Abstract
In three experiments, we explore human and simulated participants’ potential for deriving and merging analogous forms of stimulus relations. In the first experiment, five human participants were exposed to compound stimuli (stimulus pairs) by way of an automated yes–no protocol. Participants received discrimination training focusing on four three-member stimulus classes, where only two of the four classes were correctly related algebraic expressions. Training was intended to establish generalized identification of novel correct stimulus pairs and generalized identification of novel incorrect stimulus pairs. In Experiment 2, we employed a three-layer connectionist model (CM) of a yes–no protocol aimed at training and testing an analogous set of stimulus relations. Our procedures were aimed at assessing a neural network’s ability to simulate derived stimulus relations consistent with the human performances observed in Experiment 1. In Experiment 3, we employed a four-layer CM to compute the number of training epochs required to attain mastery. As with our human participants, our neural network required specific training procedures to become proficient in identifying stimuli as being members or nonmembers of specific classes. Outcomes from Experiment 3 suggest that the number of training epochs required to attain mastery for our simulated participants corresponded closely with the number of training trials required of our human participants during Experiment 1. Moreover, generalization tests revealed that human and simulated participants exhibited analogous response patterns. We discuss the evolving potential for CMs to emulate and predict human training requirements for deriving and merging complex stimulus relations during generalization tests.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Within Figure 12, there are 10 data patterns directly above each label along the x-axis, and these refer to the outcomes for the test of novel relations for human and simulated participants from Experiments 1 and 3.
References
Arntzen, E., & Holth, P. (1997). Probability of stimulus equivalence as a function of training design. The Psychological Record, 47, 309–320.
Barnes, D., & Hampson, P. J. (1993). Stimulus equivalence and connectionism: Implications for behavior analysis and cognitive science. The Psychological Record, 43, 617–638.
Bullinaria, J. A. (1997). Modeling reading, spelling and past tense learning with artificial neural networks. Brain & Language, 59, 236–266. https://doi.org/10.1006/brln.1997.1818.
Burgos, J. E. (2007). Autoshaping and automaintenance: A neural-network approach. Journal of the Experimental Analysis of Behavior, 88, 115–130. https://doi.org/10.1901/jeab.2007.75-04.
Cullinan, V., Barnes, D., Hampson, P. J., & Lyddy, F. (1994). A transfer of explicitly and nonexplicitly trained sequence responses through equivalence relations: An experimental demonstration and connectionist model. The Psychological Record, 44, 559–585.
Debert, P., Huziwara, E. M., Faggiani, R. B., De Mathis, M. E. S., & McIlvane, W. J. (2009). Emergent conditional relations in a go/no-go procedure: Figure ground and stimulus-position compound relations. Journal of the Experimental Analysis of Behavior, 92, 233–243. https://doi.org/10.1901/jeab.2009.92-233.
Devany J. M, Hayes S. C, Nelson R. O. (1985). Equivalence class formation in language-able and language-disabled children. Journal of the Experimental Analysis of Behavior, 46, 243–257.
Donahoe, J. W., & Palmer, D. C. (1989). The interpretation of complex human behavior: Some reactions to Parallel Distributed Processing, edited by J. L. McClelland, D. E. Rumelhart, and the PDP Research Group. Journal of the Experimental Analysis of Behavior, 51, 399–416. https://doi.org/10.1901/jeab.1989.51-399.
Durstenfeld, R. (1964). Algorithm 235: Random permutation. Communications of the ACM, 7(7), 420. https://doi.org/10.1145/364520.364540.
Dymond, S., & Barnes, D. (1996). A transformation of self-discrimination response functions in accordance with the arbitrarily applicable relations of sameness and opposition. The Psychological Record, 46, 271–300.
Fields, L., Reeve, K. F., Varelas, A., Rosen, D., & Belanich, J. (1997). Equivalence class formation using stimulus-pairing and yes-no responding. The Psychological Record, 47, 661–686.
Fienup, D. M., Covey, D. P., & Critchfield, T. S. (2010). Teaching brain–behavior relations economically with stimulus equivalence technology. Journal of Applied Behavior Analysis, 43, 19–33. https://doi.org/10.1901/jaba.2010.43-19.
Fienup, D. M., & Critchfield, T. S. (2011). Transportability of equivalence-based programmed instruction: efficacy and efficiency in a college classroom. Journal of Applied Behavior Analysis, 43, 763–768. https://doi.org/10.1901/jaba.2011.44-435.
Fisher, R. A., & Yates, F. (1948). Statistical tables for biological, agricultural and medical research (3rd ed.). London, UK: Oliver & Boyd.
Frank, A. J. Wasserman E. A. (2005). Associative symmetry in the pigeon after successive matching-to-sample training. Journal of the Experimental Analysis of Behavior, 84, 147–165. https://doi.org/10.1901/jeab.2005.115-04.
Greene, M. N., Morgan, P. H., & Foxall, G. R. (2017). NEURAL Networks and consumer behavior: NEURAL models, logistic regression, and the behavioral perspective model. The Behavior Analyst, 40, 393–418. https://doi.org/10.1007/s40614-017-0105-x.
Hagan, M. T., Demuth, H. B., Beale, M. H., & De Jesús, O. (2002). Neural network design. Boston, MA: PWS Publishing. https://doi.org/10.1002/rnc.727.
Hayes, S. C., Fox, E., Gifford, E. V., Wilson, K. G., Barnes-Holmes, D., & Healy, O. (2001). Derived relational responding as learned behavior. In S. C. Hayes, D. Barnes-Holmes, & B. Roche (Eds.), Relational frame theory: A post-Skinnerian account of human language and cognition (pp. 21–50). New York, NY: Plenum.
Haykin, S. O. (2008). Neural networks and learning machines (3rd ed.). Upper Saddle River, NJ: Pearson Education.
Huth, A. G., Nishimoto, S., Vu, A. T., & Gallant, J. L. (2012). A continuous semantic space describes the representation of thousands of object and action categories across the human brain. Neuron, 76, 1210–1224. https://doi.org/10.1016/j.neuron.2012.10.014.
IBM Knowledge Center (2017, January). IBM Kohonen Node. Retrieved from https://www.ibm.com/support/knowledgecenter/en/SS3RA7_15.0.0/com.ibm.spss.modeler.help/kohonennode_general.htm
Iversen, I. H. (1993). Acquisition of matching-to-sample performance in rats using visual stimuli on nose keys. Journal of the Experimental Analysis of Behavior, 59, 471–482.
Iversen, I. H. (1997). Matching-to-sample performance in rats: A case of mistaken identity. Journal of the Experimental Analysis of Behavior, 60, 27–45.
Iversen, I. H., Sidman, M., & Carrigan, P. (1986). Stimulus definition in conditional discriminations. Journal of the Experimental Analysis of Behavior, 45, 297–304.
Kay, K. N., Naselaris, T., Prenger, R. J., & Gallant, J. L. (2008). Identifying natural images from human brain activity. Nature, 452, 352–355. https://doi.org/10.1038/nature06713.
Kohonen, T. (2001). Self-organization and associative memory. Berlin, Germany: Springer-Verlag.
Larson, R., & Edwards, B. H. (2010). Calculus (9th ed.). Belmont, CA: Brooks Cole.
Larson, R., & Hostetler, R. P. (2001). Intermediate algebra (3rd ed.). Boston, MA: Houghton Mifflin.
Lew, S. E., & Zanutto, B. S. (2011). A computational theory for the learning of equivalence relations. Frontiers in Human Neuroscience, 5, 113. https://doi.org/10.3389/fnhum.2011.00113.
Lyddy, F., & Barnes-Holmes, D. (2007). Stimulus equivalence as a function of training protocol in a connectionist network. Journal of Speech & Language Pathology & Applied Behavior Analysis, 2, 14–24. https://doi.org/10.1037/h0100204.
Lyddy, F., Barnes-Holmes, D., & Hampson, P. J. (2001). A transfer of sequence function via equivalence in a connectionist network. The Psychological Record, 51, 409–428. https://doi.org/10.1037/h0100204.
Mackay, H. A., Wilkinson, K. M., Farrell, C., & Serna, R. W. (2011). Evaluating merger and intersection of equivalence classes with one member in common. Journal of the Experimental Analysis of Behavior, 96, 87–105. https://doi.org/10.1901/jeab.2011.96-87.
McCaffrey, J. (2014). Neural networks using C# succinctly. Retrieved from https://jamesmccaffrey.wordpress.com/2014/06/03/neural-networks-using-c-succinctly
McCaffrey, J. (2015). Coding neural network back-propagation using C#. Visual Studio Magazine. Retrieved from https://visualstudiomagazine.com/articles/2015/04/01/back-propagation-using-c.aspx
McCaffrey, J. (2017). Test run: Deep neural network training. Visual Studio Magazine. Retrieved from https://msdn.microsoft.com/en-us/magazine/mt842505.aspx
Ninness, C., Rumph, R., McCuller, G., Harrison, C., Ford, A. M., & Ninness, S. K. (2005). A functional analytic approach to computer-interactive mathematics. Journal of Applied Behavior Analysis, 38, 1–22. https://doi.org/10.1901/jaba.2005.2-04.
Ninness, C., Barnes-Holmes, D., Rumph, R., McCuller, G., Ford, A. M., Payne, R., et al. (2006). Transformation of mathematical and stimulus functions. Journal of Applied Behavior Analysis, 39, 299–321. https://doi.org/10.1901/jaba.2006.139-05.
Ninness, C., Dixon, M., Barnes-Holmes, D., Rehfeldt, R. A., Rumph, R., McCuller, et al. (2009). Deriving and constructing reciprocal trigonometric relations: A web-interactive training approach. Journal of Applied Behavior Analysis, 43, 191–208. https://doi.org/10.1901/jaba.2009.42-191.
Ninness, C., Lauter, J., Coffee, M., Clary, L., Kelly, E., Rumph, M., et al. (2012). Behavioral and biological neural network analyses: A common pathway toward pattern recognition and prediction. The Psychological Record, 62, 579–598. https://doi.org/10.5210/bsi.v22i0.4450.
Ninness, C., Ninness, S. K., Rumph, M., & Lawson, D. (2018). The emergence of stimulus relations: Human and computer learning. Perspective on Behavioral Science, 41, 121–154. https://doi.org/10.1007/s40614-017-0125-6.
Ninness, C., Rumph, M., Clary, L., Lawson, D., Lacy, J. T., Halle, S., et al. (2013). Neural network and multivariate analysis: Pattern recognition in academic and social research. Behavior & Social Issues, 22, 49–63. https://doi.org/10.5210/bsi.v22i0.4450.
Phan, N., Dou, D., Piniewski, B., & Kil, D. (2016). Ontology-based deep learning for human behavior prediction with explanations in health social networks. Social Network Analysis & Mining, 112, 49–60. https://doi.org/10.1007/s13278-016-0379-0.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by backpropagating errors. Nature, 323, 533–536.
Santos, P. M., Ma, M. L., & Miguel, C. F. (2015). Training intraverbal naming to establish matching-to-sample performances. Analysis of Verbal Behavior, 31, 162–182. https://doi.org/10.1007/s40616-015-0040-4.
Sidman, M., & Cresson, O. (1973). Reading and cross modal transfer of stimulus equivalence in severe retardation. American Journal of Mental Deficiency, 77, 515–523.
Sidman, M., Kirk, B., & Willson-Morris, M. (1985). Six-member stimulus classes generated by conditional-discrimination procedures. Journal of the Experimental Analysis of Behavior, 43, 21–42.
Sidman, M., & Tailby, W. (1982). Conditional discrimination vs. matching to sample: An expansion of the testing paradigm. Journal of the Experimental Analysis of Behavior, 53, 47–63. https://doi.org/10.1901/jeab.1982.37-5.
Spencer, T. J., & Chase, P. N. (1996). Speed analyses of stimulus equivalence. Journal of the Experimental Analysis of Behavior, 65, 643–659
Spielberg, S., Kennedy, K., & Curtis, B. (Co-Producers), & Spielberg, S. (Director). (2001). A.I.: artificial intelligence [Motion picture]. USA: Amblin Entertainment.
Steele, D. M., & Hayes, S. C. (1991). Stimulus equivalence and arbitrarily applicable relational responding. Journal of the Experimental Analysis of Behavior, 56, 519–555. https://doi.org/10.1901/jeab.1991.56-519.
Stewart, J. (2008). Calculus: Early transcendentals (7th ed.). Belmont, CA: Brooks/Cole.
Sullivan, M. (2002). Precalculus (6th ed.). Upper Saddle River, NJ: Prentice Hall.
Tovar, A. E., & Torres-Chávez, A. (2012). A connectionist model of stimulus class formation with a yes-no procedure and compound stimuli. The Psychological Record, 62, 747–762. https://doi.org/10.1007/s40732-016-0184-1.
Tovar, Á. E., & Westermann, G. (2017). A neurocomputational approach to trained and transitive relations in equivalence classes. Frontiers in Psychology, 8, 1–15. https://doi.org/10.3389/fpsyg.2017.01848.
Ultsch, A. (2007). Emergence in self-organizing feature maps, In Ritter, H., & Haschke, R. (Eds.) Proceedings 6th International Workshop on Self-Organizing Maps (pp. 1–7). Bielefeld, Germany: Neuroinformatics. https://doi.org/10.1016/j.beproc.2014.07.006
Urcuioli, P. J., Lionello-DeNolf, K., Michalek, S., & Vasconcelos, M. (2006). Some tests of response membership in acquired equivalence classes. Journal of the Experimental Analysis of Behavior, 86, 81–107. https://doi.org/10.1901/jeab.2006.52-05.
Urcuioli, P. J., & Swisher, M. J. (2015). Transitive and anti-transitive emergent relations in pigeons: Support for a theory of stimulus-class formation. Behavioral Processes, 112, 49–60. https://doi.org/10.1016/j.beproc.2014.07.006.
Vernucio, R. R., & Debert, P. (2016). Computational simulation of equivalence class formation using the go/no-go procedure with compound stimuli. The Psychological Record, 66, 439–440. https://doi.org/10.1007/s40732-016-0184-1.
Walker, B. D., Rehfeldt, R. A., & Ninness, C. (2010). Using the stimulus equivalence paradigm to teach course material in an undergraduate rehabilitation course. Journal of Applied Behavior Analysis, 43, 615–633. https://doi.org/10.1901/jaba.2010.43-615.
Wulfert, E., & Hayes, S. C. (1988). Transfer of a conditional ordering response through conditional equivalence classes. Journal of the Experimental Analysis of Behavior, 50, 125–144. https://doi.org/10.1901/jeab.1988.50-125.
Funding
No type of funding or other compensation was provided for conducting any part of this investigation.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethical Approval
All training and test procedures performed with respect to the human participants in the article were conducted in accordance with the ethical standards of the institutional research committee and in conformance with the 1964 Helsinki declaration and all of its subsequent amendments or similar ethical standards.
Conflict of Interest
On behalf of all authors, the corresponding author affirms that there are no conflicts of interest with regard to any aspect of this investigation.
Informed Consent
Informed consent was acquired from all participants in this investigation.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
ESM 1
(DOCX 21.2 kb)
Rights and permissions
About this article
Cite this article
Ninness, C., Rehfeldt, R.A. & Ninness, S.K. Identifying Accurate and Inaccurate Stimulus Relations: Human and Computer Learning. Psychol Rec 69, 333–356 (2019). https://doi.org/10.1007/s40732-019-00337-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40732-019-00337-6