15:00 - 16:30
Talk Session VII
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15:00 - 16:30
Wed-HS1-Talk VII-
Wed-Talk VII-
Room: HS1
Chair/s:
Simone Malejka
Can we detect regularities in our environment and adapt behavior accordingly in the absence of awareness? The demonstration of unconscious (implicit) cognition hinges on participants’ unawareness of stimuli, processes, or products involved in a task. The gold standard is to establish an indirect-without-direct effect, that is, an uninstructed effect of a stimulus on behavior under conditions that preclude any effect of the stimulus on a response according to explicit instructions. This symposium will bring together researchers working on new methods tailored to investigate the possibility of indirect-without-direct effects. The first two talks will present novel indirect and direct
measures for well-known experimental paradigms. Sascha Meyen will demonstrate a new test of reaction-time differences, which offers an improved indirect measure and provides evidence against unconscious processing in contextual cueing. In the area of priming, Thomas Schmidt will talk about a new theory of visibility focusing on the critical stimulus feature that generates the indirect effect and must be assessed in the direct measure. The final three talks will present new analyses for data that presumably show an indirect-without-direct pattern. These data often suffer from regression to the mean (RttM), defined as the statistical phenomenon that makes natural variation in repeated data look like real change. When direct measures are contaminated with measurement error, low awareness scores will tend to be followed by awareness scores closer to the mean. Itay Yaron will outline a solution to the RttM problem that uses a widely applicable bootstrapping algorithm based only on a small set of assumptions. Simone Malejka will present a method of true-score estimation based on the Bayesian principle of shrinkage, which corrects noisy data and can solve RttM and related measurement biases. Lastly, Zoltan Dienes will demonstrate how Bayes factors can provide evidence for (or against) one’s theory in the presence of measurement error by testing an interval null hypothesis of zero awareness in post-hoc trial selection.
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When does selecting out conscious trials create regression to the mean?
Wed-HS1-Talk VII-05
Presented by: Zoltan Dienes
Zoltan Dienes
University of Sussex
In implicit cognition research a standard strategy is to measure the conscious status of knowledge on each trial (e.g. with confidence or visual clarity ratings (PAS), or structural knowledge attributions) and then sub-select the trials where the knowledge is measured to be unconscious. If the accuracy is above chance that is taken to be evidence for unconscious knowledge. David Shanks has pointed out the problem of regression to the mean when people or trials are sub-selected: Because of the ubiquitous possibility of error in measurement, when a selection is made on the basis of one variable (e.g. conscious vs unconscious structural knowledge), the actual value of that variable will be closer to the mean than the measured value. Thus, trials selected to be based on unconscious structural knowledge will actually have some conscious structural knowledge. Does this critique undermine the use of trial by trial measurement, such as structural knowledge attributions in implicit learning (or confidence or PAS in subliminal perception)? I show that it does not. I show how to quantify the actual effect size that could be produced by regression to the mean in any given situation, how it may be so small as to be meaningless, and how to deal with it when it is of a decent size, using Bayes factors with an interval null hypothesis.
Keywords: implicit cognition, regression to mean, Bayes factor
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