Developmental Psychology 2006, Vol. 42, No. 6, 1206 –1219
Copyright 2006 by the American Psychological Association 0012-1649/06/$12.00 DOI: 10.1037/0012-1649.42.6.1206
Cognitive Engagement and Story Comprehension in Typically Developing Children and Children With ADHD From Preschool Through Elementary School Elizabeth P. Lorch, Richard Milich, Clarese C. Astrin, and Kristen S. Berthiaume University of Kentucky The present study examined children’s cognitive engagement with television as a function of the continuity of central or incidental content and whether this varied with age and clinical status. In Experiment 1, 9- to 11-year-old children’s response times on a secondary task were slower the later a probe occurred in a sequence of central events, and response times predicted recall. Experiment 2 extended these results to 6- to 8-year-old children. Experiment 3 revealed that children with attentiondeficit/hyperactivity disorder (ADHD) failed to show the pattern consistently observed for comparison children. The results support the hypothesis that typically developing children build a representation during viewing that reflects the causal structure of the televised story but that this skill is deficient in 4to 9-year-old children with ADHD. Keywords: ADHD, cognitive engagement, secondary probe, attention Supplemental data: http://dx.doi.org/10.1037/0012-1649.42.6.1206.supp
gagement with a televised story vary as a function of the continuation of plot-relevant content? Second, does this pattern differ as a function of age? Finally, do children with documented attention problems and story comprehension difficulties (i.e., diagnosed with attention deficit/hyperactivity disorder [ADHD]) differ in cognitive engagement from comparison children?
Knowledge of children’s story comprehension has been informed by theories of the comprehension process. A theme common to most theories is an emphasis on the causal and enabling relations between events in a story, which define plot-relevant events and give the story coherence (Ackerman, 1986; Black & Bower, 1980; Graesser & Clark, 1985; Mandler & Johnson, 1977; Trabasso, Secco, & van den Broek, 1984; van den Broek, 1997). Characteristics of the causal structure of stories have been found to predict a product of children’s comprehension—that is, memory for story events (e.g., Trabasso et al., 1984; van den Broek, 1989; van den Broek, Lorch, & Thurlow, 1996)—with effects becoming stronger with age (Nezworski, Stein, & Trabasso, 1982; Schmidt & Paris, 1983; van den Broek et al., 1996). However, much less research has investigated the online processes children may engage in to detect and use causal relations between events to build a representation of a story (Ackerman, Paine, & Silver, 1991; Trabasso & Nickels, 1992; Trabasso, Stein, Rodkin, Munger, & Baughn, 1992). One avenue of research that may contribute to knowledge of children’s online story comprehension processes is the study of systematic variations in children’s attention to televised stories (Anderson & Lorch, 1983; Huston & Wright, 1983). The present series of studies builds on this line of research by using a measure of moment-to-moment cognitive capacity usage to investigate several questions. First, does children’s cognitive en-
Influences on Children’s Attention to Television Several theoretical viewpoints on children’s television viewing concur that children, from an early age, are active viewers whose visual attention to television is guided by ongoing comprehension, expectations, and purposes for viewing (Anderson & Lorch, 1983; Huston & Wright, 1983; Salomon, 1983). For example, Anderson and Lorch (1983) proposed that for young children, a look at the television may begin in response to any of several possible reasons, including stimuli that elicit orienting responses; formal features that signal informative, appealing, child-relevant content (Alwitt, Anderson, Lorch, & Levin, 1980); and cues derived from the behavior of other children (Anderson, Lorch, Smith, Bradford, & Levin, 1981). Once a look has begun, its continuation primarily depends on the child’s ongoing judgments of whether program content is comprehensible (Anderson, Lorch, Field, & Sanders, 1981; Lorch, Anderson, & Levin, 1979; Pingree, 1986). Huston and Wright (1983) discussed similar factors as influences on visual attention but conceptualized a series of decisions children make about whether to continue a look at the television. At the beginning of a look, decisions are most heavily a function of formal characteristics, once again those that are indicative of informative, interesting, child-relevant content (Calvert, Huston, Watkins, & Wright, 1982; Huston et al., 1981). If a look continues, more extensive, deeper cognitive processing takes place, such that a subsequent decision is affected by both current comprehension
Elizabeth P. Lorch, Richard Milich, Clarese C. Astrin, and Kristen S. Berthiaume, Department of Psychology, University of Kentucky. This research was supported by National Institute of Mental Health Grant MH47386. Correspondence concerning this article should be addressed to Elizabeth P. Lorch, Department of Psychology, University of Kentucky, Lexington, KY 40506-0044. E-mail:
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and initial expectations about content (Campbell, Wright, & Huston, 1987; Rolandelli, Wright, Huston, & Eakins, 1991). As further cycles occur, later decisions are increasingly influenced by deeper processing and more elaborated expectations about story content (Hawkins, Tapper, Bruce, & Pingree, 1995; Rolandelli et al., 1991). In addition, Huston and Wright (1983) proposed that there are changes in these cycles of decisions as development advances. For younger children, the continuation of a look at the television may be more dependent on superficial features of the program than for older children, who are more likely to make elaborated decisions on the basis of deeper processing of content (Rolandelli et al., 1991). These perspectives and their associated empirical support indicate that children’s visual attention to television varies systematically in relation to program characteristics. Huston and Wright’s (1983) perspective also suggests a context for the online processing of television story structure. To the extent that children build an ongoing representation of the interrelations among story events during viewing, measures of attention may reveal effects of event centrality, causal structure, and plot development. Several investigations suggest such indications of children’s online processing of structural features of television stories. School-age children have been found to look at the television more during presentation of material that adults rated as important (i.e., central) to the plot of the story than during material rated as low in importance (Baer & Lorch, 1990; Lorch & Baer, 1997). Meadowcroft and Reeves (1989) obtained a related finding using a secondary task technique to assess cognitive engagement with the television program. The secondary task technique is based on the assumptions that mental processing requires time and that central processing capacity is limited (Basil, 1994; Kahneman, 1973). Thus, the more attentional capacity is engaged by a continuous primary task, such as viewing a television program, the less is available for a secondary task, such as a keypress in response to an occasional auditory tone (Britton, Graesser, Glynn, Hamilton, & Penland, 1983; Britton & Tesser, 1982; Thorson, Reeves, & Schleuder, 1985). Meadowcroft and Reeves (1989) found that 5- to 8-year-old children who had tested high in the development of story schema skills showed greater cognitive engagement (i.e., slower responses to secondary probes) with central content presented in a normal story structure than with the same content presented out of order and thus in the absence of a coherent story structure. Two additional studies also compared children’s responses to normally structured, coherent stories and stories in which scenes had been edited out of sequence, via existing edit points (Hawkins, Kim, & Pingree, 1991; Lorch & Castle, 1997). The use of existing edit points allowed the edited stories to be locally comprehensible but lacking in a coherent story structure. Both studies used the same types of Sesame Street stories, and in both studies children showed high visual attention to normal stories and to edited stories. Both studies, however, revealed indications that children’s attention was engaged more systematically by the normally structured stories. Lorch and Castle (1997), using the secondary task methodology, found that 5-year-old children showed greater cognitive engagement during the second half of normal stories than during the first half, but the authors did not observe this difference for the stories shown in edited form. Hawkins et al. (1991) investigated the predictability in 3- to 6-year-old children’s visual attention
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from one 3-s interval to the next (after removal of variance resulting from overall attention, age, and tape version). Consistent with the results of Lorch and Castle (1997), Hawkins et al. (1991) found much higher stability in attention late in normal stories than in edited stories. Taken together, the findings of Hawkins et al. (1991), Lorch and Castle (1997), and Meadowcroft and Reeves (1989) suggest that children’s attention is responsive to the plot relevance of story content and becomes increasingly engaged as a meaningful narrative structure develops. The major purpose of the current studies is to extend investigation of school-age children’s online cognitive processing of television story content by examining whether children’s cognitive engagement with a television program increases as sequences of story content that are central to the plot continue or decreases as sequences of content that are incidental to the plot continue. In addition, Experiment 2 investigates whether there are developmental differences in children’s cognitive engagement as a function of the continuity of central or incidental content. Finally, Experiment 3 examines the cognitive engagement of children with ADHD, whose problems in sustaining attention and comprehending causal relations have been well documented (Lorch, Eastham, et al., 2004; Milich, Lorch, & Berthiaume, 2005). Two studies have investigated directly the role of cognitive engagement in story comprehension among children with ADHD. Lorch, Eastham, et al. (2004) used a television viewing methodology to test the hypothesis that differences in cognitive engagement would account for group differences in recall of causal relations (i.e., “why” questions) when toys were present during viewing. The results of three different analytic strategies converged to support the hypothesis that greater cognitive engagement, as indexed by long looks (i.e., longer than 15 s) at the television, enabled comparison children to form a more complete representation of the relations among story events, thereby accounting for differences between the comparison and ADHD groups in understanding causal relations questions. Thus, the findings from Lorch, Eastham, et al. (2004) constitute the first compelling evidence that the amount of time spent in deeper cognitive processing during long looks helps explain the differential patterns of recall in children with ADHD and comparison children. The results of Lorch, Eastham, et al. (2004) suggest that variations in cognitive engagement may account for the problems that children with ADHD have with causal relations questions. However, the degree of cognitive engagement was inferred from time spent in long looks. The secondary task may offer a more direct way to assess cognitive engagement. To date, only one study has used this procedure with an ADHD sample (Whirley, Lorch, Lemberger, & Milich, 2003). In this study, participants were 22 boys with ADHD and 36 comparison boys, ranging in age from 9 to 11 years. Boys with ADHD responded significantly slower than the comparison boys, a common finding in reaction time studies. More important, the patterns of response times (RTs) across the central sequences differed between the two groups of boys. The comparison boys showed the predicted pattern of longer RTs (i.e., increased cognitive engagement) the longer into a central sequence the probes appeared. In contrast, the boys with ADHD actually showed shorter RTs from the beginning to the middle of the central sequences, which suggests decreased cognitive engagement as the central sequences progressed. It was only for probes occurring late in the central sequences that the RTs of the boys with ADHD
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showed the expected increase. In Experiment 3, we use a developmental perspective to compare the cognitive engagement of children with ADHD and their comparison peers, using an age range that included younger children (i.e., 4 to 9 years) than the previous secondary task studies. To address whether children’s cognitive engagement is related to the development of content that varies in plot relevance, programs were selected that contained strong narrative structures, with story lines revolving around child characters, and that enabled the identification of continuous sequences (approximately 15 s or more) of events that were central or incidental to the plot. Central sequences are those that are crucial to plot development, whereas incidental sequences are ones that could be removed without affecting the coherence of the story. Central and incidental sequences of events were operationalized in terms of the combination of two criteria: whether events in the sequence were part of the causal chain leading from the beginning to the eventual outcome of the story (as opposed to “dead end” events), and whether events were high or low in centrality, as determined by college student raters (see Experiment 1 Method section for greater detail). For each central or incidental sequence of events, several positions were identified as potential times for presentation of secondary probes. Watching and understanding the television program was defined as the primary task, and children were told that their knowledge of story events would be tested after viewing. As a child watched the program, auditory probes were presented at preselected times. The child’s secondary task was to press a key as quickly as possible whenever a probe was presented. Within the constraints of this task, visual attention was expected to be very high, so the cycles of viewing decisions described by Huston and Wright (1983) were not expected to occur. Probe RTs, however, may capture similar decisions concerning whether to engage in deeper and more elaborative processing. If children build a representation during viewing that reflects the causal structure of a televised story, they should show increasing cognitive engagement as a sequence of central story content continues, because they will encounter events that can be related to the main thread of the story. Therefore, the later a probe was presented in a sequence of central events, the slower children’s responses were expected to be. In contrast, this pattern was not expected to occur as a sequence of incidental events continued. The lack of connections to the causal chain of the story should lead to more superficial processing. Thus, children’s RTs might be unrelated to probe position or might even become shorter the later a probe was presented in a sequence of incidental events.
Experiment 1 Method Participants Participants were 27 boys and 33 girls, ages 9 –11 years (M ⫽ 10.05, SD ⫽ 0.81). Participants were primarily Caucasian (88.33%), with some African American children (8.33%) and children of other ethnic identifications (3.33%). Most parents’ education included at least some college; the average parent was a college graduate (M ⫽ 16.98 years of education). Children were recruited through advertisements in the newspaper and from an existing pool of experimental volunteers. For Experiment 1, children were screened for behavioral problems or attentional difficulties, such as ADHD, in a recruitment phone call and were not included in the study if the parent indicated the child
had ever been referred for any attentional or behavioral difficulties. Children were paid $5 for their participation, which lasted about 45 min. Data were lost from 2 participants as a result of equipment malfunction.
Materials Each participant viewed one episode of the situation comedy Growing Pains. This program was chosen specifically because its content is suitable and interesting for children. Furthermore, the plot of this specific episode (“Dad’s Birthday”) centered on the activities of one of the child characters in the family. A synopsis of the plot appears in the Appendix, which is available on the Web. With all commercials removed, the episode is 23 min in length. A detailed audiovisual script of the program was created as part of a previous study (Lorch et al., 2000). The script was divided into 407 individual units of meaning, with each unit representing a single idea or event. In accordance with procedures defined by Trabasso and van den Broek (1985), a causal network representation of the story was derived. On the basis of the causal network analysis, each idea unit was coded as to whether it was on or off the causal chain. The causal chain is a sequence of events that are causally linked together and carry the story from the beginning to the end. All story events are either part of the causal chain (i.e., causally connected to prior and subsequent events) or dead-end events (i.e., not causally connected to prior and subsequent events on the causal chain). Each idea unit also had been rated by 58 college students for its centrality to the story, on a scale from 1 to 7. The students first watched the program and then were given scripts representing the individual units in the program. In assigning their ratings, they were instructed to consider how much plot-relevant information the unit conveyed, how much would be lost if the unit were removed from the program, and how much the unit enhanced understanding of the plot. Mean centrality ratings were calculated across all students for each idea unit. For the purposes of the present study, an individual unit was considered to be central if it both was on the causal chain and had a mean centrality rating of at least 5.15 out of 7, the upper quartile of centrality ratings. An individual unit was considered to be incidental if it both was off the causal chain (a dead-end event) and had a mean centrality rating of no more than 3.30, the lower quartile of ratings. Given these classifications, nine continuous sequences of primarily central events and nine continuous sequences of primarily incidental events were identified. A description of each sequence and its order of appearance in the Growing Pains episode appears in the Appendix (available on the Web). The length of the sequences varied from 15 to 90 s, and sequences contained between 4 and 30 idea units. In central sequences, the mean centrality rating of the units was 5.53, 77% of the individual units were in the upper quartile of the ratings, and 72.4% of the individual units were on the causal chain. In incidental sequences, the mean centrality rating of the units was 2.88, 85% of the individual units were in the lower quartile of the ratings, and none of the individual units were on the causal chain. In general, individual units in either type of sequence that did not meet strict criteria for the sequence tended to be brief and isolated. To track online variations in children’s cognitive engagement with the television story, we placed 26 auditory target probes at preselected points during these sequences of story events. The basic positions for target probes were approximately 2, 7, or 12 s into the sequence.1 In addition, in
1
The nine central and nine incidental sequences exhausted all possible sequences of story events that met criteria for classification as central or incidental and continued for a minimum of 15 s. Fifteen seconds was chosen as the minimum because this is the asymptote of the attentional inertia function (Anderson & Lorch, 1983). The basic probe positions of 2, 7, and 12 s were chosen to represent early, middle, and later positions within the 15-s interval. The number of later probes was constrained by the number of sequences longer than 24 s.
COGNITIVE ENGAGEMENT WITH TELEVISION sequences lasting 24 s or longer, later probe positions were added to the basic probes. We created three series of probe assignments to counterbalance probe position across sequences. Each participant was randomly assigned to one of these three series. Each series contained 3 probes in each basic position (i.e., 2, 7, and 12 s), at each centrality level, and 4 probes in the later positions during sequences longer than 24 s, for a total of 13 central and 13 incidental target probes. We inserted 12 filler probes in the program to prevent participants from detecting a pattern or being able to predict probes and make anticipatory responses, which resulted in a total of 38 probes during the episode. A set of comprehension questions was developed for the television program. The questions tested factual information that was presented in the program during sequences containing probes. Discrete events that occurred close in time to a secondary task probe were chosen to be tested, and straightforward questions were designed to test children’s memory for each event. An example question testing memory for a central story event (see Appendix, available on the Web) is “Ben has to return the camera. What else does he have to do as part of his punishment?” (return money and apologize to neighbors). Twenty-five cued recall questions were developed; 14 tested central content, and 11 tested incidental content. Every sequence was represented by at least 1 question, with two sequences represented by 2 questions, two represented by 3 questions, and two represented by 4 questions.
Procedure The participants were brought to the on-campus television viewing facility by a parent. Each child participated individually. Before the experiment began, informed consent was obtained from the parent. After a brief conversation with the experimenter to help the child feel comfortable (e.g., talking to the child about school, activities, pets), the child was shown to his or her seat in the television viewing room. The child was seated at a table, with a computer keyboard on the table in front of him or her. The probes were generated by an IBM computer that was located on the floor next to the child. Responses were made on the keyboard on the table in front of the child. RTs for each probe were recorded (in milliseconds) from the beginning of the probe until the child responded by pressing the space bar. The probe continued until a response was made. If no response was made, the sound ended after 5 s. A television was situated on a desk near one corner of the table, with the television screen about 5 ft (1.5 m) from the child’s seat. A video camera was mounted on the wall in a position that allowed the image to include both the child and the television. For the child to look at the television, he or she needed to make a noticeable head movement. This enabled the experimenter to record looks toward and away from the television. Each child was videotaped during the entire experimental session, and visual attention to the television was later coded from the videotape. After the child was seated in the room, the experimenter obtained his or her verbal assent to participate in the study. Once the child agreed to participate, the experimenter explained the procedure to the child, emphasizing that watching the television program was the primary task and responding to the probe was the secondary task. It was explained to the participants that they would hear sounds that they should turn off as quickly as possible by pressing the space bar on a computer keyboard located on the table in front of them. Participants were instructed to keep their dominant hand directly in front of the computer keyboard throughout the entire television program. To help the child do this, the experimenter placed a loose Velcro strap across the child’s wrist. After the procedure was explained, the child was given an opportunity to practice. A computer program presented five probes that were randomly spaced throughout a 2-min period. Each child pressed the space bar in response to every practice probe. It was again emphasized to the child that watching and understanding the program was the primary task, and each child was told that after the program ended there would be some questions about the television show.
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After the child indicated verbally that he or she understood the instructions and had satisfactorily completed the practice probe program, the experimental session began. The experimenter started the videotape that played the television show and synchronized the probe program to the tape before leaving the room. When the television program was finished, the experimenter reentered the room and began the cued recall questioning session. The testing session was videotaped to allow later scoring of cued recall questions. The questions were asked in the order in which the content was presented in the show. If the child did not answer a question correctly, the experimenter supplied the correct answer and then continued to the next question until all questions were asked. The child was then debriefed, paid, and thanked for his or her participation.
Results and Discussion Dependent Measures The main dependent variable was RT to 26 target probes. Thirty-eight probes sounded during the program. Thirteen target probes occurred during sequences of central content, and 13 occurred during sequences of incidental content. The remaining probes were fillers and were not analyzed. Two participants’ RT data included one impossibly fast RT (32 ms for 1 child, 112 ms for the other). These responses were not included in the calculation of means for the categories. Two participants failed to respond to 1 probe. We obtained category means for these participants by computing means of the remaining RTs in the category. Performance on the cued recall questions was scored from transcripts of the videotape. Each answer was assigned a score of 1 for correct and 0 for incorrect. An independent rater scored 25% of participants’ cued recall responses, with 92% agreement. The proportion correct was computed for each participant separately for central and incidental questions. Participants’ visual attention to the television was coded from videotapes. Using a computer program synchronized to the beginning of the television program, we obtained a continuous record of the child’s looks at and away from the television. This continuous record of looks allowed for identification of looking status at the time of every probe as either on or off task. The mean percentage of visual attention was at ceiling (M ⫽ 0.96, SD ⫽ 0.04). RT data were analyzed after removal of all probe RTs that occurred during a look away from the television (n ⫽ 51, less than 5% of target probes). The pattern of results is identical with these RTs included, but analyses reported in this article exclude RTs to probes that sounded during looks away from the television (Lorch & Castle, 1997).
RTs Mean response times were analyzed in a repeated measures analysis of variance (ANOVA), with centrality (central and incidental) and probe position (2, 7, 12 s into sequence, and later position) as within-subject variables. Follow-up linear trend analyses tested the a priori prediction that RTs to probes would increase as the time into a sequence of central content increased. In addition, we tested whether RTs to probes would decrease as time into a sequence of incidental content increased. Mean RTs as a function of centrality and probe position are depicted in Figure 1. As hypothesized, a significant interaction was observed between centrality and probe position, F(3, 171) ⫽ 3.32, p ⬍ .022. Linear
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RT in milliseconds
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Central
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Incidental
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600
550 2
7
12
Later
Probe Position (Seconds into Sequence)
Figure 1. Mean probe response times (RTs) as a function of time into central and incidental sequences for Experiment 1.
trend analyses indicated that RTs to probes occurring during central sequences increased as time into the sequences increased, F(1, 57) ⫽ 4.30, p ⬍ .05 (effect size r ⫽ .26), and RTs to probes occurring during incidental sequences decreased as time into the sequences increased, F(1, 57) ⫽ 6.16, p ⬍ .05 (r ⫽ .31). Follow-up analyses controlling family-wise error rate indicated that the mean RT in the central, 2-s position was significantly shorter than the central, later position RT, t(58) ⫽ 2.36, p ⬍ .05 (r ⫽ .30). The incidental, 2-s position RT was significantly slower than the incidental, later position RT, t(58) ⫽ 2.29, p ⬍ .05 (r ⫽ .29). Unexpectedly, children were significantly slower in responding to probes that sounded during incidental sequences (M ⫽ 612.73) than to probes during central sequences (M ⫽ 572.15), F(1, 57) ⫽ 9.84, p ⬍ .004 (r ⫽ .38). No main effect was observed for probe position (F ⬍ 1, p ⬎ .05).
Cued Recall Performance We conducted a t test on cued recall scores to evaluate differences between performance on central and incidental content. Performance on the cued recall questions was significantly better for those questions tapping central content (M ⫽ 0.85, SD ⫽ 0.11) than for those tapping incidental content (M ⫽ 0.61, SD ⫽ 0.18), t(57) ⫽ 12.05, p ⬍ .001 (r ⫽ .84). This result replicates the pattern observed in numerous previous studies of children’s comprehension of both televised and written stories (e.g., Lorch, Bellack, & Augsbach, 1987; van den Broek, 1989; van den Broek et al., 1996). A further question concerns the possibility of a relation between cued recall performance and RTs to secondary probes. If children’s cognitive engagement with the televised story varies systemati-
cally, one might expect that recall performance would be related to differing levels of engagement. Each cued recall question tested information that was presented during or close to a unit that contained a probe. Mean RTs were computed for probes corresponding to questions that each child answered correctly and questions answered incorrectly. Regardless of centrality, children’s RTs corresponding to questions they answered correctly tended to be longer (M ⫽ 601.77) than their RTs corresponding to questions they answered incorrectly (M ⫽ 587.83), F(1, 48) ⫽ 3.71, p ⫽ .06 (r ⫽ .27). This suggests an association between online cognitive engagement with the televised story and later recall of specific material. As predicted, 9- to 11-year-old children’s cognitive engagement was related to the continuity of central or incidental content. Children’s probe RTs indicated that the longer a sequence of central story content continued, the more engaged children became, and that as a sequence of incidental content continued, they became less engaged. Children’s increased engagement was also related to better performance on story comprehension measures. Further discussion of the results from all three experiments is reserved for the general discussion.
Experiment 2 Experiment 1 examined cognitive engagement in children ages 9 to 11 years, but an unanswered question is whether younger school-aged children would also use the causal structure of a televised story to guide their engagement. Memory for story events among preschoolers and young school-age children is influenced by characteristics of the story’s causal structure, but the impact of
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causal factors increases with age (van den Broek et al., 1996). Children also show improvement with age in detecting connections between groups of events (van den Broek, 1989). In addition, young children show differential responses to global story structure, showing increased attentional engagement as normal stories develop but no systematic change in engagement to scrambled stories (Lorch & Castle, 1997). However, it is unknown whether younger children would show online sensitivity to the role different events play in a coherent story structure. Huston and Wright’s (1983) theoretical perspective suggests that with development, children may become more accomplished at using the story content to determine when deeper and more elaborative processing is necessary. Thus, Experiment 2 was designed to replicate Experiment 1 and to determine whether there are developmental differences in children’s cognitive engagement as a function of the continuity of central and incidental content. On the basis of the results of Experiment 1, it was predicted that older school-aged children would show decreasing engagement with the television as incidental content continued and increasing engagement as central content continued. Children in the younger age group may be more dependent on formal features of the television program as a guide to their attention, so their engagement may not be as sensitive to the causal structure of the story as that of older children.
Method Participants Participants were 54 younger elementary school-aged children (25 boys), ages 6 to 8 (M ⫽ 7.83, SD ⫽ 0.64), and 57 older elementary school-aged children (31 boys), ages 9 to 11 (M ⫽ 10.70, SD ⫽ 0.56), none of whom had participated in Experiment 1. Participants were primarily Caucasian (89.52%), with some African American children (5.71%) and children of other ethnic identification (4.76%). Most parents’ education included at least some college; the average parent was a college graduate (M ⫽ 16.31 years of education). Children were recruited through advertisements in the newspaper and from an existing pool of experimental volunteers. Children were paid $10 for their participation, which lasted about 1 hr. Data were lost from 6 participants as a result of equipment malfunction or failure to follow instructions.
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whether the sequence presented central or incidental content, F(3, 306) ⫽ 9.116, p ⬍ .001. As shown in Figure 2, linear trend analyses revealed that RTs increased the longer a sequence of central content continued, F(1, 104) ⫽ 34.34, p ⬍ .001 (r ⫽ .51), and that RTs decreased the longer a sequence of incidental content continued, F(1, 104) ⫽ 11.19, p ⬍ .001 (r ⫽ .31). Follow-up analyses indicated that for central sequences, RTs for each of the first three probe positions differed significantly from RTs for the later probe position, and RTs for the 2-s position differed from those for the 7-s position (ts ranged from 2.5 to 5.2, p ⬍ .05). For the incidental sequences, only the 12-s and later probe positions differed significantly, t(104) ⫽ 2.4, p ⬍ .05 (r ⫽ .23). Although older children responded more quickly (M ⫽ 601.52) to probes than younger children (M ⫽ 836.48), F(1, 102) ⫽ 36.47, p ⬍ .001 (r ⫽ .51), there were no significant differences in the patterns of probe RTs as a function of age. As found in Experiment 1, probe RTs were longer during incidental sequences (M ⫽ 741.99) than during central sequences of content (M ⫽ 689.58), F(1, 102) ⫽ 10.95, p ⬍ .001 (r ⫽ .31).
Cued Recall Performance Children correctly answered a higher proportion of questions testing central content (M ⫽ .81) than questions testing incidental content (M ⫽ .47), F(1, 103) ⫽ 474.85, p ⬍ .001 (r ⫽ .90). Although older children (M ⫽ .73) correctly answered a higher proportion of questions than younger children (M ⫽ .58), F(1, 103) ⫽ 48.01, p ⬍ .001 (r ⫽ .56), the influence of centrality on recall performance did not differ as a function of age, F(1, 103) ⬍ 1. We also examined whether children’s recall performance was related to differing levels of engagement. Overall, central content questions that were answered correctly were associated with longer RTs to probes during presentation of target content (M ⫽ 722.8 ms) than RTs to probes associated with central content questions that were answered incorrectly (M ⫽ 673.4 ms), F(1, 88) ⫽ 9.63, p ⬍ .01 (r ⫽ .31). This difference was significant for older children, t(41) ⫽ 3.70, p ⬍ .001 (r ⫽ .55), but not for younger children, t(47) ⫽ 0.90, p ⬎ .10.
Materials and Procedure The materials and procedure were identical to those of Experiment 1.
Results and Discussion Probe RTs Mean RTs were analyzed in a mixed ANOVA, with age as a between-subjects variable and centrality (central and incidental) and probe position (2, 7, 12 s into sequence, and later position) as within-subject variables. On the basis of the results for Experiment 1, follow-up linear trend analyses tested the a priori prediction that RTs to probes would increase as the time into a sequence of central content increased, whereas RTs to probes would decrease as the time into a sequence of incidental content increased. More children in the younger group (n ⫽ 11) than in the older group (n ⫽ 1) failed to respond to at least one probe, 2(1, N ⫽ 111) ⫽ 47.34, p ⬍ .01. Mean probe RTs were calculated from the remaining RTs. As hypothesized, children’s RTs to secondary probes varied jointly as a function of when in a sequence a probe occurred and
Experiment 3 The findings of Experiments 1 and 2 demonstrate that online variations in children’s cognitive engagement with a televised story are related to the continuity of central or incidental content. As indicated by probe RTs, children became increasingly engaged the longer sequences of central events continued but decreased their cognitive engagement the longer sequences of incidental events continued. Furthermore, this pattern of results was consistent from the ages of 6 to 11. These findings indicate that even the younger children were sensitive to the causal structure of the story, using it to guide their allocation of cognitive resources. Given the reliable findings obtained for comparison children in Experiments 1 and 2, Experiment 3 was designed to examine the cognitive engagement of children with ADHD, whose problems in sustaining attention and comprehending causal relations have been well documented (Lorch, Eastham, et al., 2004). As discussed earlier, two studies have addressed the issue of cognitive engagement among children with ADHD. The results of
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RT in milliseconds
1000
900
800
700
600
500 2
7
12
Later
Probe Position (Seconds into Sequence) Central
Incidental
Figure 2. Mean probe response times (RTs) as a function of time into central and incidental sequences for Experiment 2.
Lorch, Eastham, et al. (2004) suggest that variations in cognitive engagement, as operationalized by time spent in long looks, account for the problems that children with ADHD have with causal relations questions. However, the degree of cognitive engagement was inferred from time spent in long looks. By using the secondary task implemented in Experiments 1 and 2, Whirley et al. (2003) offered a more direct assessment of cognitive engagement among 9- to 11-year-old boys with ADHD. The boys with ADHD failed to show the same systematic increase in cognitive engagement observed in comparison boys as central sequences progressed. Experiment 3 was designed to replicate and extend the findings of Experiments 1 and 2 and from Whirley et al. (2003). In Experiment 3, we use a developmental perspective to compare the cognitive engagement of children with ADHD and their comparison peers, using an age range that includes younger children (i.e., 4 to 9 years) than the previous secondary task studies. Lorch and Castle (1997), using the secondary task, found that 5-year-olds demonstrated variations in cognitive engagement as a function of dramatic differences in the comprehensibility of television programming. However, in Experiment 3, like Experiments 1 and 2, the task used investigates whether children make changes in their cognitive engagement as a sequence of central or incidental events develops. Thus, Experiment 3 integrates the developmental perspective offered by Experiments 1 and 2 with the questions raised by Whirley et al. concerning children with ADHD. A second purpose of Experiment 3 was to assess children’s metacognitive understanding of the importance of events to the plot of the story. The secondary task is designed to tap momentto-moment variations in cognitive engagement in relation to the centrality of the events. In contrast, a metacognitive understanding refers to whether, after viewing the program, children are able to make explicit differentiations among events in terms of their
importance to the overall story. To accomplish this purpose, we asked the children in Experiment 3 to sort pictures of story events into categories of low, medium, or high importance, and we compared age and diagnostic group differences in sorting accuracy. In addition, we assessed the role of sorting accuracy in accounting both for moment-to-moment engagement on the secondary task and for the postviewing measure of story recall.
Method Participants This study was part of a larger longitudinal project designed to examine story comprehension and its relation to attention among children with ADHD. Although the original sample included 193 children, data for 28 children had to be eliminated. This resulted in a final sample of 64 children (49 boys, 15 girls) with a confirmed diagnosis of ADHD and 101 comparison children (61 boys, 40 girls), ranging in age from 4.0 to 9.9 years (M ⫽ 7.5 years). Each group of children was further classified into two age groups, a group of 64 younger children (between 4 and 7 years 0 months of age; M ⫽ 5.75 years), and a group of 101 older children (between 7 years 1 month and 9 years 11 months of age; M ⫽ 8.5 years). To ensure an accurate diagnosis of ADHD, we used a three-step process in the recruitment of children assigned to the ADHD group. First, all children assigned to this group were referred from medical care settings where, independently of the current study, they had received a diagnosis of ADHD/combined type according to criteria in the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; American Psychiatric Association, 2000). Second, referred children’s medical records were reviewed before they were admitted into the study. This review focused on information regarding the children’s behavior, intellectual and academic functioning, medical history, age of diagnosis, and other relevant issues. Children were excluded from the current study if information obtained during the review described a symptom picture inconsistent with ADHD/
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Table 1 Comparison of Two Diagnostic Groups in Experiment 3 on Relevant Demographic Variables ADHD (n ⫽ 64) Factor Age in years Younger group Older group Mother’s education Father’s education DSM–IV–TR Inattention Hyp/imp Oppositionality
Comparison (n ⫽ 101)
M
SD
M
SD
t(162)
p
5.88 8.61 14.02 14.09
0.85 0.96 2.20 3.38
5.72 8.48 15.63 16.30
0.76 0.85 2.25 3.05
0.82 0.72 4.44 4.18
.417 .471 .001 .001
6.11 6.19 3.49
2.21 1.98 2.31
0.16 0.18 0.27
0.46 0.48 0.71
26.23 29.20 13.69
.001 .001 .001
Note. ADHD ⫽ attention-deficit/hyperactivity disorder; DSM–IV–TR ⫽ Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.); Hyp/imp ⫽ hyperactivity/impulsivity.
combined type, if their IQ score was less than 70, or if they were taking medications that could not be discontinued for the study. The mere presence of comorbid diagnoses (e.g., oppositional defiant disorder or conduct disorder) was not cause for exclusion from the study. Children whose principal ADHD symptoms related to inattention were excluded from this study because of growing empirical evidence suggesting that children with attentional difficulties in the absence of hyperactivity and impulsivity may best be classified as suffering from a distinct disorder that is not a subtype of ADHD (Milich, Balentine, & Lynam, 2001). At a final step, the diagnosis of ADHD/combined type was confirmed by research staff using a semistructured parent interview designed to assess the presence of ADHD or oppositional defiant disorder according to DSM–IV criteria. This same interview has been used successfully for the classification of children with ADHD in previous studies (Lorch et al., 2000; Lorch, Eastham, et al., 2004; Whirley et al., 2003). An earlier study found the interrater reliability for number of ADHD symptoms endorsed in the parent interviews to be 99% (Lorch et al., 1999). Children were not assigned to the ADHD group unless this interview confirmed a diagnosis of ADHD/combined type. Children taking stimulant medication did not receive their medication on the day of the study. Children in the comparison group were recruited through newspaper advertisements and flyers. Children were excluded from participation in this group if data gathered through a parent interview and the Child Behavior Checklist (Achenbach, 1991) suggested the presence of any behavior or learning disorders. Once admitted into the study, parents had to complete a semistructured parent interview designed to assess the presence of ADHD or oppositional defiant disorder according to DSM–IV criteria. Children who met three or more criteria for inattention or hyperactivity were not included in the final analyses. Demographic characteristics for each group of children can be found in Table 1. Children in the comparison group averaged less than one symptom of ADHD. By comparison, children in the ADHD group averaged 15.84 symptoms, a difference that was statistically significant, t(162) ⫽ 33.04, p ⬍ .001 (r ⫽ .93). There were no group differences in terms of age, t(162) ⫽ 0.587, p ⬎ .10, but the groups did differ significantly in terms of mothers’ and fathers’ average years of education.2
Procedure The materials and procedure were identical to those of Experiments 1 and 2, except for the following three changes. First, because the children were participating in a longitudinal investigation in which they would repeat the secondary probe task at a later time period, two different Growing Pains episodes were used. One episode was the same as was used
in Experiments 1 and 2; the second one was newly prepared for this study, via the same procedures to identify appropriate probe positions in central and incidental sequences. Similarly, appropriate cued recall questions were generated for content that was high or low in importance. The second subsequent change in the procedures from the prior experiments was that the auditory probes placed 7 s into the sequences were deleted, so that auditory probes only were presented at 2, 12, or 24 s or later into a sequence. This change was made to streamline the procedure and because in Whirley et al. (2003) the 7-s probe did not supply unique information. For both importance levels (i.e., central and incidental), each series contained between 4 and 5 probes in each basic position, resulting in a total of 13 probes for both the central and the incidental sequences. As in Experiments 1 and 2, each probe continued until a response was made. However, because of the younger age of the sample in Experiment 3, the sound ended after 7 s if no response was made. Twelve filler probes were inserted in the program, so that each episode of the show consisted of 38 probes, including 13 during presentation of central content, 13 during incidental content, and 12 filler probes that were not analyzed. The third change in the experimental protocol was to include a sorting task after completion of the probe task. The sorting task provided a measure of understanding of the importance of story events. The task consisted of 12 pictures: 4 depicting very important events from the story, 4 depicting medium important events, and 4 depicting not important events. Selection of the 12 story events was based on the adult ratings of importance of story events, described earlier. Each event was described in a caption at the bottom of the picture. The examiner read each caption while showing the card to the child. As each card was read, it was placed on the table in front of the child. Under each of the three category labels (i.e., very important, medium important, not important) was placed a small grid, which was segmented into four spaces, each the size of a picture card. The three categories were carefully explained to the child. The child was then asked to place the picture cards under the appropriate category heading. Only four events could be placed in each category. The child was allowed to ask for any event description to be repeated and could make as many changes to the categorizations as desired.
2 Because of the group differences in parent education, analyses were repeated with mothers’ years of education as a covariate. The pattern of results was unchanged with the covariate included. Similar analyses were not undertaken for fathers’ years of education because of a higher frequency of missing data on this variable. However, the high correlation between mothers’ and fathers’ years of education (r ⫽ .89) suggests that the pattern of results would be the same.
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There were three dependent variables concerning importance judgments in the sorting task. The measure of gross errors included only those very important events the child placed in the not important pile or vice versa. The number of seconds to complete the sorting task and the total number of moves made in completing the sorting task were measures of impulsivity and of how effectively the child planned and executed sorting categorizations. These are considered core symptoms of ADHD, so these measures were included to determine whether such difficulties explained any group differences in sorting errors.
Results and Discussion The main dependent variable for the secondary task was RTs to 26 target probes. Data for children were not included in the final analyses unless the children had responded to at least two of the probes for any given probe position. Ten children were excluded because they responded to too few probes. Five of these children were in the ADHD group, whereas 5 were comparison participants, 2(1, N ⫽ 175) ⬍ 1, p ⬎ .10. Because of equipment malfunction, data for 18 children (10 comparison, 8 with ADHD) were not used. Excluding these 28 children left the final sample of 64 children in the ADHD group and 101 children in the comparison group.
Probe RTs To explore the patterns of engagement for children with ADHD versus comparison children, we performed a 2 ⫻ 2 ⫻ 2 ⫻ 3 mixed ANOVA. Diagnostic group (comparison and ADHD) and age group (younger and older) were the between-subjects variables, and sequence type (central vs. incidental) and probe position (2, 12, and 24 s and beyond into sequence) were the within-subject variables. Given the significant Centrality ⫻ Probe Position interactions found in Experiments 1 and 2, the primary effect of interest in Experiment 3 was the Diagnostic Group ⫻ Centrality ⫻ Probe
Position interaction, F(2, 161) ⫽ 3.5, p ⬍ .05. Because this interaction was significant and different patterns have been found for central and incidental sequences (Experiments 1 and 2; Whirley et al., 2003), the following results are presented separately for the two types of sequences. Central sequences. For central sequences, a significant main effect of probe position, F(2, 161) ⫽ 13.36, p ⬍ .001, was qualified by a significant Diagnostic Group ⫻ Probe Position interaction, F(2, 161) ⫽ 11.19, p ⬍ .001 (see Figure 3). Linear trend analysis of this interaction indicated that RTs to probes for comparison children increased as central content continued, F(1, 100) ⫽ 38.70, p ⬍ .001 (r ⫽ .53). Follow-up analyses revealed that RTs to the later probes were significantly longer than both the 2-s probes, t(100) ⫽ 6.22, p ⬍ .01 (r ⫽ .53), and the 12-s probes, t(100) ⫽ 5.08, p ⬍ .01 (r ⫽ .21). In contrast to the pattern for the comparison children, linear trend analysis of the RTs of children with ADHD demonstrated no significant change as central sequences progressed, F(1, 63) ⬍ 1. Younger children were slower to respond to probes than older children, F(1, 161) ⫽ 10.66, p ⬍ .001 (r ⫽ .25), but this main effect was qualified by a significant Diagnostic Group ⫻ Age Group interaction, F(1, 161) ⫽ 4.59, p ⬍ .05 (r ⫽ .17). Younger comparison children were significantly slower than their older counterparts, F(1, 99) ⫽ 24.18, p ⬍ .001 (r ⫽ .44), but younger and older children with ADHD did not differ in overall RT, F(1, 62) ⬍ 1. Incidental sequences. For incidental sequences, there was a main effect of diagnostic group, such that children with ADHD (M ⫽ 911 ms) showed significantly longer RTs than comparison children (M ⫽ 794 ms) to probes presented during incidental sequences, F(1, 161) ⫽ 5.75, p ⬍ .05 (r ⫽ .19). Younger children (M ⫽ 948 ms) responded significantly slower than their older counterparts (M ⫽ 757 ms), F(1, 161) ⫽ 15.03, p ⬍ .001 (r ⫽ .29).
1100
RT in milliseconds
1000 900 800 700 600 500 2
Later
12 Probe Position (Seconds into Sequence) ADHD, Central
Comparison, Central
ADHD, Incidental
Comparison, Incidental
Figure 3. Mean probe response times (RTs) as a function of time into central and incidental sequences for children with attention-deficit/hyperactivity disorder (ADHD) and nonreferred children for Experiment 3.
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There were no significant interactions for incidental sequences (see Figure 3).3
Cued Recall Performance Percentage correct on cued recall questions was analyzed in a 2 ⫻ 2 ⫻ 2 mixed ANOVA, with diagnostic group and age group as between-subjects variables and centrality as a within-subject variable. Significant main effects of diagnostic group, F(1, 161) ⫽ 6.05 p ⬍ .05 (r ⫽ .19); age group, F(1, 161) ⫽ 127.49, p ⬍ .001 (r ⫽ .66); and centrality, F(1, 161) ⫽ 87.98, p ⬍ .001 (r ⫽ .59), were qualified by significant Diagnostic Group ⫻ Age Group, F(1, 161) ⫽ 5.18, p ⬍ .05 (r ⫽ .18), and Centrality ⫻ Age Group, F(1, 161) ⫽ 7.48, p ⬍ .01 (r ⫽ .21), interactions. Performance of older comparison children (M ⫽ 55% correct) was significantly better than that of the older children with ADHD (M ⫽ 44%), F(1, 99) ⫽ 13.58, p ⬍ .001 (r ⫽ .35). Younger comparison children (M ⫽ 23%) and younger children with ADHD (M ⫽ 23%) did not differ in their cued recall performance, F(1, 62) ⬍ 1. Children correctly answered significantly more questions testing central content (M ⫽ 43%) than testing incidental content (M ⫽ 28%), and this was true for both the younger, t(63) ⫽ 4.3, p ⬍ .001 (r ⫽ .48), and the older, t(100) ⫽ 10.2, p ⬍ .001 (r ⫽ .71), children. However, the difference in recall of central and incidental content was larger for older children (mean difference ⫽ 20%) than for younger children (mean difference ⫽ 11%), t(163) ⫽ 2.84, p ⬍ .005 (r ⫽ .22). As in Experiments 1 and 2, we examined whether recall performance was related to differing levels of engagement. Mean RTs were computed for probes corresponding to questions that each child answered correctly and questions each child answered incorrectly, and this variable was found to interact with age group, F(1, 145) ⫽ 3.75, p ⫽ .055 (r ⫽ .16), but not with diagnostic group, F(1, 145) ⬍ 1. Regardless of centrality, older children’s RTs corresponding to questions they answered correctly (M ⫽ 798 ms) tended to be longer than their RTs corresponding to questions they answered incorrectly (M ⫽ 754 ms), t(99) ⫽ 1.86, p ⫽ .067 (r ⫽ .14), but there was no significant difference for younger children (Ms ⫽ 932 and 954 ms, respectively), t(48) ⬍ 1. This suggests that for older children, increased online cognitive engagement was associated with greater recall of story events.
Importance Judgments Three dependent variables concerning importance judgments in the sorting task were analyzed in 2 ⫻ 2 ANOVAs, with diagnostic group and age group as between-subjects variables. The three dependent variables were (a) gross errors, which involved placing low-importance pictures in the high-importance category, or vice versa; (b) number of seconds to complete the sorting task; and (c) number of moves made in completing the sorting task. For gross errors, there were significant main effects of diagnostic group, F(1, 161) ⫽ 9.01, p ⬍ .01 (r ⫽ .23), and age group, F(1,161) ⫽ 44.91, p ⬍ .001 (r ⫽ .47). Children with ADHD (M ⫽ 2.5) made more errors than comparison children (M ⫽ 1.9), and younger children (M ⫽ 2.9) made more errors than older children (M ⫽ 1.5). To determine whether problems in impulsivity or planning accounted for poorer performance among children with ADHD, we analyzed average sorting time and number of sorting
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moves. There were no main effects or interactions involving diagnostic group for either measure. The only significant effect was that younger children (M ⫽ 13.2) made significantly fewer moves than older children (M ⫽ 14.2), F(1, 159) ⫽ 4.56, p ⬍ .05 (r ⫽ .17).
Importance Judgments in Relation to Cognitive Engagement and Story Recall We examined the extent to which importance judgments, as indexed by gross errors, accounted for group differences in the pattern of cognitive engagement and in recall performance by entering gross errors as a covariate in the original ANOVAs. In terms of the pattern of cognitive engagement, gross errors did not significantly relate to RT, F(1, 160) ⫽ 2.05, p ⬎ .10, nor did any of the significant main effects or interactions change. However, gross errors did significantly predict cued recall performance, F(1, 160) ⫽ 17.61, p ⬍ .001 (r ⫽ .31), and the main effect of diagnostic group was no longer significant, F(1, 160) ⫽ 2.39, p ⬎ .10.
General Discussion The findings of these three investigations demonstrate that online variations in comparison children’s cognitive engagement with a televised story were related to the continuity of central content, and this conclusion held true for children ranging in age from 4 to 11. As indicated by probe RTs, comparison children became increasingly engaged the longer sequences of central events continued. In contrast, 4- to 9-year-old children with ADHD showed no change in cognitive engagement as central sequences progressed. In the discussion that follows, we first examine the general implications of these findings, then address developmental interpretations and consider specific ways the performance of children with ADHD differs from the patterns obtained for comparison children. The pattern of changes in cognitive engagement is consistent with earlier findings suggesting that children’s attention becomes more engaged as a coherent story segment develops (Hawkins et al., 1991; Lorch & Castle, 1997; Meadowcroft & Reeves, 1989). It is notable that the earlier studies examined relatively gross differences in story structure (i.e., events in a meaningful order vs. events in a scrambled, nonsensical order). The current study, however, provides evidence of systematic changes in school-age children’s cognitive engagement in response to more subtle differences in the meaning of events. In the stories used in these experiments, all events occurred in a sensible order. However, as 3
Although children in both diagnostic groups demonstrated high rates of visual attention to the television, comparison children (M ⫽ 96%) were more likely than children with ADHD (M ⫽ 90%) to be attending to the television when target probes were presented, t(163) ⫽ 4.11, p ⬍ .001. Therefore, we repeated the analyses, excluding probes when children were not attending to the television. This required dropping 10 additional children (3 comparison, 7 with ADHD) from the analyses, because not all cells had a sufficient number of probes. Despite the loss of power, the pattern of RT results remained the same, with a significant Group ⫻ Linear Probe Position interaction for central sequences, F(1, 151) ⫽ 11.40, p ⬍ .001, but the interaction failed to reach significance for incidental sequences, F(1, 151) ⫽ 3.36, p ⬎ .05.
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central sequences progressed, more content connected to the plot was provided, eliciting greater cognitive engagement from children. In contrast, as incidental sequences progressed, plot development was not enhanced, leading either to decreased cognitive engagement (Experiments 1 and 2) or to no systematic change in engagement (Experiment 3; Whirley et al., 2003). The current findings are consistent with the notion that, during viewing, children engage in the online construction of a story representation (Trabasso & Nickels, 1992; Trabasso et al., 1992). Sequences of central events consist primarily of events that are part of the causal chain defining the plot of the story and are perceived as very important by adult viewers. As such, the events in central sequences figure importantly in a coherent representation of the story. As children view the program and a sequence of central content continues, they encounter events that can be connected to the main thread of the story. In contrast, incidental events are not part of the causal chain and are considered generally unimportant by adult viewers. As a sequence of incidental events continues, children encounter events that cannot be linked to the plot of the story. The lack of connections to the causal chain of the story leads children to stop at more superficial processing; thus, they do not need to increase allocation of attention to events presented later in incidental sequences. These systematic changes in children’s cognitive engagement indicate that they are building a coherent representation during viewing that reflects the causal structure of the story. This online construction of a story representation also is consistent with Huston and Wright’s (1983) proposal that children may engage in cycles of decisions concerning whether to perform deeper and more elaborative processing of material. Huston and Wright’s theory was designed to predict patterns of visual attention to television under conditions that enable children to engage in alternative activities (e.g., toy play). Under such conditions, visual attention is predicted by formal and content characteristics that relate to children’s comprehension (Alwitt et al., 1980; Campbell et al., 1987). Thus, visual attention provides one indication of children’s cognitive processing during viewing. However, the task conditions of the current study elicited consistently high levels of visual attention, even from younger children. RTs to secondary probes suggest that even while children maintained visual attention, their decisions to engage in deeper processing were influenced by the centrality and continuity of content. These findings indicate that the secondary task procedure can serve as a valuable technique for revealing subtle variations in children’s cognitive engagement. The clearest indication of systematic change in cognitive engagement as a function of time into a sequence is observed when RTs to the later probes are considered. These later probes occurred after a sequence of central content had been in progress for at least 24 s. The strength of these effects for later probes suggests a conceptual parallel with the phenomenon of attentional inertia (Anderson, Choi, & Lorch, 1987). Attentional inertia refers to the observation that the longer a look at the television has been in progress, the higher is the probability that the look will continue, with this function leveling off at approximately 15 s into a look. Of special relevance to the current study, converging evidence from several measures indicates that cognitive engagement is greater if a look has continued for at least 15 s than it is during a shorter look (Anderson et al., 1987; Burns & Anderson, 1993; Lorch & Castle,
1997). Similar to the increase in cognitive engagement for very long looks, in the current study the continuation of central content also promoted increased cognitive engagement with the story, even though children maintained high levels of visual attention throughout the session. Thus, taken together, these findings highlight the importance of long sequences on level of cognitive engagement for both long looks and long sequences of a particular type of content. Consistent with other investigations (Lorch et al., 1987; Trabasso et al., 1984; van den Broek, 1989; van den Broek et al., 1996), children’s memory for central events was superior to their memory for incidental events. Thus, memory for story events reflects the causal structure of the story. Part of this effect may be due to postviewing processes of structuring the story as content is retrieved during the recall task. However, the current results suggest that children’s systematic increases or decreases in their online allocation of resources also may contribute to recall differences. Consistent with this interpretation, children tended to show greater cognitive engagement with events that they later recalled at a higher rate. This was true for central sequences across all three experiments and for incidental sequences in Experiments 1 and 3. Similar to these findings, Britton, Piha, Davis, and Wehausen (1978) provided evidence that learning from text was related to adult readers’ probe RTs.
Developmental Implications One goal of the current investigation was to examine whether older and younger elementary school children would differ in their patterns of cognitive engagement as a function of the centrality and continuity of story content. Overall, children in both age groups showed similar systematic variations in cognitive engagement. These findings indicate that even the younger children were sensitive to the causal structure of the story, using it to guide their allocation of cognitive resources. Despite these similar patterns of engagement for the two age groups, several results suggest that older children were more effective cognitive processors in their story comprehension and allocation of resources. First and not surprising, the older children were faster to respond to probes and answered more cued recall questions correctly. Second, only older children in Experiments 2 and 3 demonstrated the relation between level of engagement and recall of plot content. It may be that younger children can effectively vary allocation of attention in response to plot development but lag behind older children in translating increased attention into enhanced story recall. This hypothesized developmental progression from behavioral response to enhanced understanding is similar to one observed in online text comprehension. That is, on encountering inconsistent information in a text, both younger and older children slow their reading, but older children are more likely to recall the inconsistency (Harris, Kruithof, Terwogt, & Visser, 1981; Zabrucky & Ratner, 1986).
Cognitive Engagement in Children With ADHD In dramatic contrast to the systematic changes in cognitive engagement observed for comparison children, children with ADHD showed no variation in probe RTs as sequences of central or incidental material progressed. As such, the group difference in the pattern of cognitive engagement was even more marked than
COGNITIVE ENGAGEMENT WITH TELEVISION
that reported by Whirley et al. (2003), who found that only late in central sequences did children with ADHD begin to show increased engagement. Given that Whirley et al.’s sample was several years older than the children in Experiment 3, this suggests that children with ADHD show a pronounced developmental delay in adjusting their cognitive engagement in response to the importance of current content to the developing story. Even comparison children as young as 4 years of age showed the expected linear increase as central content progressed, whereas it was not until age 9 at the earliest (Whirley et al., 2003) that children with ADHD demonstrated an increase in cognitive engagement from the beginning to the end of central sequences. However, it should be noted that the children with ADHD in the Whirley et al. study showed a decrease in cognitive engagement during central sequences prior to the significant increase in RTs at the last probe position. This pattern has not been observed for any age group of comparison children. It should be noted that, in general, the children with ADHD had significantly longer RTs than the comparison children. This might lead one to the interpretation that children with ADHD are more engaged with the television program than comparison children. However, it is well documented that children with ADHD demonstrate slower responses on a variety of reaction time tasks, including simple reaction time tasks requiring virtually no cognitive processing (Douglas, 1999). Just as we would not argue that younger children’s longer RTs in Experiment 2 indicate they were more engaged than older children, we would not conclude processing differences on the basis of overall group RT comparisons. Instead, as we have argued throughout, it is the increase in RTs as central sequences progress that is indicative of increased engagement. The present results concerning the children with ADHD are conceptually similar to those reported by Meadowcroft and Reeves (1989). Their finding that only children with well-developed story schema knowledge showed differences in engagement as story coherence varied is consistent with prior results that children with ADHD are deficient in their appreciation of story structure variables (Lorch, O’Neil, et al., 2004; Renz et al., 2003). The results of both Experiment 3 and Meadowcroft and Reeves reinforce the view that appropriate adjustments in cognitive engagement are related to the ability to achieve coherent story representations. The results for children with ADHD also are conceptually similar to those of Lorch, Eastham, et al. (2004), who manipulated levels of visual attention and operationalized cognitive engagement in terms of long looks (i.e., at least 15 s) at the television. Findings from both investigations support the interpretation that comparison children are better able than children with ADHD to alter cognitive engagement in response to changes in story content. In Lorch, Eastham, et al.’s study, the amount of time spent in deeper cognitive processing during long looks helped to explain the differential patterns of recall in children with ADHD and comparison children. In Experiment 3, the systematic changes in cognitive engagement demonstrated by comparison children suggest that these children constructed a more complete story representation than children with ADHD. Thus, there is compelling evidence (Experiment 3; Lorch, Eastham, et al., 2004; Whirley et al., 2003) across a wide age range and different methodologies that children with ADHD have significant difficulties achieving and
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sustaining cognitive engagement. In turn, these difficulties impair the story comprehension of children with ADHD.
Limitations One limitation of the current series of experiments is that they all used one or two episodes of the same situation comedy program. The degree to which the obtained results would generalize to other types of programming or even other situation comedies is unknown. However, both Growing Pains episodes were selected on the basis of their strong narrative structures. Both episodes conformed to prototypical story structures (Mandler & Johnson, 1977), in which an initiating event creates a goal that the protagonist must reach through a series of attempts to overcome obstacles to goal attainment. As such, we expect that the current studies’ consistent finding of increased engagement as central sequences progressed would be replicated in experiments using other programs with strong narrative structures. Although the core finding of increased RTs as central sequences continued was consistent for comparison children across the three studies, there were a few inconsistent or unexpected results, especially concerning the patterns of RTs for incidental sequences. In Experiments 1 and 2, RTs to probes occurring early in incidental sequences were longer than RTs to probes in the same position in central sequences. Furthermore, although Experiments 1 and 2 revealed decreases in engagement as incidental sequences progressed, no such decrease was found in Experiment 3 for either diagnostic group. These inconsistencies for incidental sequences may reflect the possibility that factors other than the plot relevance of story content influence children’s engagement with these sequences. Because the initially long RTs to incidental probes in Experiments 1 and 2 were particularly unexpected, we examined the content of these sequences to see whether we could identify any other differences between the central and incidental sequences to account for these findings. There were no differences between sequence type in the amount of dialogue, the incidence of the laugh track, episode boundaries, or scene changes. Several of the incidental sequences appear to be for comedic purposes rather than to provide plot-relevant content. It may be that children’s attention was drawn to these moments of comic relief, but when children realized that the content was not necessary for understanding the plot, engagement was quickly reduced. The fact that in Experiment 3 incidental sequences did not provoke initially longer RTs, nor was there any change as the sequences progressed, may reflect the addition of a second television program. Perhaps it is not surprising that there is inconsistency in the patterns of responses to incidental sequences, because, by definition, these sequences are extraneous to the plot and thus are likely to produce unpredictable influences on children’s engagement. The most critical point is that no group of children ever became more engaged with incidental material as the sequences progressed. For future research, the primary focus should be on children’s engagement with central content, with incidental sequences serving merely as a control to ensure that increases in RTs are specific to central sequences.
Future Directions Additional research is needed to better understand and evaluate the development of children’s online processes of building story
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representations, both for comparison children and for those diagnosed with ADHD. Many of the conclusions about the development of story comprehension have been inferred from studies examining recall of story events. To create a fuller understanding of children’s comprehension, more research is needed using methodologies that examine online processes of story comprehension. It may be possible to use the secondary task methodology with both televised stories and written stories in a converging operations approach to gain more specific information about children’s online processing of stories (Beentjes & van der Voort, 1993). Use of televised stories makes it possible to present more lengthy and complex stories and to study a relatively wide age range of children. Use of the secondary task methodology during reading of written stories (Britton et al., 1978; Britton & Tesser, 1982), however, would permit greater control over specific content and variations in story structure. Another methodology that could be used to examine ongoing story processing, particularly in younger children, is online story narration (Renz et al., 2003; Trabasso & Nickels, 1992; Trabasso et al., 1992). This methodology examines the extent to which children use a goal-based structure to incorporate new information into their ongoing story representation. Future research also can examine how deficits in cognitive engagement for children with ADHD might account for their well-documented academic problems. Results of this and other studies (e.g., Lorch, O’Neil, et al., 2004) indicate that the academic deficits associated with ADHD go beyond mere problems in sustaining attention and instead reflect difficulties in sustaining cognitive engagement, identifying important content, and making connections among story events (Milich et al., 2005). Future research also can begin to test the efficacy of academic interventions designed to target these specific deficits. Such targeted interventions may include the mapping of important story events and their interconnections, training in the focused use of advanceorganizing techniques, and learning studying strategies that emphasize connections among story events (Berthiaume, in press). In summary, the current investigation provides evidence that 4to 11-year-old comparison children, but not children with ADHD, systematically vary their cognitive engagement while watching a televised story in a way that is appropriate for building a coherent story representation. A more thorough understanding of children’s story comprehension processes may be helpful for understanding comprehension processes in general. In school, many tasks that children complete are related to story comprehension, and an understanding of processing during these tasks may assist and guide the presentation of material to be maximally beneficial to children’s learning, especially for children who experience academic difficulties.
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Received May 24, 2005 Revision received December 12, 2005 Accepted December 12, 2005 䡲
