Augmentative and Alternative Communication, 2013; 29(4): 347–359 © 2013 International Society for Augmentative and Alternative Communication ISSN 0743-4618 print/ISSN 1477-3848 online DOI: 10.3109/07434618.2013.849754

RESEARCH ARTICLE

Exploring the Impact of Cognition on Young Children’s Ability to Navigate a Speech-Generating Device MANON ROBILLARD1, CHANTAL MAYER-CRITTENDEN1, ANNIE ROY-CHARLAND2, MICHÈLE MINOR-CORRIVEAU1 & ROXANNE BÉLANGER1 1

Speech-Language Pathology Program and 2Psychology Department, Laurentian University, Sudbury, Ontario, Canada

Abstract This study examined the impact of cognition on young children’s ability to navigate a speech-generating device (SGD) with dynamic paging. Knowledge of which cognitive factors impact navigational skills could help clinicians select the most appropriate SGD for children who have complex communication needs. A total of 65 typically developing children aged 48–77 months were assessed using the Leiter International Performance Scale-Revised (Leiter-R) and the Automated Working Memory Assessment (AWMA). Although significant correlations were found between the ability to navigate an SGD (using a taxonomic organization) and all cognitive factors except for cognitive flexibility, a stepwise linear regression revealed that sustained attention, categorization, and fluid reasoning were the most pragmatic set of factors to predict navigational skills. Future studies are needed to further understand the factors that impact children’s navigational skills.

Keywords: Augmentative and alternative communication (AAC); Speech-generating device (SGD); Navigation; Cognition; Children

clinicians during the assessment and device selection process. Wallace, Hux, and Beukelman (2010) found that cognitive flexibility impacts navigation in adults who experienced a traumatic brain injury. The influence of cognition on navigation in children remains unknown. The present study analyzed the effect of various cognitive factors (sustained attention, categorization, cognitive flexibility, fluid reasoning and working memory) on young children’s ability to navigate an SGD with dynamic paging. The goal was to decipher which cognitive factors have an impact on navigation and which can better predict navigational success (see Table I for a description of the cognitive factors). SGDs produce an electronic voice using a synthesizer or recorded speech (Lloyd, Fuller, & Arvidson, 1997). They allow active interactions by enabling a person with complex communication needs to participate in conversations (Blischak, Lombardino, & Dyson, 2003). Due to a combination of advances in empirical research and technology, there is renewed interest in the development of technology for AAC purposes (e.g., Higginbotham & Jacobs, 2011; Wilkinson & Hennig, 2007). This has contributed to the rapid evolution of SGDs, which

Introduction One of the key components in the intervention of young children who have complex communication needs is the selection of the most suitable augmentative and alternative communication (AAC) system (Light & Drager, 2007). Indeed, selecting the appropriate speech-generating device (SGD) for a young child can be challenging, especially when deciding between dynamic paging and static overlays. The arrival of new mobile technologies has rendered this process even more complex, as more options with dynamic paging are now readily available. Good navigational skills are required to find vocabulary within SGDs that have dynamic screens (Drager & Light, 2006; Reichle & Drager, 2010). Efficient use of SGDs by young children, therefore, rests on their ability to retrieve the appropriate vocabulary (Drager & Light, 2006; Wilkinson & Coombs, 2010). Finding symbols embedded within many levels of a dynamic screen can pose a particular challenge for some children, while others seem to learn to navigate with ease and without much training. Knowing how cognitive functions affect navigation could help

Correspondence: Manon Robillard, Speech-Language Pathology Program, Laurentian University, 935 Ramsey Lake Road, Sudbury, Ontario, Canada, P3E 2C6. E-mail: [email protected]

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Selecting related stimuli that progress in a corresponding order Supplying missing portions of a pattern by moving response cards into alignment with easel Recalling the position of a red dot by tapping the squares on the screen, within a series of four by four matrices Viewing blocks being tapped, then reproducing the sequence in the correct order by tapping the screen Identifying which shape does not belong and then recalling its position by tapping the screen once it disappears Stating if 2 Mister X figures are holding a ball in the same hand and then recalling the location of the balls once disappeared Recalling numbers in the correct order

Sequential Order (SO)

Verbal working memory

Verbal working memory

Verbal working memory

Visual-spatial working memory

Visual-spatial working memory

Visual-spatial working memory

Visual-spatial working memory

Fluid reasoning

Fluid reasoning

Fluid reasoning

Categorization Cognitive flexibility

Categorization

Sustained attention

Measurement

Note. Information was referenced from the Leiter-R manual (Roid & Miller, 1997) and the AWMA manual (Alloway, 2007).

Backwards Digit recall (BDR)

Counting Recall (CR)

Digit Recall (DR)

Mister X (MX)

Odd One Out (OOO)

Block Recall (BR)

Dot Matrix (DM)

Repeated Patterns (RP)

Counting the number of red circles in an array of circles and triangles, then recalling how many were counted Hearing a sequence of numbers and recalling them in a backwards order

Recognizing a whole object from a randomly displayed array of its fragmented parts

Form Completion (FC)

Classification (C) Figure Ground (FG)

Picture Context (PC)

Finding and crossing out as many items as possible that are identical to the target, within a time limit of 30-60 seconds Recognizing a pictured object that has been removed from a large display Categorizing objects and geometric designs Identifying embedded figures or designs within a complex stimulus

Implementation

Attention Sustained (AS)

Name of subtest

Table I. Implementation of Cognitive Sub-tests and Measurement for this Study.

As above

Temporary storage of verbal information that plays an important role in sub-vocal rehearsal (Alloway, Gathercole, & Pickering, 2006; Baddeley & Hitch, 1974). It is the phonological loop in Baddeley’s model.

As above

As above

Temporary maintenance and handling of visual and spatial representations. It is the visual-spatial sketchpad in Baddeley’s model (Baddeley & Hitch, 1974; Baddeley, 2000) As above

As above

The ability to classify. It develops around the age of 18 months (Gopnik & Meltzoff, 1987) As above The capacity to move from one idea to another with a certain facility (Hux & Manasse, 2003). It comes into play during problem resolution when there is a certain degree of indetermination (Deák, 2003; Chevalier & Blaye, 2008) The capacity to think logically and solve problems in novel situations (e.g. Gray, Chabris, & Braver, 2003; Horn & Cattell, 1967). It could be thought of as problem solving in situations that are independent of acquired knowledge As above

The capacity to maintain attention and alertness over a prolonged period of time (Warm, Parasuraman, & Matthews, 2008)

Definition of cognitive skills

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Cognition and Navigation in Children are now more readily available (Wilkinson & Hennig, 2007) and relatively less expensive. Some of the newest devices used as SGDs are the iPad™ (Apple Inc., 2013) and Android-based (Android, 2013) platforms. They require the addition of an AAC application, often at an extra cost. These touch- screen devices, which have plenty of processing power and the capacity for speech output, have changed the way we think of AAC technology (Hershberger, 2011). They are also highly portable, convenient, and, equally importantly, viewed as socially acceptable by peers (Alliano, Herriger, Koutsoftas, & Bartolotta, 2012; Sennott & Bowker, 2009). SGDs with dynamic paging have a screen that can display new vocabulary by linking to different pages (Lloyd et al., 1997; Reichle & Drager, 2010). “Dynamic display technologies offer the advantage of having fewer symbols necessary on each page of the display at one time while still allowing access to a larger vocabulary set” (Drager et al., 2004, pp. 1133–1134). Indeed, these dynamic screens were designed to allow an indefinite number of symbols to be programmed (Lloyd et al., 1997; Wilkinson & Hennig, 2007). In order to find words not programmed on the first page of an SGD with dynamic paging, the ability to navigate is a required skill (Reichle & Drager, 2010). However, because many words and messages are not programmed on the first screen, navigation can be challenging, which can result in a reduced efficiency of communication (Drager et al., 2004; Light & Drager, 2007; Wallace et al., 2010). These difficulties with navigation could also lead to many communication breakdowns, frustration, and abandonment of an SGD (Light & Drager, 2007). Light and Drager (2007) stated that navigation could be difficult for young children because they must hold in mind a conceptual model of the hidden pages within the SGD, as well as understand the linking ability of the symbols on the first page. With the arrival of new technologies also come more options for representing words with symbols, and for the organization of those symbols on dynamic displays (Reichle, Dettling, Drager, & Leiter, 2000). The manner in which symbols are organized will impact the speed and accuracy of retrieval (Mizuko, Reichle, Ratcliff, & Esser, 1994) and ultimately impede or enhance the ease and rate of communication (Musselwhite & St. Louis, 1988). In fact, when a child has difficulty navigating between pages of a dynamic screen, it may be necessary to decrease cognitive and visual demands by limiting the number of symbols per page and/or using a different vocabulary layout or organizational method (Wilkinson & Hennig, 2007). The traditional method of organizing symbols within an SGD is taxonomic, where vocabulary is grouped according to hierarchical categories (Light et al., 2004). Another method of organizing vocabulary is by visual scene displays, whereby vocabulary is embedded into a graphic or a photograph depicting a scene (Drager et al., 2004). Olin, Reichle, Johnson, and Monn (2010) found that retrieving symbols from an SGD with dynamic paging and visual scene displays © 2013 International Society for Augmentative and Alternative Communication

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was challenging for very young children (24–27 months and 33–36 months), yet they could learn to master this ability in a relatively small number of exposures. A study by Drager et al. (2004) established that 3-yearolds were less accurate in finding vocabulary when navigating an SGD with taxonomic grids compared to one with visual scene displays. However, for 4- and 5-year-olds, Light et al. (2004) discovered that there were no significant differences between the ability to locate vocabulary within taxonomic grids and visual scene displays of an SGD. According to Van Tatenhove (2009), children accommodate for memory and navigational challenges by remaining on the main page of their SGD, even when words needed are on other pages. Single display designs and consistent vocabulary layouts are both strategies that can be adopted to promote motor automaticity and reduce memory demands (Van Tatenhove, 2009). Indeed, the use of a consistent core vocabulary with consistent symbol location can tap into motor learning and increase automaticity (Cafiero & Stern Delsack, 2007; Van Tatenhove, 2009). Opting for a consistent vocabulary layout could, therefore, facilitate navigational learning. Knowledge of how various cognitive factors impact navigation could also facilitate the understanding of children’s navigational challenges. Previous Research on Navigation and Cognition A single study examined the role of cognitive processing on navigational skills. During a navigational task with adults who experienced a traumatic brain injury (TBI), Wallace et al. (2010) observed the role of cognitive flexibility, which is the capacity to move from one idea or strategy to another with a certain facility (Hux & Manasse, 2003). A total of 18 participants were divided into two groups based on their cognitive flexibility, which was measured using the Symbol Trails subtest of the Cognitive Linguistic Quick Test (CLQT) (HelmEstabrooks, 2001). This subtest requires alternate shifting between two sets of images. To measure navigation, participants were asked to find words on an AAC system with three levels. The effects of informative versus uninformative prompts and three image contextualizational conditions (high-context, low-context, and no-context photographs) were investigated. Results revealed that the group that passed the Symbol Trails subtest navigated significantly more accurately than the group that did not reach criterion for this subtest. All participants were more accurate with high-context and low-context photographs, compared to the photographs without context. More interestingly, cognitive flexibility was found to predict the level of training needed to master the navigation of an SGD. For people with a TBI who reach the Symbols Trails subtest criterion, minimal exposure and training with an SGD would be needed. In sum, this study showed the importance of cognitive flexibility on navigation; however, similar studies with populations other than brain-injured adults have not

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been conducted. Moreover, this study did not indicate how cognition is implicated in the navigational performance of young children or if other cognitive functions play an important role in navigation. Although the number of empirical studies linking cognitive skills and AAC are limited, researchers have suggested the importance of cognitive skills in the navigation of SGDs (Wallace et al., 2010). For instance, Thistle and Wilkinson (2012) stated that AAC communication demands more attention than oral communication due to the increased time needed to create messages with an SGD. Because attention is a critical first step in memory storage, attentional deficits could impair the ability to learn to use an SGD (Levine, Horstmann, & Kirsch, 1992). Furthermore, others have suggested that memory is implicated in the use of SGDs (e.g., Light & Drager, 2002; Light & Lindsay, 1991; Oxley & Norris, 2000; Reichle et al., 2000). Memory is required in order to remember where words are programmed because they are not always visible on the first page (Light & Drager, 2007; Oxley & Norris, 2000; Reichle et al., 2000). Memory skills are also essential when remembering both the message that one wants to communicate and the many steps related to navigation of an SGD (Rowland & Schweigert, 2003). For children that are new to navigation, visual-spatial working memory could be an important factor to consider when they are looking for a symbol in an SGD and verbal working memory could play an important role when they need to use subvocal rehearsal to remember the word for which they are searching. In addition, motor memory could play an important role for those who are familiar with the programming layout of an SGD with dynamic paging (Van Tatenhove, 2009). In summary, currently available research undeniably proposes that cognitive factors are essential in the use of SGDs. However, no current empirical data are available to indicate which cognitive factors are implicated in young children’s navigational abilities or if one or more factors play a larger role in navigational skills. The aim of the present study was, therefore, to study the relationships of multiple cognitive factors with young children’s ability to navigate an SGD with dynamic paging. Along with assessing children’s navigational ability, their sustained attention, categorization, cognitive flexibility, fluid reasoning, and working memory skills were also assessed (see Table I). Investigating the relationships of these cognitive factors with navigation was important to better understand the cognitive demands implicated in the navigation of an SGD with dynamic paging. From a clinical point of view, this research was crucial. Knowledge of which factors have an impact on navigational skills could be essential information when selecting an SGD for a young child. Results from cognitive assessments could be used in order to better plan AAC intervention. Moreover, the results of this study could play a vital role in the proper selection of an SGD that could ultimately enable a child to communicate. It was postulated that cognitive skills would impact

navigation; however, because this was the first study that investigated the relationship of cognitive scores with navigational ability, it was not possible to hypothesize the specific relationship. The specific research questions for this study were: Which cognitive skills, including sustained attention, categorization, cognitive flexibility, fluid reasoning, verbal working memory and visualspatial working memory, are correlated with young children’s ability to navigate an SGD with dynamic paging that uses a taxonomic organization? and Which, if any, of these cognitive factors best predicts young children’s ability to navigate an SGD with dynamic paging?

Method Participants Participants were 65 children aged 48–77 months (M  65.8, SD  7.19) with typical development. In order to participate, children had to attend junior kindergarten (JK) or senior kindergarten (SK) at a French language school. In all, 35 girls, and 30 boys participated. The children attended eight different French language schools from the City of Greater Sudbury in Ontario, Canada, a community where French is a minority language. Because English is the majority language, the children all had varying levels of English language skills, and most could be considered bilingual. Of the participants, 31 were enrolled in JK and 34 were enrolled in SK. In Ontario, children can start JK, their first year of school, between the ages of 3;9 (years;months) and 4;8. According to studies by Drager et al. (2004) and Light et al. (2004), 4- and 5-year-olds are more successful with taxonomic categorization than 3-year-olds. For this reason, no children under age 4 were included in this study. To increase the number of participants and include all children in SK, 6-year-olds were invited to participate in the study. Hearing impairment was ruled out using a web-based hearing test (www.digital-recordings.com), conducted by research assistants. Children passed the screening at 10dB HL, with some exceptions at 500 Hz because of the background noise and because the test was not conducted in a soundproof booth. These participants were not eliminated from the study because all of the other frequencies were within normal range. Parents did not indicate any vision problems on the questionnaire that was included with the consent form. Some children wore corrective glasses; none of had complex communication needs. The decision to include children with typical development was made in order to acquire data on basic learning strategies and difficulties related with the use of technology (Drager et al., 2004; Higginbotham, 1995; Light et al., 2004). This type of study can provide preliminary insight into the cognitive demands associated with SGDs, free from the effects of motor, sensory, perceptual, or other impairments (Light et al., 2004). Future research is required to extend these results to children with complex communication needs. Augmentative and Alternative Communication

Cognition and Navigation in Children Ethics approval was received by the Laurentian University Research Ethics Board prior to the start of the study, and only children for whom informed parents signed the consent form participated. Setting Most children were assessed within their school setting in a private room, while some children attended the Laurentian University Speech-Language Pathology Clinic for the assessment sessions. The distraction levels of the two settings were judged to be similar. Both settings offered a small private room that was similarly decorated (i.e., educational posters on the wall). The background noise levels were also judged to be similar. In both locations, only the research assistant and the child were present in the room, and hallway disturbances were judged as equivalent. In order to reduce the impact of possible fatigue on the cognitive and navigational scores, not all of the tests were administered at one time. The children were assessed over two to four sessions that varied from 30 min to 2 hours each. To control for the element of test practice on subtests that were last given, the order in which the navigational task and cognitive subtests were administered was not the same for all children. This order was randomly determined. Materials Navigation. To measure navigational skills, the application (app) Proloquo2Go™ (AssistiveWare, 2013) and the iPad2 (Apple Inc., 2013) were used. Proloquo2Go offers over 14,000 preprogrammed symbols. It was selected because it was widely used within this geographical area by children who have complex communication needs at the time this study took place. A 16-location grid was selected because pilot testing conducted with six children with typical development aged 4–6 years demonstrated that more than 16 symbols per page greatly increased the complexity of the navigational task because of the need to scan more items per page to locate symbols. The use of fewer than 16 symbols per grid may have led to a greater need to change pages and may also have increased the complexity of the task. The use of 16 symbols was also comparable to a study by Light et al. (2004), in which 12–20 symbols per page were used successfully with 4- and 5-year-olds, respectively. The total number of target words for the navigational task was also determined through pilot testing and was based on the number of items that the children could reasonably complete within a single session without needing a break. The symbols used were SymbolStix™ (N2Y Inc., 2013), which come preloaded with Proloquo2Go. The organization was taxonomic (grouping by hierarchical categories). Although young children may be more apt to use schematic organization (grouping by event contexts), the taxonomic organization that comes preloaded with Proloquo2Go was chosen to research its impact on navigation. © 2013 International Society for Augmentative and Alternative Communication

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The practice portion of the navigational task involved the retrieval of five words and the navigational task involved the retrieval of 25 words (see Supplementary material Appendix available online at http://informahealthcare. com/doi/abs/10.3109/07434618.2013.849754).The words were selected from the younger stages of receptive vocabulary tests, for example, the PPVT-4 (Peabody Picture Vocabulary Test – Fourth Edition) (Dunn & Dunn, 2007) and the ÉVIP (Échelle du vocabulaire en images Peabody) (Dunn, Thériault-Whalen, & Dunn, 1993), and were judged to be familiar for most children aged 4–6 years. They were all concrete nouns representing objects, animals or people. The preprogrammed vocabulary and taxonomic categorization from Proloquo2Go was used with one exception: The word “baby”, which was not part of the Proloquo2Go preprogrammed vocabulary, was added under the people category. For the first portion of the experimental task, the words were found under the same categories used in the practice portion, but as the task progressed, some were found under new categories. Depending on the number of subcategories involved, some symbols were retrieved at the third level of Proloquo2Go, while others were found at the fourth level. For example, in order to locate the symbol for “carrot”, the child had to first select the symbol for categories; followed by the symbol for food and drink; followed by the symbol for vegetables; and finally, the symbol for carrot. In order to not discourage the children who had difficulty navigating, the most difficult words to retrieve were placed at the end of the task and were not administered to the children if they attained the ceiling of eight consecutive errors. The words that were located under categories that were not seen during the practice portion were judged to be more difficult to find. The order of presentation of the words was determined through pilot testing; those that had the highest success rate from the pilot group were placed at the beginning, while those with the lowest success rate were placed towards the end. Items were also placed in an order that would ensure that two successive symbols would not be found under the same category. Cognitive Tests. Two cognitive tests were used for this study: The Leiter International Performance ScaleRevised (Leiter-R) (Roid & Miller, 1997), and the Automated Working Memory Assessment (AWMA) (Alloway, 2007). The Leiter-R is a non-verbal test that requires the child and clinician to communicate only with non-verbal cues such as pointing. It is standardized for the ages of 2 years to 20;11. Raw scores can be converted to scaled scores. However, because some subtests do not offer scaled scores for certain age groups, raw scores for all of the subtests were used in all analyses. The Leiter-R was selected because all subtests are nonverbal and could therefore be administered to children who have complex communication needs. According to Goldstein, Johnson, and Minshew (2001), the Leiter-R is an excellent test to measure cognition in children with reduced receptive or expressive language skills. This test was standardized in the United States using the same

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proportions of Caucasians, African-Americans, Asian Americans, and Native Americans as those found in the 1993 U.S. Census. Internal consistency reliability varied between .75 and .90 for the subtests used in the current study (Roid & Miller, 1997). Attention Sustained (AS) was selected to measure the children’s ability to sustain attention. Picture Context (PC) and Classification (C) were selected to measure categorization. Figure Ground (FG) was selected as a measure of cognitive flexibility. Form Completion (FC), Sequential Order (SO) and Repeated Patterns (RP) were selected to measure fluid reasoning. These subtests were selected as per their description in the Leiter-R manual. The Automated Working Memory Assessment is a test of visual-spatial and verbal working memory. It is a computerized assessment that requires children to point to the screen for some of the tasks. Standard scores are available for ages 4 years to 22;11. The administered AWMA subtests that measure visual-spatial working memory are Dot Matrix (DM), Block Recall (BR), Odd One Out (OOO) and Mister X (MX). The subtests that were administered to measure verbal working memory are Digit Recall (DR), Counting Recall (CR) and Backwards Digit Recall (BDR). The subtests used in this study have correlation coefficients for test-retest reliability that vary between .83 and .90 (Alloway, 2007). Individuals who participated in the standardization of the AWMA resided in England and included children from diverse countries. Information regarding the implementation for the cognitive subtests and what they measure can be found in Table I. Procedure Navigational Task. One trained research assistant was responsible for this portion of data collection. Before starting the navigational task, the children were asked if they had previously used an iPad, iPhone, or Android device. The research assistant then described and demonstrated how to use the Proloquo2Go application on the iPad2 and how to find words within the levels. A generic explanation of the symbols, taxonomic organization, and device operation was provided to the children. For example, it was explained that words could be found under folders representing categories, that the home button linked to the first page, and that the back button linked to the previous page. This demonstration took an average of 5–10 min. Because Proloquo2Go was not available with a French speech synthesizer, the volume was turned off to control for the participants’ varying levels of English skills. The research assistant confirmed the retrieval of symbols by verbalizing the words in French. Practice Portion. The children were asked to retrieve six practice words on the iPad2. In order to control for the children’s ability to correspond the symbol to the referent and to ensure that only navigation was being measured, the symbols were presented in a booklet,

alone on one page, at the same time as the word for the symbol was said aloud. The booklet remained open, displaying the target symbol, while the children navigated within the pages of the iPad2 to find the symbols. During the practice portion, the research assistant gave the children verbal prompts if they were unable to find a symbol (e.g., What category does that word belong to? or You can use the home button to return to the first page). Before starting the formal task, the six practice words all had to be successfully retrieved (with or without prompting); if they were not retrieved independently, the research assistant modeled the correct path to teach the navigation needed to find the symbol. The child then began the formal task. Formal Task. For the formal navigational task, the children were asked to retrieve 25 words within the pages of Proloquo2Go using the iPad2. The procedure was identical to the practice portion, with the exception that prompts were not given. As in the practice portion, symbols were always presented in a separate booklet at the same time as the research assistant said aloud the word for the target symbol. The booklet remained open in front of the child, displaying the symbol, during the entire navigational task. The child could look at the symbol at anytime to ensure proper symbol selection. This was done in order to ensure that navigation, not symbol knowledge, was being measured. The child was instructed that, by turning the page of the image booklet, an item could be skipped if the symbol could not be retrieved. An item was scored as correct if the child correctly selected the symbol that matched the target in the booklet. When a symbol was incorrectly retrieved, no point was given for that item. If the children did not make a selection within 5 min, a reminder was given that by turning the page of the symbol booklet, an item could be skipped. The test ended once all of the 25 items were presented or after the child reached the ceiling of failing to retrieve eight consecutive symbols. Eight children reached the ceiling and did not complete the navigation test. They were given a score of 0 for the items not administered, but were not omitted from the analysis. For each child, the total number of correct answers was used to calculate the percentage of words correctly retrieved out of 25 items; this percentage represented the navigational score. Procedural and Inter-rater Reliability. With parent permission, the sessions were all videotaped. For a randomly selected sample of 20% of the participants (13 of 65 children), another research assistant viewed the videos to ensure procedural reliability. The second assistant also coded the children’s answers to calculate interrater agreement. Both procedural and item-by-item inter-rater reliability were 100%. An inter-rater reliability analysis using the Cohen’s Kappa statistic was also performed to determine consistency among raters. The inter-rater reliability for the raters was found to be Kappa  1.00 (p  .001). Augmentative and Alternative Communication

Cognition and Navigation in Children Cognitive Tests. Trained research assistants administered the subtests of the Leiter-R and the AWMA. Each subtest took an average of 10 min to administer. As per standard administration, the Leiter-R was completed without verbal instructions. The AWMA was administered using a portable computer with a 15-inch (38.1 cm) screen. Because the AWMA is an English program, the sound was turned off and the research assistant gave the instructions in French.

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Correlation between Navigation and Cognition Relationships between navigation scores, age, and the cognitive variables were examined (see Table II for correlations). Significant positive correlations were found between all but one of the cognitive measures and the navigation scores. More precisely, for the Leiter-R subtests, the higher the scores for Attention Sustained, Picture Context, Classification, Sequential Order, Repeated Patterns and Form Completion, the higher the navigation scores. Furthermore, for the AWMA subtests, the higher the scores for Digit Recall, Dot Matrix, Odd-One-Out, Counting recall, Block Recall, and Backwards Digit Recall, the higher the navigation scores. The correlation between the navigation scores and Figure Ground (Leiter-R) scores was not statistically significant. Significant correlations were also found between the age of the participants and their navigation scores, in favor of the older participants. Correlations were also computed between cognitive measures. All observed correlations were positive between cognitive measures; however, not all correlations were statistically significant. For the sake of brevity, for example, the Attention Sustained subtest of the Leiter-R was significantly correlated with all of the verbal and visual-spatial working memory subtests of the AWMA. It was also interesting to find that Form Completion (Leiter-R subtest) was not significantly correlated with any of the verbal or visual-spatial working memory subtests from the AWMA.

Results Navigation scores varied from 12–100% success. The mean and standard deviation for the total sample was 65.57 and 24.91, respectively. The mean for the 4-yearold children was 44.82 (SD  24.99, n  17), for the 5-year-old children it was 66.25 (SD  19.28, n  32) and for the 6 year olds it was 86.26 (SD  16.39, n  16). Because standardized scores were not available for the Picture Context and Classification subtests of the Leiter-R for the 6-year-olds, raw scores from all subtests of the Leiter-R and AWMA were used for the analysis. Means and standard deviations for all of the cognitive measures are presented as raw scores in Table IV. To rule out previous exposure to an iPad as a factor for navigational success, the correlation between exposure and navigation scores was analyzed. This was measured by asking the children if they had ever used an iPad or similar technology. Only 14 of the 65 children had not used this type of technology. Exposure was not statistically significant (r   0.14, p  .26); experience using an iPad or other similar technology did not appear to impact the children’s ability to retrieve symbols within the application.

Prediction of Navigational Performance This analysis examined the ability to use cognitive measures and age to predict navigational skills. The aim

Table II. Correlations, Means and Standard Deviations of Navigation, Age and the Cognitive Factors.

Variables Navig. Age AS PC C FG FC SO RP DR DM OOO CR MX BR BDR M SD

Navig.

Age

AS

PC

C

FG

FC

SO

RP

DR

DM

OOO

CR

MX

BR

BDR

—

.63* —

.68* .76* —

.52* .41* .33* —

.39* .49* .41* .21 —

.19 .29* .22 .16 .26* —

.29* .14 .23 .23 .15 .18 —

.49* .27* .32* .38* .23 .48* .39* —

.31* .06 .18 .17 .04 .30* .15 .41* —

.41* .43* .39* .21 .41* .19 .07 .12 .11 —

.35* .39* .47* .16 .26* .26* .09 .15 .04 .17 —

.43* .54* .59* .13 .35* .30* .16 .20 .13 .41* .46* —

.45* .59* .59* .16 .49* .25* .13 .25* .27* .51* .33* .53* —

.25* .40* .43* .30* .24 .12 .03 .07 .27* .27* .30* .41* .41* —

.46* .52* .58* .23 .48* .29* .16 .19 .17 .23 .50* .56* .50* .40* —

65.57 24.91

65.80 7.18

51.98 17.78

22.26 3.44

16.88 1.61

14.72 2.43

22.62 4.05

11.09 4.76

11.08 4.49

18.74 4.79

13.03 3.34

9.35 4.02

7.95 3.13

5.60 2.55

11.86 4.46

.34* .48* .53* .29* .37* .22 .20 .22 .22 .60* .42* .56* .59* .32* .42* — 4.57 3.47

Navig., Navigation Score; AS, Attention Sustained, Leiter-R; PC, Picture Context, Leiter-R; C, Classification, Leiter-R; FG, Figure Ground, Leiter-R; FC, Form Completion; Sequential Order, Leiter-R; RP, Repeated Patterns, Leiter-R; DR, digit recall, AWMA; DM, dot matrix, AWMA; OOO, Odd One Out, AWMA; CR, counting recall, AWMA; MX, Mister X, AWMA; BR, Block Recall, AWMA; BDR, Backward Digit Recall, AWMA. *p  .05. © 2013 International Society for Augmentative and Alternative Communication

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was to find the most pragmatic set of factors that could best predict navigational skills, that is, the best prediction possible with the least amount of factors possible. To the best of our knowledge, no previous research had suggested a theoretical reason providing an order in which variables should be included; therefore, a stepwise linear regression was a relevant statistical choice in this case (Tabachnick & Fidell, 2013). This statistical analysis starts out empty, with independent variables added one-at-a-time if they meet statistical criteria for inclusion but deleted at any step if they no longer contribute in a significant manner to the regression (Tabachnick & Fidell, 2013). The final results include the variables that make the most important contribution to the regression model. Consequently, a stepwise linear regression was performed with the scores from the navigational task as the dependent variable; all of the raw scores from the Leiter-R and the AWMA subtests, as well as age in months, were used as the independent variables. The results revealed that Attention Sustained was the first variable included in the model that explained navigational skills. It explained 46% of the variance, F(1, 63)  54.26. The addition of Picture Context (a measure of categorization) added 10% to the variance explained, F(1, 62)  14.05. Sequential Order (another measure of categorization) added an extra 4% to the variance explained, F(1, 61)  6.11. None of the other independent variables made a significant contribution to the model. Regression coefficients are presented in Table III. Profile Analyses A series of univariate analysis of variance (ANOVA) were computed for each cognitive measure in order to compare the cognitive abilities of children with the highest scores in navigation to those with the lowest scores. More specifically, the sample was divided in thirds as a function of their navigation scores. Because 65 was not divisible by 3 and some of the children at the cusp of the thirds received identical scores, the groups were divided as follows: 21 children with the highest navigation scores (84–100), 20 with the lowest navigation scores (12–56), and 24 with mid-range scores (60–80). Analyses were

Table III. Regression Coefficients and Percentage of Variance Explained.

Predictor variables Model 1 AS Model 2 AS PC Model 3 AS PC SO *p  .05.

B

SE B

b

0.95

0.13

.68*

0.80 2.42

0.13 0.65

.57* .33*

0.73 1.93 1.16

0.12 0.65 0.47

.52* .27* .22*

R2

ΔR2

.46* .56*

.10*

.60*

.04*

conducted between the group with the highest scores and the group with the lowest scores. Means and standard deviations of the cognitive measures for the groups are presented in Table IV. For all cognitive measures, except for Figure Ground, F(1, 39)  1.72, p  .20, participants with higher navigational skills also had significantly better scores on the cognitive measures (for the sake of brevity, between F(1, 39)  40.14, η2p .51, p  .001 for Attention Sustained, and F(1, 39)  4.86, η2p .11, p  .04 for Form Completion). Age The children were also divided into three groups according to their age: 4 (n  17), 5 (n  32), and 6 (n  16). A one-way ANOVA confirmed that a difference was present between the age groups, F(2, 62)  17.20, p  .001. A post-hoc Tukey test showed that there was a significant difference between the navigational scores of the 4-year-olds and the 5-year-olds (sig  .002), as well as the 4-year-olds and the 6-year-olds (sig  .000). A significant difference was also present between the 5-year-olds and the 6-year-olds (sig  .006).

Discussion The first aim of this study was to explore which cognitive factors impact the ability to navigate an SGD in young children. Results revealed that all cognitive factors, except for cognitive flexibility (Figure Ground subtest of the Leiter-R), were positively linked to navigational ability. In other words, the better the cognitive ability, Table IV. Means and Standard Deviations of Navigation, Age and Cognitive Subtests (Presented as Raw Scores).

All participants Variable Navigation Age AS PC C FG FC SO RP DR DM OOO CR MX BR BDR

Low navigation

High navigation

M

SD

M

SD

M

SD

65.57 65.80 51.98 22.26 16.88 14.72 22.62 11.09 11.08 18.74 13.03 9.35 7.95 5.60 11.86 4.57

24.91 7.19 17.78 3.44 1.61 2.43 4.05 4.76 4.49 4.79 3.34 4.02 3.13 2.55 4.46 3.47

34.30 60.35 38.25 19.95 15.80 14.45 21.30 8.50 9.20 15.60 11.55 7.30 5.60 5.30 9.60 2.75

14.32 6.18 11.35 4.48 1.40 1.57 3.51 3.59 2.91 5.15 2.19 2.64 1.79 2.20 3.15 3.29

91.43 71.57 66.76 24.05 17.62 14.48 24.05 14.00 13.43 20.67 14.38 11.19 9.43 7.00 14.57 5.95

5.70 5.17 16.80 1.36 0.92 3.14 4.40 5.60 4.70 2.74 4.34 4.42 3.09 1.92 4.83 2.94

AS, Attention Sustained, Leiter-R; PC, Picture Context, Leiter-R; C, Classification, Leiter-R; FG, Figure Ground, Leiter-R; FC, Form Completion; Sequential Order, Leiter-R; RP, Repeated Patterns, Leiter-R; DR, digit recall, AWMA; DM, dot matrix, AWMA; OOO, Odd One Out, AWMA; CR, counting recall, AWMA; MX, Mister X, AWMA; BR, Block Recall, AWMA; BDR, Backward Digit Recall, AWMA. Augmentative and Alternative Communication

Cognition and Navigation in Children the better the navigational skills. Given that age was also correlated with navigation, it is also true that the older the child, the better the navigational ability. Indeed, 6-year-olds performed better than 5- and 4-year-olds, and 5-year-olds performed better than 4-year-olds. The children’s performance on the navigation task varied greatly, especially for the youngest children. This demonstrates that there is great variability as to the ability to navigate an SGD that uses a taxonomic organization such as Proloquo2Go, with minimal instruction. Many of the young children had difficulty locating vocabulary (i.e., mean accuracy of 45% for the 4-year-old children and 66% for the 5-year-old children), despite typical cognitive development. Older children may be better able to retrieve symbols without training using this type of organization. Results were similar when we compared the cognitive profiles of the children who had the highest navigation scores to the cognitive profiles of the children who had the lowest navigation scores. All subtests had a significantly different mean score between the two groups, except for Figure Ground. These results were most interesting in light of the fact that Wallace et al. (2010) observed a link between cognitive flexibility and navigation for adults who experienced a TBI. Even though they only explored this particular cognitive factor, they did find a significant link to navigational skills. The fact that the populations in these two studies differed greatly could explain the difference in results. Another possible explanation could be the differences in how the construct of cognitive flexibility was measured in the two studies. Indeed, the validity of all of the measures that were used to assess the cognitive factors could be questioned because it is difficult to isolate one cognitive factor from another. Our findings could also be attributable to other factors, such as the average or above average scores obtained on the cognitive flexibility measure, Figure Ground. In fact, only two of our 65 participants had a below average Figure Ground score, reducing available variance. One reason for the elevated Figure Ground scores could be associated with sampling from a bilingual population who often perform better on perceptual tasks that require finding an embedded object in a visual background (Bialystok, 2001; Diaz, 1985) such as Figure Ground. Future studies should include monolinguals from a linguistic majority context and children with lower cognitive flexibility skills to further study and fully understand its contribution to children’s navigational skills. A different cognitive flexibility measure could also be used in future research. Nevertheless, all other measures of cognitive processing in the current study did show a significant link to navigational skills. Sustained attention, categorization, fluid reasoning, verbal working memory, and visuospatial working memory were all tied to children’s ability to navigate an SGD. As suggested by Wallace et al. (2010), cognitive abilities are linked to the navigational ability. The empirical data to demonstrate that cognition © 2013 International Society for Augmentative and Alternative Communication

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is an important aspect of navigation in children is now available. Furthermore, it is now possible to understand how cognition can greatly impact young children’s ability to use an SGD. Among other factors, cognitive skills should be taken into consideration during the selection process for the most appropriate AAC system for children. The current study also intended to discover which subset of factors could best predict navigational ability using a taxonomic organization. A stepwise linear regression analysis revealed that sustained attention, categorization, and fluid reasoning were the best predictors of navigation in young children. Sustained attention was the first factor to contribute to the regression model. Because attention is an important factor for all other cognitive functions (Zarghi et al., 2011), it is not surprising that it was the best predictor of navigation. Sustained attention allows for more concentration on a task, and is therefore important for the location of a message on an SGD in the presence of other visual and auditory distracters (Wallace et al., 2010). Furthermore, because sustained attention allows one to stay on task for a long period of time (Levine et al., 1992), this skill would allow a longer search for a symbol without getting distracted or giving up. It should be noted that sustained attention would be mostly important for children who are new navigators, such as the participants in this study, because the automaticity that would come from repeated use could greatly reduce the attentional demands (Light & Lindsay, 1991) needed to navigate an SGD. Motor memory from having a consistent vocabulary layout could also reduce navigational challenges (Van Tatenhove, 2009). In fact, future research should explore the importance of cognition as a function of navigational experience. Because the symbols in the application Proloquo2Go were organized by taxonomic categories, it is easy to understand why categorization was the second factor added to the regression model. Knowledge of the category under which a symbol might be programmed would facilitate navigation by reducing the time needed to search for it within multiple categories. It could also reduce the frustration that would come from not finding a symbol. In 2010, Reichle and Drager reported that efficient use of SGDs with dynamic displays and taxonomical organization required the ability to classify. As a matter of fact, in clinical settings, a child’s ability to categorize is often used to gauge the ability to navigate an SGD with dynamic paging and taxonomic organization. This study is the first to empirically support this clinical trend. A child who has difficulty categorizing would therefore have difficulty navigating within an application such as Proloquo2Go or other applications or devices with taxonomic categorization. Children who have difficulty navigating may have less difficulty finding words within an SGD with schematic organization. Fluid reasoning, the third factor that predicted navigation, might be important in navigation because

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it allows children to problem solve in order to find symbols. Thus, a child who has no knowledge of where to find a particular symbol could use information gathered from the process of looking for other symbols to make inferences about its location. Reasoning could also help when children need to correct erroneous selections. Consequently, children with higher reasoning skills would have better navigational outcomes because they would be able to resolve where to find symbols by using rationalization. Although all of the working memory subtests of the AWMA were correlated with navigation, none were retained as the best set of predictors. The significant correlations with navigation could be explained by the fact that all of the memory subtests were also significantly correlated with sustained attention. This is in line with findings by Schweizer and Moosbrugger (2004), who established that a substantial link is present between sustained attention and working memory. Thus, the attention component of the memory subtests may have caused the significance in correlations with navigation. Another hypothesis is that, because the navigational task in this study required finding single words and not sequencing or combining words to make sentences, the working memory system may not have been sufficiently taxed. Higher working memory demands may be required to sequence symbols, because verbal working memory would be needed to remember the words generated by the SGD and those that are still needed to complete the sentence. Future studies should include sequencing tasks before ruling out the impact of working memory on navigation and its ability to predict navigation. Also, because the experimental navigation task was structured with a booklet present at all times with the target symbol, some of the working memory demands were potentially reduced. Similarly, although age was correlated with navigational skills, it was not retained as one of the best predictors when it was combined with the cognitive factors. When assessing children’s ability to use an SGD with dynamic paging, it would be important to consider factors other than age, which is not among the best predictors of navigation. The three subtests of the Leiter-R that were revealed as significant in predicting navigation have further importance in terms of clinical implications. Because the Leiter-R is a non-verbal test, it could be used in a clinical setting with children who have complex communication needs who may benefit from the use of an SGD. When assessing a young child, clinicians could use the Attention Sustained, Picture Context, and Sequential Order subtests, along with traditional non-standardized tools, to help predict the ease with which the child will learn to navigate between screens to find the desired symbols with a taxonomic organization. Because intervention to learn to use an SGD may not always be available, there are some situations when it may be important to select a device that can be used without training. Results from traditional AAC non-standardized assessments, in combination with cognitive testing, may reveal that a child

needs intervention and exposure to learn to navigate, or that a device with static overlays or a different organization (e.g., schematic organization) may be more beneficial. Another suggestion for children who have difficulty navigating would be to place the burden of navigation on an adult or facilitator in the beginning stages. By providing the child with the appropriate page for the activity at hand, participation would be facilitated without navigation. This strategy could be useful until the child learns to navigate independently. Results from the Leiter-R may provide information to help clinicians to decide if a child can use an SGD with dynamic paging to communicate, but these results should not be the only factor on which to base the decision; a trial period may also be a good way of predicting success. This study focused on the performance of children on initial introduction of an SGD with a taxonomic organization with only minimal instruction. Performance might vary with a different organization and would be expected to increase with instruction and practice. Future research is required to investigate these learning curves. There are two important questions that have not been addressed by this study and that remain to be answered: What level of navigational skills are required to be a competent communicator for children this age (how good do navigational skills need to be for effective SGD use), and How do we predict if a child is going to be good enough at navigation? The inclusion of these research questions in future studies would provide clinicians with important information regarding the selection of the most appropriate SGD for children with complex communication needs. It should be noted that difficulties with navigation should not preclude AAC intervention; there are no cognitive prerequisites to AAC. Limitations Procedural limitations were present for this study. The different settings (i.e., private room in a school versus speech-language pathology clinic) may have impacted the children’s performance. Although parents did not report any difficulties with vision, we did not directly measure visual acuity. Thus, differences in visual acuity could have played a role in navigation. Furthermore, difficulty with representation may have played a role for some children, even though symbols for words assessed were presented in a booklet during the navigational task. Future studies should incorporate testing of vision and representational skills. It should be noted that the sample was small for the use of a stepwise regression statistical procedure (Tabachnick & Fidell, 2013). Results should thus be interpreted with caution and future research should explore the question with a larger sample. For the profile analyses, because the p value was not adjusted to deal with the issue of multiple post hoc tests, there is an increased risk of a Type 1 error. The fact that some of the children did not complete the navigation task Augmentative and Alternative Communication

Cognition and Navigation in Children because they reached the ceiling of eight consecutive errors may have impacted the results. The validity of the cognitive factors could also be questioned because they are difficult to isolate. Furthermore, this study focused on one specific approach to navigation using a taxonomic organization; future research is required to investigate the navigation performance of young children using different organizations within dynamic display systems. Nevertheless, this research suggests that cognitive abilities are an important factor to consider when using SGDs. Because participants of this study did not have complex communication needs, it is not known if the same cognitive factors would impact navigation in children who actually need an SGD. It is also not known if children belonging to different age groups would perform similarly. The navigational task for this study required finding single words only. A navigational task requiring the sequencing of symbols could be more taxing on the working memory system. The fact that the iPad volume was turned off may also have negatively impacted the scores. Finally, the Leiter-R has a physical component because the children are required to place cards into slots, manipulate shapes, and cross out images. It is not known if this test could be adapted for a population with fine motor impairments, which, according to Binger and Light (2006), represents 20% of the children who need AAC. Future Research Further research is needed with children who have complex communication needs. Future studies should assess particular populations, such as children with autism who often use SGDs. Research with younger children aged 2- and 3-years-old is needed to determine their ability to navigate an SGD with dynamic paging and taxonomic organization. With this younger population, the impact of cognition on the use of visual scene displays is also needed. This would help clinicians select devices for the emergent communicator. The impact of having language impairments on navigation should also be analyzed in future studies. More importantly, research is needed to determine how SGDs should be programmed to reduce attention, categorization, and reasoning demands, thus benefitting more children who use technology to communicate. For children who have difficulty navigating, futures studies are needed to determine how much training would be required to learn to retrieve symbols in an SGD with a dynamic display. Ultimately, it would be important to research the training level necessary for those children to become competent communicators.

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Leiter-R), categorization (Picture Context and Classification, Leiter-R), fluid reasoning (Form Completion, Sequential Order and Repeated Patterns, Leiter-R), working memory (AWMA), and age were all correlated with navigation. Yet, cognitive flexibility (Figure Ground, Leiter-R), which was correlated with navigation in adults who suffered a traumatic brain injury (Wallace et al., 2010), was not correlated with navigation for young children with typical development. Among the factors correlated with navigation, the subset that best predicted children’s navigational skills with a taxonomic organization included sustained attention, categorization, and fluid reasoning. Even though verbal working memory and visual-spatial working memory were correlated with navigation, subtests assessing memory skills were not retained in the subset of predictive factors of navigational ability. The AWMA was not as useful a tool. Likewise, age, which was also correlated with navigation, did not result as one of the best predictors of navigational ability. It is important to know which cognitive factors can contribute to navigational success or difficulties, because the proper selection of an initial SGD may lead the way to positive AAC outcomes. The prescription of the first SGD could set the stage for successful communication or failure. The improper selection of an SGD could cause the child to become frustrated and caregivers to decide to no longer use a device for communication purposes. When choosing an SGD for young children, attention, categorization, and reasoning skills, along with nonstandardized AAC evaluation tasks, can be assessed to help predict their success with dynamic paging using a taxonomic organization. More precisely, the Attention Sustained, Picture Context, and Sequential Order subtests of the Leiter-R can be utilized to help predict the ability to navigate an iPad using Proloquo2Go with a 16-location grid. Lastly, studies are needed to investigate how to program SGDs with dynamic paging to reduce attention, categorization, and reasoning demands of navigation.

Acknowledgements This research was performed as part of the first author’s doctoral dissertation. The authors would like to thank the Conseil Scolaire Public du Grand Nord de l’Ontario (CSPGNO) for the partnership that allowed this research to be conducted. This research was made possible through a partial financial contribution from Health Canada. The views expressed here do not necessarily represent the official views of Health Canada. Thank you to the students who participated in the data collection: Mélissa Therrien, Melissa Lariviere, France Rainville and Sylvie Rondeau.

Conclusion Cognitive skills have an impact on the navigational skills of typically developing children who are new to AAC use. Sustained attention (Attention Sustained, © 2013 International Society for Augmentative and Alternative Communication

Declaration of interest: The authors report no conflicts of interests. The authors alone are responsible for the content and writing of this paper.

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References Alloway, T. P. (2007). The Automatic Working Memory Assessment (AWMA) [Computer Software]. London: Harcourt Assessment. Alloway, T. P., Gathercole, S. E., & Pickering, S. J. (2006). Verbal and visuospatial short-term and working memory in children: Are they separable? Child Development, 77, 1698–1716. doi: 10.1111/ j.1467-8624.2006.00968.x Alliano, A., Herriger, K., Koutsoftas, A. D., & Bartolotta, T. E. (2012). A review of 21 iPad applications for augmentative and alternative communication purposes. Perspectives on Augmentative and Alternative Communication, 21, 60–71. doi: 10.1044/aac21.2.60 Android. (2013). Android. Retrieved from http://www.android.com/ Apple Inc. (2013). Apple. Retrieved from http://www.apple.com/ AssistiveWare. (2013). Proloquo2go [Mobile application software]. Retrieved from http://itunes.apple.com/ Baddeley, A. D. (2000). The episodic buffer: A new component of working memory? Trends in Cognitive Science, 4, 417–423. doi: 10.1016/S1364-6613(00)01538-2 Baddeley, A. D., & Hitch, G. (1974). Working memory. In G. H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory 8 (pp. 47–90). New York: Academic Press. Bialystok, E. (2001). Bilingualism in development: Language, literacy, and cognition. New York: Cambridge University Press. Binger, C., & Light, J. (2006). Demographics of preschoolers who require AAC. Language, Speech, and Hearing Services in Schools, 37, 200–208. doi: 0161-1461/06/3703-0200 Blischak, D. M., Lombardino, L. J., & Dyson, A. T. (2003). Use of speech-generating devices: In support of natural speech. Augmentative and Alternative Communication, 19, 29–35. doi: 10.1080/0743461032000056478 Cafiero, J. M., & Stern Delsack, B. (2007). AAC and autism: Compelling issues, promising practices and future directions. Perspectives on Augmentative and Alternative Communication, 16, 23–26. doi: 10.1044/aac16.2.23 Chevalier, N., & Blaye, A. (2008). Cognitive flexibility in preschoolers: The role of representation and activation and maintenance. Developmental Science, 11, 339–353. doi: 10.1111/j.14677687.2008.00679.x Deák, G. O. (2003). The development of cognitive flexibility and language abilities. In R. Kail (Ed.), Advances in Child Development and Behavior (pp. 271–327). San Diego: Academic Press. Diaz, R. M. (1985). Bilingual cognitive development: Addressing three gaps in current research. Child Development, 56, 1376–1388. doi: 10.1111/1467-8624.ep7252620 Drager, K. D. R., & Light, J. C. (2006). Designing dynamic display AAC systems for young children with complex communication needs. Perspectives on Augmentative and Alternative Communication, 15, 3–7. Drager, K. D. R., Light, J. C., Carlson, R., D’Silva, K., Larsson, B., Pitkin, L., & Stopper, G. (2004). Learning of dynamic display AAC technologies by typically developing 3-year-olds: Effect of different layouts and menu approaches. Journal of Speech, Language, and Hearing Research, 47, 1133–1148. doi: 10.1044/1092-4388 Dunn, L. M., Thériault-Whalen, C. M., & Dunn, L. M. (1993). Échelle de Vocabulaire en Images Peabody. Adaptation francaise du Peabody Picture Vocabulary Test-Revised. Manuel pour les formes A et B. Toronto, ON: Psycan. Dunn, L. M., & Dunn, D. M. (2007). Peabody Picture Vocabulary Test (4th ed.). Minneapolis, MN: NCS Pearson, Inc. Goldstein, G., Johnson, C. R., & Minshew, N. J. (2001). Attentional processes in autism. Journal of Autism & Developmental Disorders, 31, 433–440. doi: 0162-3257/01/0800-0433 Gopnik, A., & Meltzoff , A. (1987). The development of categorization in the second year and its relation to other cognitive and linguistic development. Child Development, 58, 1523–1531. doi: 009-3920/87/5806-0013 Gray, J. R., Chabris, C. F., & Braver, T. S. (2003). Neural mechanisms of general fluid intelligence. Nature Neuroscience, 6, 316–322. doi: 10.1038/nn1014

Helm-Estabrooks, N. (2001). Cognitive linguistic quick test (CLQT). San Antonio, TX: The Psychological Corporation. Hershberger, D. (2011). Mobile technology and AAC apps from an AAC developer’s perspective. Perspectives on Augmentative and Alternative Communication, 20, 28–33. doi: 10.1044/aac20.1.28 Higginbotham, J. D. (1995). Use of nondisabled subjects in AAC research: Confessions of a research infidel. Augmentative and Alternative Communication, 11, 2–5. doi: 10.1080/ 07434619512331277079 Higginbotham, J., & Jacobs, S. (2011). The future of the android operating system for augmentative and alternative communication. Perspectives on Augmentative and Alternative Communication, 20, 52–56. doi: 10.1044/aac20.2.52 Horn, J. L., & Cattell, R. B. (1967). Age differences in fluid and crystallized intelligence. Acta Psychologica, 26, 107–129. doi: http://dx.doi.org/10.1016/0001-6918(67)90011-X Hux, K., & Manasse, N. (2003). Assessment and treatment of cognitive-communication impairments. In K. Hux (Ed.), Assisting survivors of traumatic brain injury (pp. 93–133). Austin, TX: ProEd. Levine, S., Horstmann, H. M., & Kirsch, N. L. (1992). Performance considerations for people with cognitive impairment in accessing assistive technologies. Journal of Head Trauma Rehabilitation, 7, 46–58. doi: 10.1097/00001199-199209000-00007 Light, J., & Drager, K. (2002). Improving the design of AAC technologies for young children. Assistive Technology, 14, 17–32. doi: 10.1080/10400435.2002.10132052 Light, J., & Drager, K. (2007). AAC technologies for young children with complex communication needs: State of the science and future research directions. Augmentative and Alternative Communication, 23, 204–216. doi: 10.1080/07434610701553635 Light, J., Drager, K., McCarthy, J., Mellott, S., Millar, D., Parrish, C. . . Welliver, M. (2004). Performance of typically developing four- and five-year-old children with AAC systems using different language organization techniques. Augmentative and Alternative Communication, 20, 63–88. doi: 10.1080/07434610410001655553 Light, J., & Lindsay, P. (1991). Cognitive science and augmentative and alternative communication. Augmentative and Alternative Communication, 7, 186–203. doi: 0743-4618/91/0703-0186 Lloyd, L. L., Fuller, D. R., & Arvidson, H. H. (1997). Augmentative and Alternative Communication: A handbook of principles and practices. Toronto: Allyn and Bacon. Mizuko, M., Reichle, J., Ratcliff , A., & Esser, J. (1994). Effects of selection techniques and array sizes on short-term visual memory. Augmentative and Alternative Communication, 10, 237–244. doi: 10.1080/07434619412331276940 Musselwhite, C. R., & St. Louis, K. W. (1988). Communication programming for persons with severe handicaps. Austin, TX: Pro- Ed. Olin, A. R., Reichle, J., Johnson, L., & Monn, E. (2010). Examining dynamic visual scene displays: Implications for arranging and teaching symbol selection. American Journal of Speech-Language Pathology, 19, 284–297. doi: 10.1044/1058-0360 Oxley, J. D., & Norris, J. A. (2000). Children’s use of memory strategies: Relevance to voice output communication aid use. Augmentative and Alternative Communication, 16, 79–94. doi: 0743-4618/00/1602-0079 Reichle, J., Dettling, E. E., Drager, K. D., & Leiter, A. (2000). A comparison of correct responses and response latency for fixed and dynamic displays: Performance of a learner with severe developmental disabilities. Augmentative and Alternative Communication, 16, 154–163. doi: http://dx.doi.org/10.1080/ 07434610012331279014 Reichle, J., & Drager, K. D. R. (2010). Examining issues of aided communication display and navigational strategies for young children with developmental disabilities. Journal of Developmental and Physical Disabilities, 22, 289–311. doi: http://dx.doi. org/10.1007/s10882-010-9191-3 Roid, G. H., & Miller, L. J. (1997). Leiter international performance scale revised (Leiter-R). Wood Dale, IL: Stoelting. Augmentative and Alternative Communication

Cognition and Navigation in Children Rowland, C., & Schweigert, P. D. (2003). Cognitive skills and AAC. In J. C. Light, D. R. Beukelman, & J. Reichle (Eds.), Communicative competence for individuals who use AAC: From research to effective practice (pp. 241–275). Baltimore, MD: Paul H. Brookes. Schweizer, K., & Moosbrugger, H. (2004). Attention and working memory as predictors of intelligence. Intelligence, 32, 329–347. doi: 10.1016/j.intell.2004.06.006 Sennott, S., & Bowker, A. (2009). Autism, AAC, and Proloquo2Go. Perspectives on Augmentative and Alternative Communication, 18, 137–145. doi: 10.1044/aac18.4.137 Tabachnick, G. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Boston: Allyn and Bacon. Thistle, J. J., & Wilkinson, K. (2012). What are the attention demands of aided AAC? Perspectives on Augmentative and Alternative Communication, 21, 17–22. doi: 10.1044/aac 21.1.17 Van Tatenhove, G. M. (2009). Building language competence with students using AAC devices: Six challenges. Perspectives on Augmentative and Alternative Communication, 18, 38–47. doi: 10.1044/aac18.2.38

Supplementary material available online Supplementary material Appendix.

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Wallace, S., Hux, K., & Beukelman, D. (2010). Navigation of a dynamic screen AAC interface by survivors of severe traumatic brain injury. Augmentative and Alternative Communication, 26, 242–254. doi: 10.3109/07434618.2010.521895 Warm, J. S., Parasuraman, R., & Matthews, G. (2008). Vigilance requires hard mental work and is stressful. Human Factors, 50, 433–441. doi: 10.1518/001872008X312152 Wilkinson, K. M., & Coombs, B. (2010). Preliminary exploration of the effect of background color on the speed and accuracy of search for an aided symbol target by typically developing preschoolers. Early Childhood Services, 4, 171–183. Wilkinson, K. M., & Hennig, S. (2007). The state of research and practice in augmentative and alternative communication for children with developmental/intellectual disabilities. Mental retardation and developmental disabilities research reviews, 13, 58–69. doi: 10.1002/mrdd.20133 Zarghi, A., Zali, A., Tehranidost, M., Zarindast, M. R., Ashrafi , F., Doroodgar, S., & Khodadadi, S. M. (2011). Demographic variables and selective, sustained attention and planning through cognitive tasks among healthy adults. Basic and Clinical Neuroscience, 2, 58–67.

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