Unconscious memory suppression

A global neuronal workspace model of functional neurological disorders

We introduce here a general model of Functional Neurological Disorders based on the following hypothesis: a Functional Neurological Disorder could correspond to a consciously initiated voluntary top-down process causing involuntary lasting consequences that are consciously experienced and subjectively interpreted by the patient as involuntary. We develop this central hypothesis according to Global Neuronal Workspace theory of consciousness, that is particularly suited to describe interactions between conscious and non-conscious cognitive processes. We then present a list of predictions defining a research program aimed at empirically testing their validity. Finally, this general model leads us to reinterpret the long-debated links between hypnotic suggestion and functional neurological disorders. Driven by both scientific and therapeutic goals, this theoretical paper aims at bringing closer the psychiatric and neurological worlds of functional neurological disorders with the latest developments of cognitive neuroscience of consciousness.

The Pervasive Problem of Post Hoc Data Selection in Studies on Unconscious Processing

Studies on unconscious mental processes typically require that participants are unaware of some information (e.g., a visual stimulus). An important methodological question in this field of research is how to deal with data from participants who become aware of the critical stimulus according to some measure of awareness. While it has previously been argued that the post hoc selection of participants dependent on an awareness measure may often result in regression-to-the-mean artifacts (Shanks, 2017), a recent article (Sklar et al., 2021) challenged this conclusion claiming that the consideration of this statistical artifact might lead to unjustified rejections of true unconscious influences. In this reply, we explain this pervasive statistical problem with a basic and concrete example, show that Sklar et al. fundamentally mischaracterize it, and then refute the argument that the influence of the artifact has previously been overestimated. We conclude that, without safeguards, the method of post hoc data selection should never be employed in studies on unconscious processing.

The Challenge of Inferring Unconscious Mental Processes

Studies of unconscious mental processes often compare a performance measure (e.g., some assessment of perception or memory) with a measure of awareness (e.g., a verbal report or forced-choice response) of the critical cue or contingency taken either concurrently or separately. The resulting patterns of bivariate data across participants lend themselves to several analytic approaches for inferring the existence of unconscious mental processes, but it is rare for researchers to consider the underlying generative processes that might cause these patterns. We show that bivariate data are generally insufficient to discriminate single-process models, with a unitary latent process determining both performance and awareness, from dual-process models, comprising distinct latent processes for performance and awareness. Future research attempting to isolate and investigate unconscious processes will need to employ richer types of data and analyses.

Raising awareness about measurement error in research on unconscious mental processes

Experimental psychologists often neglect the poor psychometric properties of the dependent measures collected in their studies. In particular, a low reliability of measures can have dramatic consequences for the interpretation of key findings in some of the most popular experimental paradigms, especially when strong inferences are drawn from the absence of statistically significant correlations. In research on unconscious cognition, for instance, it is commonly argued that the lack of a correlation between task performance and measures of awareness or explicit recollection of the target stimuli provides strong support for the conclusion that the cognitive processes underlying performance must be unconscious. Using contextual cuing of visual search as a case study, we show that given the low reliability of the dependent measures collected in these studies, it is usually impossible to draw any firm conclusion about the unconscious character of this effect from correlational analyses. Furthermore, both a psychometric meta-analysis of the available evidence and a cognitive-modeling approach suggest that, in fact, we should expect to see very low correlations between performance and awareness at the empirical level, even if both constructs are perfectly related at the latent level. Convincing evidence for the unconscious character of contextual cuing and other effects will most likely demand richer and larger data sets, coupled with more powerful analytic approaches.

Correlation analysis to investigate unconscious mental processes: A critical appraisal and mini-tutorial

As a method to investigate the scope of unconscious mental processes, researchers frequently obtain concurrent measures of task performance and stimulus awareness across participants. Even though both measures might be significantly greater than zero, the correlation between them might not, encouraging the inference that an unconscious process drives task performance. We highlight the pitfalls of this null-correlation approach and provide a mini-tutorial on ways to avoid them. As reference, we use a recent study by Salvador et al. (2018) reporting a non-significant correlation between the extent to which memory was suppressed by a Think/No-Think cue and an index of cue awareness. In the Null Hypothesis Significance Testing (NHST) framework, it is inappropriate to interpret failure to reject the null hypothesis (i.e., correlation = 0) as evidence for the null. Furthermore, psychological measures are often unreliable, which can dramatically attenuate the size of observed correlations. A Bayesian approach can circumvent both problems and compare the extent to which the data provide evidence for the null versus the alternative hypothesis (i.e., correlation > 0), while considering the usually low reliabilities of the variables. Applied to Salvador et al.'s data, this approach indicates no to moderate support for the claimed unconscious nature of participants' memory-suppression performance-depending on the model of the alternative hypothesis. Hence, more reliable data are needed. When analyzing correlational data, we recommend researchers to employ the Bayesian methods developed here (and made freely available as R scripts), rather than standard NHST methods, to take account of unreliability.

Sleep reactivation did not boost suppression-induced forgetting

Sleep's role in memory consolidation is widely acknowledged, but its role in weakening memories is still debated. Memory weakening is evolutionary beneficial and makes an integral contribution to cognition. We sought evidence on whether sleep-based memory reactivation can facilitate memory suppression. Participants learned pairs of associable words (e.g., DIET-CREAM) and were then exposed to hint words (e.g., DIET) and instructed to either recall ("think") or suppress ("no-think") the corresponding target words (e.g., CREAM). As expected, suppression impaired retention when tested immediately after a 90-min nap. To test if reactivation could selectively enhance memory suppression during sleep, we unobtrusively presented one of two sounds conveying suppression instructions during sleep, followed by hint words. Results showed that targeted memory reactivation did not enhance suppression-induced forgetting. Although not predicted, post-hoc analyses revealed that sleep cues strengthened memory, but only for suppressed pairs that were weakly encoded before sleep. The results leave open the question of whether memory suppression can be augmented during sleep, but suggest strategies for future studies manipulating memory suppression during sleep. Additionally, our findings support the notion that sleep reactivation is particularly beneficial for weakly encoded information, which may be prioritized for consolidation.

How Do I Know What My Theory Predicts?

To get evidence for or against a theory relative to the null hypothesis, one needs to know what the theory predicts. The amount of evidence can then be quantified by a Bayes factor. Specifying the sizes of the effect one’s theory predicts may not come naturally, but I show some ways of thinking about the problem, some simple heuristics that are often useful when one has little relevant prior information. These heuristics include the room-to-move heuristic (for comparing mean differences), the ratio-of-scales heuristic (for regression slopes), the ratio-of-means heuristic (for regression slopes), the basic-effect heuristic (for analysis of variance effects), and the total-effect heuristic (for mediation analysis).