proficiency. Padilla, Bajo, Cañas and Padilla (1995) found that professional interpreters performed better on short-term and working memory tests as compared to (pre- and post-training) interpreting students and non-interpreter controls. Tzou, Eslami, Chen and Vaid (2012) also used a between-group comparison to test for effects of training, investigating interpreting and working memory performance in students with either one or two years of training. There was a non-significant trend for Year 2 students to show higher working memory span than Year 1 students (and individuals with greater L2 proficiency had significantly higher working

memory span). Similarly, Dong and Xie (2014) found a significant group difference between students who had undergone one year of interpreting training, and students who had undergone three years of training, in a set-shifting test (the WCST), with the Year 3 students outperforming the Year 1 students. The Year 1 students also outperformed bilingual

controls matched for language proficiency, suggesting that both aptitude and training may contribute to this advantage.

These between-group comparisons provide weak and indirect support for a training-mediated working memory advantage. In studies comparing groups of students, or else comparing students with professionals (e.g., Dong & Xie, 2014; Padilla et al., 1995; Tzou et al., 2012), any advantage might be because students with poorer working memory drop out or fail (or repeat) the course, or because students with poorer working memory do not go on to become professional interpreters. On the other hand, differences between Year 1 and Year 2 or 3 students may simply be due to a general “studying” effect, as it is feasible that studying any subject might improve working memory.

There are, however, several longitudinal studies that use either behavioural

(Babcock et al., 2017; Dong & Liu, 2016) or neural measures (Hervais-Adelman et al., 2017;

Van de Putte et al., 2018) to investigate cognitive skills in trainees before and after training.

Dong and Liu (2016) employed a longitudinal design to test non-language cognitive skills in three groups before and after different types of language training. One group received interpreting training, one received translation training, and the third received general English language training. They found that interpreting training led to an advantage in switching and working memory updating as compared with L2 training and translation training. Babcock et al. (2017) employed a similar longitudinal design, testing short-term

memory, working memory, executive function, alerting and orienting networks and task switching in a group of interpreting students and a group of translation students (before and after their 2-year Masters programme), and a group of non-language students at two

different time points approximately 2 years apart. They found no between-group differences before training. All three groups’ performance improved across tasks after training. However, only the interpreting students showed a significant increase in performance in measures associated with short-term memory (a letter-span task), suggesting that training in simultaneous interpreting may result in verbal short-term

memory advantages. These studies show that while training in interpreting does not lead to consistent improvements in all tests associated with working memory, short-term memory, task switching and executive function, some behaviourally measurable cognitive functions do appear to change with training. If we assume that cognitive resources are limited (Kahneman, 1973), then improvements in one area of cognitive functioning could lead to greater resource availability for other cognitive functions – such as prediction.

Evidence of cognitive change due to interpreting training also comes from neural studies. For instance, Hervais-Adelman et al. (2017) measured cortical thickness in

interpreting students before and after training in simultaneous interpreting. A control group was tested at the same intervals. They found that around 14 months of interpreting training led to increases in cortical thickness in areas of the brain associated with speech-sound processing, executive control of attentional resources, and working memory (with decreases in cortical thickness found in the control group). This suggests that simultaneous

interpreting training may confer cognitive advantages (see also Van de Putte et al., 2018).

However, it is still unclear what the advantage of such cortical thickness is for the simultaneous interpreting task itself.

4.1.3 Evidence that predictive processing can change depending on cognitive