Normalization between stimulus elements in a model of Pavlovian conditioning: Showjumping on an elemental horse

Abstract

Harris and Livesey. Learning & Behavior, 38, 1-26, (2010) described an elemental model of associative learning that implements a simple learning rule that produces results equivalent to those proposed by Rescorla and Wagner (1972), and additionally modifies in "real time" the strength of the associative connections between elements. The novel feature of this model is that stimulus elements interact by suppressively normalizing one another's activation. Because of the normalization process, element activity is a nonlinear function of sensory input strength, and the shape of the function changes depending on the number and saliences of all stimuli that are present. The model can solve a range of complex discriminations and account for related empirical findings that have been taken as evidence for configural learning processes. Here we evaluate the model's performance against the host of conditioning phenomena that are outlined in the companion article, and we present a freely available computer program for use by other researchers to simulate the model's behavior in a variety of conditioning paradigms.

Exploring the role of stimulus similarity on the summation effect in causal learning

“…Importantly, Soto et al argue that factors such as spatial contiguity should be critical in determining whether summation should be obtained. A similar role for spatial contiguity can be found in Harris and Livesey's (2010; see also Thorwart et al, 2012) model, who propose that the closer two stimuli are, the more their representations interact with each other (through normalizing gain 7 SUMMATION IN CAUSAL LEARNING control), producing configural processing. In the rest of this paper, we use the term similarity specifically in regard to all the manipulations of stimulus properties that are considered by these models to mediate the generalization process: more similarity will thus indicate more configural processing; less similarity more elemental processing.…”

“…There was a substantial reduction in the rating to AB from Experiment 1 to Experiment 2 (from an average around 19 to an average of around 17), but it is not clear whether this reduction was due to the change in spatial separation of A and B, or to any of the other changes across experiments (maximum value of the rating scale, stimulus shapes and color, etc.). As the previous literature suggests spatial separation as a potential stimulus factor controlling the level of elemental vs. configural processing of stimuli (see Melchers et al, 2008;Soto et al, 2014a;Thorwart et al, 2012), Experiment 3 was performed to further assess the potential effect of spatial separation of cues on the summation effect.…”

“…The opposite effect occurs when elemental processing is encouraged by presenting the cues in different positions on the screen: under this condition, blocking is more likely to be observed (Glautier, 2002;Livesey & Boakes, 2004). Given these results, contemporary theories have incorporated stimulus properties such as spatial position and organization as another aspect of stimulus similarity (Soto et al, 2014a;Thorwart et al, 2012). For example, Soto et al's (2014) model combines the notion of consequential regions proposed by Shepard (1987) with contemporary Bayesian approaches to generalization (Courville, 2005;Courville, Daw, & Touretzky, 2006) and makes the same prediction as flexible associative models: similar stimuli should produce a weaker summation effect than dissimilar stimuli.…”

Three Ways That Non-associative Knowledge May Affect Associative Learning Processes

“…Several authors have suggested that pretraining, task instructions, and spatial stimulus characteristics can alter the encoding strategy that participants use or the way they mentally represent cues, which in turn affects generalization between compounds and individual cues (e.g., see Melchers et al, 2008 for a review). The potential for these changes in stimulus representation to impact on learning is sometimes discussed in terms of flexible shifting between elemental and configural learning (Melchers et al, 2008) or shifts within an elemental learning system (e.g., Wagner and Brandon, 2001; Livesey and Harris, 2008; Thorwart et al, 2012, 2016). Such changes in stimulus representation reduce generalization from A to AB and thus result in a weak expectation of the outcome in AB+ trials.…”

“…This expectation generalizes to AB+ trials. As described above, the default assumption of many associative learning theories is that the associative strengths of the cues that are present will combine in an additive fashion (Rescorla and Wagner, 1972), although there are many hypothesized reasons why this summation might be less than perfectly additive (see McLaren and Mackintosh, 2000, 2002; Wagner and Brandon, 2001; Harris, 2006; Harris and Livesey, 2010; Thorwart et al, 2012 to name just a few). Thus the process that provides a means of generalization is assumed to automatically produce an expectation of the outcome based on some combination of the associative strengths of the cues present.…”