A Translational Comparative Investigation of Two Token Reinforcement Systems for ASD Programming

Despite the popularity of token reinforcement in applied settings, little research has been conducted on strategies for optimizing its effectiveness in autism service delivery; however, this work is well-suited for a translational exploration. Employing an alternating treatments design, this paper serves as a pilot study, demonstrating how a random ratio (RR) token-exchange schedule can be arranged and compared to a standard fixed ratio (FR) schedule to examine differences in preference, in trial duration, rate, and accuracy, and in challenging behaviors. Two children diagnosed with autism spectrum disorder (ASD) participated during regularly scheduled therapy sessions. During Phase 1, we arranged the random ratio token board (RRTB) to allow participants to draw from a cup of red and blue tokens. Tokens could be exchanged for the backup reinforcer contingent on drawing 10 red tokens or one blue token. This was compared to a static token board (STB), during which the client always had to draw 10 white tokens before exchanging. During this phase, one participant showed a strong fixation on the blue tokens, consistent with a phenomenon known as sign tracking. This led researchers to revise the arrangement of the RRTB so that staff used a random number list instead of different colored tokens to determine when participants could exchange. Following Phase 2, differences in trial rate and challenging behaviors revealed that one participant performed better with the RRTB, despite preferring the STB, while the other participant performed better with the STB, despite preferring the RRTB. Limitations and future directions are discusse

Investigating Looking Behaviors with a Humanoid Robot

Faculty advisor: Jed ElisonSeveral research studies have shown that children with autism spectrum disorders (ASD) often display impairments in their ability to engage in many social behaviors that are crucial for the development of social-emotional competence, empathy, and expressive language. Because most children with autism show strong preferences for nonsocial information such as objects and machines (Adamson, Deckner, & Bakeman, 2010; Tapus et al., 2012), researchers have explored using humanoid robots to help children with autism develop skills for social interaction (Tapus et al., 2012). In this study, we used data from 55 typically developing toddlers (M= 33 months) who participated in a 10-minute semi-structured play session with a humanoid robot, the NAO V4 (Aldebaran Robotics). The NAO robot was pre-programmed to advance through seven structured social interactions, such as Simon Says, I Spy, a tai chi routine, and a dance to “If You’re Happy and You Know It.” Using this data, we examined children’s engagement with the robot, specifically their looking preferences during the interaction phases with the NAO.This research was supported by the Undergraduate Research Opportunities Program (UROP)