Parsing the Passive: Comparing Children With Specific Language Impairment to Sequential Bilingual Children

Abstract

Twenty-five monolingual (L1) children with specific language impairment (SLI), 32 sequential bilingual (L2) children, and 29 L1 controls completed the Test of Active & Passive Sentences-Revised (van der Lely 1996) and the Self-Paced Listening Task with Picture Verification for actives and passives (Marinis 2007). These revealed important between-group differences in both tasks. The children with SLI showed difficulties in both actives and passives when they had to reanalyse thematic roles on-line. Their error pattern provided evidence for working memory limitations. The L2 children showed difficulties only in passives both on-line and off-line. We suggest that these relate to the complex syntactic algorithm in passives and reflect an earlier developmental stage due to reduced exposure to the L2. The results are discussed in relation to theories of SLI and can be best accommodated within accounts proposing that difficulties in the comprehension of passives stem from processing limitations.

ACKNOWLEDGMENTS

This research was supported by the Economic and Social Research Council research grant “Real-time processing of syntactic information in children with English as a Second Language & children with Specific Language Impairment” awarded to Theodoros Marinis (RES-061-23-0137). We thank Nicola Dawson and Daniel Gibbons for the data collection of the children with Specific Language Impairment; Halit Firat for the data collection of the L2 children; Vicky Chondrogianni for the data collection of the L1 children; the SENCOs, schools, and speech and language therapists for helping us in the recruitment; and the families and children for participating in this project. This study was presented at the 2012 ASHA Convention, the 45th BAAL conference, and at the departmental seminars of the University of Geneva, the University of Warsaw, and Bangor University. We thank the audiences of these events and two anonymous reviewers for their constructive comments and suggestions.

Notes

¹For alternative analyses of passives that do not involve movement, see Langacker (1991).

²Cf. O'Brien et al. (2006) and Crain et al. (2009).

³See Wexler (2004) for a version of this account within the minimalist framework. A slightly different hypothesis was formulated by Fox & Grodzinsky (1998), according to which children have difficulties with passives because they lack the ability to transmit the external theta role to the agent in the by-phrase.

⁴Effects of age were also found in Rice, Wexler & Francois (2001) and suggest that with increasing age comprehension difficulties of passives may resolve.

⁵A similar prediction for the processing of tense morphemes was borne out by Montgomery & Leonard (1998, 2006).

⁶In fact, if the L2 children have acquired passives in Turkish, this could facilitate their performance in English. Given that we did not test the L2 children in Turkish, this issue remains open for future research.

⁷These were administered to all children.

⁸η² (eta-squared) estimates the effect size in the sample. According to Cohen (1992), 0.1 is a small effect, 0.25 is a medium effect, and 0.4 is a large effect.

⁹The full materials for this task are available through the IRIS digital depository (http://www.iris-database.org) and the website of the University of Reading (http://www.personal.reading.ac.uk/~lls05tm/).

¹⁰A conservative rate of 33% (4 out of 12) was counted as chance level since this is a four-choice task but one of the pictures was a semantic distracter.

¹¹False alarms are trials that require a “yes” answer but where the participants provide a “no” response.

¹²Extreme values are extremely long or short RTs and could result from interruption of the trial or pressing the button extremely quickly. Outliers are also considerably longer or shorter RTs than the mean, but are often the end of the tail. See Ratcliff (1993) for the difference between extreme values and outliers in RT experiments and the rationale behind eliminating extreme values and replacing outliers.