Testing the limits of the Perruchet effect in choice response time tasks.

Abstract

The Perruchet effect refers to a dissociation between trends in the conscious expectancy of an event and trends in the strength or the speed of responding to that event, which suggests that learned changes in the performance of a response may be automatic. Despite being consistently demonstrated in conditioning studies and simple reaction time (RT) tasks, mixed results have been found in the choice variant of the Perruchet effect, especially when expectancy and responding are measured concurrently (that is, on the same trial). The present experiments examined why the dissociation disappears when concurrent measurement is used by directly comparing trials on which expectancy is measured to trials on which expectancy is not measured. In Experiment 1, expectancy was measured on a randomly chosen 50% of trials, whereas expectancy was measured every fourth trial in Experiment 2. In both experiments, the Perruchet effect was weakened on trials that immediately followed an expectancy rating but was still clearly evident on other trials, suggesting that automatic facilitation of RT based on recent trial history is temporarily masked, rather than abolished, by a concurrent expectancy judgment. (PsycINFO Database Record

Validation of a Bayesian Adaptive Estimation Technique in the Stop-Signal Task

“…Automatic learning effects are well documented in simple go/no go and choice RT tasks even when the trial sequences employed on those tasks are unsystematic (for instance, the Perruchet effect [30, 31, 32, 33]). In the SST, a combination of automatic and intentional, strategic changes in responding may occur across the course of an experimental session, such as incremental slowing of go responses, or deliberate anticipation of Stop signals (e.g.…”

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

“…The result has now been replicated across several paradigms involving classical conditioning and voluntary responding (see Perruchet, 2015 for a review). Current debates about the validity of this dissociation center around whether the pattern observed in anticipatory behavior is a bona fide example of associative learning (e.g., Weidemann et al, 2009, 2016; Barrett and Livesey, 2010; Mitchell et al, 2010) and whether participants truly hold these two conflicting belief biases concurrently (Livesey and Costa, 2014; Lee Cheong Lem et al, 2015). However, to date there has been no attempt to explore how these beliefs affect future learning.…”

Saving the Perruchet effect: A role for the strength of the association in associative learning

“…These authors only found a difference between these two groups in a concurrent measurement condition, supporting that the effect of expectancies on performance is stronger when expectancies are collected before each RT trial. In another study, Lee Cheong Lem, Harris, and Livesey (2015) measured RT in blocks in which expectancy trials were intermixed with trials that did not have an expectancy measurement. Even though they did not directly compare RT slopes, Lee Cheong Lem et al (2015) did not find much of a difference between participants who consistently followed the GF and so-called “inconsistent” participants on trials where expectancies were not collected, but only in expectancy trials.…”

“…In another study, Lee Cheong Lem, Harris, and Livesey (2015) measured RT in blocks in which expectancy trials were intermixed with trials that did not have an expectancy measurement. Even though they did not directly compare RT slopes, Lee Cheong Lem et al (2015) did not find much of a difference between participants who consistently followed the GF and so-called “inconsistent” participants on trials where expectancies were not collected, but only in expectancy trials. In our study, expectancies were collected in the first block of trials, before the RT blocks, and we observed that expectancies influenced performance.…”