Conscious awareness modulates processing speed in the redundant signal effect

Abstract

Evidence for the influence of unaware signals on behaviour has been reported in both patient groups and healthy observers using the Redundant Signal Effect (RSE). The RSE refers to faster manual reaction times to the onset of multiple simultaneously presented target than those to a single stimulus. These findings are robust and apply to unimodal and multi-modal sensory inputs. A number of studies on neurologically impaired cases have demonstrated that RSE can be found even in the absence of conscious experience of the redundant signals. Here, we investigated behavioural changes associated with awareness in healthy observers by using Continuous Flash Suppression to render observers unaware of redundant targets. Across three experiments, we found an association between reaction times to the onset of a consciously perceived target and the reported level of visual awareness of the redundant target, with higher awareness being associated with faster reaction times. However, in the absence of any awareness of the redundant target, we found no evidence for speeded reaction times and even weak evidence for an inhibitory effect (slowing down of reaction times) on response to the seen target. These findings reveal marked differences between healthy observers and blindsight patients in how aware and unaware information from different locations is integrated in the RSE.

Keywords: Continuous flash suppression; Redundant Signal Effect; Visual awareness.

Fig. 1

a Schematic representation of the time-course of a trial for a Single unmasked target. Schematic displays of the spatial locations of the visual targets of experiment 1(b), experiment 2(c) and experiment 3(d)

Fig. 2

a Violin plot with boxplot of the reaction times split by condition. b Violin plots with box plots of the reaction times to the Double Contralateral condition split by Aware and Unaware trials, and the Single unmasked trials. Each dot represents the mean reaction time for each participant. c The proportion of PAS 1, 2, 3, and 4 responses per participant. Each participant of experiment 1 is represented by a line. d A boxplot of the reaction time per condition and available PAS response with the data by trial of experiment 1 overlaid as a dot plot. c, d were made in R with the package ggplot2 (Wickham 2016)

Fig. 3

a Violin plot with boxplot of the reaction times split by condition. b Violin plots with box plots of the mean of the reaction times to the Double Asymmetric and Double Contralateral condition collapsed together and split by Aware and Unaware trials, and the Single unmasked condition. Each dot represents the mean for each participant. c The proportion of PAS 1, 2, 3, and 4 responses per participant. Each participant of experiment 2 is represented by a line. d A boxplot of the reaction time per condition and available PAS response with the data by trial of experiment 2 overlaid as a dot plot. c, d were made in R with the package ggplot2 (Wickham 2016)

Fig. 4

a Violin plot with boxplot of the reaction times split by condition. b Violin plots with box plots of the mean of the reaction times to the Double Diagonal, Double Ipsilateral, and Double Contralateral condition collapsed together and split by Aware and Unaware trials, and the Single unmasked condition. Each dot represents the mean for each participant. c The proportion of PAS 1, 2, 3, and 4 responses per participant. Each participant of experiment 3 is represented by a line. d A boxplot of the reaction time per condition and available PAS response with the data by trial of experiment 3 overlaid as a dot plot. c, d were made in R with the package ggplot2 (Wickham 2016)

Fig. 5

Histogram of redundancy gain ranging from − 100 to 100 ms for all double target conditions from the three experiments. The data has been split into aware and unaware trials and the dotted lines represent the mean for each of the distributions. The solid lines represent the comparison to the Single unmasked conditions. The arrows are included to highlight the difference between the distribution means and the baseline

Fig. 6

a Model plotted with the marginal effects values predicted with the function ggpredict() in the package ggeffects (Lüdecke 2018). The predictions of the model are shown together with the datapoints from all three experiments split by PAS response (1, 2, 3, 4) and the number of targets. b The marginal effects of model prediction without the data plotted over the prediction lines. Both figures were made in R with the package ggplot2 (Wickham 2016) as a part of the Tidyverse package (Wickham et al. 2019). The error margins represent the 95% CI as predicted for the model