EXPRESS: Inhibitory causal structures in serial and simultaneous feature negative learning

2021 ◽  
pp. 174702182110222
Author(s):  
Peter F. Lovibond ◽  
Jessica C. Lee

We have previously reported that human participants trained with a simultaneous feature negative discrimination (intermixed A+ / AB- trials) show only modest transfer of inhibitory properties of the feature B to a separately trained excitor in a summation test (Lee & Lovibond, 2021). Self-reported causal structure suggested that many participants learned that the effect of the feature B was somewhat specific to the excitor it had been trained with (modulation), rather than learning that the feature prevented the outcome (prevention). This pattern is reminiscent of the distinction between negative occasion-setting and conditioned inhibition in the animal conditioning literature. However, in animals, occasion-setting is more commonly seen with a serial procedure in which the feature (B) precedes the training excitor (A). Accordingly, we ran three experiments to compare serial with simultaneous training in an allergist causal judgment task. Transfer in a summation test was stronger to a previously modulated test excitor compared to a simple excitor after both simultaneous and serial training. There was a numerical trend towards a larger effect in the serial group, but it failed to reach significance and the Bayes Factor indicated support for the null. Serial training had no differential effect on self-reported causal structure, and did not significantly reduce overall transfer. After both simultaneous and serial training, transfer was strongest in participants who reported a prevention structure, replicating and extending our previous results to a previously modulated excitor. These results suggest that serial feature negative training does not promote a qualitatively different inhibitory causal structure compared to simultaneous training in humans.

2020 ◽  
Vol 74 (1) ◽  
pp. 150-165
Author(s):  
Jessica C Lee ◽  
Peter F Lovibond

Traditional associative learning theories predict that training with feature negative (A+/AB-) contingencies leads to the feature B acquiring negative associative strength and becoming a conditioned inhibitor (i.e., prevention learning). However, feature negative training can sometimes result in negative occasion setting, where B modulates the effect of A. Other studies suggest that participants learn about configurations of cues rather than their individual elements. In this study, we administered simultaneous feature negative training to participants in an allergist causal learning task and tested whether evidence for these three types of learning (prevention, modulation, configural) could be captured via self-report in the absence of any procedural manipulation. Across two experiments, we show that only a small subset of participants endorse the prevention option, suggesting that traditional associative models that predict conditioned inhibition do not completely capture how humans learn about negative contingencies. We also show that the degree of transfer in a summation test corresponds to the implied causal structure underlying conditioned inhibition, occasion-setting, and configural learning, and that participants are only partially sensitive to explicit hints about causal structure. We conclude that feature negative training is an ambiguous causal scenario that reveals individual differences in the representation of inhibitory associations, potentially explaining the modest group-level inhibitory effects often found in humans.


Author(s):  
Steven Glautier ◽  
Ovidiu Brudan

Abstract. In the current investigation, we classified participants as inhibitors or non-inhibitors depending on the extent to which they showed conditioned inhibition in a context that had been used for extinction of a conditioned response. This classification enabled us to predict participant responses in a second experiment which used a different design and a different experimental task. In the second experiment a feature-negative discrimination survived reversal training of the feature to a greater extent in the non-inhibitors than in the inhibitors and this result was supported by Bayesian analyses. We propose that the fundamental distinction between inhibitors and non-inhibitors is based on a tendency to utilize first-order (direct associations) or second-order (occasion-setting) strategies when faced with ambiguous information and that this classification is a stable individual differences attribute.


2018 ◽  
Author(s):  
Steven Glautier ◽  
Ovidiu Brudan

There is a revised version at https://osf.io/uspdb/ In the current investigation we classified participants as inhibitors or non-inhibitors depending on the extent to which they showed conditioned inhibition in a context thathad been used for extinction of a conditioned response. This classification enabled us to predict participant responses in a second experiment which used a different design and a different experimental task. In the second experiment a feature-negative discrimination survived reversal training of the feature to a greater extent in thenon-inhibitors than in the inhibitors and this result was supported by Bayesian analyses. We propose that the fundamental distinction between inhibitors andnon-inhibitors is based on a tendency to utilise first-order (direct associations) or second-order (occasion-setting) strategies when faced with ambiguous information andthat this classification is a stable individual differences attribute.


2019 ◽  
Author(s):  
Steven Glautier ◽  
Ovidiu Brudan

In the current investigation we classified participants as inhibitors or non-inhibitors depending on the extent to which they showed conditioned inhibition in a context that had been used for extinction of a conditioned response. This classification enabled us to predict participant responses in a second experiment which used a different design and a different experimental task. In the second experiment a feature-negative discrimination survived reversal training of the feature to a greater extent in the non-inhibitors than in the inhibitors and this result was supported by Bayesian analyses. We propose that the fundamental distinction between inhibitors and non-inhibitors is based on a tendency to utilise first-order (direct associations) or second-order (occasion-setting) strategies when faced with ambiguous information and that this classification is a stable individual differences attribute. (138 words)


2018 ◽  
Author(s):  
Steven Glautier ◽  
Ovidiu Brudan

There is a revised version at https://osf.io/6haqn/ In the current investigation we classified participants as inhibitors or non-inhibitors depending on the extent to which they showed conditioned inhibition in a context that had been used for extinction of a conditioned response. This classification enabled us to predict participant responses in a second experiment which used a different design and a different experimental task. Our results were repeated in two replications and supported by Bayesian analyses. In the second experiment a feature-negative discrimination survived reversal training of the feature to a greater extent in the non-inhibitors than in the inhibitors. We propose that the fundamental distinction between inhibitors and non-inhibitors is based on a tendency to utilise first-order (direct associations) or second-order (occasion-setting) strategies when faced with ambiguous information and that this classification is a stable individual differences attribute.


2010 ◽  
Vol 92 (3) ◽  
pp. 239-250 ◽  
Author(s):  
XIAOJUAN MI ◽  
KENT ESKRIDGE ◽  
DONG WANG ◽  
P. STEPHEN BAENZIGER ◽  
B. TODD CAMPBELL ◽  
...  

SummaryQuantitative trait loci (QTLs) mapping often results in data on a number of traits that have well-established causal relationships. Many multi-trait QTL mapping methods that account for correlation among the multiple traits have been developed to improve the statistical power and the precision of QTL parameter estimation. However, none of these methods are capable of incorporating the causal structure among the traits. Consequently, genetic functions of the QTL may not be fully understood. In this paper, we developed a Bayesian multiple QTL mapping method for causally related traits using a mixture structural equation model (SEM), which allows researchers to decompose QTL effects into direct, indirect and total effects. Parameters are estimated based on their marginal posterior distribution. The posterior distributions of parameters are estimated using Markov Chain Monte Carlo methods such as the Gibbs sampler and the Metropolis–Hasting algorithm. The number of QTLs affecting traits is determined by the Bayes factor. The performance of the proposed method is evaluated by simulation study and applied to data from a wheat experiment. Compared with single trait Bayesian analysis, our proposed method not only improved the statistical power of QTL detection, accuracy and precision of parameter estimates but also provided important insight into how genes regulate traits directly and indirectly by fitting a more biologically sensible model.


2020 ◽  
Vol 46 (4) ◽  
pp. 422-442
Author(s):  
Paula Balea ◽  
James Byron Nelson ◽  
Pedro M. Ogallar ◽  
Jeffrey A. Lamoureux ◽  
Manuel Aranzubia-Olasolo ◽  
...  

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