Generality of the Summation Effect in Human Causal Learning

2009 ◽  
Vol 62 (5) ◽  
pp. 877-889 ◽  
Author(s):  
Fabian A. Soto ◽  
Edgar H. Vogel ◽  
Ramón D. Castillo ◽  
Allan R. Wagner

Considerable research has examined the contrasting predictions of the elemental and configural association theories proposed by Rescorla and Wagner (1972) and Pearce (1987), respectively. One simple method to distinguish between these approaches is the summation test, in which the associative strength attributed to a novel compound of two separately trained cues is examined. Under common assumptions, the configural view predicts that the strength of the compound will approximate to the average strength of its components, whereas the elemental approach predicts that the strength of the compound will be greater than the strength of either component. Different studies have produced mixed outcomes. In studies of human causal learning, Collins and Shanks (2006) suggested that the observation of summation is encouraged by training, in which different stimuli are associated with different submaximal outcomes, and by testing, in which the alternative outcomes can be scaled. The reported experiments further pursued this reasoning. In Experiment 1, summation was more substantial when the participants were trained with outcomes identified as submaximal than when trained with simple categorical (presence/absence) outcomes. Experiments 2 and 3 demonstrated that summation can also be obtained with categorical outcomes during training, if the participants are encouraged by instruction or the character of training to rate the separately trained components with submaximal ratings. The results are interpreted in terms of apparent performance constraints in evaluations of the contrasting theoretical predictions concerning summation.

2019 ◽  
Vol 73 (2) ◽  
pp. 260-278
Author(s):  
Tara Zaksaite ◽  
Peter M Jones

Rescorla and Wagner’s model of learning describes excitation and inhibition as symmetrical opposites. However, tasks used in human causal learning experiments, such as the allergist task, generally involve learning about cues leading to the presence or absence of the outcome, which may not reflect this assumption. This is important when considering learning effects which provide a challenge to this model, such as the redundancy effect. The redundancy effect describes higher causal ratings for the blocked cue X than for the uncorrelated cue Y in the design A+/AX+/BY+/CY–, the opposite pattern to that predicted by the Rescorla–Wagner model, which predicts higher associative strength for Y than for X. Crucially, this prediction depends on cue C gaining some inhibitory associative strength. In this article, we used a task in which cues could have independent inhibitory effects on the outcome, to investigate whether a lack of inhibition was related to the redundancy effect. In Experiment 1, inhibition for C was not detected in the allergist task, supporting this possibility. Three further experiments using the alternative task showed that a lack of inhibition was related to the redundancy effect: the redundancy effect was smaller when C was rated as inhibitory. Individual variation in the strength of inhibition for C also determined the size of the redundancy effect. Given that weak inhibition was detected in the alternative scenario but not in the allergist task, we recommend carefully choosing the type of task used to investigate associative learning phenomena, as it may influence results.


Author(s):  
Jan De Houwer ◽  
Tom Beckers

Abstract. De Houwer and Beckers (in press , Experiment 1) recently demonstrated that ratings about the relation between a target cue T2 and an outcome are higher when training involves CT1+ and T1T2+ followed by C+ trials than when training involves CT1+ and T1T2+ followed by C- trials. We replicated this study but now explicitly asked participants to rate the causal status of the cues both before and after the C+ or C- trials. Results showed that causal ratings for T2 were significantly higher after C+ trials than before C+ trials and that T2 received significantly lower ratings after C- trials than before C- trials. The results thus provide the first evidence for higher-order unovershadowing and higher-order backward blocking. In addition, the ratings for T1 revealed that first-order backward blocking (i.e., decrease in ratings for T1 as the result of C+ trials) was stronger than first-order unovershadowing (i.e., increase in ratings for T1 as the result of C- trials).


2008 ◽  
Vol 34 (2) ◽  
pp. 303-313 ◽  
Author(s):  
Harald Lachnit ◽  
Holger Schultheis ◽  
Stephan König ◽  
Metin Üngör ◽  
Klaus Melchers

2008 ◽  
Vol 34 (4) ◽  
pp. 423-436
Author(s):  
Chris J. Mitchell ◽  
Justin A. Harris ◽  
R. Frederick Westbrook ◽  
Oren Griffiths

2017 ◽  
Vol 45 (3) ◽  
pp. 300-312
Author(s):  
Ryoji Nishiyama ◽  
Takatoshi Nagaishi ◽  
Takahisa Masaki

2017 ◽  
Author(s):  
Omar D. Pérez ◽  
Rene San Martín ◽  
Fabián A. Soto

AbstractSeveral contemporary models of associative learning anticipate that the higher responding to a compound of two cues separately trained with a common outcome than to each of the cues alone -a summation effect-is modulated by the similarity between the cues forming the compound. Here, we explored this hypothesis in a series of causal learning experiments with humans. Participants were presented with two visual cues that separately predicted a common outcome and later asked for the outcome predicted by the compound of the two cues. Importantly, the cues’ similarity was varied between groups through changes in shape, spatial position, color, configuration and rotation. In variance with the predictions of these models, we observed similar and strong levels of summation in both groups across all manipulations of similarity (Experiments 1-5). The summation effect was significantly reduced by manipulations intended to impact assumptions about the causal independence of the cues forming the compound, but this reduction was independent of stimulus similarity (Experiment 6). These results are problematic for similarity-based models and can be more readily explained by rational approaches to causal learning.


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