افزایش تنوع واکنش در کودکان مبتلا به اختلال طیف اوتیسم با استفاده از برنامه تاخیر تقویتی

Individuals with autism spectrum disorder (ASD) often present with deficits in variability in responding across multiple repertoires. However, research to date has resulted in little empirical evaluation of remediation strategies for such deficits. We investigated the effects of lag schedules of reinforcement on response variability using a computer-based task designed for the purpose of the study. The U-value statistic was used as a measure of variability in responding to determine if increasing the lag criterion would correspondingly increase levels of variability. Participants included children with ASD (Group 1) and neurotypical children (Group 2). Results showed that U-values were higher when reinforcement was contingent on increased variability, indicating the effectiveness of higher lag values on response variability. A significant difference in response variability between groups provided evidence for the disparity in such responding in children with ASD compared to their neurotypical peers. Group 1 showed consistently lower U-values than Group 2 indicating lower response variability. However, data from this study clearly show that lag schedules of reinforcement may be employed to increase response variability in ASD.

schedules of reinforcement Lag schedules of reinforcement have been used to increase variability in responding in both the basic (Cherot et al., 1996 and Neuringer and Huntley, 1992) and applied (Cammilleri and Hanley, 2005 and Napolitano et al., 2010) research fields within behavior analysis. Lag schedules involve the delivery of reinforcement if a response, or sequence of responses, differs from a pre-determined previous number of responses (Page & Neuringer, 1985). For example, under a lag 3 schedule, a response would be reinforced if it differed from the previous three responses, while under a lag 10 schedule a response would be reinforced if it differed from the previous 10 responses. Lag schedules have also been used to investigate the operant nature of variability, commonly compared to a control condition where reinforcement is provided independent of variability. To date, the majority of investigations involving lag schedules have been in the area of basic research. Increasingly, these data have begun to inform the applied research field, with several studies emerging in recent years detailing the use of lag schedules to increase variability in play and language in a clinical population (e.g., Cammilleri and Hanley, 2005, Lee and Sturmey, 2006 and Napolitano et al., 2010). The progression in utilizing such procedures to increase variability in response patterns, from basic to applied research, is indicative of the value that researchers and practitioners are now placing on variability as a target behavior in humans. Furthermore, lag schedules may offer an efficient and reliable tool for increasing response variability when this type of responding is necessary or desirable. While several studies have emerged that employ lag schedules to increase variability, the majority of research reported has been limited to a lag 1 schedule. In light of the outcomes that basic researchers have demonstrated, e.g., inducing marked increases in response variability with pigeons using lag 5, 10 and 50 schedules (Abreu-Rodrigues et al., 2005 and Page and Neuringer, 1985), the use of the higher lag values with human participants is a worthwhile area for further investigation. In 2012, two studies (Heldt and Schlinger, 2012 and Susa and Schlinger, 2012) involved the use of lag schedules to increase variability in the verbal behavior of participants with developmental disabilities. Susa and Schlinger (2012) used increasing lag schedules of reinforcement (lag 1, lag 2 and lag 3) in a changing criterion design to increase the variability of responses to a social question, “How are you?” The participant was a boy diagnosed with Autism. The authors observed that the average number of previous responses from which each response varied increased as the lag schedule value increased. Heldt and Schlinger (2012) sought to increase variability in the tacting responses of two boys, one of whom had mild intellectual disability and one of whom had Fragile X Syndrome. In this study, a lag 3 schedule was implemented directly following the baseline phase. The authors found that variability in tacting increased for both participants and these gains were maintained at a three-week follow up. Individuals with autism spectrum disorder (ASD) have been shown to demonstrate deficits in variability in responding across multiple repertoires (Boucher, 1977, Lee et al., 2002 and Mullins and Rincover, 1985). The current research aimed to identify possible remediation strategies for this. To this end, it investigated the effect of lag schedules of reinforcement on the variability of response sequences on a computer-based task. The task involved a computer program in the form of a game that required participants to fulfill a lag criterion in order to progress through it. A primary aim of the study was to determine if increasing the lag criterion would increase the level of variability as measured by the U-value statistic. The U-value statistic is perhaps the most widely used measure of variability in responding across the basic research literature. It determines the distribution of probabilities of a response, with equal probabilities indicating high variability and unequal probabilities indicating low variability ( Neuringer, Kornell & Olufs, 2001). In other words, higher U values (closer to 1) indicate higher variability while lower U-values (closer to 0) indicate lower variability. In the current study, it was expected that U-values would be higher when reinforcement was contingent on increased variability, i.e., higher lag values would produce higher U-values. A secondary aim of the study was to compare variability in responding on the computer-based task across two groups of participants, one comprised of neurotypical children and one comprised of children with ASD.

The current study revealed a number of interesting findings. Firstly, the significant between-groups effect provides further evidence for the difference in response varibility in children with ASD compared to their neurotypical peers. Group 1 achieved consistently lower U-values across the study settings than Group 2, indicating lower variability in responding. Data from this study clearly show that lag schedules of reinforcement can increase response variability. Importantly, they also show that increasing the lag value results in higher variability. The current study indicates that, when using lag schedules with human participants, there may be a ceiling, beyond which extinction of variability can occur. This is likely due to the magnitude of reinforcement decreasing while response effort increases. Findings from the current study, where a lag 8 was the highest schedule value, indicate that a lag 6 may be the optimum schedule for high levels of variability in human participants with and without ASD. However, there were six participants (Group 1, Participants 5 & 6; Group 2, Participants 11, 12, 14 & 20) of the 20 in this study that showed little or no sensitivity to the changing lag criterion. These participants demonstrated relatively high variability when reinforcement was not contingent on it (during the control setting) and this may be relevant in explaining their lack of responsiveness to the altering lag schedule. On examination of the animal research in this area, Abreu-Rodrigues et al. (2005) reported that one of their four pigeon subjects demonstrated high and stable levels of variability across lag 1, lag 5 and lag 10 conditions. This mirrored the participants in the current study who demonstrated this pattern of high and stable variability in responding. Further research is required to identify which participants are most and least responsive to lag schedules and to attempt to indentify the participant characteristics related to both outcomes. A preliminary investigation of the relationship between severity of autism sympoms and response variability was conducted as part of this study, with some indicators that an inverse relationship between these two variables may occur. Future research may yield useful information regarding the effect of autism severity on the level of response variability in this population. Considering the prevalence of low variability in responding among individuals with ASD, the importance of identifying procedures for ameliorating invariable response patterns cannot be underestimated. The current study highlights an important avenue for further research as it has demonstrated that the use of lag schedules of reinforcement can yield encouraging outcomes for individuals with ASD who demonstrate low variability in responding.