Is there evidence for unaware evaluative conditioning in a valence contingency learning task?

ABSTRACT

In three experiments we investigated whether memory-independent evaluative conditioning (EC) and other memory-independent contingency learning (CL) effects occur in the valence contingency task (VCT). In the VCT, participants respond to the valence of a target word that is preceded by a nonword. Across trials, each nonword is mostly combined with either positive or negative targets. Schmidt and De Houwer (2012. Contingency learning with evaluative stimuli. Experimental Psychology, 59, 175–182. doi:10.1027/1618-3169/a000141) showed faster and more often correct responses on trials that conformed to this contingency. Additionally, the authors found EC on valence ratings assessed after the VCT. All effects occurred also in the absence of contingency memory. Our Experiments 1a and 1b replicated the CL effects on measures assessed during the VCT (RT, errors) and showed that they occurred in the absence of contingency memory, but they did not replicate the EC effect assessed after the VCT. In Experiment 2, we tested whether this dissociation between EC and other CL effects was due to the different phases (during vs. after VCT) with a CL measure that could be used in both phases. On this measure, the CL effect was memory-dependent after, but not during the VCT. Across measures and experiments, we thus find memory-independent CL during the VCT, but not afterwards.

KEYWORDS:

• Contingency learning
• evaluative conditioning
• memory
• awareness
• implicit learning
• colour-word contingency task

Acknowledgement

We thank Johann Jacoby and David Shanks for statistical advice and Wolfgang Walther for programming Experiment 1b

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Sometimes authors aim at investigating contingency awareness rather than contingency memory, but we are indeed interested in contingency memory. Following common terminology, we use both the terms awareness and memory to refer to contingency knowledge assessed after the learning phase.

2 We always used the default prior with a Cauchy distribution centred on 0 and width of r = 0.707 (Rouder, Speckman, Sun, Morey, & Iverson, 2009). Bayes factors are based on Bayesian t-tests calculated with the R package BayesFactor.

3 The intercept in the regression analysis and the contingency main effects in the ANOVA and linear mixed models are of course not exactly analogous but in the absence of moderation by memory the interpretation would be similar.

4 We had planned to test 96 participants.

5 This and similar follow-up tests were conducted on split datasets to reach convergence.

6 To also gauge the memory-independent EC effect across our own studies, we ran a regression analysis based on only these studies, which did not yield a significant intercept, b = 0.070, SE = 0.069, t = 1.02, p = .309.

7 We used the R package metaBMA (Heck & Gronau, 2017). For the prior on the distribution for the effect size of the EC effect, we used a Cauchy distribution centered on zero and a scale parameter of r = .707. As suggested in the article by Gronau et al. (2017), we used an Inverse-Gamma (1, 0.15) distribution as a prior for the between-study heterogeneity.

Additional information

Funding

This research was supported by Deutsche Forschungsgemeinschaft [grant number GA 1520/2-1].