Stimulus localization in the absence of awareness shown in individuals
established with an objective measure
Student: Mathijs Bergers Student number: 10671730
Supervisor: Timo Stein Date: June 18, 2020
Abstract
Much is unknown of the neurobiological basis for consciousness. To gain more insight on the subject, this study was performed to show if stimulus localization could occur in the absence of awareness established with an objective measure. A 2-AFC spatial response task was performed with a psychophysical approach, using backward masking (five masking strengths) to suppress the target stimuli. The target stimuli could be either left, right or not present at all. The participants (N = 4) had to localize the target stimuli (to measure performance sensitivity) and determine its presence (to measure awareness sensitivity). One task consisted of 720 trials equally divided over 5 blocks. When looking at individual results of the participants, three out of four showed localization without awareness. A trend was observed when all the participants together were looked at: the weaker the masking strength, the higher the performance and awareness sensitivity. These results give the implication that unconscious processing without awareness could take place. However, a great diversity in consciousness studies makes it difficult to cross-examine these results.
Introduction
One of the most fundamental topics in neuroscience and psychology today is to understand the neurobiological basis that results in conscious awareness. Consciousness is a subjective experience, where a person is aware of him or herself and of their connection to the environment. To obtain these subjective experiences, an excited cerebellar cortex is required (Posner, Saper, Schiff, & Plum, 2007). Thereby, people can communicate verbally and by body language about information that is processed consciously. However, a lot of information is not perceived consciously and get processed unconsciously (Dehaene & Changeux, 2011). To get a better understanding of consciousness is of enormous scientific and clinical importance, in order to improve clinical assessment of consciousness in unresponsive patients with brain damage (Gosseries, Di, Laureys, & Boly, 2014). In order to get a better understanding of the neural basis to conscious awareness and its function, a lot can be learned from unconscious processes.
A phenomenon called blindsight (Weiskrantz, 1986) causes doubt as to whether there is function of consciousness. Although blindsight patients suffer from damage to the visual cortex and report “blindness” to the contralateral hemifield of their vision, they are still able to guess the presence or location of stimuli with considerable accuracy (Stoerig & Pöppel, 1986; Weiskrantz, Warrington, Sanders, & Marshall, 1974). In healthy patients such clinical blindsight effects has been observed too. Performance on localization of “unseen” targets was significantly higher than chance (Graves & Jones, 1992; Hesselmann, Hebart, & Malach, 2011; Meeres & Graves, 1990; Salti et al., 2015).
However, these studies used subjective measures to show unconscious processing. These measures rely on the introspective reports of the participants. In other words, the participants report what they saw (Seth, Dienes, Cleeremans, Overgaard, & Pessoa, 2008). The use of these subjective measures is highly doubted by some researchers. These measures would
rely on the criterion of awareness set by the observer. Therefore it would measure what the observer reports, and not what can be reported. (Eriksen, 1960; Hannula, Simons, & Cohen, 2005; Lloyd, Abrahamyan, & Harris, 2013).
Furthermore, when observers showed above chance performance while reporting stimuli subjectively invisible, these seemingly unconscious effects might just be weakly conscious (Persuh, Larock, & Berger, 2018; Schmidt, 2015; Snodgrass, Bernat, & Shevrin, 2004; Stein, Kaiser, & Hesselmann, 2016). Kolb and Braun (1995) tried to disprove this by showing no relation between confidence ratings and performance in a masked condition. Nonetheless, their results could not be reproduced (Morgan, Mason, & Solomon, 1997; Robichaud & Stelmach, 2003).
An alternative to these subjective measures of awareness are objective measures, which makes use of the Signal Detection Theory. These objective measures determine if stimuli were perceived consciously by the observer, based on their task performance. In that way, the problem that the observers set their own criterion of awareness gets tackled (Hannula et al., 2005). Peters and Lau (2015) applied these criterion-free objective measures, but no unconscious perception was found. The reason they did not find unconscious perception might be because they used orientation discrimination to measure task performance. Orientation discrimination is thought to be processed in the primary visual cortex, as seen in a study of cats and macaque monkeys (Hubel & Wiesel, 1974). Another way of testing unconscious processing is localization, where the participant has to localize the target instead of determine the orientation of it. Therefore, it is a lower level way to test unconscious processing. In monkeys, localization is found to be processed in the superior colliculi (Horwitz & Newsome, 1999). According to Leopold (2012) the superior colliculi might play a role in blindsight and thus the possibility arises that unconscious processing can be found using localization instead of orientation discrimination. A pilot study of Stein, Lam and Pinto (n.d.) used such a localization
measure in a 20-AFC spatial response task and indicated performance above chance without objective awareness.
The goal of this study is to generalize these pilot findings to less locations (2-AFC) and to test this with the classical psychophysical approach (few subjects, thousands of trials per subject). A spatial response task is used to determine if stimulus localization could occur in the absence of awareness established with an objective measure. The participants had to detect (absent or present) and localize (left or right) the stimuli. To show if stimulus localization could occur outside awareness, unconscious processing (detection d’) must not differ from zero while performance (localization d’) is above chance (Erdelyí, 1986; Snodgrass et al., 2004). In addition, performance sensitivity (localization d’) must be significantly higher than the awareness sensitivity (detection d’) (Franz & von Luxburg, 2015; Reingold & Merikle, 1988; Schmidt & Vorberg, 2006). Based on the outcome of the pilot study (Stein et al., n.d.), is expected to find performance above chance while detection d’ is equal to zero. However, performance sensitivity will not be higher than awareness sensitivity. So only the classical criterion for zero-awareness will be met and not the criterion of higher sensitivity for the performance measure.
Material and Method
Participants. Four right-handed students of the University of Amsterdam (3 male, 1
female, M[age] = 24.0 years, SD = 2.16). All participants were healthy and had normal or corrected-to-normal visual acuity. Because of limitations due to the COVID-19 outbreak, the experiments were performed by trainees, so all participants knew the purpose of the experiment.
Apparatus. Due to the same limitations described previously, every participant used
their own laptop or computer. Experiments were written in MATLAB (R2018b), using the Psychophysics Toolbox extensions (Brainard, 1997). Stimuli were shown against an almost
white background differing between participants from RGB[217 217 217] to RGB[230 230 230] (see Appendix – Table 1 for participant specific details). All participants were told to keep their viewing distance towards the screen and environmental factors (e.g. lightning in the room) approximately the same every trial.
Stimuli. The target was a faint grey square (14 pixels x 14 pixels) and differed in
greyness between participants (from RGB[179 179 179] to RGB[191 191 191]). It could be presented either left or right relative to the fixation point on equal distance. A meta-contrast mask was used that looked like a plus sign consisting of five blocks (each block 14 pixels x 14 pixels), without the block in the middle. The gap in the middle is where the target stimuli was presented, which fitted perfectly in the mask. Five masking strengths (M1 to M5) were used which differed in luminance of the mask and in time. A blank screen was presented in-between the target stimulus and the masking stimuli (M1: RGB[0 0 0], 0 ms; M2: RGB[0 0 0], 16.7 ms; M3: RGB[128 128 128], 33.3 ms; M4: RGB[179 179 179], 50 ms; M5: RGB[179 179 179], 83.3 ms). M1 was the strongest mask and M5 the weakest mask.
Spatial response task. Every trial started with a black fixation cross (RGB[255 255
255]) shown in the centre of the screen (1s), followed by a flash of the target stimulus (16.7 ms). The strongest masking stimuli (16.7 ms) followed right after the target stimuli. With the other masks a blank screen was shown in-between the target stimulus and the masking stimuli (16.7), varying in presentation time (highest presentation time for the weakest mask). All masks were shown at the same time at both sides of the fixation cross (Figure 1).The task was divided in two sequences: the normal spatial response task and the reversed spatial response task. At the normal spatial response task the participants indicated the target location first and the target awareness second. At the reversed spatial response task this sequence was switched.
All participants were instructed to perform the task as accurate as possible, since there was no time pressure. For the localization task, the participants had to indicate the location of
the target (left or right) by pressing the “right-arrow key” when the target was to the right, and the “left-arrow key” when the target was to the left. Then they had to say if the target stimuli was absent or presents by pressing a key (1 = absent, 2 = present).
The target stimulus was only present in 67% of the trials, so the participants had to guess sometimes at the localization task. All masking strengths were used evenly. The spatial response task consisted of 720 trials, divided over five blocks of each 144 trials with at least 20 seconds rest in-between.
All participants performed the spatial response task multiple times (M = 21.8, SD= 2.75), were participant one performed the task 19 times, participant two 20 times, participant three 23 times and participant four 25 times.
Analysis. For objective target visibility, all masking strengths were analysed. According
to the signal detection theory, the hit rate was defined as the proportion of trials where target was present, and the observer responded that the target was present. The false alarm rate was defined as the rate of trials where the target was absent, and the observer responded that the target was present. To calculate detection d’, the inverse of the cumulative distribution function of the false alarm rate is subtracted from the inverse of the cumulative distribution function of
Figure 1. Example of a trial of the spatial response task. Every trial started with a fixation point (1s),
followed by a target stimulus in 67% of the all trials. Target stimulus could be left or right. Mask 1 (16.7 ms) followed directly after the target stimulus. The other four masks (16.7 ms) followed 16.7 ms to 83.3 ms after the target stimulus fainted. The masks differed not only in presentation time, but also in luminance. After the participants had to give an indication of the location first (left or right), followed by an indication of the detection (absent or present). At the inversed spatial response task localization and detection were switched.
the hit rate (Kingdom & Prins, 2016). Thereafter, detection d’ was compared against zero using a one-sample t-test. If the result is not significant, it means the stimulus could not be detected above chance level. This indicates that the participants were not aware of the stimulus.
To analyse the target location performance (localization d’), all masking strengths were used. The hit rate was defined as the number of trials where the observer responded with the correct location (e.g. left response on left location) divided by all trials where the target was present. The false alarm rate was defined as the number of trials where the observer responded with the wrong location (e.g. left response on right location) divided by all trials where the target was present. Then the inverse of the cumulative distribution function was applied to both hit rate and false alarm rate, after which the inversed false alarm rate was subtracted from the inversed hit rate. To obtain localization d’ this number was divided by √2 for it being a 2-AFC task (Kingdom & Prins, 2016).
One way to show unconscious processing regarding to the classic dissociation paradigm, is to show positive results towards performance while the unconscious processing (detection d’) does not differ from zero. (Erdelyí, 1986; Snodgrass et al., 2004). So detection d’ was compared to zero using a one sample t-test and the accuracy (hit rate) of target localization performance was compared against chance (50%, two locations).
Although there might be a problem using this method of showing unconscious processing. Franz & von Luxburg (2015) claimed that in order to show unconscious processing it is not enough to show a significant result in one task (performance) and a non-significant result in another task (awareness). Namely, the significant result at performance cannot show that awareness was absent. Therefore, both tasks need to be compared to each other, with the performance sensitivity being significantly higher than the awareness sensitivity. This could be accomplished by showing a dissociation between both (Reingold & Merikle, 1988; Schmidt & Vorberg, 2006). Therefore a paired sample t-test of localization d’ and detection d’ was done,
where the performance sensitivity had to be higher than the detection sensitivity in order to reach the criterion of higher sensitivity.
All data was analysed using MATLAB (R2018b) and SPSS (IBM SPSS Version 26.0)
Results
Participants together. Fig. 2A displays the means of detection d’ and localization d’
of all participants under five different conditions of masking strength. No ANOVA could be performed due to the limited amount of participants (N = 4). Two interesting features were observed when looking at the graph. First, when the masking strength was getting weaker, both performance sensitivity as detection sensitivity are getting higher. Second, at the highest masking strengths (M1, M2 and M3) localization d’ was higher than detection d’.
Participants individually. Originally, this study was supposed to be a high sample size
study in which all the participants had to perform one block. When analysed, the participants would have been the random factor in order to show if the results would be consistent across all participants. In that case, assumptions could be made upon a population. However, because of the circumstances, this study had a low sample size (N = 4) in which every participant performed at least 19 blocks. Each participant is analysed individually with the blocks as random factor. Therefore this analysis would show if the results are consistent across blocks for each subject.
Analysing each participant separately, three out of four subjects showed localization without awareness. All masking strengths of both tasks were compared to zero using a one-sample t-test (one-tailed). At participant 1 detection d’ did not significantly differ from zero at M2, t(18) = -1.39, p = .09, while localization d’ did, t(18) = 2.10, p = .025 (Fig. 2B). At M1 participant 2 showed also no significantly difference at detection d’, t(19) = 0.37, p = .36, but
Figure 2. Mean d’ of both tasks (detection and localization) under five different masking strengths.
Masking strength 1 is the strongest mask and masking strength 5 the weakest. A) Mean d’ of all participants together. B/C/D/E) Mean d’ of participant 1 (B), participant 2 (C), participant 3 (D), and participant 4 (E). Participant 1 showed significant unconscious processing in masking strength 2, whereas participant 2 and 3 showed significant unconscious processing in masking strength 1. No significance was found in participant 4. In all 5 graphs the error bars represent the standard error of the mean.
at localization d’ it did, t(19) = 5.56, p < .001 (Fig. 2C). Also at M1, detection d’ did not differ from zero at participant 3, t(22) = 1.72, p = .05, while localization d’ did differ from zero, t(22) = 8.08, p < .001 (Fig. 2D). Only at participant 4, no masking strength was found where detection d’ did not differ from zero while localization d’ did (Fig. 2E).
However, in order to show localization without awareness at the masking strengths mentioned above, it is not sufficient to show merely significance in localization and no significance in detection (Franz & von Luxburg, 2015). A differentiation between detection and localization must be shown too (Reingold & Merikle, 1988; Schmidt & Vorberg, 2006). Therefore, a paired sample t-test of localization d’ and detection d’ was performed at these specific masking strengths. Participant 1, t(18) = -2.35, p = .03, participant 2, t(19) = -2.15, p = .045, and participant 3, t(22) = -2.29, p = 0.032, all showed differentiation between detection and localization.
Discussion
The aim of this study was to test whether it is possible to show localization without awareness established by a objective measure. The main results indicate that this is possible. First, when all participants were analysed together, at one masking strength it seemed that awareness sensitivity was close to zero while performance seemed a bit higher. Second, with each participant analysed individually, three out of four participants showed at one masking condition localization above chance while detection d’ is equal to zero. This corresponds with the proposed hypothesis. However, also performance sensitivity showed to be significant higher than sensitivity at these particular masking strengths, which was not expected.
Therefore, these participants meet the strict objective measures for unconsciousness, showing both detection sensitivity close to zero while localization sensitivity differs from zero
(Franz & von Luxburg, 2015), and a differentiation between detection and localization (Reingold & Merikle, 1988; Schmidt & Vorberg, 2006).
These results give the implication that unconscious processing can take place in individuals without awareness, contradicting the results of Peters and Lau (2015) who applied orientation discrimination in their study. These contradicting results might be due to the different neural mechanisms which are thought to underlie target localization and orientation discrimination (Horwitz & Newsome, 1999; Hubel & Wiesel, 1974). When the results of this study can be replicated using a big sample size, it gives an interesting insight in the mechanisms of blindsight (Leopold, 2012).
The pilot study of Stein et al. (n.d.) also showed a differentiation of localization and detection in one mask, however it did not meet the criterion of zero-awareness for detection. This might be because they employed only two masking strengths, instead of five masking strengths in this study, creating awareness also in the strongest masking strength. the results in the current study are analysed, two participants showed a significant effect at M2, while one participant showed a significant effect at M1.
These little differences in masking strengths of these studies makes the data hard to compare. Moreover, these differences are just a tip of the iceberg. There is an important discussion going on, regarding the diversity in the study of unconsciousness. Breitmeyer (2015) showed the presence of 24 non-invasive approaches (e.g. backward pattern masking) to prevent a stimulus from reaching the threshold of visual consciousness. Comparing studies with different approaches can easily lead to wrong conclusions. But that is not all. Awareness could be shown with either objective measures, which are based on task performance, or subjective measures, which rely on the introspective reports of the participants (Seth et al., 2008). However, there is no consensus about which measure must be employed and both measures have been criticized (Dehaene & Naccache, 2001; Lau, 2007; Macmillan, 1986; Merikle,
Smilek, & Eastwood, 2001). Because of this, a variety of statistical analysis could be performed on these measures, such as null hypothesis significance testing and Bayesian statistics (Sand & Nilsson, 2016). Moreover, there is a variety in experimental setups. This could be caused by different disciplines. For example, cognitive psychology fixates on the awareness of stimuli, whereas at social psychology the interaction of these stimuli towards the behaviour of the observer is more important. However, these disciplines use the same words towards consciousness. Subsequently, Moors & Houwer (2006) concluded that consciousness and unconsciousness are used as distinct terms. This suggest that they would have the same meaning in every context, while there are actually multiple definitions of both consciousness and unconsciousness, all with different underlying neural mechanisms.
This great diversity in the study of consciousness makes it hard to interpret and compare findings of different studies, for example this study against the pilot study of Stein et al. (n.d.). That is why Rothkirch & Hesselmann (2017) proposed some solutions. First, specific protocols need to be written down in order to analyse and asses awareness in the same way, as to standardize the procedure. These protocols should aim for the most valuable methods possible. In this way there will still be diversity, but it will be a lot easier to compare the distinct measures of awareness. Second, to overcome the definition problem, a clear-cut classification needs to be composed to address the different descriptions of unconsciousness. For example, Dehaene et al. (2006) distinguished different types of processing based on the presence or absence of top-down attention and the bottom-up stimulus strength. Finally, Rothkirch & Hesselmann (2017) suggested that all data need to be accessible for everyone in order to replicate the results and to try different statistical tests.
Returning to the current study, a few limitations need to be noted. Due to the worldwide Covid-19 outbreak (World Health Organization, 2020) in May the Dutch government announced that public instances like schools are closed and social interactions must be limited
(Schaart, 2020), causing implications for this study. Namely, no participants could be tested at the University of Amsterdam. Therefore, this study was performed at home on just four students. This led to a low sample size, and thus this study could not say anything about the population. Another limitation is related to the environment the participants performed their tests. Every student performed this study at home at their own computer, causing different circumstances for every participant.
Regarding to future research these limitations must be overcome by having more participants and testing them under the same conditions. Looking from a broader perspective, it would be really interesting to adapt the recommendations of Rothkirch & Hesselmann (2017). So, specific protocols towards consciousness needs to be formulated, classifications composed and data made accessible for everyone. Then it will be easier to compare different studies and to come to an understanding of the neural mechanisms of consciousness.
In conclusion, this study showed how to objectively measure conscious awareness and suggested that stimulus localization might occur outside awareness in individual subjects. When the measurement of consciousness will improve it would be of enormous scientific and clinical significance.
Acknowledgements
Special thanks to Timo Stein of the Brain & Cognition Centre (University of Amsterdam) for his guidance and feedback during the writing of this thesis.
Thanks to Timo Stein, Rianne Lam and Yair Pinto for giving access to their unpublished pilot study.
Appendix
Appendix - Table 1: Participant specific details.
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