Discussion

Four major findings emerge from the current study. First, when parsing ambiguous wh-questions, young children aged 5–6 years do not show a clear attachment preference; this preference arises at age 7–8, attesting to an adultlike use of incremental parsing strategies. Second, children age 5–6 revise less than 7–8-year-olds and adults, who show a similar rate of revision, again attesting to adultlike parsing at age 7–8. Third, although 7–8-year-olds and adults revise more, they still show a substantial number of misparsing, even higher in adults. Fourth, we found a specific link between the ability to revise and indices of cognitive control, and more particularly inhibition and updating. Participants with stronger cognitive control abilities revise better than

those with weaker cognitive control. In the following sections, we discuss the developmental trajectory of incremental parsing and revision and the link between syntactic revision and cognitive control.

Developmental trajectory of incremental parsing and revision As reviewed in the Introduction, incremental parsing has been largely attested in the adult literature (e.g., Sussman & Sedivy, 2003; for a review, see Phillips & Wagers, 2007), but less so in children (Lassotta et al., 2016; Love, 2007; Omaki et al., 2014; Roberts et al., 2007). We explored incremental parsing through the study of ambiguous bi-clausal wh-questions, which can be parsed in attaching the wh-argument to the main verb or to the embedded verb. A preference for main verb attachment in this context is viewed as evidence for incremental parsing, as it attests to wh-attachment to the first available gap. We found no evidence for active gap filling in 5–6-year-old children, who showed a MV preference in only 40 % of the cases. However, inspection of individual profiles in that group shows that some children actually do show a preference: 46 % of them went for MV attachment in 75 % or more of their responses. A change is observed at age 7–8 years, where we found a MV preference at the group level (68 %).

However, although the majority of the children showed a MV bias in that group (63 %), a number of them still failed to show active gap filling. Our data show that incremental parsing still develops beyond age 7–8, as adults show a massive MV preference, both at the group level (94 %), and at the individual level (96 %).

The MV attachment bias in wh-questions appears to be modulated by two linguistic factors: verb semantics and argument structure (argument vs. adjunct wh-, see Table 20). In Lassotta and colleagues (2016), the very same argument wh-structure in French was used as in the current study, but with heavy MVs (raconter ‘tell’ and expliquer ‘explain’) instead of the light verb dire ‘say’ used here. As can be seen in Table 20 (column ‘Active gap filling’), children’s MV bias is much lower with the light verb than with heavy verbs, and a similar tendency is present in adults, although less pronounced. Lassotta and colleagues (2016) also uncovered that MV attachment is shaped by verb argument structure: both children and adults showed slightly stronger MV attachment in argument wh-questions, in which the wh-element is an argument of the verb, than in adjunct wh-questions, in which the wh-element is an optional complement (Table 20). Hence, the main point of departure between children and adults lies in the role of verb semantics: while children showed a strong MV preference with heavy verbs, the preference is absent with light verbs in younger 5–6-year-olds, and still mild in 7–8-year-olds. In contrast,

adults show a strong MV preference independently of verb semantics. This suggests that children’s initial parsing strategy is contingent on verb semantics, attaching the wh-element to the heavy verb, which progressively evolves toward a more efficient, default strategy of active gap filling. This possibility is in line with findings showing children’s sensitivity to verb semantics in visual world eye-tracking studies reporting over-reliance on verb information in the interpretation of garden-path sentences like (4), while adults make use of both verb information and information about the referential context (Kidd et al., 2011; Snedeker & Trueswell, 2004).

Table 20

Overview of rates of active gap filling, misparsing and revision (in %) in studies on wh-attachment in French bi-clausal wh-questions (Lassotta et al., 2016, and current study), depending on main clause argument structure and main verb semantics

Note. Active gap filling is measured through MV attachment in ambiguous questions, misparsing through MV attachment in filled-gap questions, and revision through EV increase between ambiguous and filled-gap questions.

Now turning to the parsing of filled-gap questions, the current study uncovered revision, manifested by an increase of EV responses as compared to ambiguous wh-questions, in all age groups, showing that participants were sensitive to the filled-gap PP signaling the parser to reject MV attachment (see Table 20, column ‘Revision’). The results revealed that among children, revision increases with age, 5–6-year-olds showing fewer revision (22 %) than 7–8-year-olds (63 %). Interestingly, adults revised at a similar rate as 7–8-year-olds (62 %). At the individual

level, 43 % of the 5–6-year-olds showed revision (i.e., an EV increase of at least +25 %; 13 % systematically revising in all items), against 75 % of the 7–8-year-olds and 77 % of the adults (in both groups 42 % revising in all items). This shows, on the one side, fully operational revision skills by age 7–8 years, and on the other side substantial inter-individual variability in all age groups, some 5–6-year-old children being already able to revise while some older children and adults (occasionally) fail.

Considering how the two linguistic factors discussed in regard to MV attachment preference affect revision, it turns out that while children revise argument wh-questions as often with heavy verbs (43 %) as with light verbs (43 %), adults succeed less with heavy verbs (21 %) than with light verbs (62 %). This suggests that adults are sensitive to the semantic properties of the verb when it comes to revision, while children are not. With respect to verb argument structure, Lassotta and colleagues (2016) reported that whereas adults revise adjunct and argument wh-questions equally often (both 21 %), children only revise argument wh-questions. Adjunct wh-questions involve an optional filled-gap whereas argument wh-questions involve an obligatory filled-gap, which determines the nature of the error signal (optional in the former and obligatory in the latter). Children thus appear to be very sensitive to the nature of the error signal in that revision is easier for them with more salient, obligatory filled-gaps than with less salient, optional filled-gaps.

Considering parsing errors, that is, interpretations based on the attachment of the wh-element to the MV although its argument slot is already occupied by a PP, both the current results and Lassotta et al.’s (2016) attest to substantial error rates in adults. We focus here on argument questions, because attaching the wh-element to the filled MV gap in adjunct questions can actually be made grammatical (e.g., “Where did Aline in the living room explain that …?”

could be parsed as “Where about in the living room?”; see Lassotta et al., 2016, for a discussion of this option). Both studies show that adults and children make more errors with heavy main verbs, which appears to play a role in attracting MV attachment (see Table 20, column

‘Misparsing’). But more crucially, both studies show that adults misparse filled-gap argument questions significantly more than children: 79 % errors vs. 52 % with heavy MVs, and 32 % vs.

12 % (on average across the two child groups) with light verbs. Interestingly, 7–8-year-olds showed only 5 % errors with light verbs. Our finding that adults sometimes generate erroneous parses is actually in line with the few studies that tested adults’ actual comprehension of complex structures involving either garden-path of object movement, which also attested to unexpectedly high rates of comprehension errors (e.g., Christianson et al., 2001; Ferreira, 2003; Villata &

Franck, 2020; Villata et al., 2018). One possible explanation for the high rate of ungrammatical

parses in adults, even with light verbs, lies in their strong reliance on the active gap filling parsing strategy, which could make the initial parse too stable to be reconsidered and hence no alternative parse is generated (i.e., revision failure). Another possible interpretation of misparses is that an alternative parse is generated, but finally not selected due to lingering memory traces (or semantic persistence) of the early parse that are too strong to be outruled (e.g., Slattery et al., 2013; Staub, 2007; Sturt, 2007; Gompel et al., 2006). We discuss both revision and misparsing in relation to cognitive control skills in the next section.

Role of cognitive control in syntactic revision

Different cognitive control indices were collected through the N-Back and DCCS tasks, tapping into inhibition, switching, and updating. A general – and unsurprising – observation is that adults show higher performances than children, that is higher accuracy (N-back d’ and lure accuracy), lower latencies (N-back hit latency), and lower costs (all six DCCS indexes).

Considering accuracy indices, performance was similar between 5–6-year-olds and 7–8-year-olds (N-back lure accuracy, DCCS perceptual inhibition accuracy cost, single-switch accuracy cost, and multiple-switch accuracy cost), with one exception: the N-back d’ is lower in 5–6-year-olds than in 7–8-year-olds, who do not differ from adults. Considering latencies, we found that older children outperformed younger children in all indexes (N-back hit latency, DCCS perceptual inhibition latency cost, single-switch latency cost, and multiple-switch latency cost). These findings are in line with previous studies showing a continuous development of cognitive control abilities in childhood (e.g., for DCCS: Diamond et al., 2005; N-back: Pelegrina et al., 2015). We also used the Corsi task as control test, and found developmental changes here, too, 5–6-year-olds’

level being lower than in 7–8-year-olds, whose level is lower than in adults. Normative data from Farrell Pagulayan and colleagues (2006) reported similar Corsi levels (age 7: 5.0; age 8: 5.2).

Only very few studies using N-back tasks with children at that age are available. A categorical picture N-back task (without lures) showed hit latencies that fit our data (age 6:

760 ms, age 10: 490 ms, adults: 480 ms; Ciesielski et al., 2006) and also demonstrated that the underlying updating abilities continuously develop beyond age 8. Normative 2-back data (without lures) indicated a lower d’ (age 7: 0.7; age 8: 0.8) and longer hit latencies (age 7: 1’220 ms; age 8:

1’180 ms; Pelegrina et al., 2015) as compared to our results (age 7–8: d’ = 1.7 and hit latency = 601 ms). We presume that the letter N-back used in this normative study was much less child-friendly than our N-back task with animated pictures and the gaming context including positive feedback and goal-set, and that the task setting influenced performance (positive feedback

enhancing intrinsic motivation, situational interest, and task performance; e.g., Harackiewicz, 1979). So, our N-back appears to be more ideal for young populations than standard letter N-backs. Also, despite the child-friendly design and procedure, adults were well engaged in our N-back task as attested by the fact that neither their d’ (1.87) nor their lure accuracy (86 %) were at ceiling. Hence, the new N-back task we designed appears suitable to multiple age groups, thus providing an excellent tool to the study of cognitive control development.

We saw that both the linguistic indices (active-gap filling and revision) and the cognitive control indices develop with age, and that revision difficulties substantially vary across children within the same age group. Moreover, although children have been argued to struggle with syntactic revision due to immature syntactic processing mechanisms (e.g., Trueswell & Gleitman, 2004; Trueswell et al., 1999), we saw that adults still struggle. Hence, it seems plausible that revision difficulties in both children and adults do not (only) lie in the immaturity of parsing strategies, but rather in the involvement of factors external to the language domain (e.g., Mazuka et al., 2009; Novick et al., 2005; Omaki & Lidz, 2015). Indeed, we found significant links between syntactic revision and these components of cognitive control. Participants with higher N-back and DCCS performance were found better in rejecting the erroneous MV interpretation, and opting for the alternative and correct EV interpretation. More specifically, revision success is tied to correctly and quickly detecting 2-back targets and in discarding 1-back and 3-back lures. These two indexes measure the ability to constantly update what constitute the last two items encoded in memory, and to inhibit stored objects from memory that are retrieved in virtue of their identity with the current object being processed, although they are not targets. In line with these findings, participants with a lower single-switch cost at the DCCS also tended to revise more frequently.

This index measures the ability to switch sorting rules which in fact means deactivating (or inhibiting) a previously relevant rule and actively selecting a new rule to be currently used (possibly also involving updating).

In accordance with our prediction of a selective link between revision and some components of cognitive control, we found no correlation between revision and perceptual inhibition assessed in the DCCS task, and no correlation with spatial working memory assessed in the Corsi task. We also failed to find a significant correlation between revision and the multiple-switch cost of the DCCS, although this measure is expected to measure switching.

Multiple-switch cost was measured in the third block of the DCCS task, after participants performed two blocks in which they first had to sort cards according to one criterion, and then according to the other. Although we expected this third block to be particularly difficult, given that participants have to regularly switch, between the two sorting criteria, it turned out that this

switching cost was low (see Table 18). It is thus plausible that this measure did not mobilize cognitive control as expected, explaining why it fails to correlate with revision.

The attested link between N-back performance and revision in our study is in line with previous findings showing that repeated training with the N-back task improves adults’ ability to revise temporarily ambiguous sentences (Hussey et al., 2017; Hussey & Novick, 2012; Novick et al., 2013). Interestingly, even a brief training with a trial preceding the comprehension of garden-path sentences was found to reduce comprehension errors and facilitate revision in adults (Hsu et al., 2020). To-date, only one study used N-back training with children (age 9) and found it to be effective, i.e., increasing fluid intelligence on untrained tasks, and lasting over three months (Jaeggi et al., 2011). Future investigations could examine if such a training effect also transfers to revision performance and test younger children as well.

In summary, the overall data collected in the current study shows developmental changes in both the linguistic and non-linguistic domains. At the linguistic level, we found that (a) the incremental parsing strategy of active gap filling is absent in 5–6-year-olds, starts to emerge at age 7–8, and keeps consolidating until adulthood; (b) 5–6-year-olds sometimes manage to revise their initial parse, but less often than children aged 7–8 and adults who revise alike; (c) adults more often reach an illicit, ungrammatical parse than children aged 5–8. At the level of cognitive control, (d) the key measures of updating and inhibition that correlate with revision (d’, lure accuracy, and hit latency) improve between age 5–6 and 7–8, between age 7–8 and adults, or both.

The overall pattern can be accounted if one assumes that two factors pull syntactic revision in opposite directions. On the one side, the strength of the initial engagement towards main verb attachment, i.e., the strength of incremental processing, increases the difficulty to disengage from that initial path and revise. On the other side, the strength of cognitive control abilities reduces the difficulty to disengage from that initial analysis. In other words, the more efficient incremental parsing is, the more cognitive control needs to be efficient to overcome the initial analysis.

Younger children benefit from the fact that they do not yet apply active gap filling, but are penalized by their immature cognitive control. In contrast, adults benefit from mature cognitive control, allowing them to revise more easily than young children, but they are penalized by their strong incremental parsing strategy, leading them to still get stuck on their initial parse in almost one third of the garden-path sentences. Children aged 7–8 seem to show a nearly optimal balance between the two factors, with improved cognitive control but a mild active-gap filling strategy, allowing them to revise as much as adults, but generate less misinterpretations.

3.2.5 Conclusion

The examination of children’ and adults’ processing of complex wh-questions has revealed that young children do not show a clear active gap filling strategy of incremental parsing, which keeps developing even after age 8. However, young children are capable of syntactic revision, an ability that improves until age 7–8 years, age at which it stabilizes. In line with the few existing studies on adults’ actual comprehension of complex sentences, adults were found to frequently misinterpret garden-path sentences, even more so than children. The systematic investigation of participants’ cognitive control skills in parallel to the linguistic measures collected allowed us to extend for the first time to children the finding of a link between revision and cognitive control. Our study may open a path for future research on how sentence processing, and revision in particular, could be improved by cognitive training in children and whether long-term benefits are discernable.

ACKNOWLEDGMENTS

This work was supported by grant 100014-126924 from the Swiss National Fund for Scientific Research to Julie Franck. We thank the schools, parents, and especially the children who participated in the experiments. Many thanks also to Sandra Villata and Daniele Panizza for the exciting discussions and collaboration during the designing of this study. We are greatly in debt to our colleague and friend, Akira Omaki, who passed away as we were writing this manuscript. His tremendous inspiration will always remain

C ^HAPTER 4:

F INDING CLOSURE

“The more I learn, the more I learn how little I know.”

—Socrates