Unconscious arithmetic: Assessing the robustness of the results reported by Karpinski, Briggs, and Yale (2018)

Perceptual and semantic same-different processing under subliminal conditions

Although other types of subliminal integrative processing are widely refuted by recent studies, subliminal same-different processing (SSDP) remains unchallenged to this day. Using shapes, categorical images, and Chinese characters as stimuli, the current study assessed whether SSDP can occur on a perceptual and semantic basis. Although some significant results were found, the effects are much weaker than previous studies, with Bayes factors suggesting that these effects are not reliable. It is therefore concluded that substantiating claims of SSDP requires more reliable evidence than currently available.

Unconscious integration: Current evidence for integrative processing under subliminal conditions

Integrative processing is traditionally believed to be dependent on consciousness. While earlier studies within the last decade reported many types of integration under subliminal conditions (i.e. without perceptual awareness), these findings are widely challenged recently. This review evaluates the current evidence for 10 types of subliminal integration that are widely studied: arithmetic processing, object-context integration, multi-word processing, same-different processing, multisensory integration and 5 different types of associative learning. Potential methodological issues concerning awareness measures are also taken into account. It is concluded that while there is currently no reliable evidence for subliminal integration, this does not necessarily refute 'unconscious' integration defined through non-subliminal (e.g. implicit) approaches.

The unreliable influence of multivariate noise normalization on the reliability of neural dissimilarity

Representational similarity analysis (RSA) is a key element in the multivariate pattern analysis toolkit. The central construct of the method is the representational dissimilarity matrix (RDM), which can be generated for datasets from different modalities (neuroimaging, behavior, and computational models) and directly correlated in order to evaluate their second-order similarity. Given the inherent noisiness of neuroimaging signals it is important to evaluate the reliability of neuroimaging RDMs in order to determine whether these comparisons are meaningful. Recently, multivariate noise normalization (NN_M) has been proposed as a widely applicable method for boosting signal estimates for RSA, regardless of choice of dissimilarity metrics, based on evidence that the analysis improves the within-subject reliability of RDMs (Guggenmos et al. 2018; Walther et al. 2016). We revisited this issue with three fMRI datasets and evaluated the impact of NN_M on within- and between-subject reliability and RSA effect sizes using multiple dissimilarity metrics. We also assessed its impact across regions of interest from the same dataset, its interaction with spatial smoothing, and compared it to GLMdenoise, which has also been proposed as a method that improves signal estimates for RSA (Charest et al. 2018). We found that across these tests the impact of NN_M was highly variable, as also seems to be the case for other analysis choices. Overall, we suggest being conservative before adding steps and complexities to the (pre)processing pipeline for RSA.

Unconscious preparation: Effects of prime visibility on semantic generalization of task priming

Studies found that subliminal primes can be associated with specific tasks to facilitate task performance, and such learning is highly adaptive and generalizable. Meanwhile, conditioning studies suggest that aversive/reward learning and generalization actually occur at the semantic level. The current study shows that prime-task associations can also be generalized to novel word/neighbour primes from the same semantic category, and this occurs without contingency awareness. Previous studies have counterintuitively suggested that both the learning of task priming and the semantic priming of word neighbours depend on the lack of visibility. Here, we show that semantic generalization indeed depends on reduced visibility, but cannot occur subliminally. The current study shows for the first time that semantic learning and generalization can occur without any emotional or motivational factors, and that semantic priming can occur for arbitrary-linked stimuli in a context completely devoid of semantics.

A Traveler’s Guide to the Multiverse: Promises, Pitfalls, and a Framework for the Evaluation of Analytic Decisions

Decisions made by researchers while analyzing data (e.g., how to measure variables, how to handle outliers) are sometimes arbitrary, without an objective justification for choosing one alternative over another. Multiverse-style methods (e.g., specification curve, vibration of effects) estimate an effect across an entire set of possible specifications to expose the impact of hidden degrees of freedom and/or obtain robust, less biased estimates of the effect of interest. However, if specifications are not truly arbitrary, multiverse-style analyses can produce misleading results, potentially hiding meaningful effects within a mass of poorly justified alternatives. So far, a key question has received scant attention: How does one decide whether alternatives are arbitrary? We offer a framework and conceptual tools for doing so. We discuss three kinds of a priori nonequivalence among alternatives—measurement nonequivalence, effect nonequivalence, and power/precision nonequivalence. The criteria we review lead to three decision scenarios: Type E decisions (principled equivalence), Type N decisions (principled nonequivalence), and Type U decisions (uncertainty). In uncertain scenarios, multiverse-style analysis should be conducted in a deliberately exploratory fashion. The framework is discussed with reference to published examples and illustrated with the help of a simulated data set. Our framework will help researchers reap the benefits of multiverse-style methods while avoiding their pitfalls.