|
| Theoretical Topics in Experimental Analysis |
| Tuesday, November 14, 2017 |
| 4:00 PM–4:50 PM |
| Loft B, Niveau 3 |
| Area: EAB |
| Keyword(s): Computational Modeling, Mathematical Modeling, Stimulus Equivalence |
| Chair: Don (Yuhan) Li (The University of Auckland) |
| |
| The Computational Analysis of Behaviour: Multi-Variate Models of Behaviour |
| Domain: Theory |
| DON LI (The University of Auckland) |
| Abstract: A study of behaviour investigates the relationship between experimentally controlled environmental variables and the likelihood of response (Skinner, 1950). Skinner proposed that response rate should be the canonical datum for indexing the likelihood of response. However, the validity of response rate does not dominate all other types of dependent variable, especially for experimental designs for which rates cannot be calculated or when derived variables are required. Regardless of the type of dependent variable, theories of behaviour attempt to link environmental variables to some behavioural metric. This constrains those theories to particular dimensions of behaviour. An alternative approach is to construct a theory that links environmental variables to a constellation of behavioural metrics, thereby explaining "behaviour" as opposed to a particular behavioural metric. Computational models of behaviour comprise a set of models that have this multivariate property. Because computational models of behaviour output punctuate responses, almost any arbitrary behavioural metric can be calculated. Hence, one may fit a computational model of behaviour to any vector of dependent variables. The present paper outlines the philosophy of the Computational Analysis of Behaviour and illustrates the efficacy of the philosophy with a demonstration of multi-objective optimisation with Catania's Operant Reserve (Catania, 2005). |
| |
| CANCELED: From Conditioning to Cognition? Associative Versus Propositional Learning and the Formation of Stimulus Equivalence Relations |
| Domain: Theory |
| DAVID W. DICKINS (University of Liverpool) |
| Abstract: There has been much debate about the explanatory power of associative versus propositional accounts of human learning (e.g..Mitchell et al., 2009). How may this notional dichotomy be applied to the laboratory study of stimulus equivalence classes? Is the commonest training procedure, arbitrary matching-to-sample, simply a cluster of conditional discriminations, homologous with those which can be demonstrated in other species, or even in the isolated ganglia of Aplysia, or does it depend, in human participants at least, upon propositional processes, in which a language-like apprehension of the relation between the stimuli and not just a link between representations of the stimuli is entailed? When the same matching-to-sample procedure is used during unreinforced tests for derived relations can a seemingly simpler associative account still be maintained, or do such tests help to generate, in a propositional manner, the relations they aim to demonstrate? The literature on alternative methods of training and testing is reviewed to see if there are or might be circumstances in which propositional processes can be confidently excluded in the formation of stimulus equivalence classes. Mitchell, C.J., De Houver, J., Lovibond, P.F., 2009. The propositional nature of human associative learning. Behav. Brain Sci. 183-246. |
| |
| Keyword(s): Computational Modeling, Mathematical Modeling, Stimulus Equivalence |
|
| |