Detecting (Un)seen Change: The Neural Underpinnings of (Un)conscious Prediction Errors

Progressing, not regressing: A possible solution to the problem of regression to the mean in unconscious processing studies

How convincing is current evidence for unconscious processing? Recently, a major criticism suggested that some, if not much, of this evidence might be explained by a mere statistical phenomenon: regression to the mean (RttM). Excluding participants based on an awareness assessment is a common practice in studies of unconscious processing, and this post hoc data selection might lead to false effects that are driven by RttM for aware participants wrongfully classified as unaware. Here, we examined this criticism using both simulations and data from 12 studies probing unconscious processing (35 effects overall). In line with the original criticism, we confirmed that the reliability of awareness measures in the field is concerningly low. Yet, using simulations, we showed that reliability measures might be unsuitable for estimating error in awareness measures. Furthermore, we examined other solutions for assessing whether an effect is genuine or reflects RttM; all suffered from substantial limitations, such as a lack of specificity to unconscious processing, lack of power, or unjustified assumptions. Accordingly, we suggest a new nonparametric solution, which enjoys high specificity and relatively high power. Together, this work emphasizes the need to account for measurement error in awareness measures and evaluate its consequences for unconscious processing effects. It further suggests a way to meet the important challenge posed by RttM, in an attempt to establish a reliable and robust corpus of knowledge in studying unconscious processing.

Awareness and consciousness in humans and animals - neural and behavioral correlates in an evolutionary perspective

Awareness or consciousness in the context of stimulus perception can directly be assessed in well controlled test situations with humans via the persons' reports about their subjective experiences with the stimuli. Since we have no direct access to subjective experiences in animals, their possible awareness or consciousness in stimulus perception tasks has often been inferred from behavior and cognitive abilities previously observed in aware and conscious humans. Here, we analyze published human data primarily on event-related potentials and brain-wave generation during perception and responding to sensory stimuli and extract neural markers (mainly latencies of evoked-potential peaks and of gamma-wave occurrence) indicating that a person became aware or conscious of the perceived stimulus. These neural correlates of consciousness were then applied to sets of corresponding data from various animals including several species of mammals, and one species each of birds, fish, cephalopods, and insects. We found that the neural markers from studies in humans could also successfully be applied to the mammal and bird data suggesting that species in these animal groups can become subjectively aware of and conscious about perceived stimuli. Fish, cephalopod and insect data remained inconclusive. In an evolutionary perspective we have to consider that both awareness of and consciousness about perceived stimuli appear as evolved, attention-dependent options added to the ongoing neural activities of stimulus processing and action generation. Since gamma-wave generation for functional coupling of brain areas in aware/conscious states is energetically highly cost-intensive, it remains to be shown which animal species under which conditions of lifestyle and ecological niche may achieve significant advantages in reproductive fitness by drawing upon these options. Hence, we started our discussion about awareness and consciousness in animals with the question in how far these expressions of brain activity are necessary attributes for perceiving stimuli and responding in an adaptive way.

The Predictive Role of Low Spatial Frequencies in Automatic Face Processing: A Visual Mismatch Negativity Investigation

Visual processing is thought to function in a coarse-to-fine manner. Low spatial frequencies (LSF), conveying coarse information, would be processed early to generate predictions. These LSF-based predictions would facilitate the further integration of high spatial frequencies (HSF), conveying fine details. The predictive role of LSF might be crucial in automatic face processing, where high performance could be explained by an accurate selection of clues in early processing. In the present study, we used a visual Mismatch Negativity (vMMN) paradigm by presenting an unfiltered face as standard stimulus, and the same face filtered in LSF or HSF as deviant, to investigate the predictive role of LSF vs. HSF during automatic face processing. If LSF are critical for predictions, we hypothesize that LSF deviants would elicit less prediction error (i.e., reduced mismatch responses) than HSF deviants. Results show that both LSF and HSF deviants elicited a mismatch response compared with their equivalent in an equiprobable sequence. However, in line with our hypothesis, LSF deviants evoke significantly reduced mismatch responses compared to HSF deviants, particularly at later stages. The difference in mismatch between HSF and LSF conditions involves posterior areas and right fusiform gyrus. Overall, our findings suggest a predictive role of LSF during automatic face processing and a critical involvement of HSF in the fusiform during the conscious detection of changes in faces.