JSLHR

Research Article

Learning of Grammar-Like Visual Sequences by Adults With and Without Language-Learning Disabilities Jessica M. Aguilara and Elena Plantea

Purpose: Two studies examined learning of grammar-like visual sequences to determine whether a general deficit in statistical learning characterizes this population. Furthermore, we tested the hypothesis that difficulty in sustaining attention during the learning task might account for differences in statistical learning. Method: In Study 1, adults with normal language (NL) or language-learning disability (LLD) were familiarized with the visual artificial grammar and then tested using items that conformed or deviated from the grammar. In Study 2, a 2nd sample of adults with NL and LLD were presented auditory word pairs with weak semantic associations (e.g., groom + clean) along with the visual learning task. Participants were instructed to attend to visual sequences and to ignore the

auditory stimuli. Incidental encoding of these words would indicate reduced attention to the primary task. Results: In Studies 1 and 2, both groups demonstrated learning and generalization of the artificial grammar. In Study 2, neither the NL nor the LLD group appeared to encode the words presented during the learning phase. Conclusion: The results argue against a general deficit in statistical learning for individuals with LLD and demonstrate that both NL and LLD learners can ignore extraneous auditory stimuli during visual learning.

A

Evans et al., 2009; Plante et al., 2002; Richardson et al., 2006). Statistical learning studies of language or language-like structures (i.e., artificial grammars) frequently use auditory stimuli. However, a number of studies have demonstrated that learners can recognize statistical patterns in visual (e.g., Arciuli & Simpson, 2011; Fiser & Aslin, 2002; Kidd, 2012; Marcus, Fernandes, & Johnson, 2007; Reber, 1967, 1976; Saffran, Johnson, Aslin, & Newport, 1999; Theissen, 2011) and tonal stimuli (e.g., Creel, Newport, & Aslin, 2004; Dawson & Gerken, 2009; Marcus et al., 2007; Saffran et al., 1999). In contrast to the literature on typical learners, the only studies to address nonverbal statistical learning for individuals with language impairments have used various serial reaction time paradigms (Gabriel, Maillart, Guillaume, Stefaniak, & Meulemans, 2011; Gabriel et al., 2013; Gabriel, Stefaniak, Maillart, Schmitz, & Meulemans, 2012; Hedenius et al., 2011; Tomblin, Mainela-Arnold, & Zhang, 2007). These paradigms look at manual reaction time changes as participants learn statistical dependencies among continuous sequences of visual stimuli. Hedenius et al. (2011) reported that individual differences in their serial reaction time task predicted grammatical deficits in children with specific language

n individual’s ability to detect statistical regularities in their environment plays an important role in perceptual and cognitive learning, including language acquisition, skill learning, and object recognition. Recognition of statistical regularities results in rapid learning in the absence of explicit instruction (Aslin & Newport, 2012; Gómez, 2006). This phenomenon has been examined for individuals with normal language (NL) and, to a lesser extent, those with language and learning disabilities. In studies that have focused on participants with language impairment, several have used stimuli designed to mimic aspects of natural languages (Bahl, Plante, & Gerken, 2009; Evans, Saffran, & Robe-Torres, 2009; Plante, Gómez, & Gerken, 2002; Richardson, Harris, Plante, & Gerken, 2006). Such studies frequently provided evidence of poor learning compared with their typically developing peers (Bahl et al., 2009;

a

University of Arizona, Tucson

Correspondence to Jessica M. Aguilar: [email protected] Editor: Rhea Paul Associate Editor: Jessica Richardson Received May 15, 2013 Revision received October 7, 2013 Accepted December 8, 2013 DOI: 10.1044/2014_JSLHR-L-13-0124

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Key Words: adults, language, language disorders, procedural learning, statistical learning

Disclosure: The authors have declared that no competing interests existed at the time of publication.

Journal of Speech, Language, and Hearing Research • Vol. 57 • 1394–1404 • August 2014 • A American Speech-Language-Hearing Association

impairment (SLI). Some studies have revealed that participants with SLI can demonstrate initial sequence learning similar to their typically developing peers (Gabriel et al., 2011; Tomblin et al., 2007). However, the individuals with SLI took longer to learn to criterion in one study (Tomblin et al., 2007), and they did not show consolidation of sequence learning when tested 3 days later in another (Hedenius et al., 2011). Gabriel and colleagues (2011) showed similar learning for a probabilistic serial reaction time task but found significant group differences for a serial reaction time task that included more difficult second-order contingencies (Gabriel et al., 2013). Poor performance on this type of task is consistent with the idea that developmental language impairment reflects a broader deficit in procedural learning (Ullman & Pierpont, 2005). However, there are several major limitations to this theoretical claim. One is that serial reaction time tasks do not reflect important properties of language and contain the unrelated construct of manual reaction time (see Kail, 1994; Windsor, 2002, for reviews). This raises the possibility that serial reaction time deficits are associated deficits rather than being causally related to grammatical deficits. If so, deficits in serial reaction time would be similar to other known areas of nonverbal deficit in the SLI population (e.g., Johnston & Ellis Weismer, 1983; Kamhi, Catts, Mauer, Apel, & Gentry, 1988; Kamhi, Ward, & Mills, 1995; Savich, 1984; Swisher, Plante, & Lowell, 1994). A visual learning task that represents properties also present for specific grammatical forms found in natural languages would provide a stronger test of an amodal learning deficit that relates conceptually to language deficits. Here, we use an artificial language that is composed of threeelement visual strings. The strings are structured so that the presence of the first element predicts the third element, but the specific middle element that appears in the string is not contingent on either element (i.e., aXb, cXd, where a predicts b and c predicts d). This constitutes the grammar of the artificial language. This is analogous to English verb forms such as “is verbing” in which “is + verb” predicts the presence of “-ing,” but the specific verb that follows “is” is free to vary. This type of visual stimuli reflects the types of statistical relationships found in natural grammars. As such, this visual grammar would offer a nonverbal parallel to the area of deficit that is considered a hallmark feature of language impairment. A second concern with the claim that an amodal procedural learning deficit might account for poor language skills is that there is now evidence that those with impaired language can learn grammar-like forms under optimized conditions. Recently, Torkildsen, Dailey, Aguilar, Gómez, and Plante (2013) presented learners selected for poor language skills with a simple artificial grammar. The grammatical structures took the form of “aX” and “Yb” in which closed class “a” and “b” elements were paired with open class “X” and “Y” elements. This study demonstrated that participants with a language learning disability (LLD) were able to learn the statistical relationship between elements only if the “X” and “Y” elements were represented by a wide

variety of different nonwords. Presenting the same number of exemplars of the grammar, but with a limited variety of “X” and “Y” elements, failed to result in learning. This outcome was consistent with previous studies of normal learners in which high variability produced learning of grammars that were unlearnable under low-variability conditions (Gómez, 2002; Gómez & Maye, 2005). Therefore, it appears that a statistical learning deficit can be reduced or eliminated under certain conditions, even by those with impaired language. This suggests that a strong test of the theory that an amodal procedural learning deficit underlies language impairment would require learning conditions known to facilitate learning in both typical learners and those with poor language skills. Here, we explore learning in the visual domain for adults with impaired language skills. If the principles that describe learning in the presence of impaired language are amodal, then adults with impaired language should show learning deficits in the visual domain for strings that reflect verb phrase structure in English. Studies of typical learners have shown that grammar-like patterns can be learned rapidly in the visual domain (e.g., Arciuli & Simpson, 2011; Fiser & Aslin, 2002; Kidd, 2012; Marcus et al., 2007; Reber, 1967, 1976; Saffran et al., 1999; Theissen, 2011). However, the extent to which this applies to those with impaired language is unknown. We adapted an artificial grammar that has been implemented in the auditory domain (Gómez, 2002; Gómez & Maye, 2005; Grunow, Spaulding, Gómez, & Plante, 2006) to study this type of learning in the visual domain.

Attention and Learning The aXb and cXd strings used in the present study were previously used in the auditory domain in a study of adults with poor language skills (Grunow et al., 2006). Learning by the LLD participants in the Grunow et al. study showed a (nonsignificant) group trend toward poorer learning overall. In fact, this trend has been noted in multiple studies of adults with LLDs (Grunow et al., 2006; Plante et al., 2002; Richardson et al., 2006). The performance of those with LLDs frequently reflects an attenuated version of the pattern seen in typical adults. Language-learningdisabled adults often correctly select fewer grammatically correct test items and make more false-positive responses than their NL peers when tested on the grammar. This response pattern would also be expected if one group (the LLD group) had received less input compared with another (the control group) during the familiarization phase. Although both typical and impaired participants are given identical input in these studies, fluctuations in attention may serve to functionally reduce the input that is processed by the learner. Three experiments examining attention and statistical learning with typical adults support this notion. Toro, Sinnett, and Soto-Faraco (2005) found that when attentional resources were depleted, word segmentation based on statistical regularities was compromised. Therefore, one

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potentially critical aspect for learning may be the control of attention to the stimuli. When input to the learner exceeds his or her own attentional capacity, the stimuli may compete for his or her limited attentional resources (Cowan et al., 2005; Lavie, 2005; Lavie & Fox, 2000; Tellinghuisen & Nowak, 2003). There is reason to suspect that attention might be implicated for learners with LLD. Previous studies have provided evidence that, compared with typically developing peers, children with SLI demonstrate deficits in auditory attention, but not visual attention (Finneran, Francis, & Leonard, 2009; Spaulding, Plante, & Vance, 2008). Although similar work has not been completed for adults, these studies raise the possibility that difficulty maintaining attention to input may reduce learning by those with impaired language. In the present study, we purposefully stress attentional capacity by presenting stimuli that could compete for attentional resources. If the attentional account of poor learning is true, it should affect individuals with impaired language more than those with NL. One way to introduce competition for attention is to provide extraneous input simultaneous with the input to be learned. Studies of sustained attention in typical adults report that forced attention to auditory information during a visual learning task results in poor learning (Tellinghuisen & Nowak, 2003). Therefore, simultaneous presentation of auditory input during visual learning can tax attentional capacity when attention is divided between the two types of stimuli. Conversely, when typical adults are instructed specifically to ignore auditory stimuli during visual processing, their learning does not suffer (Mozolic, Hugenschmidt, Peiffer, & Laurienti, 2008; Tellinghuisen & Nowak, 2003). Likewise, the presence of extraneous visual stimuli during a visual learning task appears to have a less detrimental effect than stimuli presented in both visual and auditory modalities can have (Lavie & Fox, 2000). This work suggests that typical learners should be able to sustain attention to a visual learning task, even when additional but irrelevant auditory stimuli are presented simultaneously. However, learners with already limited attentional capacity (i.e., those with a language impairment) might have more difficulty sustaining their attention to the visual learning task. If so, they should experience more attentional breaks during learning. This should result in both poorer learning and greater incidental encoding of the irrelevant auditory stimuli.

The Present Studies In two studies, we examine learning of an artificial grammar presented in the visual domain. This grammar is defined by three-symbol strings (aXb, cXd) in which the identity of the first symbol (a) predicts the identity of the third symbol (b), but the middle symbol (X) is unrelated to either the first or the third. This is analogous to the present progressive verb tense in which “is + verb” (or “are + verb”) predicts the occurrence of “-ing” with any one of a number of root verbs occurring between “is” and “-ing.”

In Study 1, we examined whether a statistical learning deficit is present when visual stimuli are presented under conditions previously shown to facilitate learning in the auditory domain. Specifically, we used the principle of high variability for the nonconditional elements of the grammatical strings (i.e., X element in aXb and cXd strings). If adults with LLD are poor learners of this visual artificial grammar, particularly under conditions that facilitate learning in general, this would provide support for the idea of an amodal procedural learning deficit underlying language impairment. In Study 2, we examined the extent to which learners with and without LLD are susceptible to breaks in sustained attention during learning by testing their incidental encoding of irrelevant auditory stimuli. If limited attention contributes to poor learning for individuals with LLD, these participants will demonstrate poorer learning of the grammar-like visual sequences than their NL peers, and they will experience more encoding of irrelevant auditory stimuli presented during the learning task.

Study 1 Method Participants Twenty-four adults participated in this study. The participants were undergraduate students at the University of Arizona and native speakers of English. Twelve (five men, seven women) belonged to the LLD group. They ranged in age from 18 to 21 years (M = 19.14, SD = 1.21). Twelve adults (five men, seven women) were members of the NL group. They ranged in age from 18 to 21 years (M = 19.22, SD = 1.17). All adults passed a pure-tone hearing screening (500, 1000, 2000, and 4000 Hz at 25 dB HL bilaterally; American National Standards Institute [ANSI], 1996). To rule out mental retardation, all participants were administered the Test of Nonverbal Intelligence—3 (Brown, Sherbenou, & Johnsen, 1997) and were required to score above 75 (70 + 1 SEM) to remain in the study. The scores on this measure (see Table 1) did not differ significantly between groups, t(22) = –1.7, p = .10. Adults were classified as having either LLD or NL status based on a combination of self-report and clinical testing. All members of the LLD group self-identified as having either a learning disability or a history of speechlanguage services. None of the adults in the NL group reported a history for these conditions. We used the methods of Fidler, Plante, and Vance (2011) to confirm their selfreported status. This method uses scores from three languagebased measures that assess phonology, syntax, and semantics. A modified version of the Token Test (Morice & McNicol, 1985) was used as a measure of receptive syntactic processing. The Word Definitions subtest of the Clinical Evaluation of Language Fundamentals, Fourth Edition (CELF–4; Semel, Wiig, & Secord, 2003) was used to assess semantics. A 15-item spelling test was used to assess written phonology. The scores from these three language-based measures

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Table 1. Test scores for the participant groups in Study 1. Normal language Variable Age TONI-3a Modified Token Testb CELF–4 Word Definitions Dictated spellingc

Language-learning disability

M

SD

M

SD

19.22 107.41 40.66 11.58 12.00

1.17 13.01 2.18 2.46 2.29

19.14 98.83 34.08* 8.41* 7.25*

1.21 11.85 2.84 2.10 3.62

Note. TONI-3 = Test of Nonverbal Intelligence, third edition; CELF–4 = Clinical Evaluation of Language Fundamentals, Fourth Edition. a

Standard score (M = 100, SD = 15). bRaw score out of 44 possible items. cRaw score out of 15 possible items.

*p < .05.

were statistically weighted and combined into a composite. Positive scores indicated that a participant’s score was consistent with scores previously obtained from adults with a childhood history of speech-language impairment (positive for impairment). A negative score indicated that a participant’s score was consistent with scores previously obtained from adults without a childhood history of speech-language impairment (negative for impairment). The individual test scores are reported in Table 1. Adults who self-reported a history of learning disability or speech-language services and whose test scores confirmed poor language skill were retained for this study. Adults whose weighted test scores were consistent with NL were gender and age matched to members of the LLD group for this study. This procedure assured that individuals who may have received services earlier in life, but no longer had impaired language skills, were not selected to participate. As a result, all individuals in the LLD group currently had poor language skills relative to their typical peers. Materials Visual stimuli included symbol strings used to familiarize learners with the grammar and strings used to test learning (see Figure 1). Sequences were black symbols reflecting an aXb and cXd grammar on a white 2 × 4 in. rectangular display against a black background on a 14-in. computer screen. All stimuli used during a familiarization phase of the experiment reflected an “aXb” and “cXd” grammar in which a–b and c–d elements always occurred as a pair, and the third (X) element occurred in the middle position. The “X” elements came from a pool of 40 symbols. Special care was taken to provide different symbols Figure 1. Examples of visual sequences conforming to the aXb and cXd grammatical forms.

representing “a-b,” “c-d,” and “X” elements that were not easily confused. The study consisted of a familiarization phase in which participants were exposed to stimuli corresponding to the two sequences (aXb, cXd). Forty intervening “X” elements were presented twice during study, once in the “aXb” condition and once in the “cXd” condition for a total of 80 familiarization sequences. Test items consisted of 24 symbol sequences. Of these, 12 sequences were consistent with the familiarization input (i.e., a was paired with b, and c was paired with d, and the order of the elements was correct). Two types of “consistent” items were used. These two types permitted an examination of whether individuals simply remembered exemplars seen previously (correct seen items) or could generalize the underlying grammar of the sequences (correct generalization items). Generalization sequences followed either the aXb or cXd grammar but contained previously unseen X elements. There were three of each of the aXb and cXd strings for both correct seen and correct generalization items. The items that were consistent with the grammar were compared with 12 strings that were inconsistent with the grammar. The 12 items inconsistent with the input included six sequences with violations of item pairing (co-occurrence violations; aXd, cXb) and six sequences with violations of item positions (linear order violations; bXa, dXc). The two types of incorrect items were used to examine participants’ attention to linear order violations and attention to violations involving co-occurrence of symbols in the visual grammar. A previous artificial grammar learning study in the auditory domain (Torkildsen et al., 2013) revealed that learners were more sensitive to linear order than co-occurrence violations. Procedure The visual learning task took approximately 4 min for presentation of all 80 familiarization sequences. During familiarization, each symbol sequence was displayed for 2,000 ms. A black screen was displayed for 1,000 ms between symbol sequences. Participants were instructed to attend to the visual stimuli and that they would be tested later on what they had learned. Immediately following familiarization, participants completed a visual learning test phase. Participants were

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instructed to press the yes key for sequences that are consistent with what was previously seen and no key if the sequence is not consistent with the previous items. Participants were told to emphasize accuracy over speed of response, and they were not given feedback during the test phase. Analysis Plan The dependent variable in statistical learning experiments of the type described here is typically the number of items accepted that are consistent or inconsistent with the input (i.e., number of “yes” responses to different item types). Participants who learned something about the input should respond yes to more test items that are consistent with the input than items that are inconsistent. Therefore, the operational definition of learning is a statistically significant difference between the number of yes responses to consistent and inconsistent test items. The analysis plan recognizes that two-item forcedchoice responses can be susceptible to an overall bias toward either yes or no responses. Such biases can either inflate or decrease the overall rate of yes responses. Such shifts could lead to spurious group differences in the number of correct responses if the two groups differ in terms of an overall response bias rather than in their actual learning. In contrast, even if an underlying response bias is present, learning could still be detected as a differential acceptance rate of the different item types. However, for a significant learning effect to emerge, the effect would have to be larger than any inherent response bias within each group. The goal of separating a potential response bias from true learning can be accomplished by subjecting the data to a 2 × 4 mixed analysis of variance (ANOVA), with group (LLD vs. NL) as a between-group variable and item type (correct seen, correct generalization, linear order violation, co-occurrence violation) as within-participant variables. If participants have learned the sequences presented to them, they should accept correct seen items more often than those that violate the grammatical pattern (linear order and cooccurrence violation items). However, if only a response bias is present (or no learning occurs), then the overall rate of yes responses will not differ across item types.

Participants could learn only the nature of the specific items they were familiarized with, or learn the underlying grammar that defined the relation between elements in the string. The latter requires generalization to items not seen during familiarization. Evidence of generalization is operationally defined as greater yes responses to correct generalization items than incorrect items. In addition, we were interested in the learners’ sensitivity to different aspects of the input, which may lead them to reject different types of violations of the underlying grammar. For example, learners might be sensitive to the order in which elements appear in the string (linear order) or the pairing of a + b and c + d elements regardless of their position in the string (co-occurrence of items). Accordingly, we asked whether participants would be more sensitive to linear order than co-occurrence violations. To address these issues, we specified a series of planned comparisons to be tested using least squares analyses of significant item type of Item × Group effects from the ANOVA. We planned to compare both the correct seen and correct generalization items with each of the incorrect item types. Because these are all directional hypotheses, one-tailed tests were used. Finally, because there were no a priori hypotheses concerning specific group differences for each item type, a Tukey’s honestly significant difference (HSD) post hoc analysis was selected to describe the location of significant group differences in the event of a Group × Item interaction.

Results A mixed ANOVA with group as a between-group factor and item type (correct seen, correct generalization, co-occurrence violation, and linear order violation) as a within-group factor was used to analyze performance (see Table 2). The main effect for group was not significant, F(1, 22) = 0.43, p = .5186, h2 = .02, nor was the Group × Item Type interaction, Wilks’s F(1, 66) = 2.81, p = .0655, hp2 = .12. There was a significant main effect for item type, Wilks’s F(1, 66) = 48.23, p = .0001, hp2 = .38. A least squares analysis indicated that correct seen items were accepted significantly more often than both linear order violation items,

Table 2. Means and standard deviations of item types for the participant groups in Study 1. Group Normal language Variable Correct seen Correct generalization Linear order violation Co-occurrence violation

Language-learning disability

M

SD

M

SD

5.66a 3.66b 1.41a,b 2.41a

0.49 2.05 1.62 2.27

4.75a 2.75b 2.08a,b 4.33a

1.76 1.81 1.88 1.82

Note. Learning is operationally defined as significantly higher acceptance of correct items compared with incorrect items. Subscripts indicate significant differences among item types within each group.

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t(22) = 6.83, p = .0001, d = 1.98, and co-occurrence violation items, t(22) = 3.89, p = .0004, d = 1.06. This outcome is consistent with memory for items seen during familiarization. Participants also accepted more correct generalization items than linear order violation items, t(22) = 2.14, p = .0436, d = 0.75, but not co-occurrence violation items, t(22) = –0.24, p = .8067, d = –0.24.

Brief Discussion The results from Study 1 provided evidence of both learning and generalization by the participants. The participants were able to differentiate between correct items they had seen during familiarization and both types of incorrect items, which were composed of the same nonword elements. They also showed evidence that they could generalize the underlying grammatical relation between initial and final elements by accepting more items that contained new middle elements than items that violated the linear order of the initial and final elements. However, they were less sensitive to violations involving how grammatical elements could be combined (i.e., co-occurrence violations). This is consistent with a previous report of greater sensitivity to linear order versus co-occurrence violations for an artificial grammar presented in the auditory domain (Torkildsen et al., 2013). Despite the lack of a robust group difference, examination of Figure 2 reveals a general (nonsignificant) trend toward decreased acceptance of correct items and increased acceptance of incorrect items by the LLD group relative to the NL group that has also been reported in previous studies (Grunow et al., 2006; Richardson et al., 2006; Torkildsen et al., 2013). This trend could signal an attentional effect on learning for the LLD group, as described in the introduction. We tested this hypothesis in Study 2.

Figure 2. Mean performance on correct seen, correct generalization, linear order violation, and co-occurrence violation test items by participant groups for Study 1. Error bars indicate standard error. NL = normal language; LLD = language-learning disability.

Study 2 Method Participants Fifty-six adults participated in this study. The participants were undergraduate students at the University of Arizona and native speakers of English. Twenty-eight (14 men, 14 women) belonged to the LLD group. They ranged in age from 18 to 30 years (M = 19.56, SD = 2.15). Twenty-eight adults (14 men, 14 women) were members of the NL group. They ranged in age from 18 to 21 years (M = 19.11, SD = 0.87). All adults passed a pure-tone hearing screening (500, 1000, 2000, and 4000 Hz at 25 dB HL bilaterally; ANSI, 1996). To rule out mental retardation, all participants were administered the Test of Nonverbal Intelligence—3 (Brown et al., 1997) and were required to score above 75 (70 + 1 SEM) to remain in the study. The scores on this measure (see Table 1) did not differ significantly between groups, t(54) = 1.04, p = .29. Adults were classified as having either LLD or NL status based on a combination of self-report and clinical testing. All members of the LLD group self-identified as having either a learning disability or a history of speechlanguage services. None of the adults in the NL group reported a history for these conditions. We used the methods of Fidler et al. (2011), as described in Study 1. The scores are also reported in Table 3. In the present study, adults who self-reported a history of learning disability or speechlanguage services and whose test scores confirmed poor language skill were retained for this study. Adults whose weighted test scores were consistent with NL and were gender and age matched to members of the LLD group for this study. Compared with the participants in Study 1, the participants tended toward slightly stronger language skills. For the LLD group, this difference was statistically significant for the CELF–4 Word Definitions subtest, t(38) = 2.19, p = .0344, d = 0.65, but not for the other measures. It was also the case that this subtest differed significantly for the NL groups, t(38) = 2.46, p = .0184, d = 0.68, in Studies 1 and 2. In this case, the participants in Study 2 had lower scores on this test than did those in Study 1. Materials Visual stimuli. Visual stimuli were identical to those used in Study 1 to test statistical learning. Auditory stimuli. Auditory stimuli consisted of 40 word pairs (80 words total). All words were English high-frequency nouns and verbs of three to five letters in length. Words were selected from the English Lexicon Project website (http://elexicon.wustl.edu/default.asp). Auditory stimuli were selected such that they formed pairs of words that represented weak semantic associations between the pair (i.e., groom and clean as opposed to groom and bride). This was done so that, when presented with the first word of a pair, participants should be unlikely to report the paired word, unless they had previously encoded those word pairs during the experiment. The weak semantic relation was

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Table 3. Test scores for the participant groups in Study 2. Group Normal language Variable Age TONI-3a Modified Token Testb CELF–4 Word Definitions Dictated spellingc a

Language-learning disability

M

SD

M

SD

19.11 104.78 39.92 13.25 11.5

0.87 13.39 2.30 1.71 2.13

19.56 101.14 35.46* 10.03* 6.92*

2.15 12.60 4.41 2.15 3.36

Standard score (M = 100, SD = 15). bRaw score out of 44 possible items. cRaw score out of 15 possible items.

*p < .05.

verified by asking 22 adults who were not participants in either Study 1 or Study 2 to generate a related word when given the first word of the pair. If four or more individuals spontaneously generated the second word of the pair (e.g., clean) when presented with the first (e.g., groom), the pair was eliminated from the stimulus set. The 40 word pairs retained for use in this study include only those word pairs with a low (0%–18%) probability of association, based on the rate at which the 22 adults reported the paired word when given the first word of the pair. It is important to note that, unlike the participants in Study 2, these 22 adults had not previously been exposed to the word pairs. Therefore, their data provide a baseline for reporting for individuals who have not encoded the words at all. The auditory stimuli were then digitally recorded by a male speaker of Standard English dialect and edited to produce approximately equal loudness for all words. Procedure The visual learning task was identical to that used in Study 1, but with the addition of simultaneously presented auditory stimuli. Auditory stimuli were presented through speakers at a low but clearly audible level (i.e., approximately 50 dB HL) at a rate of one word pair per visual symbol sequence. Each word pair was presented twice for a total of 40 pairs presented twice with 80 visual sequences. Participants were instructed to concentrate on the visual stimuli, as they would be tested on what they learned from these stimuli later, and to ignore the auditory stimuli. Recall that Mozolic et al. (2008) and Tellinghuisen et al. (2003) previously demonstrated that typical learners were able to learn their primary visual learning tasks under similar conditions. After the visual learning test phase, participants completed a “surprise” word association task. Participants were presented with the first word of the semantically associated word pairs that were played during the visual learning task and asked to generate the first word that came to mind. Speed was emphasized to ensure that any effect of prior exposure to the words rather than a deeper consideration of semantics would influence responses. This should reflect whether word pairs were attended during the visual familiarization phase.

Analysis Plan As in Study 1, the principal analysis used to establish whether learning occurred was a 2 × 4 mixed ANOVA, with group (LLD vs. NL) as a between-group variable and item type (correct seen, correct generalization, linear order violation, co-occurrence violation) as a within-participant variable. This was followed by a least squares analysis of the item types to determine whether the operational definitions of learning and generalization specified in Study 1 were supported. Because there were no a priori hypotheses concerning group, significant Group × Item Type interactions were further analyzed using a Tukey’s HSD post hoc test. The auditory word pairs were analyzed for the number of words presented during familiarization that were also reported by each group during the word association test. This was done using an independent groups t test. Recall that we established the base rate of reporting the weakly semantically associated word pairs for people who had not previously been exposed to the word pairs (see the Materials section above). This provides a basis for comparison for performance of the participants of Study 2.

Results Visual Learning Task A mixed ANOVA with group as a between-group factor and item type (correct seen, correct generalization, co-occurrence violation, and linear order violation) as a within-group factor was used to analyze performance (see Table 4). The main effect for group was not significant, F(1, 54) = 2.49, p = .12, h2 = .04. There was a significant main effect for item type, Wilks’s F(1, 162) = 69.03, p = .0001, hp2 = .41. This was qualified by a significant Group × Item Type interaction, Wilks’s F(1, 162) = 4.05, p = .0116, hp2 = .07. As Figure 3 suggests, this reflected a general pattern for the NL group to accept more correct items than the LLD group, whereas the LLD group tended to accept more incorrect items than their NL counterpart. Least squares comparisons confirmed that participants in the NL group were more likely to accept correct seen items than both linear order violation items, t(54) = 11.23, p = .0001, d = 4.27, and co-occurrence violation items,

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Table 4. Acceptance rates for item types for the participant groups in Study 2. Group Normal language Variable Correct seen Correct generalization Linear order violation Co-occurrence violation

Language-learning disability

M

SD

M

SD

5.21a 3.50b 0.64a,b 2.25a

1.06 2.44 0.95 2.08

4.53a 2.96b 1.78a,b 3.35a

1.07 1.77 1.81 1.66

Note. Learning is operationally defined as significantly higher acceptance of correct item types compared with incorrect item types. Subscripts indicate significant differences among item types within each group.

t(54) = 6.58, p = .0001, d = 1.42. This is consistent with memory for items seen during familiarization. Participants in the NL group also accepted more correct generalization items than linear order violation items, t(54) = 5.25, p = .0001, d = 1.17, but not co-occurrence violation items, t(54) = 1.85, p = .0705, d = 0.51. This is consistent with generalization of the underlying pattern to new strings. The LLD groups showed the same overall pattern of response to the different item classes. Least squares comparisons also confirmed the participants in the LLD group accepted more correct seen items than both linear order violation items, t(54) = 6.67, p = .0001, d = 1.51, and cooccurrence violation items, t(54) = 2.62, p = .0115, d = 0.71. Participants in the LLD group also accepted more correct generalization items than linear order violation items, t(54) = 2.17, p = .0347, d = 0.65, but not co-occurrence violation items, t(54) = –0.58, p = .5644, d = –0.22. The ANOVA was followed up with a Tukey’s HSD and the post hoc analysis of group differences. No group differences were found for any item type (see Figure 3). Auditory Task Results for generation of auditory word pairs revealed no difference between groups, t(54) = –0.04, p = .96, d = 0.01: Figure 3. Mean performance on correct seen, correct generalization, linear order violation, and co-occurrence violation test items by participant groups for Study 2. Error bars indicate standard error.

NL group (M = 7.3, SE = 0.93); LLD group (M = 7.4, SE = 1.15; see Figure 4). This corresponded to 18.1% of all paired words for the NL group and 18.2% for the LLD group. This is at the upper range (0%–18%) of words reported by individuals who had not previously been exposed to the word pairs (see the Materials section above).

General Discussion The findings for this visual learning paradigm parallel those of earlier studies in the auditory domain. In both Study 1 and Study 2, the LLD group responses followed the general pattern of learning shown by the NL group, but showed a nonsignificant trend toward slightly lower acceptance of correct items and slightly greater acceptance of incorrect items. This general response pattern for visual stimuli has also been reported for an equivalent task in the auditory domain (Grunow et al., 2006). The results of Studies 1 and 2 also indicate that learners generalize beyond the stimuli they were familiarized with and demonstrated some understanding of the underlying grammatical relation among visual symbols. However, they were only able to distinguish between correct generalization items and linear order violation items, but not co-occurrence violation items. This pattern was also reported for an auditory artificial Figure 4. Mean word pairs reported by participant groups for Study 2. Error bars indicate standard error.

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grammar study (Torkildsen et al., 2013). Thus, the overall pattern of learning demonstrated here in the visual domain is comparable to similar studies in the auditory domain. Although we hypothesized that the attenuated response pattern of the LLD group might be attributable to weak sustained attention to the learning task, Study 2 provided no evidence that attentional breaks during the visual learning task occurred more often for the LLD group than for the NL group. In addition, neither group reported previously heard words more often than people who had never heard the words previously paired (established during the development of the word stimuli). Both groups of Study 2 participants reported similarly low numbers of words that had been presented during the visual learning task. Furthermore, replication of the pattern of visual learning results from Study 1 demonstrated that the simultaneous presentation of auditory stimuli with the visual learning task in Study 2 was unlikely to have affected the learning response for either the NL or the LLD group. Although this outcome for the LLD group was unexpected, it is consistent with previous studies in which typical adults were instructed to attend to stimuli in one modality (e.g., visual) while ignoring stimuli in another (e.g., auditory; Mozolic et al., 2008; Rees, Frith, & Lavie, 2001; Tellinghuisen & Nowak, 2003). These results indicate that LLD participants, like their NL counterparts, are also able to suppress information in the auditory modality during attention to the visual modality. The present study established that participants with LLD were able to demonstrate several important aspects of statistical learning. First, they were able to differentiate between strings that were seen before and those that used the same elements, but were arranged in ungrammatical ways. Second, they were able to generalize the a + b and c + d grammatical rule to strings that included new middle elements. This learning was accomplished with just minutes of familiarization with exemplars that followed the grammatical rule. Furthermore, learning required no direct instruction concerning the nature of the input. This result, and that of similar studies in the auditory domain (Grunow et al., 2006; Richardson et al., 2006; Torkildsen et al., 2013), refutes a strong form of the theory that a procedural learning deficit underlies developmental language impairment (Ullman & Pierpont, 2005). This theory suggests that the ability to accumulate information on statistical patterns, including those representing grammatical forms, is deficient in SLI. In this study, adults with impaired language demonstrated relatively rapid learning of two grammatical patterns without direct instruction. In the present study, we used a relatively large set of middle items in the three-item string (aXb) so that this aspect of the grammar reflected high variability relative to the items that defined the fixed relationship (e.g., a + b in aXb). This particular use of variability within the input is known to enhance learning (Gómez, 2002; Gómez & Maye, 2005; Grunow et al., 2006). All studies of LLD to date in which the principle of variability has been used have also resulted in no significant group differences under highvariability conditions, suggesting that individuals with LLD

can learn grammar-like properties when variability is optimized. Unlike previous studies, which exclusively used auditory input, the effect here was found with visual input. Therefore, it appears that rapid learning of nonadjacent dependencies in both the visual and auditory domains can be achieved by those with LLD. However, it is important to note that because we did not explicitly manipulate variability in the present study, the effect may be limited to learning conditions that include high variability of the lexical-like items. The present visual learning studies revealed that individuals in both the NL group and the LLD group were more sensitive to linear order violations than to co-occurrence violations. Torkildsen et al. (2013) demonstrated a similar pattern of results for linear order violation items versus co-occurrence violation items in the auditory domain. This same item type preference might also be seen if participants made judgments on the basis of the first two adjacent elements of the visual sequence while disregarding the third element. Participants may be more sensitive to the linear order violations because they would never have seen b elements proceed X elements during training. Conversely, they would be less sensitive to the co-occurrence violation items because they would have seen the a and c elements proceed, and the b and d elements follow the X elements in the aXb and cXd examples presented during familiarization. If so, these results suggest that adults are more sensitive to adjacent dependencies than to nonadjacent dependencies, and this may have assisted their learning of the relationship between the nonadjacent elements. This would be consistent with prior reports that adjacent dependencies are more easily learned than nonadjacent dependencies (Gómez, 2002; Newport & Aslin, 2004) and that initial learning of sequential dependencies can assist later learning of the nonadjacent dependencies (Lany, Gómez, & Gerken, 2007). The present study extends the previous work on visual learning by individuals with impaired language beyond the serial reaction time paradigms used to date with languageimpaired groups. Recall that the previous serial reaction time studies demonstrated differences in the rate of learning (Gabriel, Stefaniak, Maillart, Schmitz, & Meulemans, 2012; Tomblin et al., 2007) and learning consolidation (Hedenius et al., 2011) for children with impaired language compared with their normal peers. Behavioral studies of learning using serial reaction time tasks have demonstrated that this sequence-specific learning is likely to involve higher order relationships involving chunks or clusters of sequences extending beyond the immediately adjacent pairs (Cleermans & McClelland, 1991). Additionally, serial reaction time tasks involve learning of manual-visual sequential information, which may be more difficult for individuals with SLI than learning visual sequences alone. The visual artificial grammar task in the present studies measured learning of a statistical relation between nonadjacent elements in a short string, which is generally considered a harder task than learning relations among adjacent elements (Gómez, 2002; Gómez & Maye, 2005; Lany et al., 2007; Newport & Aslin, 2004).

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In Study 2, the addition of auditory stimuli did not appear to affect learning for either group. Previous studies have provided evidence that, compared with typically developing peers, children with SLI demonstrate deficits in auditory attention, but not visual attention (Finneran et al., 2009; Gomes, Duff, & Wolfson, 2004; Spaulding et al., 2008). It may be that attentional capacity limits are only weakly affected or unaffected in the visual domain, permitting our LLD participants to maintain attention to the visual task. An alternate explanation may be related to the attentional demands of the primary visual task compared with the passive auditory task. Toro et al. (2005) found attentional effects only when the additional task was more demanding than the primary learning task or when the additional stimuli were in the same modality as the learning task. We did not use these strategies in the present study because we wanted to use a learning condition in which typical learners were able to ignore competing stimuli in order to determine whether performance by those with LLD represented a potential deficit. The results of Study 2 are consistent with the view that it is more difficult to find attentional costs across modalities than within modalities (Soto-Faraco & Spence, 2002; Toro et al., 2005). In summary, previous literature has documented that learning by individuals with poor language skills can be accomplished under conditions in which learning is also optimal for those with NL (Gómez, 2002; Gómez & Maye, 2005; Grunow et al., 2006; Torkildsen et al., 2013). Although these previous studies have used auditory stimuli exclusively, the present study suggests that those with poor language skills can learn strings that conform to an underlying grammatical rule, presented in the visual domain, after only a brief period of familiarization with the grammar. This calls into question whether a strong form of the procedural learning theory of language impairment is tenable, given that those with language impairment can show good learning at least under some conditions. Instead, findings of relatively good learning by those with impaired language indicate that understanding the conditions under which this can be achieved may be more important than whether an amodal procedural learning deficit is present. Furthermore, it does not appear that those with poor language skills were any more susceptible to interference from auditory stimuli during visual learning. However, the question remains concerning whether resistance to irrelevant stimuli would be equally robust during auditory learning.

References American National Standards Institute. (1996). Specifications for audiometers (ANSI S3.6-1996). New York, NY: Author. Arciuli, J., & Simpson, I. C. (2011). Statistical learning in typically developing children: The role of age and speed of stimulus presentation. Developmental Science, 14, 464–473. Aslin, R. N., & Newport, E. L. (2012). Statistical learning: From acquiring specific items to forming general rules. Current Directions in Psychological Science, 21, 170–176.

Bahl, M., Plante, E., & Gerken, L. A. (2009). Processing prosodic structure by adults with language-based learning disability. Journal of Communication Disorders, 42, 313–323. Brown, L., Sherbenou, R., & Johnsen, S. (1997). Test of Nonverbal Intelligence—3. Bloomington, MN: Pearson. Cleermans, A., & McClelland, J. L. (1991). Learning the structure of event sequences. Journal of Experimental Psychology: General, 120, 235–253. Cowan, N., Elliot, E. M., Scott Saults, J., Morey, C. C., Mattox, S., Hismjatullina, A., & Conway, A. R. A. (2005). On the capacity of attention: Its estimation and its role in working memory and cognitive aptitudes. Cognitive Psychology, 51, 42–100. Creel, S. C., Newport, E. L., & Aslin, R. N. (2004). Distant melodies: Statistical learning of nonadjacent dependencies in tone sequences. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 1119–1130. Dawson, C., & Gerken, L. (2009). From domain-generality to domain-sensitivity: 4-month-olds learn an abstract repetition rule in music that 7-month-olds do not. Cognition, 111, 378–382. Evans, J. L., Saffran, J. R., & Robe-Torres, K. (2009). Statistical learning in children with specific language impairment. Journal of Speech, Language, and Hearing Research, 52, 321–335. Fidler, L. J., Plante, E., & Vance, R. (2011). Identification of adults with developmental language impairments. American Journal of Speech-Language Pathology, 20, 2–13. Finneran, D. A., Francis, A. L., & Leonard, L. B. (2009). Sustained attention in children with specific language impairment. Journal of Speech, Language, and Hearing Research, 52, 915–929. Fiser, J., & Aslin, R. N. (2002). Statistical learning of higher-order temporal structure from visual shape sequences. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28, 458–467. Gabriel, A., Maillart, C., Guillaume, M., Stefaniak, N., & Meulemans, T. (2011). Exploration of serial structure procedural learning in children with language impairment. Journal of the International Neuropsychological Society, 17, 336–343. Gabriel, A., Maillart, C., Stefaniak, N., Lejeune, C., Desmottes, L., & Meulemans, T. (2013). Procedural learning in specific language impairment: Effects of sequence complexity. Journal of the International Neuropsychological Society, 19, 264–271. Gabriel, A., Stefaniak, N., Maillart, C., Schmitz, X., & Meulemans, T. (2012). Procedural visual learning in children with specific language impairment. American Journal of Speech-Language Pathology, 21, 329–341. Gomez, H., Duff, M., & Wolfson, G. (2004, June). Performance on auditory and visual simple, selective and divided attention tasks by children with poor and age appropriate receptive language. Paper presented at the annual meeting of the Society for Research on Child Language Disorders, Madison, WI. Gómez, R. L. (2002). Variability and detection of invariant structure. Psychological Science, 13, 431–436. Gómez, R. L. (2006). Dynamically guided learning. In Y. Munakata & M. Johnson (Eds.), Attention & performance XXI: Processes of change in brain and cognitive development (pp. 87–110). Oxford, United Kingdom: Oxford University Press. Gómez, R. L., & Maye, J. (2005). The developmental trajectory of nonadjacent dependency learning. Infancy, 7, 183–206. Grunow, H., Spaulding, T. J., Gómez, R. L., & Plante, E. (2006). The effects of variation on learning word order rules by adults with and without language-based learning disabilities. Journal of Communication Disorders, 39, 158–170. Hedenius, M., Persson, J., Tremblay, A., Adi-Japha, E., Verissimo, J., Dye, C. D., . . . Ullman, M. T. (2011). Grammar predicts

Aguilar & Plante: Learning and Attention

1403

procedural learning and consolidation deficits in children with specific language impairment. Research in Developmental Disabilities, 32, 2362–2375. Johnston, J., & Ellis Weismer, S. (1983). Mental rotation abilities of language-disordered children. Journal of Speech and Hearing Research, 26, 397–403. Kail, R. (1994). A method for studying the generalized slowing hypothesis in children with specific language impairment. Journal of Speech and Hearing Research, 37, 418–421. Kamhi, A. G., Catts, H., Mauer, D., Apel, K., & Gentry, B. (1988). Phonological and spatial processing abilities in language- and reading-impaired children. Journal of Speech and Hearing Disorders, 53, 316–327. Kamhi, A. G., Ward, M. F., & Mills, E. A. (1995). Hierarchical planning abilities in children with specific language impairments. Journal of Speech and Hearing Research, 38, 1108–1116. Kidd, E. (2012). Implicit statistical learning is directly associated with the acquisition of syntax. Developmental Psychology, 48, 171–184. Lany, J., Gómez, R. L., & Gerken, L. A. (2007). The role of prior experience in language acquisition. Cognitive Science, 31, 481–507. Lavie, N. (2005). Distracted and confused? Selective attention under load. Trends in Cognitive Sciences, 9, 75–82. Lavie, N., & Fox, E. (2000). The role of perceptual load in negative priming. Journal of Experimental Psychology: Human Perception and Performance, 26, 1038–1052. Marcus, G. F., Fernandes, K. J., & Johnson, S. P. (2007). Infant rule learning facilitated by speech. Psychological Science, 18, 387–391. Morice, R., & McNicol, D. (1985). The comprehension and production of complex syntax in schizophrenia. Cortex, 21, 567–580. Mozolic, J. L., Hugenschmidt, C. E., Peiffer, A. M., & Laurienti, P. J. (2008). Modality-specific selective attention attenuates multisensory integration. Experimental Brain Research, 184, 39–52. Newport, E. L., & Aslin, R. N. (2004). Learning at a distance: I. Statistical learning of non-adjacent dependencies. Cognitive Psychology, 48, 127–162. Plante, E., Gómez, R., & Gerken, L. A. (2002). Sensitivity to word order cues by normal and language/learning disabled adults. Journal of Communication Disorders, 35, 453–462. Reber, A. S. (1967). Implicit learning of artificial grammars. Journal of Verbal Learning and Verbal Behavior, 6, 855–863. Reber, A. S. (1976). Implicit learning of synthetic languages: The role of the instructional set. Journal of Experimental Psychology: Human Learning and Memory, 2, 88–94. Rees, G., Frith, C., & Lavie, N. (2001). Processing of irrelevant visual motion during performance of an auditory attention task. Neuropsychologia, 39, 937–949.

Richardson, J., Harris, L., Plante, E., & Gerken, L. (2006). Subcategory learning in normal and language learning-disabled adults: How much information do they need? Journal of Speech and Hearing Research, 49, 1257–1266. Saffran, J. R., Johnson, E. K., Aslin, R. N., & Newport, E. L. (1999). Statistical learning of tone sequences by human infants and adults. Cognition, 70, 27–52. Savich, P. (1984). Anticipatory imagery ability in normal and language-disabled children. Journal of Speech and Hearing Research, 27, 494–501. Semel, E., Wiig, E. H., & Secord, W. A. (2003). Clinical Evaluation of Language Fundamentals (4th ed.). San Antonio, TX: The Psychological Corporation. Soto-Faraco, S., & Spence, C. (2002). Modality-specific auditory and visual temporal processing deficits. The Quarterly Journal of Experimental Psychology: Human Experimental Psychology, 55(A), 23–40. Spaulding, T. J., Plante, E., & Vance, R. (2008). Sustained selective attention skills of preschool children with specific language impairment: Evidence for separate attentional capacities. Journal of Speech, Language, and Hearing Research, 51, 16–34. Swisher, L., Plante, E., & Lowell, S. (1994). Nonlinguistic deficits of language-impaired children complicate the interpretation of their nonverbal IQ scores. Language, Speech, and Hearing Services in Schools, 25, 235–240. Tellinghuisen, D. J., & Nowak, E. J. (2003). The inability to ignore auditory distracters as a function of visual task perceptual load. Perception & Psychophysics, 65, 817–828. Theissen, E. D. (2011). Domain general constraints on statistical learning. Child Development, 82, 462–470. Tomblin, J. B., Mainela-Arnold, E., & Zhang, X. (2007). Procedural learning in adolescents with and without specific language impairment. Language Learning and Development, 3, 269–293. Torkildsen, J. v. K., Dailey, N. S., Aguilar, J. M., Gómez, R., & Plante, E. (2013). Exemplar variability facilitates rapid learning of an otherwise unlearnable grammar by individuals with language-based learning disability. Journal of Speech, Language, and Hearing Research, 56, 618–629. Toro, J. M., Sinnett, S., & Soto-Faraco, S. (2005). Speech segmentation by statistical learning depends on attention. Cognition, 97, B25–B34. Ullman, M. T., & Pierpont, E. I. (2005). Specific language impairment is not specific to language: The procedural deficit hypothesis. Cortex, 41, 399–433. Windsor, J. (2002). Contrasting general and process-specific slowing in language impairment. Topics in Language Disorders, 22, 49–61.

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