Abstract Grammar acquisition is a high level cognitive function that requires the extraction of complex rules. While it has been proposed that offline time might benefit this type of rule extraction, this remains to be tested. Here, we addressed this question using an artificial grammar learning paradigm. During a short-term memory cover task, eighty-one human participants were exposed to letter sequences generated according to an unknown artificial grammar. Following a time delay of 15 min, 12 h (wake or sleep) or 24 h, participants classified novel test sequences as Grammatical or Non-Grammatical. Previous behavioral and functional neuroimaging work has shown that classification can be guided by two distinct underlying processes: (1) the holistic abstraction of the underlying grammar rules and (2) the detection of sequence chunks that appear at varying frequencies during exposure. Here, we show that classification performance improved after sleep. Moreover, this improvement was due to an enhancement of rule abstraction, while the effect of chunk frequency was unaltered by sleep. These findings suggest that sleep plays a critical role in extracting complex structure from separate but related items during integrative memory processing. Our findings stress the importance of alternating periods of learning with sleep in settings in which complex information must be acquired.

Citation: Nieuwenhuis ILC, Folia V, Forkstam C, Jensen O, Petersson KM (2013) Sleep Promotes the Extraction of Grammatical Rules. PLoS ONE 8(6): e65046. https://doi.org/10.1371/journal.pone.0065046 Editor: Edwin Robertson, Harvard Medical School, United States of America Received: June 12, 2012; Accepted: April 24, 2013; Published: June 5, 2013 Copyright: © 2013 Nieuwenhuis et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by grants from the Dutch National Science Foundation (grant numbers NWO 051-04-100), Fundação para a Ciência e Tecnologia (PTDC/PSI-PCO/110734/2009; IBB/CBME, LA, FEDER/POCI 2010), Max Planck Institute for Psycholinguistics, Donders Institute for Brain, Cognition and Behaviour, and Vetenskapsrådet. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

Introduction Considerable evidence suggests that new skill learning or acquisition of new declarative information does not only take place during training or exposure to new material, but continues to develop during offline periods following learning in which one is not directly engaged in the task. Both offline time spent awake and asleep can be beneficial [1], [2], [3]. Recently, studies have indicated that offline time might promote the formation of relations between pieces of information. For example, in transitive inference learning, the association of related memories preferentially develops following offline time periods, with the greatest improvement after sleep [4]. Also, following exposure to an artificial language, infants learn simple relations between syllables only after a period of sleep [5]. It has therefore been hypothesized that sleep is specifically beneficial for generalization and abstraction [6]. A grammar specifies a complex set of relations between words that supports us in comprehending how concepts are interrelated in sentences, for example, in determining who did what to whom. Acquiring a new grammar requires complex rule extraction. The human brain is equipped with implicit learning mechanisms that extract such patterns from the information to which it is exposed [7], [8] (note, we use the terms “implicit” and “implicit learning” in their classical sense, meaning a lack of meta-cognitive knowledge and in particular the absence of any stated use of explicit “problem solving” strategies [7], [8], [9]). While above findings have begun to establish a role for sleep in associative memory processing, it is currently unknown whether offline (sleep) time contributes to the implicit abstraction of complex structure needed for grammar acquisition. Moreover, the mechanistic route(s) by which these offline benefits develop remain largely uncharacterized. Artificial grammar learning has been extensively used to study grammar acquisition (e.g. [7], [8], [9], [10], [11], [12], [13], [14]). In such tasks, a complex set of rules constituting the grammar (see for instance Fig. 1A), defines the order of symbols in a sequence. The typical artificial grammar learning experiment includes a short acquisition session followed by a classification test. During the acquisition phase, participants are engaged in a short-term memory task using an acquisition sample generated from a grammar. After the acquisition session, the participants are informed that the sequences were generated according to a complex system of rules, however, no information about the rules is provided. The participants then have to classify new items as Grammatical or Non-Grammatical guided by their immediate intuitive impression (i.e., guessing based on “gut feeling”). The participants usually perform reliably above chance, suggesting that they acquired knowledge about relevant aspects of the underlying grammar [13], [14]. Based on the fact that subjects are typically unable to provide sufficient, if any, reasons to motivate their classification decisions it is assumed that the classification performance is based largely on implicit acquisition mechanisms, for reviews see [9], [11], [15], [16] however also see [17], [18], [19]. It has been suggested that artificial grammar learning depends on the implicit acquisition of structural knowledge (i.e., “rule-based” representations). This knowledge could be extracted from the combined sample of acquisition sequences by integrative processes combining the structural information present in the individual sequences [7], [8], [12]. PPT PowerPoint slide

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larger image TIFF original image Download: Figure 1. Artificial grammar and stimulus material. A) The Reber grammar used. B) Total Pool: all 569 unique 5–12 letter sequences. C) Acquisition Set: 100 representative sequences, used in the short-term memory task. D) Classification Set: 120 sequences arranged in a 2×2 factorial design, with the factors grammaticality (Grammatical, Non-Grammatical), and associative chunk strength (High, Low), used in the classification task. https://doi.org/10.1371/journal.pone.0065046.g001 Alternative theoretical frameworks have questioned the acquisition of abstract (“rule”) knowledge during artificial grammar learning and suggest instead that what participants actually learn are parts of the presented sequences (“chunks” such as bigrams and trigrams). The acquired knowledge of these chunks would subsequently be sufficient to account for the performance of grammaticality judgments [20], [21]. In order to address this issue the associative chunk strength (ACS) measure was developed [11], [12]. ACS is quantified in terms of the frequency with which local subsequences (e.g., bi- and trigrams) of a classification item occurred in the Acquisition Set. In this approach, acquired structural (“rule based”) and instance specific information are quantified by grammaticality status and ACS, respectively. Several studies have been performed to test the independent contributions of grammaticality and ACS on classification by arranging the sequences in the Classification Set in a 2×2 factorial design (e.g. [11], [12], [14], [22], [23], [24]). In these studies the Classification Set contains both items that violate the grammar but include many highly frequent subsequences, and Grammatical sequences that omit highly frequent subsequences. From the studies in which the ACS of the test items was manipulated, it is clear that both grammaticality and ACS affect grammar judgments made by the participants. However, when participants perform robustly (e.g., after several days of implicit acquisition) the effects are largely independent and the grammaticality effect is significantly larger than the effect of ACS [13], [14], [25]. In addition, functional neuroimaging data show that the effects of grammaticality and ACS affect different brain regions and typically no interaction between these factors are found in the brain [22], [26]. Taken together, the evidence suggests that artificial grammar learning can be conceptualized both in terms of structure-based rule acquisition and surface-based statistical learning mechanisms. It is, however, unknown how an offline delay between acquisition and classification interacts with these mechanisms. Interestingly, many artificial grammar studies, especially those that investigate more extensive and complex artificial grammars, apply an acquisition phase that spans over several consecutive days (e.g. [13], [14], [22], [27]). This suggests that offline wake and/or sleep time between acquisition and classification might benefit artificial grammar learning. Building on the emerging findings of a role for offline time in associative memory processing, the current study investigates whether offline wake and/or sleep time facilitates artificial grammar learning. Moreover, we specifically examine which mechanistic route (or combination of routes) mediates such delayed memory benefits by using ACS controlled classification items. Given that offline time might promote the formation of relations between pieces of information [4], [5] and the hypothesis that sleep is particularly beneficial for generalization and abstraction [6], we hypothesize that (1) offline time, and sleep in particular, will enhance artificial grammar learning; and that (2) offline (sleep) time specifically benefits abstraction of grammar rules without enhancing the effect of ACS.

Discussion Our findings demonstrate that the brain-state (wake or sleep) during an offline delay modulate the classification performance after artificial grammar learning. In particular, classification performance was enhanced after a delay containing sleep. A clear dissociation was seen in the route by which this benefit of sleep developed: the improvement after sleep was due to an enhanced ability to discriminate between Grammatical and Non-Grammatical sequences (grammaticality effect) and was not be explained by an increased ability to recognize highly frequent chunks and reject low-frequency chunks (effect of ACS). Notably, the effect of offline sleep time on grammar acquisition did not appear to depend on the length of the offline delay or the time of day of either exposure or classification. Sleep did not increase the proportion of sequences with highly frequent chunks (High ACS) that was endorsed as Grammatical. Participants in all groups endorsed a higher proportion of High than Low ACS sequences, but sleep did not modulate this effect. This is interesting considering that it’s known that one of the routes modulating classification performance is becoming familiar with sequence chunks that were highly frequent during exposure [11], [12], [22]. Moreover, it has been shown repeatedly that one of the effects of sleep is to strengthen individual memories (e.g. [29], [30], [31], [32]). Hence, strengthening of the memory for highly frequent chunks could have represented one possible route through which sleep improves classification performance; however, we did not find evidence for this. Instead, it appears that the classification performance was improved via the grammaticality route, since after sleep, the ability to discriminate between Grammatical and Non-Grammatical sequences increased significantly. It has been argued that the ability to correctly judge grammaticality status during classification arises from integration and abstraction of the grammatical information present in the collective Acquisition Set of sequences used during exposure to the grammar [7], [8], [11]. The effect of sleep on the grammaticality route suggests that sleep promotes such integrative processes. During sleep, high-level rule abstraction processes appear to take place in which memories of individual sequences are combined, resulting in abstract knowledge reaching beyond the previously encountered exemplars. This knowledge might be a partially veridical representation of the underlying grammar rules. However, this does not imply that participants have acquired full “knowledge” of the grammar rules, either implicit or explicit, in this study, since they still classify many sequences incorrectly. Over time and with further implicit exposure, one would expect, however, that this knowledge would effectively converge on a representation equivalent to the underlying grammar, as in for example natural language acquisition [13], [25], [33]. Since Reber [7], artificial grammar learning is one of the main paradigms to investigate implicit learning. It is typically assumed that the classification performance is based on implicit acquisition mechanisms because subjects are in general unable to provide sufficient reasons to motivate their classification decisions [9], [14], [16], [22], [25]. It has also been argued that learning based on ACS reflects explicit declarative memory mechanism involving the medial temporal lobe [23], [34], while implicit learning of grammaticality status independent of ACS reflects an implicit procedural learning mechanism involving the basal ganglia and the prefrontal cortex [22], [24]. Although we did not test for explicit knowledge directly in the current study, previous results obtained in very similar artificial grammar learning paradigms [13], [14], [25] suggests that classification performance is to great extent based on implicit acquisition mechanisms. If this is so, then the current results suggest that sleep also enhances implicitly learned material, and might therefore not be limited to consolidating explicitly learned material [35], [36]. As noted in the introduction, we use the terms “implicit” and “implicit learning” to mean a lack of meta-cognitive knowledge and in particular the absence of any stated use of explicit “problem solving” strategies [7], [8], [9]. We note that recent experimental findings suggest that partial subjective awareness of the structural knowledge can develop in some artificial grammar learning tasks [17], [18], [19] and that a detailed investigation of the effect of sleep on the development of partial awareness would be a topic for future studies. Since brain activity during sleep was not measured directly, it cannot be excluded that an unidentified factor occurring during the night time portion of the diurnal cycle, co-occurring with but independent of sleep, contributed to the reported findings, although this seems implausible. Nonetheless, several studies report a correlation between the level of improvement on associative and relational memory tasks and the amount of (a specific stage of) sleep, suggesting an active role for sleep [37], [38], [39]. Both Rapid Eye Movement (REM) sleep [37], [40] and Non-REM (NREM) sleep [38], [39] have been associated with improvements in the reprocessing and optimization of the high-level information contained in the learned material. Several studies have shown that different groups of brain regions are selectively sensitive to the grammaticality status and to the ACS level of sequences, respectively [22], [23], [24]. Reduced activation levels for High versus Low ACS items were shown in early visual regions and the medial temporal lobe, while the caudate nucleus appears to be more active for Grammatical versus Non-Grammatical items [22], [23], [24]. Moreover, Broca’s region is sensitive to the grammaticality status of items and is not sensitive to ACS [22], [24], [26]. Future research will have to elucidate whether increased activity in the brain regions underlying the grammaticality route can be observed during sleep and whether such activity predicts post-sleep improvement of rule abstraction performance. Our findings stress the importance of alternating periods of exposure to a new grammar with periods of sleep for optimal grammar acquisition. More generally, a night of sleep can be beneficial after exposure to material that requires high-level rule abstraction.

Supporting Information Appendix S1. Task instructions exposure phase. A) Written instructions, handed out to participants prior to short-term memory task. B) On screen instructions, appeared on computer screen before the short-term memory task started. Instructions were translated from Dutch. https://doi.org/10.1371/journal.pone.0065046.s001 (DOCX) Appendix S2. Task instructions test phase. A) Written instructions, handed out to participants prior to the classification task. B) On screen instructions, appeared on computer screen before the classification task started. Instructions were translated from Dutch. https://doi.org/10.1371/journal.pone.0065046.s002 (DOCX)

Acknowledgments We thank Matthew Walker for his thoughtful insights and constructive contributions to the manuscript and Jae Brodsky for her advice on the statistical analysis.

Author Contributions Conceived and designed the experiments: ILN CF KMP. Performed the experiments: ILN CF VF. Analyzed the data: ILN. Contributed reagents/materials/analysis tools: ILN CF. Wrote the paper: ILN VF CF OJ KMP.