Contents lists available at ScienceDirect Behavioural Processes journal homepage: www.elsevier.com/locate/behavproc Generalization of learned variability across multiple dimensions in humans Xiuyan Kong a, ⁎ , James S. McEwan a , Lewis A. Bizo a,b , Mary T. Foster a,b a School of Psychology, University of Waikato, New Zealand b School of Psychology and Behavioural Science, University of New England, Australia ARTICLE INFO Keywords: Behavioral variability Generalization Human U-value ABSTRACT This study examined whether trained variability would generalize across dimensions of the target response. Two experiments used a computerized rectangle drawing task that required participants to click and drag a mouse cursor to create rectangles on a computer screen. In Experiment 1, one group received points when successive rectangles varied in their size, shape and location (VAR), another group were yoked to the VAR group and received points that were allocated to them using a yoking procedure (YOKE), regardless of the variability in the size, shape or location of the rectangle drawn. Variability was higher for a dimension when variability on that dimension was directly reinforced. In Experiment 2, three groups of participants received points when rectangles varied on two dimensions; each group differed in the two dimensions that required variation. Variability was again higher for the reinforced dimensions for two of the three groups. Comparison with the YOKE group showed that the variability on those dimensions where variability was not directly reinforced was affected by re- inforcement for variability on the other dimensions. Specifically, the variability in Shape and Location was significantly higher when these two dimensions occurred with other dimensions where variability was reinforced (as in Experiment 2) compared to when they were not required to vary (as in the YOKE group). This suggests that, for these two groups, the reinforced variability on the other two dimensions generalized to the third dimension. Implications of this finding to our understanding of factors that promote behavioral variability are discussed. 1. Introduction The generalization of stimulus control over responses that occurs across stimuli and contexts has both theoretical and applied sig- nificance. In applied settings, generalization is usually considered to be a key criterion for judging the success of an intervention (Arnold- Saritepe et al., 2009; T. F. Stokes and Baer, 1977; T. F. Stokes and Osnes, 1989). It has previously been demonstrated that behavioral variability is sensitive to the contingencies of reinforcement and can come under discriminative stimulus control (Denney and Neuringer, 1998; Neuringer, 2002; Neuringer and Jensen, 2013; Page and Neuringer, 1985). One question that is of both theoretical and applied interest is whether trained variability will generalize to new contexts. One of the suggestions for promoting generalization in applied settings is to train diverse responses to increase the likelihood of responses contacting reinforcement outside of the training environments (e.g., T. F. Stokes and Baer, 1977; T. F. Stokes and Osnes, 1989). Reinforcing varying aspects of the response during training can be seen as a step to the training of diverse responses. In applied settings, training of response variability does not usually precede training the prerequisite behaviors or responses ̶ such training would fit with “training diverse exemplars” as described by T. F. Stokes and Baer (1977). Schmidt and Bjork (1992) suggested that training responses to vary could facilitate the general- ization of such behavior to novel contexts. There have been a limited number of studies that have investigated the extent to which learned variability would generalize across beha- viors or contexts. This question is important because if a child was trained to vary in the number of colours they used when painting, might we also see some increase in variability in their use of building blocks or other forms of creative play? Some insight into an answer to that question is provided by P. D. Stokes et al. (2008) who trained partici- pants using a computer based task to move a lit box from the top of a pyramid, shown on a screen, to different endpoints at the bottom of the pyramid. They reported that requiring variability in the paths taken to reach designated endpoints for reinforcement resulted in participants using more different paths than when this was not the case. When tested, after training, on a different pyramid and on endpoints that had https://doi.org/10.1016/j.beproc.2018.10.020 Received 22 March 2018; Received in revised form 18 October 2018; Accepted 29 October 2018 ⁎ Corresponding author. E-mail addresses: kitt_kong@hotmail.com (X. Kong), james@behavioursolutions.nz (J.S. McEwan), lbizo@une.edu.au (L.A. Bizo), mary@behavioursoluitons.nz (M.T. Foster). Behavioural Processes 158 (2019) 32–40 Available online 01 November 2018 0376-6357/ © 2018 Elsevier B.V. All rights reserved. T