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Research Article  |   September 2009
Sensitivity and perceptual awareness increase with practice in metacontrast masking
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Journal of Vision September 2009, Vol.9, 18. doi:10.1167/9.10.18
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      Caspar M. Schwiedrzik, Wolf Singer, Lucia Melloni; Sensitivity and perceptual awareness increase with practice in metacontrast masking. Journal of Vision 2009;9(10):18. doi: 10.1167/9.10.18.

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      © 2016 Association for Research in Vision and Ophthalmology.

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Abstract

Can practice effects on unconscious stimuli lead to awareness? Can we “learn to see”? Recent evidence suggests that blindsight patients trained for an extensive period of time can learn to discriminate and consciously perceive stimuli that they were previously unaware of. So far, it is unknown whether these effects generalize to normal observers. Here we investigated practice effects in metacontrast masking. Subjects were trained for five consecutive days on the stimulus onset asynchrony (SOA) that resulted in chance performance. Our results show a linear increase in sensitivity ( d′) but no change in bias ( c) for the trained SOA. This practice effect on sensitivity spreads to all tested SOAs. Additionally, we show that subjects rate their perceptual awareness of the target stimuli differently before and after training, exhibiting not only an increase in sensitivity, but also in the subjective awareness of the percept. Thus, subjects can indeed “learn to see.”

Introduction
It is well accepted that the brain is a highly plastic organ that can undergo major changes in function and morphology not only during development, but also in an adult state. This plasticity has been intensively investigated in studies of perceptual learning and it has been shown that the sensitivity to stimulus features can be drastically improved with practice (Ahissar & Hochstein, 1998; Goldstone, 1998). Furthermore, not only basic perceptual skills are subject to practice-dependent changes, but also higher cognitive functions such as visuo-spatial attention (Green & Bavelier, 2003) or working memory (Olesen, Westerberg, & Klingberg, 2004). However, the potential plasticity of another characteristic of the human brain, namely its capacity to produce perceptual awareness (PA), has not been thoroughly investigated. Specifically, it is so far unknown whether practice can render a previously invisible stimulus visible, whether we can “learn to see.” In a clinical context, this is an important question, as patients with acquired impairments of conscious perception, such as blindsight patients, might be trained to become conscious again. The very essence of blindsight is a dissociation between chance performance for simple yes-no responses (a subjective measure of awareness) and above chance performance in forced-choice procedures (an objective measure of awareness). Practice can lead to improved performance in forced-choice procedures in human blindsight patients (Bridgeman & Staggs, 1982; Chokron et al., 2008; Henriksson, Raninen, Näsänen, Hyvärinen, & Vanni, 2007; Raninen, Vanni, Hyvärinen, & Näsänen, 2007; Stoerig, 2006; Zihl, 1980; Zihl & Werth, 1984) as well as in monkeys with bilateral ablation of primary visual cortex (V1) (Dineen & Keating, 1981; Humphrey, 1974). Although very high levels of accuracy can be reached in forced-choice tasks, the dissociation between objective and subjective measures often remains unchanged (Sahraie et al., 1997). However, without a concomitant increase in subjective awareness the psychological strain of acquired blindness is not alleviated, and blindsight capabilities are not used in everyday life. A recent systematic study in a sample of 12 cortically blind human subjects who carried out daily discrimination training over a period of three months found not only an increase in sensitivity deep in the blind visual field, but also an increase in reported PA of the stimuli (Sahraie et al., 2006; also see Zihl & von Cramon, 1985). These results are interesting because they indicate that cortical blindness resulting from brain damage is at least partially reversible. Furthermore, the result that—apart from sensitivity—acknowledged awareness of the stimuli increases with practice points to the possibility that not only basic visual functions, but also awareness itself is trainable. If that should indeed be the case, it would be an indication that the “threshold of visual awareness” is not fixed, even in the case of permanent cortical damage. However, it is so far unknown whether such training effects generalize to normal observers. 
A common practice in the study of unconscious perception is to use masking in order to present stimuli at or below the threshold of visual awareness. In metacontrast masking (Alpern, 1953), a trailing, non-overlapping mask is used to render a stimulus that precedes the mask invisible. Depending on the task and the stimuli used, a U-shaped function of performance is usually obtained, with a minimum at a positive, non-zero stimulus onset asynchrony (SOA) between stimulus and the subsequent mask (Breitmeyer & Ogmen, 2006, p. 43ff). Metacontrast masking has been used to study unconscious perception in normal subjects, for example to induce conditions of “relative” blindsight (Lau & Passingham, 2006), subliminal priming (Neumann & Klotz, 1994; Vorberg, Mattler, Heinecke, Schmidt, & Schwarzbach, 2004), and to investigate the relationship between attention and awareness (Kentridge, Nijboer, & Heywood, 2008). Part of the attraction of metacontrast masking stems from the huge database accumulated over a century of behavioral research (for a recent review, see Breitmeyer & Ogmen, 2006). 
Here, we used metacontrast masking to study the effects of training on sensitivity and awareness in normal subjects. We hypothesized that if the information of the target stimulus is available to the visual system although the stimulus is not consciously perceived, and if the “threshold of visual awareness” is not fixed, training should render previously invisible stimuli visible (also see Kanwisher, 2001). To test this hypothesis, we first measured sensitivity as a function of stimulus onset asynchrony (SOA) in a two alternative forced-choice (2AFC) form discrimination task in individual subjects. We then chose the SOA that yielded zero sensitivity and continued to train our subjects on this SOA for five consecutive days. To test the upper limits of the effects of training, we also trained an individual subject over a period of 24 days. After training, we assessed the subjects' performance over a wide range of SOAs and at a non-trained transfer position. Additionally, we measured the PA of the stimuli that subjects reported before and after training. Using Signal Detection Theory (SDT) we find that subjects not only improve linearly in sensitivity (while their bias is largely constant), but that their reported PA of the stimuli also increases after training. These improvements are still detectable after several months. We thus hypothesize that awareness itself is indeed trainable. 
Methods
Subjects
Eight subjects (five male, mean age 24, range 22–27) participated in the main experiment lasting five sessions, and one additional subject (female, age 23) was trained over a period of 24 sessions. All had normal or corrected-to-normal vision and no history of neurological and/or psychiatric disease. Handedness was assessed using the Edinburgh Inventory (Oldfield, 1971). The mean laterality quotient was H = 74.49 (range 58.3–91.6), indicating that all nine subjects were right-handed. All subjects gave written informed consent. 
Stimuli
Stimuli were displayed on a CRT monitor (Sony GDM 5502, resolution 1024 × 768) at a refresh rate of 100 Hz. Subjects viewed the screen from 1 m distance. The visible screen size was 21.05° × 16.17°. Stimulus presentation and response recording were controlled by Presentation software (version 10.3, www.neurobs.com), running under Microsoft Windows 98. All stimuli were presented on a dark gray background (6.26 cd/m 2). A white fixation cross (53 cd/m 2, 0.56° × 0.56°) was displayed at the center of the screen. 
Stimuli were presented at 3.2° either above or below fixation (position was counterbalanced across subjects). A square (0.41° × 0.41°) and a diamond (0.63° × 0.63°) were used as target stimuli. The outlines of the targets were 0.02° wide and had a luminance of 34.42 cd/m 2. The mask stimulus was a star-shaped figure made up of two squares and two diamonds directly neighboring the positions of the target stimuli on their inside and outside borders. The mask outlines had a width of 0.065° on the outside and a width between 0.043° and 0.086° on the inside (with regard to the stimuli). The mask had the same luminance as the target stimuli. Enlarged versions of targets and mask can be seen in Figure 1
Figure 1
 
A square (0.41° × 0.41°) and a diamond (0.63° × 0.63°) were used as target stimuli. The outlines of the targets were 0.02° wide. The mask stimulus was a star-shaped figure made up of two squares and two diamonds directly neighboring the positions of the target stimuli on their inside and outside borders (gap width 0.02°).
Figure 1
 
A square (0.41° × 0.41°) and a diamond (0.63° × 0.63°) were used as target stimuli. The outlines of the targets were 0.02° wide. The mask stimulus was a star-shaped figure made up of two squares and two diamonds directly neighboring the positions of the target stimuli on their inside and outside borders (gap width 0.02°).
A combination of para- (forward) and metacontrast (backward) masking has been shown to be especially effective with regard to masking (Macknik & Livingstone, 1998). We thus used such a combination to mask our target stimuli in order to create conditions where we would obtain a d′ = 0 and to avoid potential ceiling effects. Each trial started with a fixation period of variable length (between 1000 and 1500 ms). Subsequently, the paracontrast mask was presented for 50 ms. After a fixed inter-stimulus interval (ISI) of 30 ms, the target was presented for 10 ms. The metacontrast mask was presented for 50 ms at stimulus onset asynchronies (SOA) ranging from 20 ms to 160 ms (20, 40, 60, 80, 100, 120, 140, 160 ms) for the threshold estimation or at an individually determined SOA during the training sessions. Responses were recorded only after mask offset. 
Procedure
The main task was to discriminate the two target stimuli (square from diamond) in a forced-choice procedure: Subjects were instructed to quickly press the button “S” on a keyboard whenever they saw the diamond and to press the button “L” whenever they saw a square. Accuracy and not speed was emphasized. Subjects were also informed that both stimulus alternatives would appear in a randomized order but with equal probabilities and that there were no catch trials. In addition to the discrimination task, subjects rated their PA of the stimuli on a three-point-scale (see below). 
Subjects were asked to maintain fixation on the center of the screen throughout the experimental sessions. Instructions were given in both verbal and written form. The experiments were conducted in a darkened room. Constant head position was assured by the use of a chinrest with forehead support. 
The experiment took place over a period of five consecutive days. On the first day, we assessed for each subject the SOA that would yield the maximal masking in a typical threshold experiment. This SOA was then used for the training. The first training session was conducted directly after the assessment of the critical SOA on the first day. On days two to four, only training sessions were conducted. On the fifth and last day, the last training session was administered. After this last training session, we again assessed the masking threshold, followed by the transfer task. Each part of the experiment will be described in detail below. 
Threshold estimation
Before threshold estimation, subjects carried out practice trials of “slow motion” versions of the upcoming experimental trials in order to familiarize them with the stimuli and with the task. For the threshold estimations, we presented target stimuli at SOAs ranging from 20 ms to 160 ms. Each target was presented 40 times at each SOA, yielding 80 trials per SOA and a total of 640 trials. After every 160 trials, we introduced a break of variable length. The occurrence of SOAs was randomized but counterbalanced over blocks. The sequence of target stimuli was fully randomized and no feedback was given. 
In addition to target discrimination, subject rated their PA of the respective stimulus on a three-point-scale after every trial. To this end, we presented a screen with the question “How visible was the stimulus? Invisible/unclear/clearly visible” after the subject's forced-choice response. Subjects were instructed to respond with key presses for “invisible” if they did not see the stimulus, for “unclear” if they saw something but could not identify it, and for “clearly visible” if they unmistakably saw the stimulus. These questions were not aimed at the subjects' confidence in their reports, but at their phenomenal impression of the stimulus. Thus, our scale differs from previously used scales that either assessed only confidence (Persaud, McLeod, & Cowey, 2007; Wilimzig, Tsuchiya, Fahle, Einhäuser, & Koch, 2008), or a compound of confidence and PA (Wessinger, Fendrich, & Gazzaniga, 1999). This is an important distinction, because it has been shown that confidence and awareness are not equivalent measures and recover differently after repeated exposure in blindsight patients (Sahraie, Weiskrantz, & Barbur, 1998). Our operationalization of PA was discussed with the subjects before the first threshold estimation. It was further pointed out to the subjects that there were neither right nor wrong answers to the question and that they should rate only their perception of the target stimulus and not the whole stimulus sequence or the mask stimulus. The threshold estimation took approximately 1 h. Thresholds and PA were estimated before the first and after the last training session. 
Training sessions
From the threshold estimation, we extracted the SOA with the lowest performance individually for each subject. This SOA was then used in the training sessions. Data from the PA rating was not used to choose the training SOA, and no rating of PA was required during training. Subjects engaged in the forced-choice discrimination task for 600 trials per training session (a total of 3000 trials). After every 100 trials, a break was introduced. Subjects received feedback (correct/incorrect) after every response as well as after every block of 100 trials (percentage correct). Correct/incorrect feedback was displayed at fixation for 1000 ms in green letters (font Arial, font size 20 pt, 37.68 cd/m 2) or red letters (9.05 cd/m 2), respectively. A training session lasted approximately 30 min. 
To ensure stable high motivation across subjects, we developed a pay-off scheme based on monetary reward. Subjects would receive AAAAAAAAAAAAA5 for each of the training sessions. If they improved in their performance by at least 10%, they could earn an additional AAAAAAAAAAAAA2 per day. However, if they showed no gain or even a decrease in performance as compared to the previous day, they would lose AAAAAAAAAAAAA2. Performance levels (in percentage correct) were written down after each training session and subjects were reminded of their previous performance as well as the pay-off scheme before each training session. Also, subjects were reinstructed about the discrimination task. 
Transfer task
On the last day of the experiment, we assessed whether the exercise on the trained stimulus position would transfer to another position. To this end, we switched the stimulus position to its mirror position above or below the fixation cross. For instance, if a subject had been trained on the position above the fixation cross, the new stimulus position would now be below the fixation cross. Subjects had no prior knowledge that the stimulus position would be switched. We employed the same SOAs and the same procedure as during the other training sessions (see “Training sessions”). Subjects could earn an additional AAAAAAAAAAAAA2 if they reached at least the performance of the last session on the originally trained position. 
Retest
During the retest session, which took place between five and ten months after the last training session, we again assessed objective and subjective thresholds, followed by a test of sensitivity at the transfer position (procedure as for the initial transfer task). Subjects received 15AAAAAAAAAAAAA for their participation in the retest. 
Analysis
In order to calculate d′ and c values, square trials were considered signal trials and diamond trials were considered noise trials (Wickens, 2002, p.114). For the threshold data, this yielded 40 signal and 40 noise trials per SOA. We calculated d′ and c for each SOA independently, thus the values indicate discriminability of signal and noise trials at a given SOA and not discriminability between SOAs. For the training data, 300 signal and 300 noise trials were available per session. To correct for extreme false alarm or hit rate proportions, we used the loglinear correction. In this approach, 0.5 is added to the number of hits and false alarms, and 1 is added to the number of signal trials and the number of noise trials. The loglinear correction reduces bias in the calculation of d′ when corrections have to be applied in order to avoid infinite z score values (Hautus, 1995). 
2AFC tasks are usually considered to be bias-free. However, this assumption does not always hold true (Macmillan & Creelman, 2005, p. 170ff). Furthermore, it has been shown that perceptual learning can lead to changes in sensitivity as well as bias (Wenger, Copeland, Bittner, & Thomas, 2008; Wenger & Rasche, 2006; also see Seitz, Nanez, Holloway, Koyama, & Watanabe, 2005). Thus, we included a measure of bias into our analysis. For the calculation of bias, three possible measures can be calculated: the criterion location c (the distance between the criterion and the point at which the signal over the noise distribution cross), the relative bias c′ (the criterion location relative to the corresponding d′), and the likelihood ratio β (the ratio of the heights of the signal and the noise distribution). We chose to calculate c for our purposes, because it is the only measure of bias that is orthogonal to but associated with sensitivity (d′) and fulfills the monotonicity condition at and below chance levels (Macmillan & Creelman, 1990). Furthermore, the range of c is similar to that of d′; this eases the comparison of the change in the two measures. 
Data from the PA rating could not be analyzed in a SDT framework, because we did not include catch trials in our paradigm. Thus, given that the PA rating aimed at subjective impression of both the square and the diamond target stimuli, we did not have noise trials in this measure. Further, the data could not be analyzed as a type 2 task as the decision axis was not the decision in the 2AFC task but the stimulus itself (Galvin, Podd, Drga, & Whitmore, 2003). We instead calculated mean PA rating values at each SOA for correct responses, errors, and pooled correct responses and errors (see below). 
In all analyses of variance (ANOVA) with more than one degree of freedom we used the Greenhouse-Geisser correction (McCall & Appelbaum, 1973). We report adjusted degrees of freedom and adjusted p values. 
Results
Discrimination performance
First threshold
Metacontrast masking yields either functions that show a linear relationship to SOA (type A masking) with maximal masking at a SOA of 0 ms or U-shaped functions of SOA (type B masking) with a maximal effect at a time point greater than 0 ms. Usually, these functions are obtained from the hit rates. In order to assess whether our combination of para- and metacontrast masking yielded comparable functions in our paradigm with combined forward and backward masking, we first analyzed d′ and c values in repeated measures ANOVAs with the factor SOA (20, 40, 60, 80, 100, 120, 140, 160 ms). For d′, we indeed found a significant effect of SOA ( F(3.394, 23.757) = 5.620, p = 0.0036, η 2 = 0.445), whereas for c, the effect of SOA did not reach significance ( F(4.003, 28.018) = 1.178, p = 0.342, η 2 = 0.144). This indicates that the experimental manipulation of the SOA led to changes in sensitivity, but not to changes in bias. As can be seen from Figure 2, both d′ and c vary with SOA in a complex way. To ease the interpretation of the results, we aligned the SOAs to the SOA at which the individual subject was subsequently trained. We then centered our analysis on the trained SOA per subject and further included one neighboring SOAs above and below, respectively. Five subjects were trained at a SOA of 80 ms, two at a SOA of 100 ms, and one at a SOA of 60 ms. As can be seen from Figure 3, the reordering yielded a clearly U-shaped function of SOA for d′, but not for c
Figure 2
 
Average sensitivity ( d′) and bias ( c) from threshold 1 and threshold 2. Error bars represent the standard error of the mean.
Figure 2
 
Average sensitivity ( d′) and bias ( c) from threshold 1 and threshold 2. Error bars represent the standard error of the mean.
Figure 3
 
Sensitivity ( d′) and bias ( c) from threshold 1 and threshold 2, individually aligned to the trained SOA. Error bars represent the standard error of the mean.
Figure 3
 
Sensitivity ( d′) and bias ( c) from threshold 1 and threshold 2, individually aligned to the trained SOA. Error bars represent the standard error of the mean.
An additional ANOVA of this reduced sample of SOA (untrained below, trained, untrained above) confirmed the significant effect of SOA for d′ ( F(1.513, 10.591) = 11.186, p = 0.004, η 2 = 0.615) and its non-significant effect for c ( F(1.504, 10.526) = 0.378, p = 0.636, η 2 = 0.051). Additionally, a within subjects contrast showed a significant quadratic trend of SOA for d′ ( F(1, 7) = 50.178, p = 0.0002, η 2 = 0.878). Thus, the obtained functions of sensitivity are comparable to U-shaped type B metacontrast masking functions. 
To make sure that subjects were trained on a SOA that was at the objective threshold of conscious perception, we ran a two-sided t-test of their sensitivity values at that SOA against 0. We indeed found that the d′ of the trained SOA was not significantly different from 0 (mean difference 0.067, T(7) = 0.620, p = 0.555), indicating that subjects were objectively unaware of the stimuli. By contrast, the sensitivity at the two neighboring untrained SOAs was significantly higher than 0 (below: mean difference 0.4323, T(7) = 4.448, p = 0.003; above: mean difference 0.4472, T(7) = 3.907, p = 0.0058). Thus, at these SOAs the subjects were objectively aware of the stimuli. 
A two-sided t-test of the corresponding values of c against 0 was also not significant at any of the three SOAs (all p >= 0.3842). This indicates that subjects were not biased to respond with either response alternative at these SOAs, as would be expected from a 2AFC task with equal probabilities for the two response alternatives. 
Training sessions
To assess whether subjects improved in their performance throughout the training sessions, we entered d′ and c values of the five training sessions into a repeated measures ANOVA. For d′, we found a significant effect of session ( F(1.824, 12.767) = 15.578, p = 0.0004, η 2 = 0.690); a within subjects contrast yielded a significant linear trend ( F(1, 7) = 21.737, p = 0.0023, η 2 = 0.756) and a quadratic trend that approached significance ( F(1, 7) = 5.214, p = 0.0564, η 2 = 0.427). Figure 4 shows that subjects improved linearly in their sensitivity in the course of the training sessions, with a trend towards reaching a plateau for the two final sessions. Sensitivity increased on average by a d′ of 1.16 ( SD 0.74) from the first to the fifth session. 
Figure 4
 
Sensitivity ( d′) and bias ( c) over the training sessions and for the transfer task. Error bars represent the standard error of the mean.
Figure 4
 
Sensitivity ( d′) and bias ( c) over the training sessions and for the transfer task. Error bars represent the standard error of the mean.
For c, we did not find a significant effect of session ( F(3.283, 22.983) = 0.852, p = 0.488, η 2 = 0.109). In fact, none of the c values obtained for the individual sessions differed significantly from 0 (two-sided t-tests, all p >= 0.2655, uncorrected). Thus, while subjects improved linearly in sensitivity, their bias to respond with one or the other response alternative did not change during the training (also see Figure 4). 
The average d′ for the first training session was 0.51 ( SD 0.32) and significantly different from 0 ( T(7) = 4.470, p = 0.0029). To address the question of when during the training session the subjects crossed the objective threshold, we split up the first session into six blocks of 100 trials (50 signal trials and 50 noise trials) each. After applying the loglinear correction, we tested d′ for each block against 0 using two-sided paired t-tests. Only the first block was not significantly different from 0 (mean difference 0.3614, T(7) = 2.219, p = 0.0708). However, when two subjects were excluded that had a d′ > 1 already in the first block, we found that for the remaining subjects, the first as well as the second block were not significantly different from 0 (first block: mean difference 0.1278, T(5) = 1.272, p = 0.2594; second block: mean difference 0.3009, T(5) = 1.740, p = 0.1424). Thus, the subjects crossed the objective threshold early during the first session. 
Perceptual learning is often characterized by an initial rapid learning phase that is later followed by slow, sustained learning. To characterize the learning during the first session, we analyzed the average d′ of all subjects in each of the six blocks by means of a linear regression. We found that on average, d′ increased linearly not only over all sessions, but also within the first session, ranging from d′ = 0.36 ( SD 0.48) in the first block to d′ = 0.59 ( SD 0.38) in the sixth block. Block number explained a significant proportion of variance in d′, R 2 = 0.6601, F(1,4) = 7.7677, p < 0.05. When we excluded the two subjects that had a d′ > 1 in the first block, similar results were obtained ( R 2 = 0.7906, F(1,4) = 15.0987, p < 0.02). Here, the average d′ ranged from d′ = 0.13 ( SD 0.25) in the first block to d′ = 0.49 ( SD 0.40) in the sixth block. Linear regressions for the remaining sessions 2–5 were all non-significant (all p > 0.083). Thus, learning was characterized by a steep linear increase in sensitivity during the first session, followed by a more variable, gradual increase over the remaining sessions. 
Second threshold
After the subjects had significantly improved in their sensitivity to discriminate the two stimuli during the training sessions, we now tested how this improvement affected the objective threshold as a function of SOA. We hypothesized that the training effect could either be specific to the trained SOA, affect all SOAs, or affect only a subsample of the SOAs. Thus, we analyzed the performance in the second threshold estimation for both the full range of SOAs as well as the SOAs aligned to the trained SOA. 
For the full range of SOAs, we ran a repeated measures ANOVA with factors session (first, second) and SOA (20, 40, 60, 80, 100, 120, 140, 160 ms), again for both the d′ and the c values. For d′, we found a significant main effect of session ( F(1, 7) = 27.311, p = 0.0012, η 2 = 0.796), and a significant main effect of SOA ( F(3.121, 21.849) = 6.467, p = 0.0024, η 2 = 0.480), but no significant interaction ( F(3.115, 21.805) = 0.359, p = 0.7903, η 2 = 0.049). Thus, training led to a significant improvement in the forced-choice discrimination task that affected all SOAs, and not only the trained SOA (see Figure 2). 
The same analysis for c yielded a significant main effect of session ( F(1, 7) = 8.084, p = 0.0249, η 2 = 0.536), a significant main effect of SOA ( F(3.535, 24.746) = 8.090, p = 0.0003, η 2 = 0.536), as well as a significant interaction of session and SOA ( F(4.004, 28.027) = 3.204, p = 0.0275, η 2 = 0.314). Post-hoc two-sided t-tests (uncorrected) revealed that the bias changed significantly with training only for the two shortest SOAs (20 ms: mean difference −0.4375, T(7) = −3.628, p = 0.0084; 40 ms: mean difference −0.2030, T(7) = −2.512, p = 0.0403; all other p >= 0.0651). Hence, for the two shortest SOAs, subjects became more biased to respond with “diamond” after the training (also see Figure 2B). 
Following our approach from the analysis of the first threshold, we again aligned the SOAs with the trained SOA and analyzed these data with repeated measures ANOVAs for both d′ and c. For d′, we confirmed the significant main effect of session ( F(1, 7) = 30.553, p = 0.0009, η 2 = 0.814) and a significant main effect of SOA ( F(1.343, 9.402) = 7.702, p = 0.0158, η 2 = 0.524), but no interaction between session and SOA ( F(1.640, 11.480) = 2.301, p = 0.1494, η 2 = 0.114). As can be seen from Figure 3A, the obtained function of SOA moves up and changes its shape, which indicates that no significant masking took place anymore after the training. For c, we found no significant effect or interaction (all p >= 0.0907). 
Post-hoc t-tests revealed that the d′ values for the trained SOA indeed significantly differed before and after the training (mean difference −1.114, T(7) = −5.122, p = 0.001, two-sided, uncorrected), and that the d′ for the trained SOA was significantly different from 0 after the training (mean difference 1.1821, T(7) = 5.009, p = 0.0007, one-sided, uncorrected). The corresponding estimate of c did not differ between the two thresholds (mean difference −0.0964, T(7) = −1.214, p = 0.264, uncorrected, two-sided), nor did it differ from 0 after training (mean difference 0.0209, T(7) = 0.318, p = 0.760, uncorrected, two-sided). The differences in d′ and c for the trained SOA between the first and second threshold are displayed in Supplementary Figure 1 for each subject individually. 
Transfer task
In order to evaluate whether the training on one stimulus position showed transfer to another stimulus position, we compared d′ and c of the transfer task with the results from the first and last training sessions in paired two-sided t-tests. We reasoned that if the results from the transfer task were significantly lower than the results from the last session and lower than or equal to the results from the first session, this would argue for no transfer to an untrained stimulus position. However, if the results from the transfer task were equal or higher than the results from the last session, this would point towards independence of the training effects from the stimulus position. In the case of partial transfer, we would expect the results from the transfer task to lie somewhere between the results from the first and last training session. 
The average d′ reached in the transfer task was 1.02 ( SD 0.68), while the average c was −0.10 ( SD 0.24). This indicates that for d′, the results from the transfer task were significantly better than the results from the first training session (mean difference 0.5068, T(7) = −2.895, p = 0.0232), while there was no significant difference between the transfer and the fifth training session (mean difference −0.6579, T(7) = 2.219, p = 0.0620). Upon further inspection of the data, we found that the individual subjects' differences between the d′ values from the fifth session and the transfer session were negative in six out of eight cases. In the two remaining cases, the differences were positive (0.34 and 0.30). When we excluded these two outliers, we found that the remaining subjects performed significantly worse in the transfer task as compared to the last training session (mean difference −0.9854, T(5) = 3.522, p = 0.0169). Thus, most subjects showed partial but not full transfer of the training effects to the untrained stimulus position. Data for all subjects is displayed in Figure 4
For c, a two-sided t-test against 0 for all subjects revealed that the bias was not significantly different from 0 in the transfer task (mean difference −0.1043, T(7) = −1.210, p = 0.2655). Furthermore, the differences between the bias in the transfer task and the bias in the first and in the last session were both not significantly different from 0 either (first session: mean difference −0.0776, T(7) = −0.854, p = 0.4212; fifth session: mean difference −0.1074, T(7) = −1.209, p = 0.2660). This indicates that a change of the stimulus position did not lead to a significant change in bias. 
Perceptual awareness rating
For the PA rating, our analyses aimed at three questions: 1. Do the PA ratings follow the same or a similar function of SOA as the objective measures that we obtained in the two alternative forced-choice task? 2. Do PA ratings increase as a result of training? 3. Do the PA ratings differ depending on accuracy? We reasoned that the mean PA rating for correct responses should be higher than the mean PA rating for errors before as well as after the training if there was a genuine increase in PA, whereas no difference in the PA rating for correct and incorrect responses would indicate biasing effects such as over- or underconfidence. These three questions where first addressed in a repeated measures ANOVA with factors session (first, second), accuracy (correct responses, errors), and SOA (20, 40, 60, 80, 100, 120, 140, 160 ms). We found a significant main effect of session ( F(1, 7) = 7.869, p = 0.0263, η 2 = 0.529), a significant main effect of accuracy ( F(1, 7) = 18.730, p = 0.0034, η 2 = 0.728), and an effect of SOA that only approached significance ( F(2.770, 19.391) = 3.021, p = 0.0580, η 2 = 0.301). Furthermore, we found a significant interaction of session and accuracy ( F(1, 7) = 21.890, p = 0.0023, η 2 = 0.758) as well as a significant interaction of accuracy and SOA ( F(3.524, 24.669) = 3.509, p = 0.0249, η 2 = 0.334), but no interaction of session and SOA ( F(3.662, 25.635) = 1.122, p = 0.3652, η 2 = 0.138), and no interaction of session, accuracy, and SOA ( F(3.686, 25.805) = 0.729, p = 0.5702, η 2 = 0.094). These results indicate that the mean PA ratings do not differ between SOAs, but that they do differ before and after training, and that this difference is modulated by accuracy. To further elucidate these results, we first split up our data set by session in order to investigate whether the mean PA rating was higher for correct than for incorrect responses both before as well as after the training. This was confirmed for the data from the first threshold by means of a repeated measures ANOVA with factors SOA (20, 40, 60, 80, 100, 120, 140, 160 ms) and accuracy (correct responses, errors). We found a significant effect of accuracy ( F(1, 7) = 9.321, p = 0.0185, η 2 = 0.571), but no effect of SOA ( F(3.574, 25.015) = 2.107, p = 0.1158, η 2 = 0.231), and no interaction ( F(3.551, 24.858) = 0.771, p = 0.5406, η 2 = 0.099). Thus, the PA rating was sensitive to variations in accuracy at each SOA before the training, showing higher PA for correctly identified stimuli than for incorrectly identified stimuli (mean difference 0.1056, SE 0.35). The same analysis for the PA scores of the second threshold yielded identical results: We found a significant effect of accuracy ( F(1, 7) = 23.6, p = 0.0018, η 2 = 0.771), but no effect of SOA ( F(3.891, 27.237) = 1.858, p = 0.1480, η 2 = 0.210), and no interaction ( F(3.177, 22.236) = 2.352, p = 0.0967, η 2 = 0.252). Thus, the PA scores retained their sensitivity for accuracy even after the training, with higher scores for correct responses than for errors (mean difference 0.2482, SE 0.51). The results from the first and second threshold are illustrated in Figures 5B and 5C
Figure 5
 
Mean perceptual awareness ratings from threshold 1 and threshold 2 by accuracy. (A) Mean perceptual awareness rating for correct responses from threshold 1 and threshold 2. (B) Mean perceptual awareness rating for correct responses and incorrect responses from threshold 1. (C) Mean perceptual awareness rating for correct responses and incorrect responses from threshold 2. Error bars represent the standard error of the mean.
Figure 5
 
Mean perceptual awareness ratings from threshold 1 and threshold 2 by accuracy. (A) Mean perceptual awareness rating for correct responses from threshold 1 and threshold 2. (B) Mean perceptual awareness rating for correct responses and incorrect responses from threshold 1. (C) Mean perceptual awareness rating for correct responses and incorrect responses from threshold 2. Error bars represent the standard error of the mean.
Based on the previous analyses, we now continued to split up the PA ratings by accuracy for further analyses. In order to answer the question whether subjects become more aware of the stimuli after the training we analyzed the mean PA rating only for correct responses in a repeated measure ANOVA with factors session (first, second) and SOA (20, 40, 60, 80, 100, 120, 140, 160 ms). We found a significant effect of session ( F(1, 7) = 10.459, p = 0.0144, η 2 = 0.599) as well as a significant effect of SOA ( F(2.817, 19.719) = 7.729, p = 0.0015, η 2 = 0.525), and no interaction between these two factors ( F(2.966, 20.760) = 2.672, p = 0.0745, η 2 = 0.276). These results indicate that subjects became more aware of the stimuli that they also correctly identified (see Figure 5A). This seems to be the case not only for the trained SOA, but also for the untrained SOAs, thus paralleling the findings from the forced-choice task. To further elucidate the origin of the increase in PA for correct responses, we also calculated the proportion of “invisible,” “unclear” and “clearly visible” responses for correct responses for the first and the second threshold at the trained SOA. To this end, we normalized the amount of the respective PA ratings by the amount of hits and compared these values for the two thresholds. As can be seen from Figure 6, the number of “unclear” correct responses did not change significantly from the first to the second threshold (mean difference 0.0557, T(7) = 0.5340, p = 0.6099), while the number of “invisible” responses dropped to almost the same extent (mean difference 0.2044, T(7) = 2.4506, p = 0.0441) as the number of “clearly seen” responses increased (mean difference 0.26, T(7) = 2.8121, p = 0.0261). 
Figure 6
 
Proportion of perceptual awareness ratings for hits from threshold 1 and threshold 2.
Figure 6
 
Proportion of perceptual awareness ratings for hits from threshold 1 and threshold 2.
We also conducted a repeated measure ANOVA with factors session (first, second) and SOA (20, 40, 60, 80, 100, 120, 140, 160 ms) for erroneous responses. A higher awareness for errors would be difficult to interpret, but can be taken as an indication of a more liberal use of the scale or overconfidence after the training. We only found an effect of session that approached significance ( F(1, 7) = 5.361, p = 0.0538, η 2 = 0.434). Thus, the PA for incorrectly identified stimuli did not change significantly, however, an effect of overconfidence with regard to the PA rating cannot fully be ruled out, given the statistical trend in the data for errors. 
Retest
Four of the eight trained subjects could be retested between five and ten months after their last training session. For the threshold performance, we compared d′ and c between the three data sets over all SOAs at the single subject level. At a significance level of p = 0.05, we found the sensitivity over all SOAs to be significantly lower in the first threshold session than in the retest in all four subjects, while the sensitivity in the second threshold session was significantly higher than or equal to the sensitivity reached in the retest in three out of four subjects. This indicates that the subjects retained some but not all of their training effects. The criterion c was not significantly different from 0 in three out of four subjects. For the transfer position, we found neither sensitivity nor criterion to be significantly different from the previous assessment ( d′: mean difference −0.1607, T(3) = −1.681, p = 0.1913; c: mean difference −0.0367, T(3) = −0.2694, p = 0.8041). For the PA rating, we found that the mean PA rating for correct responses over all SOAs was significantly lower in the first threshold session than in the retest in three out of four subjects. The mean PA rating for correct responses was significantly higher in the second threshold than in the retest in three subjects as well. However, the mean PA rating for correct responses was significantly higher than the mean PA rating for errors in only two subjects, while it was significantly lower for one subject, and not significantly different for another subject. Thus, although the subjects preserved high sensitivity levels even without further practice, the differential effect of training for correct responses and errors on the PA rating was not fully retained in all subjects after a period of five to ten months. 
Individual subject
We also trained an individual subject for 24 consecutive sessions (14400 trials) to obtain an approximation of the upper limit of the training effects. Figure 7 shows the results of the first and second threshold estimations at the upper stimulus position. The SOA yielding the lowest d′ value in the first threshold was 120 ms ( d′ = 0.06, c = 0.03). The subject was subsequently trained on this SOA. The d′ reached in the first session was 1.05 ( c = 0.14), and 2.48 in the last session ( c = 0.19). We used a linear regression to analyze the development of d′ and c during the course of training. We found that session number explained a significant proportion of variance in d′, R 2 = 0.908, F(1, 22) = 216.960, p < 0.0001. Session number also explained a significant proportion of variance in c, R 2 = 0.422, F(1, 22) = 16.090, p = 0.001. However, session number explained only about half of the variance in c, while it explained most of the variance in d′. Also, d′ showed substantial improvement, reaching from 1.05 to 3.04 (range 1.99). At the same time, c stayed close to the unbiased 0 point, reaching from −0.06 to 0.30 (range 0.37). We interpret these results as to be in agreement with the results from the bigger sample trained for five consecutive sessions: While there is a significant linear improvement of sensitivity, the bias shows little or no systematic change in the course of training (see Figure 8). 
Figure 7
 
Sensitivity ( d′) and bias ( c) from threshold 1 and threshold 2 for an individual subject trained for 24 sessions.
Figure 7
 
Sensitivity ( d′) and bias ( c) from threshold 1 and threshold 2 for an individual subject trained for 24 sessions.
Figure 8
 
Sensitivity ( d′) and bias ( c) over the training sessions and for the transfer task for an individual subject trained for 24 sessions.
Figure 8
 
Sensitivity ( d′) and bias ( c) over the training sessions and for the transfer task for an individual subject trained for 24 sessions.
As can be seen from Figure 7A and in agreement with the results from the bigger sample, we found an increase in sensitivity ( d′) at almost every SOA for the second threshold. This increase was especially pronounced for the trained SOA (first threshold d′ = 0.06, second threshold d′ = 3.25). The subject also became more biased to respond with “diamond”: Whereas the criterion at the trained SOA was 0.03 for the first threshold, it increased to 0.62 for the second threshold (see Figure 7B). 
For the transfer, d′ dropped to 0.62 ( c = 0.37). The results from the transfer task were lower than the results from the last session ( d′ = 2.48) and lower than the results from the first session d′ = 1.05). This result points towards no transfer of the increased sensitivity to the untrained stimulus position for this subject. 
To elucidate whether the individual subject became more aware of the stimuli as a result of the training, we calculated the mean PA ratings per SOA for correct responses and errors for each of the two thresholds. As in the larger sample of subjects, we first compared the PA ratings for correct responses and errors of the first threshold. This was done by means of a paired two-sided t-test, using each SOA as a sample. As the mean PA rating in the first threshold was higher for correct responses than for errors only in four out of eight SOAs (20, 40, 100, 160 ms), the result of this test was not significant (mean difference 0.0114, T(7) = 0.492, p = 0.6378). Nevertheless, we went on to compare the PA rating for correct responses and the PA rating for errors of the first and second threshold, again using paired two-sided t-tests using each SOA as a sample. The PA rating for correct responses was significantly higher in the second than in the first threshold (mean difference −0.2189, T(7) = 5.287, p = 0.0011), while the PA rating for errors did not change (mean difference −0.0930, T(7) = 2.003, p = 0.0852). Also, the PA rating for correct responses was significantly higher than the PA rating for errors in the second threshold (mean difference 0.1373, T(7) = 4.608, p = 0.0025). Thus, the individual subject trained for 24 sessions became more aware of the correctly identified stimuli, while she showed no change in PA for incorrectly identified stimuli after the training. The subject was also retested after eight months. At a significance level of p = 0.05, the sensitivity was significantly lower in the first threshold session than in the retest, while there was no significant difference between the second threshold session and the retest. Thus, the training effect on sensitivity was retained. However, the criterion c was significantly higher than 0 over all SOAs in the retest, thus indicating that the subject was now biased to respond with “diamond.” For the PA rating, we found that the mean rating for correct responses was significantly higher during the retest than during the first threshold session, and significantly higher during the second threshold session that during the retest. The differences in the mean PA rating for errors and the mean PA rating for correct responses only approached significance ( p = 0.0585). These results indicate that a limited amount of the improvements in PA were retained even after eight months, which is in agreement with most of the other retested subjects. 
Discussion
The results of this experiment show that subjects' sensitivity to discriminate between two metacontrast-masked stimuli improves rapidly with practice in a linear manner. This effect is not specific to the trained timing of the stimuli, but generalizes to other timings as well. Furthermore, some limited training effects can be observed for a mirror-symmetric position relative to the horizontal meridian. Critically, when subjects are asked to report their PA of the masked stimuli, an increase in subjective PA can be measured after the training. The training effects for both the objective as well as the subjective measure persist to a limited extent over a period of several months. We thus hypothesize that awareness is trainable, a conclusion that is in accordance with recent findings in blindsight patients (Sahraie et al., 2006). 
Improvements in objective measures
A number of studies have reported improvements in performance after training under conditions of impaired conscious perception in normal observers. For example, training effects have been shown to exist in forward (Coyne, 1981) and backward pattern masking paradigms (Braff, Saccuzzo, Ingram, McNeill, & Langford, 1980; Hertzog, Williams, & Walsh, 1976; Maehara & Goryo, 2003; Schiller, 1965; Schubö, Schlaghecken, & Meinecke, 2001; Ward & Ross, 1977; Wolford & Kim, 1992; Wolford, Marchak, & Hughes, 1988), as well as under conditions of crowding (Chung, 2007; Huckauf & Nazir, 2007). Our results are also consistent with previous reports on learning effects in metacontrast masking. Hernandez and Lefton (1977) observed improvements in sensitivity and no changes in response bias over twelve sessions in a foveal metacontrast detection task. Similarly, Hogben and Di Lollo (1984) reported improvements in a target location identification task over five sessions. However, none of these studies specifically investigated whether subjects can become objectively aware of stimuli that they were previously unaware of. Accordingly, initial thresholds were above d′ = 0 in both previous metacontrast masking experiments (although no statistics are provided regarding this issue). Additionally, in these two studies subjects were not trained on the SOA that yielded the worst performance, but concurrently on all SOAs. Thus, learning could have taken place at a SOA for which the target stimulus was consciously perceivable, and then transferred to other SOAs. 
Contrary to our and the above findings, two studies investigating subliminal priming under metacontrast masking conditions observed no change in discrimination performance ( d′ = 0) after training with feedback. In a study by Klotz and Neumann (1999), subjects were required to discriminate whether or not a specific target stimulus was present in any of two locations. Even though feedback was given and motivation was upheld by financial reward, subjects failed to improve significantly in their discrimination performance over up to 640 trials. In a similar study Vorberg et al. (2004) found no improvements in the ability to discriminate the direction of a metacontrast masked target over more than 3000 trials with error feedback. What might account for the differences between these two studies and our results? In the study of Klotz and Neumann subjects had to attend to two potential target locations, whereas subjects were attending to just one location in our task. It has been shown that perceptual learning can occur without awareness of the target stimuli under certain conditions (Watanabe, Nanez, & Sasaki, 2001). However, if attentional resources are further limited (by the attentional blink) learning does not occur anymore (Seitz, Lefebvre, Watanabe, & Jolicoeur, 2005). Thus, the combination of impaired awareness and distributed attention might have precluded improvements in discrimination performance. In the study by Vorberg and colleagues, subjects were instructed to withhold their responses for 600 ms, whereas in our study, subjects could respond as soon as the mask turned off. It has been argued that subject can be briefly aware of a stimulus, but that delaying the response to a stimulus can lead to rapid forgetting and thus to an underestimation of initial awareness (Dennett & Kinsbourne, 1992; Lachter & Durgin, 1999; Lachter, Durgin, & Washington, 2000). If improvements in discrimination performance do not go along with an improvement in memory, this difference in task might account for the difference between our study and the study by Vorberg and colleagues. 
How might the improvements in sensitivity observed in our study come about? One possibility is that subjects learned to suppress the masks. Studies in pattern masking, where it is possible to change the mask while keeping the target stimulus constant, have shown that subjects can learn to suppress specific masks with training, and that this effect breaks down when the masks, but not when the target stimuli are changed (Schubö et al., 2001; Wolford et al., 1988). These findings are in line with recent evidence from monkey inferotemporal cortex, where suppression of the single unit response to the mask was the most consistent finding when monkeys were trained to identify masked objects (Op de Beeck, Wagemans, & Vogels, 2007). In metacontrast masking, it has been shown that the suppression of the metacontrast mask by a second mask can lead to target recovery (Breitmeyer & Ogmen, 2006, p. 254ff). Thus, it is not unlikely that mask suppression is contributing to the observed improvements in target discrimination. Another possibility is that the signal of the target stimulus was strengthened (Gold, Bennett, & Sekuler, 1999). In discrimination tasks, an ideal observer would correlate input with templates of the possible targets, and respond according to the best fit. Refinement of the templates leads to less overlap, thus increasing discriminability. Accordingly, in monkeys trained to discriminate orientations the orientation selectivity of V1 neurons increases (Schoups, Vogels, Qian, & Orban, 2001). 
Learning could also have taken place in the time domain. There is good evidence that observers can improve substantially in their ability to discriminate short time intervals between visual stimuli (Westheimer, 1999) and two-flash discrimination performance improves after practicing masked letter recognition (Wolford et al., 1988). Similar conclusions were drawn by Ventura (1980) with respect to training effects on brightness ratings under foveal metacontrast conditions. Thus, subjects may have learned to discriminate visual events in time that they perceived as one perceptual event before the training in our paradigm as well. Alternatively, subjects may have learned to use the fixed-interval forward mask as a temporal marker for the upcoming stimulus, thus facilitating the allocation of temporal attention in a specific time window (Rolke, 2008; Rolke & Hofmann, 2007). Attention has been shown to attenuate metacontrast masking (Boyer & Ro, 2007; Tata, 2002; also see Breitmeyer & Ogmen, 2006, p. 243ff for a critical discussion of attentional effects in metacontrast masking), and effects of training on attention have also been hypothesized to be the basis of perceptual learning (Vidnyanszky & Sohn, 2005). All four proposed mechanisms can in principle explain the improvement in objective performance at the trained position, as well as the transfer effects from the trained SOA to other SOAs. The location specificity of training effects in metacontrast masking has so far not been investigated. Transfer to other stimulus locations is often used to make inferences upon the cortical area where perceptual learning takes place. If transfer is abolished by small stimulus displacements, this is usually taken as evidence for training effects in a lower visual area with small receptive fields (e.g., Karni & Sagi, 1991). In our study, we found evidence for a limited transfer of improvements in sensitivity to another stimulus location. However, because of the large separation of the tested stimulus locations, the rather special position of the stimuli on the vertical meridian, and the mirror-symmetric transfer location no inferences on the likely site of learning are possible (Dill, 2002). 
Whatever the specific mechanism is, the crossing of the “threshold of visual awareness” with training indicates that some attribute of the representation of the target stimuli must have changed in order to become accessible to conscious perception. It has been hypothesized that conscious representations are stronger, more stable and more distinct than unconscious representations (Cleeremans, 2008). In principle, training could have led to a strengthening, stabilization and (most importantly) increased distinctiveness of the representation of the target stimulus. In this framework, a representation becomes available to phenomenal consciousness and report once it has reached sufficient quality through learning. Alternatively, mechanisms that read out representations might have become more tuned to the elusive representations of the target stimuli, or more able to use specific features of the target stimuli to discriminate between them. Both accounts require that a representation of the masked target stimulus was actually available even though masking took place. This assumption is supported by at least three lines of evidence: First, even when the target stimulus is perceptually invisible due to metacontrast masking, forced-choice detection can still result in above chance performance (Schiller & Smith, 1966; but see Otto, Ogmen, & Herzog, 2006). Second, the orientation of metacontrast masked targets can be recovered from V1 activity using functional magnetic resonance imaging (Haynes & Rees, 2005). Third, experiments in subliminal priming under metacontrast masking conditions consistently find evidence for access to target information in the absence of conscious target perception (Klotz & Neumann, 1999; Neumann & Klotz, 1994; Vorberg et al., 2004). 
Improvements in subjective measures
Our subjects showed improved discrimination of stimuli as a result of training. When adopting an operationalization of consciousness that relies purely on objective, forced-choice measures, these data already suggest that subjects can indeed become aware of stimuli that they were not aware of before the training. However, although commonly used, such an operationalization of PA has repeatedly been criticized for not taking into account the subjective experience of the subject (e.g., Wiens, 2007). Importantly, even when both objective and subjective measures of awareness are assessed, they do not always yield the same results. For example, Lau and Passingham (2006) found that subjects rated their PA of metacontrast masked stimuli higher for longer SOAs than for shorter SOAs at identical levels of accuracy. Similarly, the blindsight patient GY is able to correctly discriminate motion directions at low and at high motion speed in his blind hemifield while reporting awareness only in the latter condition (Sahraie et al., 1997). Thus, subjects' accuracy does not always directly reflect their subjective awareness. Accordingly, improvements in objective measures of awareness after training do not necessarily go along with improvements in subjective awareness: Studies in blindsight patients show that remarkable levels of accuracy can be reached in objective tasks in the absence of acknowledged awareness (for a review, see Cowey, 2004). Similarly, investigations of implicit sequence learning indicate that objective performance can improve with practice without conscious awareness of the learned sequences (Destrebecqz & Cleeremans, 2001). In masking of faces by faces, subjects ability to discriminate between fearful and non fearful faces increases over time as indexed by objective measures; however, subjective awareness does not change significantly (Szczepanowski & Pessoa, 2007). These studies further corroborate the notion that high performance on a forced-choice task does not necessarily warrant the conclusion that subjects are aware. Although a dissociation of objective and subjective performance was not the focus of the current study, it is thus possible that subjects improved in our objective task without concomitant changes in subjective awareness. 
Depending on the task and stimuli employed, metacontrast masking has been shown to not only impair the discriminability of briefly presented target stimuli, but also to change the subjective experience of these stimuli, for example their brightness (e.g., Petry, 1978). Here, we employed PA ratings asking for stimulus clarity to test for such effects. However, we did not find U-shaped functions of these ratings with SOA. This might be due to the high initial difficulty of the task over all SOAs, a strong central tendency in the rating responses, and/or the fact that metacontrast masked targets are not invisible in the strict sense, but only degraded to such an extent that discrimination becomes impossible. Irrespective of the question whether the PA rating yields a U-shaped function, the ratings vary with accuracy before and after the training. This validates that our ratings capture an aspect of the subjective experience of the subjects, because on average, correct responses should correlate with higher subjective visibility than incorrect responses. Critically, we find an increase in subjective visibility after training which is specific for correct responses. This effect indicates that the subjective quality of the percept changes, irrespective of the fact that the stimulus might have been noticeable (but not identifiable) before the training. Whether this change result from the changes in sensitivity or whether it occurs independently of those remains an open question. 
It has also been proposed that PA depends on criterion setting (Lau, 2008). In this framework, higher order representations of signal and noise are learned from internal signal and noise distributions. The internal signal and noise distributions as well as the learned higher order distributions are separated by a criterion. Whether we report perceiving or do not report perceiving a stimulus (PA) depends on the criterion that separates the learned higher order signal distribution from the learned higher order noise distribution. The underlying internal signal and noise distributions represent the efficiency of information processing, not PA. According to the theory, the internal signal distributions can be captured in 2AFC tasks and the resulting sensitivity measure, whereas PA ratings tap into the higher order representations. Since the higher order representations need to be learned, they will not always reflect the underlying distributions of internal signal and noise properly, and can thus lead to a bias in reported PA, while leaving the sensitivity measure unaffected. In this case, perceptual learning could lead to changes in sensitivity that are accompanied by changes in PA (more efficient information processing and higher PA), changes in sensitivity that are not accompanied by changes in PA (more efficient information processing in the absence of changes in PA), and changes in PA only. In a 2AFC discrimination task with equal stimulus probabilities, optimal performance can only be reached if a stable criterion is established around the point where the two signal distributions intersect, which is indeed what we observe in our data. This criterion, however, is not the criterion that is relevant for reporting PA of the stimulus (although it may be important in learning higher order representations). It thus remains conceivable that perceptual learning leads to a change in criterion for reporting PA without a concomitant change in criterion for the 2AFC task. 
Implications for the study of consciousness
An absolute threshold is an unrealistic assumption in the study of consciousness (Eriksen, 1960). However, especially in the field of subliminal perception, a threshold of conscious perception has to be established in order to demonstrate perception without awareness. This is and has been a highly controversial issue. One approach to overcome these controversies has been the implementation of so called “objective” measures of awareness and the application of SDT (Holender, 1986). As in our study, a measure of sensitivity (such as d′) is calculated as an aggregate over many trials, and a t-test of this measure against 0 is taken as an indication of whether a stimulus was consciously perceived or not. However, our study shows that even a d′ = 0 does not allow for the conclusion that stimuli are presented at a fixed threshold of conscious perception. If an adequate number of trials and feedback are provided, and sufficiently high motivation is assured subjects can become “aware” of stimuli that they were previously unaware of. Interestingly, blindsight studies in monkeys usually involve heavy training schedules after surgery, which might explain the rarity of cases where the animals have not been found to show blindsight abilities (e.g., Dineen & Keating, 1981). But even without explicit training, spontaneous improvements in performance have for example been reported after continuous testing over many years in human blindsight patients (e.g., Trevethan, Sahraie, & Weiskrantz, 2007). Furthermore, being able to become aware also poses a challenge to studies that aim to contrast consciously perceived and not consciously perceived trials (Baars, 1997, p. 18ff). Differences observed between perceived and unperceived conditions might not be due to differences between conscious and unconscious perception, but the result of insufficiently trained tasks. Investigating the transition from unaware to aware states might be a valuable alternative to the contrastive approach. Further studies will be needed to show how the amount of training maps onto stages of awareness, and whether awareness itself is a trainable function or whether it is training in other functions such as perception or attention which renders previously invisible stimuli visible. Whatever the case may be, our results show that awareness can be modified though training, and that subject can indeed “learn to see.” 
Supplementary Materials
Supplementary Figure 1 - Supplementary Figure 1 
Supplementary Figure 1. Differences in sensitivity (d′) and bias (c) for the trained SOA between threshold 1 and threshold 2 per subject. 
Acknowledgments
This work was supported by the Max Planck Society. We would like to thank Paula Morgenstern and Steffen Angermair for their help with data acquisition, and Michael H. Herzog and Axel Kohler for their comments on the manuscript. 
Commercial relationships: none. 
Corresponding author: Caspar M. Schwiedrzik. 
Email: schwiedrzik@mpih-frankfurt.mpg.de. 
Address: Max Planck Institute for Brain Research, Department of Neurophysiology, Deutschordenstraße 46, 60528 Frankfurt am Main, Germany. 
References
Ahissar, M. Hochstein, S. Walsh, V. Kulikowski, J. (1998). Perceptual learning. Perceptual constancy. Why things look as they do. (pp. 455–498). Cambridge, MA: Cambridge University Press.
Alpern, M. (1953). Metacontrast. Journal of the Optical Society of America, 43, 648–657. [PubMed] [CrossRef] [PubMed]
Baars, B. J. (1997). In the theater of consciousness: The workspace of the mind. Oxford: Oxford University Press.
Boyer, J. Ro, T. (2007). Attention attenuates metacontrast masking. Cognition, 104, 135–149. [PubMed] [CrossRef] [PubMed]
Braff, D. Saccuzzo, D. Ingram, R. McNeill, B. Langford, R. (1980). Effect of practice on visual backward masking. Perceptual and Motor Skills, 50, 511–517. [PubMed] [CrossRef] [PubMed]
Breitmeyer, B. Ogmen, H. (2006). Visual masking: Time slices through conscious and unconscious vision. Oxford: Oxford University Press.
Bridgeman, B. Staggs, D. (1982). Plasticity in human blindsight. Vision Research, 22, 1199–1203. [PubMed] [CrossRef] [PubMed]
Chokron, S. Perez, C. Obadia, M. Gaudry, I. Laloum, L. Gout, O. (2008). From blindsight to sight: Cognitive rehabilitation of visual field defects. Restorative Neurology and Neuroscience, 26, 305–320. [PubMed] [PubMed]
Chung, S. T. (2007). Learning to identify crowded letters: Does it improve reading speed? Vision Research, 47, 3150–3159. [PubMed] [CrossRef] [PubMed]
Cleeremans, A. (2008). Consciousness: The radical plasticity thesis. Progress in Brain Research, 168, 19–33. [PubMed] [PubMed]
Cowey, A. (2004). The 30th Sir Frederick Bartlett lecture Fact, artefact, and myth about blindsight. Quarterly Journal of Experimental Psychology A: Human Experimental Psychology, 57, 577–609. [PubMed] [CrossRef]
Coyne, A. C. (1981). Age differences and practice in forward visual masking. Journal of Gerontology, 36, 730–732. [PubMed] [CrossRef] [PubMed]
Dennett, D. C. Kinsbourne, M. (1992). Time and the observer: The where and when of consciousness in the brain. Behavioral and Brain Sciences, 15, 183–247. [CrossRef]
Destrebecqz, A. Cleeremans, A. (2001). Can sequence learning be implicit New evidence with the process dissociation procedure. Psychonomic Bulletin & Review, 8, 343–350. [PubMed] [CrossRef] [PubMed]
Dill, M. Fahle, M. Poggio, T. (2002). Specificity versus invariance of perceptual learning: The example of position. Perceptual learning. (pp. 219–231). Cambridge, MA: MIT Press.
Dineen, J. Keating, E. G. (1981). The primate visual system after bilateral removal of striate cortex Survival of complex pattern vision. Experimental Brain Research, 41, 338–345. [PubMed] [PubMed]
Eriksen, C. W. (1960). Discrimination and learning without awareness: A methodological survey and evaluation. Psychological Review, 67, 279–300. [PubMed] [CrossRef] [PubMed]
Galvin, S. J. Podd, J. V. Drga, V. Whitmore, J. (2003). Type 2 tasks in the theory of signal detectability: Discrimination between correct and incorrect decisions. Psychonomic Bulletin & Review, 10, 843–876. [PubMed] [CrossRef] [PubMed]
Gold, J. Bennett, P. J. Sekuler, A. B. (1999). Signal but not noise changes with perceptual learning. Nature, 402, 176–178. [PubMed] [CrossRef] [PubMed]
Goldstone, R. L. (1998). Perceptual learning. Annual Review of Psychology, 49, 585–612. [PubMed] [CrossRef] [PubMed]
Green, C. S. Bavelier, D. (2003). Action video game modifies visual selective attention. Nature, 423, 534–537. [PubMed] [CrossRef] [PubMed]
Hautus, M. J. Bavelier, D. (1995). Corrections for extreme proportions and their biasing effects on estimated values of d′. Behavior Research Methods, Instruments, & Computers, 27> 46–51. [CrossRef]
Haynes, J. D. Rees, G. (2005). Predicting the orientation of invisible stimuli from activity in human primary visual cortex. Nature Neuroscience, 8, 686–691. [PubMed] [CrossRef] [PubMed]
Henriksson, L. Raninen, A. Näsänen, R. Hyvärinen, L. Vanni, S. (2007). Training-induced cortical representation of a hemianopic hemifield. Journal of Neurology, Neurosurgery & Psychiatry, 78, 74–81. [PubMed] [CrossRef]
Hernandez, L. L. Lefton, L. A. (1977). Metacontrast as measured under a signal detection model. Perception, 6, 696–702. [PubMed] [CrossRef] [PubMed]
Hertzog, C. K. Williams, M. V. Walsh, D. A. (1976). The effect of practice on age differences in central perceptual processing. Journal of Gerontology, 31, 428–433. [PubMed] [CrossRef] [PubMed]
Hogben, J. H. Di Lollo, V. (1984). Practice reduces suppression in metacontrast and in apparent motion. Perception & Psychophysics, 35, 441–445. [PubMed] [CrossRef] [PubMed]
Holender, D. (1986). Semantic activation without conscious identification in dichotic listening, parafoveal vision, and visual masking: A survey and appraisal. Behavioral and Brain Sciences, 9, 1–66. [CrossRef]
Huckauf, A. Nazir, T. A. (2007). How odgcrnwi becomes crowding: Stimulus-specific learning reduces crowding. Journal of Vision, 7, (2):18, 1–12, http://journalofvision.org/7/2/18/, doi:10.1167/7.2.18. [PubMed] [Article] [CrossRef] [PubMed]
Humphrey, N. K. (1974). Vision in a monkey without striate cortex: A case study. Perception, 3, 241–255. [PubMed] [CrossRef] [PubMed]
Kanwisher, N. (2001). Neural events and perceptual awareness. Cognition, 79, 89–113. [PubMed] [CrossRef] [PubMed]
Karni, A. Sagi, D. (1991). Where practice makes perfect in texture discrimination: Evidence for primary visual cortex plasticity. Proceedings of the National Academy of Sciences of the United States of America, 88, 4966–4970. [PubMed] [Article] [CrossRef] [PubMed]
Kentridge, R. W. Nijboer, T. C. Heywood, C. A. (2008). Attended but unseen: Visual attention is not sufficient for visual awareness. Neuropsychologia, 46, 864–869. [PubMed] [CrossRef] [PubMed]
Klotz, W. Neumann, O. (1999). Motor activation without conscious discrimination in metacontrast masking. Journal of Experimental Psychology: Human Perception and Performance, 25, 976–992. [CrossRef]
Lachter, J. Durgin, F. (1999). Metacontrast masking functions: A question of speed? Journal of Experimental Psychology: Human Perception and Performance, 25, 936–947. [CrossRef]
Lachter, J. Durgin, F. Washington, T. (2000). Disappearing percepts: Evidence for retention failure in metacontrast masking. Visual Cognition, 7, 269–279. [CrossRef]
Lau, H. C. (2008). A higher order Bayesian decision theory of consciousness. Progress in Brain Research, 168, 35–48. [PubMed] [PubMed]
Lau, H. C. Passingham, R. E. (2006). Relative blindsight in normal observers and the neural correlate of visual consciousness. Proceedings of the National Academy of Sciences of the United States of America, 103, 18763–18768. [PubMed] [Article] [CrossRef] [PubMed]
Macknik, S. L. Livingstone, M. S. (1998). Neuronal correlates of visibility and invisibility in the primate visual system. Nature Neuroscience, 1, 144–149. [PubMed] [CrossRef] [PubMed]
Macmillan, N. A. Creelman, C. D. (1990). Response bias: Characteristics of detection theory, threshold theory, and “nonparametric” indexes. Psychological Bulletin, 107, 401–413. [CrossRef]
Macmillan, N. A. Creelman, C. D. (2005). Detection theory: A user's guide. Mahwah, NJ: Lawrence Erlbaum Associates.
Maehara, G. Goryo, K. (2003). Perceptual learning in visual backward pattern masking. Perceptual and Motor Skills, 97, 1137–1149. [PubMed] [CrossRef] [PubMed]
McCall, R. B. Appelbaum, M. I. (1973). Bias in the analysis of repeated-measures designs: Some alternative approaches. Child Development, 44, 401–415. [CrossRef]
Neumann, O. Klotz, W. (1994). Attention and performance: XV. Conscious and nonconscious information processing. (pp. 123–150). Cambridge, MA: MIT Press.
Oldfield, R. C. (1971). The assessment and analysis of handedness: The Edinburgh inventory. Neuropsychologia, 9, 97–113. [PubMed] [CrossRef] [PubMed]
Olesen, P. J. Westerberg, H. Klingberg, T. (2004). Increased prefrontal and parietal activity after training of working memory. Nature Neuroscience, 7, 75–79. [PubMed] [CrossRef] [PubMed]
Op de Beeck, H. P. Wagemans, J. Vogels, R. (2007). Effects of perceptual learning in visual backward masking on the responses of macaque inferior temporal neurons. Neuroscience, 145, 775–789. [PubMed] [CrossRef] [PubMed]
Otto, T. U. Ogmen, H. Herzog, M. H. (2006). The flight path of the phoenix—The visible trace of invisible elements in human vision. Journal of Vision, 6, (10):7, 1079–1086, http://journalofvision.org/6/10/7/, doi:10.1167/6.10.7. [PubMed] [Article] [CrossRef]
Persaud, N. McLeod, P. Cowey, A. (2007). Post-decision wagering objectively measures awareness. Nature Neuroscience, 10, 257–261. [PubMed] [CrossRef] [PubMed]
Petry, S. (1978). Perceptual changes during metacontrast. Vision Research, 18, 1337–1341. [PubMed] [CrossRef] [PubMed]
Raninen, A. Vanni, S. Hyvärinen, L. Näsänen, R. (2007). Temporal sensitivity in a hemianopic visual field can be improved by long-term training using flicker stimulation. Journal of Neurology, Neurosurgery & Psychiatry, 78, 66–73. [PubMed] [CrossRef]
Rolke, B. (2008). Temporal preparation facilitates perceptual identification of letters. Perception & Psychophysics, 70, 1305–1313. [PubMed] [CrossRef] [PubMed]
Rolke, B. Hofmann, P. (2007). Temporal uncertainty degrades perceptual processing. Psychonomic Bulletin & Review, 14, 522–526. [PubMed] [CrossRef] [PubMed]
Sahraie, A. Trevethan, C. T. MacLeod, M. J. Murray, A. D. Olson, J. A. Weiskrantz, L. (2006). Increased sensitivity after repeated stimulation of residual spatial channels in blindsight. Proceedings of the National Academy of Sciences of the United States of America, 103, 14971–14976. [PubMed] [Article] [CrossRef] [PubMed]
Sahraie, A. Weiskrantz, L. Barbur, J. L. (1998). Awareness and confidence ratings in motion perception without geniculo-striate projection. Behavioural Brain Research, 96, 71–77. [PubMed] [CrossRef] [PubMed]
Sahraie, A. Weiskrantz, L. Barbur, J. L. Simmons, A. Williams, S. C. Brammer, M. J. (1997). Pattern of neuronal activity associated with conscious and unconscious processing of visual signals. Proceedings of the National Academy of Sciences of the United States of America, 94, 9406–9411. [PubMed] [Article] [CrossRef] [PubMed]
Schiller, P. H. (1965). Monoptic and dichoptic visual masking by patterns and flashes. Journal of Experimental Psychology, 69, 193–199. [PubMed] [CrossRef] [PubMed]
Schiller, P. H. Smith, M. C. (1966). Detection in metacontrast. Journal of Experimental Psychology, 71, 32–39. [PubMed] [CrossRef] [PubMed]
Schoups, A. Vogels, R. Qian, N. Orban, G. (2001). Practising orientation identification improves orientation coding in V1 neurons. Nature, 412, 549–553. [PubMed] [CrossRef] [PubMed]
Schubö, A. Schlaghecken, F. Meinecke, C. (2001). Learning to ignore the mask in texture segmentation tasks. Journal of Experimental Psychology: Human Perception and Performance, 27, 919–931. [PubMed] [CrossRef] [PubMed]
Seitz, A. R. Lefebvre, C. Watanabe, T. Jolicoeur, P. (2005). Requirement for high-level processing in subliminal learning. Current Biology, 15, R753–755. [PubMed] [CrossRef] [PubMed]
Seitz, A. R. Nanez, J. E. Holloway, S. R. Koyama, S. Watanabe, T. (2005). Seeing what is not there shows the costs of perceptual learning. Proceedings of the National Academy of Sciences of the United States of America, 102, 9080–9085. [PubMed] [Article] [CrossRef] [PubMed]
Stoerig, P. (2006). Blindsight, conscious vision, and the role of primary visual cortex. Progress in Brain Research, 155, 217–234. [PubMed] [PubMed]
Szczepanowski, R. Pessoa, L. (2007). Fear perception: Can objective and subjective awareness measures be dissociated? Journal of Vision, 7, (4):10, 1–17, http://journalofvision.org/7/4/10/, doi:10.1167/7.4.10. [PubMed] [Article] [CrossRef] [PubMed]
Tata, M. S. (2002). Attend to it now or lose it forever: Selective attention, metacontrast masking, and object substitution. Perception & Psychophysics, 64, 1028–1038. [PubMed] [CrossRef] [PubMed]
Trevethan, C. T. Sahraie, A. Weiskrantz, L. (2007). Form discrimination in a case of blindsight. Neuropsychologia, 45, 2092–2103. [PubMed] [CrossRef] [PubMed]
Ventura, J. (1980). Foveal metacontrast: I Criterion content and practice effects. Journal of Experimental Psychology: Human Perception and Performance, 6, 473–485. [PubMed] [CrossRef] [PubMed]
Vidnyanszky, Z. Sohn, W. (2005). Learning to suppress task-irrelevant visual stimuli with attention. Vision Research, 45, 677–685. [PubMed] [CrossRef] [PubMed]
Vorberg, D. Mattler, U. Heinecke, A. Schmidt, T. Schwarzbach, J. Kaernbach,, C. Schrger,, E. Mller, H. (2004). Invariant time course of priming with and without awareness. Psychophysics beyond sensation. Laws and invariants of human cognition. (pp. 271–288). Mahwah, NJ: Lawrence Erlbaum Associates.
Ward, T. B. Ross, L. E. (1977). Laterality differences and practice effects under central backward masking conditions. Memory & Cognition, 5, 221–226. [CrossRef] [PubMed]
Watanabe, T. Nanez, J. E. Sasaki, Y. (2001). Perceptual learning without perception. Nature, 413, 844–848. [PubMed] [CrossRef] [PubMed]
Wenger, M. J. Copeland, A. M. Bittner, J. L. Thomas, R. D. (2008). Evidence for criterion shifts in visual perceptual learning: Data and implications. Perception & Psychophysics, 70, 1248–1273. [PubMed] [CrossRef] [PubMed]
Wenger, M. J. Rasche, C. (2006). Perceptual learning in contrast detection: Presence and cost of shifts in response criteria. Psychonomic Bulletin & Review, 13, 656–661. [PubMed] [CrossRef] [PubMed]
Wessinger, C. M. Fendrich, R. Gazzaniga, M. S. (1999). Variability of residual vision in hemianopic subjects. Restorative Neurology and Neuroscience, 15, 243–253. [PubMed] [PubMed]
Westheimer, G. (1999). Discrimination of short time intervals by the human observer. Experimental Brain Research, 129, 121–126. [PubMed] [CrossRef] [PubMed]
Wickens, T. D. (2002). Elementary signal detection theory. Oxford: Oxford University Press.
Wiens, S. (2007). Concepts of visual consciousness and their measurement. Advances in Cognitive Psychology, 3, 349–359. [Article] [CrossRef]
Wilimzig, C. Tsuchiya, N. Fahle, M. Einhäuser, W. Koch, C. (2008). Spatial attention increases performance but not subjective confidence in a discrimination task. Journal of Vision, 8, (5):7, 1–10, http://journalofvision.org/8/5/7/, doi:10.1167/8.5.7. [PubMed] [Article] [CrossRef] [PubMed]
Wolford, G. Kim, H. Healy,, A. F. Kosslyn,, S. M. Shiffrin, R. M. (1992). The role of visible persistence in backward masking. From learning processes to cognitive processes. Essays in honor of William K. Estes. –180). Hillsdale, NJ: Lawrence Erlbaum Associates.
Wolford, G. Marchak, F. Hughes, H. (1988). Practice effects in backward masking. Journal of Experimental Psychology: Human Perception and Performance, 14, 101–112. [PubMed] [CrossRef] [PubMed]
Zihl, J. (1980). “Blindsight”: Improvement of visually guided eye movements by systematic practice in patients with cerebral blindness. Neuropsychologia, 18, 71–77. [PubMed] [CrossRef] [PubMed]
Zihl, J. von Cramon, D. (1985). Visual field recovery from scotoma in patients with postgeniculate damage A review of 55 cases. Brain, 108, 335–365. [PubMed] [CrossRef] [PubMed]
Zihl, J. Werth, R. (1984). Contributions to the study of “blindsight”—II The role of specific practice for saccadic localization in patients with postgeniculate visual field defects. Neuropsychologia, 22, 13–22. [PubMed] [CrossRef] [PubMed]
Figure 1
 
A square (0.41° × 0.41°) and a diamond (0.63° × 0.63°) were used as target stimuli. The outlines of the targets were 0.02° wide. The mask stimulus was a star-shaped figure made up of two squares and two diamonds directly neighboring the positions of the target stimuli on their inside and outside borders (gap width 0.02°).
Figure 1
 
A square (0.41° × 0.41°) and a diamond (0.63° × 0.63°) were used as target stimuli. The outlines of the targets were 0.02° wide. The mask stimulus was a star-shaped figure made up of two squares and two diamonds directly neighboring the positions of the target stimuli on their inside and outside borders (gap width 0.02°).
Figure 2
 
Average sensitivity ( d′) and bias ( c) from threshold 1 and threshold 2. Error bars represent the standard error of the mean.
Figure 2
 
Average sensitivity ( d′) and bias ( c) from threshold 1 and threshold 2. Error bars represent the standard error of the mean.
Figure 3
 
Sensitivity ( d′) and bias ( c) from threshold 1 and threshold 2, individually aligned to the trained SOA. Error bars represent the standard error of the mean.
Figure 3
 
Sensitivity ( d′) and bias ( c) from threshold 1 and threshold 2, individually aligned to the trained SOA. Error bars represent the standard error of the mean.
Figure 4
 
Sensitivity ( d′) and bias ( c) over the training sessions and for the transfer task. Error bars represent the standard error of the mean.
Figure 4
 
Sensitivity ( d′) and bias ( c) over the training sessions and for the transfer task. Error bars represent the standard error of the mean.
Figure 5
 
Mean perceptual awareness ratings from threshold 1 and threshold 2 by accuracy. (A) Mean perceptual awareness rating for correct responses from threshold 1 and threshold 2. (B) Mean perceptual awareness rating for correct responses and incorrect responses from threshold 1. (C) Mean perceptual awareness rating for correct responses and incorrect responses from threshold 2. Error bars represent the standard error of the mean.
Figure 5
 
Mean perceptual awareness ratings from threshold 1 and threshold 2 by accuracy. (A) Mean perceptual awareness rating for correct responses from threshold 1 and threshold 2. (B) Mean perceptual awareness rating for correct responses and incorrect responses from threshold 1. (C) Mean perceptual awareness rating for correct responses and incorrect responses from threshold 2. Error bars represent the standard error of the mean.
Figure 6
 
Proportion of perceptual awareness ratings for hits from threshold 1 and threshold 2.
Figure 6
 
Proportion of perceptual awareness ratings for hits from threshold 1 and threshold 2.
Figure 7
 
Sensitivity ( d′) and bias ( c) from threshold 1 and threshold 2 for an individual subject trained for 24 sessions.
Figure 7
 
Sensitivity ( d′) and bias ( c) from threshold 1 and threshold 2 for an individual subject trained for 24 sessions.
Figure 8
 
Sensitivity ( d′) and bias ( c) over the training sessions and for the transfer task for an individual subject trained for 24 sessions.
Figure 8
 
Sensitivity ( d′) and bias ( c) over the training sessions and for the transfer task for an individual subject trained for 24 sessions.
Supplementary Figure 1
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