Introduction {#s1}
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Visual perceptual learning is the process in which the observers improve their discrimination of fine differences of basic visual features, such as contrast, orientation, motion direction, etc., through practice. For several decades visual perceptual learning has been regarded as a distinct format of learning because it is specific to the orientation and retinal location of the trained stimulus ([@bib18]; [@bib2]; [@bib21]; [@bib7]; [@bib16]; [@bib8]; [@bib32]). Such learning specificity has inspired theories that interpret visual perceptual learning as a result of training induced neural plasticity in the early visual areas ([@bib18]; [@bib10]; [@bib25]; [@bib4]). For example, it has been proposed that training could sharpen neuronal orientation tuning in the primary visual cortex (V1), so that neurons become more sensitive to the fine changes of orientation differences ([@bib25]; [@bib17]). The learning specificity has also constrained alternative reweighting theories of visual perceptual learning ([@bib7]; [@bib16]; [@bib32]; [@bib14]; [@bib15]; [@bib11]; [@bib12]). These theories propose that training may not change the tuning properties of sensory neurons. Rather, the inputs from activated neurons are reweighted through training to improve readout at a later decision stage. Reweighting theories are supported by neurophysiological evidence. For example, motion direction learning in monkeys is correlated with changes in motion-driven responses of neurons in the lateral intraparietal area (LIP) that is related to the transformation of motion information into decisions (saccadic choices), but not with changes of neurons in the medial temporal area (MT) that represents motion direction signals ([@bib11]).

However, in a series of \'double training\' studies we demonstrated that visual perceptual learning of various tasks can transfer significantly, and often completely, to new orientations or locations ([@bib31]; [@bib28], [@bib29]; [@bib36], [@bib35]). In a double training experimental design the observers are additionally exposed to the new orientation or location via practicing an irrelevant task besides the primary learning task. For example, perceptual learning of foveal orientation discrimination (e.g., which of two consecutively presented gratings is more clockwise-tilted?) initially shows little transfer to an orthogonal orientation. However, if the observers are exposed to an orthogonal orientation via an irrelevant contrast discrimination task (e.g., which of two gratings has higher contrast?), learning transfers completely to the orthogonal orientation ([@bib36]). Similarly, perceptual learning of Vernier discrimination (e.g., whether a lower grating is placed to the left or right of an upper grating) can also transfer significantly and often completely to a new retinal location when the observers are additionally exposed to the transfer location via an irrelevant contrast or orientation discrimination task ([@bib28], [@bib29]). These results suggest two important insights regarding visual perceptual learning. First, visual perceptual learning is mainly a high-level rule-based learning process that occurs beyond the retinotopic and orientation selective visual areas, so that learning is in principle transferrable to untrained conditions ([@bib36]). Second, learning specificity may be related to the untrained conditions, rather than the trained conditions as the field has been assuming (i.e., plasticity with the trained early visual cortical neurons or reweighting of the inputs from these neurons). The second insight, which stands completely different from the interpretations of specificity by the field, forms the basis of the current study.

During training at a specific orientation or location in a typical visual perceptual learning experiment, most mental resources are devoted to the trained stimuli. For example, an observer has to focus the attention on the near-threshold difference between the reference orientation and the target orientation at a specific location. As a result the untrained orientations and locations, and thus the visual neurons representing these orientations and locations, are neither bottom-up stimulated nor top-down attended during training. We thus suspect that this insufficient bottom-up stimulation of, and/or top-down attention to, the untrained conditions are responsible for the orientation and location specificity. In other words, because visual neurons representing untrained conditions are not properly activated as a result, high-level perceptual learning may not be able to functionally connect to these neurons for learning transfer.

Our previous double training experiments are unable to separately identify the potential bottom-up and/or top-down contributions because suprathreshold stimuli are used in the secondary exposure task. In the current study we applied a continuous flash suppression (CFS) technique ([@bib26]) to suppress the exposure stimulus into sub-consciousness (see Materials and methods). We further manipulated the subconscious stimulus conditions to make the exposure task to be bottom-up only or top-down only. The results show that either bottom-up stimulation of the untrained condition, or top-down attention to it, is sufficient to enable substantial and often complete transfer of learning. These results provide a solution to the mystery of learning specificity that has dominated the history of perceptual learning research. With learning specificity considered as a by-product of training, the field should move on to study the brain mechanisms of perceptual learning without much of specificity-related constraints. Moreover, more efficient training paradigms can be designed to generate perceptual learning without the unwanted specificity in practical settings.

Results {#s2}
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Orientation specificity and transfer: The effects of bottom-up or top-down influences at the untrained orientations {#s2-1}
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### Baseline: Orientation specificity {#s2-1-1}

We first established the baseline for orientation specificity in an orientation discrimination learning task. The observers practiced orientation discrimination with a foveal Gabor stimulus at 36° or 126° for 5 daily sessions, which reduced the thresholds by 42.1 ± 3.4% (mean ± se; t~9~ = 12.46, p\<0.001, 95% CI = 34.4% to 49.7%, Cohen's d = 3.94; two-tailed paired t-test in this and later analyses when the significance of the improvement was tested) at the trained orientation ([Figure 1a and g](#fig1){ref-type="fig"}). Training only reduced the orientation thresholds with the same stimulus at the untrained orthogonal orientation by 11.8 ± 4.6% (t~9~ = 2.56, p = 0.030, 95% CI = 1.4% to 22.2%, Cohen's d = 0.81). The improvement was significantly lower at the untrained orientation than at the trained orientation (t~9~ = 6.39, p\<0.001, 95% CI = 19.56% to 40.98%, Cohen's d = 2.02). We used a transfer index (TI) to compare the transfer effects among various conditions. The transfer index was the improvement at the transfer condition divided by the improvement at the trained condition, with TI = 0 indicating complete learning specificity, and TI = 1 indicating complete learning transfer. Here TI = 0.25 ± 0.10 (t~9~ = 2.47, p = 0.036, 95% CI = 0.02 to 0.49, Cohen\'s d = 0.78; [Figure 1h](#fig1){ref-type="fig"}), indicating that learning is mostly orientation specific.10.7554/eLife.14614.003Figure 1.Orientation discrimination learning and the effect of bottom-up stimulation of the transfer orientation on learning transfer.(**a**) The baseline. Training was conducted at one orientation and the transfer of learning was tested at an orthogonal orientation to reveal orientation specificity. (**b**) CFS configurations for subconscious presentation of a Gabor at the orthogonal transfer orientation. The flashing noise was presented from 0 to 530 ms in the dominant eye. From 212 to 318 ms, a Gabor at the transfer orientation was also presented in the non-dominant eye. (**c**) Stimuli for bottom-up stimulation. Bottom-up stimulation was achieved by different CFS stimuli for instruction presentations before the experiment and for actual experimental presentations. Instruction stimuli: Before the experiment, the observers were instructed to report the color of a dot centered on the noise pattern in the dominant eye. A blank screen was shown to the non-dominant eye. Actual stimuli: In the actual experiment, a Gabor at the orthogonal transfer orientation was also flashed in the non-dominant eye. The observers were not told, neither were they aware of, the presence of the orthogonal Gabor. Here orientation discrimination training and bottom-up stimulation of the orthogonal transfer orientation were performed in different blocks of trials. (**d**) The mean and individual learning and transfer data with training and bottom-up stimulation of the transfer orientation. (**e**) Control experiment. Same as 1d except that there was no orientation discrimination training. (**f**) Control experiment. Same as 1d except that there was no actual presence of the orthogonal Gabor. (**g**) A summary of learning and transfer in the baseline, training plus bottom-up stimulation, bottom-up stimulation alone, and training plus noise-only conditions. (**h**) A summary of the transfer indices in the baseline, training plus bottom-up stimulation, and training plus noise-only conditions. Error bars indicate ± 1 standard error of the mean. DE - dominant eye. \*p\<0.05; \*\*p\<0.01; \*\*\*p\<0.001. See [Figure 1---source data 1](#SD1-data){ref-type="supplementary-material"} for raw data.**DOI:** [http://dx.doi.org/10.7554/eLife.14614.003](10.7554/eLife.14614.003)10.7554/eLife.14614.004Figure 1---source data 1.The first data sheet summarizes the mean and individual data presented in figure panels 1a, 1d, 1e, and 1f.The other data sheets contain raw staircase data for 1a, 1d, 1e, and 1f.**DOI:** [http://dx.doi.org/10.7554/eLife.14614.004](10.7554/eLife.14614.004)

### The effects of bottom-up exposure of the untrained orientation on learning transfer {#s2-1-2}

Previously we have shown that orientation discrimination learning can completely transfer to an orthogonal orientation if the observers receive additional exposure to the transfer orientation via an irrelevant secondary task such as contrast discrimination ([@bib36]). To separate the potential bottom-up and top-down contributions of the secondary task, we applied a continuous flashing suppression (CFS) technique ([@bib26]) to render the exposure stimulus (a Gabor orthogonal to the trained orientation) subconscious. This was achieved by presenting the flashing noise to the dominant eye, which suppressed the perception of the orthogonal Gabor in the non-dominant eye ([Figure 1b](#fig1){ref-type="fig"}).

We further manipulated the CFS stimulus configuration to achieve bottom-up stimulation of the orthogonal transfer orientation. Before the experiment, we instructed ten new observers to report the color (red/green) of a dot that was centered on the noise pattern ([Figure 1c](#fig1){ref-type="fig"} instruction stimuli; the mean correct rate was 96.6 ± 0.3%) in the dominant eye while showing a blank screen to the non-dominant eye. But in the actual experiment an orthogonal Gabor was present in the non-dominant eye ([Figure 1c](#fig1){ref-type="fig"} actual stimuli). Since the observers were neither told, nor aware of, the existence of the orthogonal Gabor, and their attention was directed to the dot-color report task, the subconsciously presented orthogonal Gabor bottom-up stimulated the visual cortical neurons at the transfer orientation without attracting top-down attention.

The observers received orientation training and bottom-up stimulation of the orthogonal transfer orientation in separate blocks of trials in the same session. Five sessions of practice reduced the orientation discrimination thresholds at the trained orientation significantly by 50.6 ± 6.0% (t~9~ = 8.87, p\<0.001, 95% CI = 35.9% to 60.5%, Cohen's d = 2.80), as well as at the untrained but bottom-up stimulated orthogonal orientation by 33.7 ± 4.6% (t~9~ = 7.33, p\<0.001, 95% CI = 23.3% to 44.1%, Cohen's d = 2.32) ([Figure 1d and g](#fig1){ref-type="fig"}). There was still a significant difference between the two improvements (t~9~ = 3.84, p = 0.004, 95% CI = 5.9% to 23.0%, Cohen's d = 1.21), and the transfer index TI = 0.72 ± 0.08 (t~9~ = 9.09, p\<0.001, 95% CI = 0.54 to 0.90, Cohen\'s d = 2.87; [Figure 1h](#fig1){ref-type="fig"}), indicating substantial but incomplete learning transfer.

A control experiment indicated that the improvement at the untrained orthogonal orientation did not result from the bottom-up stimulation alone. Eight new observers received equal amount of bottom-up stimulation of the orthogonal transfer orientation without practicing the orientation discrimination task. The bottom-up stimulation changed the orientation discrimination thresholds at the stimulated orientation insignificantly by 9.7 ± 4.3% (t~7~ = 2.25, p = 0.06, 95% CI = -0.05% to 19.9%, Cohen's d = 0.85, [Figure 1e and g](#fig1){ref-type="fig"}).

A second control experiment ruled out the possibility that the dynamic white noise, which would activate visual neurons tuned to all orientations, was sufficient to enable the same amount of learning transfer. The experimental design was the same as in [Figure 1c](#fig1){ref-type="fig"}, except that no orthogonal Gabor was present in the actual stimuli. Again training reduced orientation thresholds significantly by 46.7 ± 3.4% (t~9~ = 13.92, p\<0.001, 95% CI = 39.1% to 54.3%, Cohen's d = 4.40) in ten observers ([Figure 1f and g](#fig1){ref-type="fig"}). However, no significant threshold improvement was evident at the orthogonal transfer orientation (13.4 ± 8.8%, t~9~ = 1.53, p = 0.16, 95% CI = -6.5% to 33.2%, Cohen's d = 0.51), which was significantly lower than the improvement at the trained orientation (t~9~ = 4.13, p = 0.003, 95% CI = 15.1% to 51.6%, Cohen's d = 1.44). The TI = 0.24 ± 0.24 in this condition (t~9~ = 0.99, p = 0.35, 95% CI = -0.31 to 0.79, Cohen\'s d = 0.31; [Figure 1h](#fig1){ref-type="fig"}). The error bar of the transfer index was large because one observer actually showed complete learning transfer.

An independent-samples Kruskal-Wallis test revealed significant differences among the transfer indices of the baseline condition ([Figure 1a](#fig1){ref-type="fig"}), the training plus bottom-up stimulation condition ([Figure 1d](#fig1){ref-type="fig"}), and the training plus noise-only condition ([Figure 1f](#fig1){ref-type="fig"}) (p = 0.009). Post-hoc Dunn's multiple comparison indicated that the training plus bottom-up stimulation condition had significantly more transfer than the baseline condition (p = 0.004 without correction; p = 0.011 with correction) and the training plus noise-only condition (p = 0.022 without correction or 0.067 with correction). There was no significant difference of the transfer effects between the training plus noise-only condition and the baseline condition (p = 0.54 without correction or 1.00 with correction).

There results together suggest that bottom-up stimulation of the transfer orientation can enable substantial but partial transfer of orientation learning. Moreover, high-level perceptual learning may functionally connect to salient orientation signals for learning transfer even if these signals are subconscious.

### The effects of top-down attention to the untrained orientation on learning transfer {#s2-1-3}

Next we studied the effect of top-down attention to the transfer orientation on learning transfer. To isolate the top-down effect, before the experiment the observers were shown the flashing noise in the dominant eye. They were also shown a Gabor at the orthogonal transfer orientation, or an uppercase letter C, in the non-dominant eye when the other eye was closed ([Figure 2a](#fig2){ref-type="fig"} instruction stimuli). They were asked to report, or guess if they had to, whether a Gabor or a letter C was shown every trial by key press. However, during actual training experiment neither the Gabor nor the letter C was presented ([Figure 2a](#fig2){ref-type="fig"} actual stimuli). Faked feedback was provided to give the observers a false impression that 70--80% of their responses were correct in each block of trials. This stimulus manipulation thus initiated top-down attention to the orthogonal transfer orientation without actual bottom-up stimulation.10.7554/eLife.14614.005Figure 2.Orientation discrimination learning and the effect of top-down attention to the transfer orientation on learning transfer.(a) CFS configurations for the top-down attention condition. Instruction stimuli: Before the experiment the observers were instructed through a demo that an orthogonal Gabor or an uppercase letter C would be presented to the non-dominant eye, and that they needed to report whether it was a Gabor or a C even if they could not perceive it. Actual stimuli: In actual testing the Gabor/C was replaced by a blank screen. Orientation discrimination training and the top-down attention condition were conducted in different blocks of trials. (**b**) The mean and individual learning and transfer data with training and top-down attention to the transfer orientation. (**c**) Control experiment. Same as 2b except that there was no orientation discrimination training. (**d**) A summary of learning and transfer in the top-down attention and control conditions. (**e**) A summary of the transfer indices in the current training plus top-down attention condition and the previous baseline (replotted from [Figure 1a](#fig1){ref-type="fig"}) and training plus noise-only (replotted from [Figure 1f](#fig1){ref-type="fig"}) conditions. Error bars indicate ± 1 standard error of the mean. DE - dominant eye. \*p\<0.05; \*\*p\<0.01; \*\*\*p\<0.001. See [Figure 2---source data 1](#SD2-data){ref-type="supplementary-material"} for raw data.**DOI:** [http://dx.doi.org/10.7554/eLife.14614.005](10.7554/eLife.14614.005)10.7554/eLife.14614.006Figure 2---source data 1.The first data sheet summarizes the mean and individual data presented in figure panels 2b and 2c.The other data sheets contain raw staircase data for 2b and 2c.**DOI:** [http://dx.doi.org/10.7554/eLife.14614.006](10.7554/eLife.14614.006)

Training and top-down attention to the transfer orientation improved orientation thresholds at the trained orientation by 39.5 ± 3.5% (t~9~ = 11.21, p\<0.001, 95% CI = 31.5% to 47.4%, Cohen's d = 3.54) in ten observers ([Figure 2b and d](#fig2){ref-type="fig"}). The threshold reduction at the orthogonal orientation was also significant (29.0 ± 3.4%, t~9~ = 8.45, p\<0.001, 95% CI = 21.3% to 36.8%, Cohen's d = 2.67), indicating the effectiveness of top-down orientation attention in enabling learning transfer. The improvement at the untrained orientation was significantly lower than that at the trained orientation (t~9~ = 2.53, p = 0.032, 95% CI = 1.1% to 19.8%, Cohen's d = 0.80), and the transfer index was 0.77 ± 0.10 (t~9~ = 7.86, p\<0.001, 95% CI = 0.55 to 1, Cohen\'s d = 2.48; [Figure 2e](#fig2){ref-type="fig"}), suggesting substantial but partial learning transfer.

A control experiment with eight observers indicated that, without actual training of orientation discrimination, top-down attention to the transfer orientation alone led to an insignificant reduction of orientation threshold at the transfer orientation (8.0 ± 4.4%, t~7~ = 1.81, p = 0.11, 95% CI = -2.4% to 18.4%, Cohen's d = 0.64) ([Figure 2c and d](#fig2){ref-type="fig"}). This control ruled out the possibility that top-down attention alone improved orientation discrimination at the transfer orientation.

An independent-samples Kruskal-Wallis test revealed significant differences among the transfer indices of the baseline condition ([Figure 1a](#fig1){ref-type="fig"}), the training plus noise-only condition ([Figure 1f](#fig1){ref-type="fig"}), and the training plus top-down attention condition (p = 0.007). Post-hoc Dunn's multiple comparison indicated that the training plus top-down attention condition had significantly more transfer than the baseline condition (p = 0.003 without correction; p = 0.008 with correction) and the training plus noise-only condition (p = 0.019 without correction or 0.058 with correction). These results indicate that top-down attention, like bottom-up stimulation, is sufficient to enable substantial but partial learning transfer to the untrained orthogonal orientation.

### The effects of combined bottom-up stimulation and top-down attention to the untrained orientation on learning transfer {#s2-1-4}

We eventually achieved complete learning transfer with combined bottom-up stimulation and top-down attention with the transfer orientation. The stimulus configuration was identical to the above top-down attention configuration, except that the orthogonal Gabor at the transfer orientation was indeed present in the actual experiment for 80% of the trials ([Figure 3a](#fig3){ref-type="fig"}), and the upper case C was present for the remaining 20%. The observers needed to guess whether a Gabor or a C was present, and auditory feedback was given on incorrect responses.10.7554/eLife.14614.007Figure 3.Orientation discrimination learning and the effect of combined bottom-up stimulation and top-down attention on learning transfer.(**a**) CFS configurations for the combined bottom-up stimulation and top-down attention condition. It only differed from [Figure 2a](#fig2){ref-type="fig"} in that the orthogonal Gabor was present in 80% of the trials in the actual stimuli, and an uppercase letter C was present in the remaining 20%. (**b**) The mean and individual learning and transfer data with training and combined bottom-up stimulation and top-down attention to the transfer orientation. (**c**) Control experiment. Same as 3b except that there was no orientation discrimination training. (**d**) A summary of learning and transfer in the combined bottom-up and top-down condition and the control condition. (**e**) A summary of the transfer indices in the current combined bottom-up and top-down condition and the previous baseline (replotted from [Figure 1a](#fig1){ref-type="fig"}) and training plus noise-only (replotted from [Figure 1f](#fig1){ref-type="fig"}) conditions. Error bars indicate ± 1 standard error of the mean. DE - dominant eye. \*p\<0.05; \*\*p\<0.01; \*\*\*p\<0.001. See [Figure 3---source data 1](#SD3-data){ref-type="supplementary-material"} for raw data.**DOI:** [http://dx.doi.org/10.7554/eLife.14614.007](10.7554/eLife.14614.007)10.7554/eLife.14614.008Figure 3---source data 1.The first data sheet summarizes the mean and individual data presented in figure panels 3b and 3c.The other data sheets contain raw staircase data for 3b and 3c.**DOI:** [http://dx.doi.org/10.7554/eLife.14614.008](10.7554/eLife.14614.008)

Training significantly reduced the orientation thresholds at both the trained orientation (45.1 ± 5.4%, t~8~ = 8.25, p\<0.001, 95% CI = 32.5% to 57.8%, Cohen's d = 2.49) and the orthogonal transfer orientation (39.0 ± 4.3%, t~8~ = 9.00, p\<0.001, 95% CI = 29.0% to 49.0%, Cohen's d = 2.38) in nine observers ([Figure 3b--d](#fig3){ref-type="fig"}). There was no significant difference between the two improvements (t~8~ = 1.20, p = 0.26, 95% CI = -5.7% to 18.0%, Cohen's d = 0.40). The transfer index was 0.94 ± 0.12 (t~8~ = 7.78, p\<0.001, 95% CI = 0.66 to 1.22, Cohen\'s d = 2.59; [Figure 3e](#fig3){ref-type="fig"}), indicating nearly complete learning transfer to the untrained orthogonal orientation.

In a control experiment, the combined bottom-up stimulation and top-down attention without actual orientation training only led to a small threshold improvement at the exposed orientation (14.6 ± 4.4%, t~7~ = 3.31, p=0.013, 95% CI = 4.2% to 25.1%, Cohen's d = 1.17) ([Figure 3c and d](#fig3){ref-type="fig"}). The improvement was significantly lower than that in [Figure 3b](#fig3){ref-type="fig"} with orientation training (t~15~ = 3.93, p=0.001, 95% CI = 11.2% to 37.6%, Cohen's d = 1.91, two-tailed unpaired t-test). This control ruled out the possibility that the combined bottom-up stimulation and top-down attention to the transfer orientation alone caused the substantial improvement of orientation discrimination shown in [Figure 3b](#fig3){ref-type="fig"}. In [Figure 3b and c](#fig3){ref-type="fig"} the Gabor/C judgments were near chance (0.80) after some initial increase, suggesting effective suppression of stimulus perceptions with continuous flashing noise.

An independent-samples Kruskal-Wallis test revealed significant differences among the transfer indices of the baseline condition ([Figure 1a](#fig1){ref-type="fig"}), the training plus noise-only condition ([Figure 1f](#fig1){ref-type="fig"}), and the training plus combined bottom-up and top-down condition (p = 0.002). Post-hoc Dunn's multiple comparison indicated that the training plus combined bottom-up and top-down condition had significantly more transfer than the baseline condition (p = 0.001 without correction; p = 0.003 with correction) and the training plus noise-only condition (p = 0.007 without correction or 0.022 with correction).

In [Figure 3b and c](#fig3){ref-type="fig"} the Gabor/C judgments were near chance (0.80), suggesting effective suppression of the perception of the orthogonal Gabor by the continuous flashing noise. We further confirmed this suppression effect with 18 observers (10 from [Figure 1d](#fig1){ref-type="fig"}, 3 from [Figure 2b](#fig2){ref-type="fig"}, and 5 from [Figure 3b](#fig3){ref-type="fig"}) after they completed their experiments. Each observer was presented a sub-conscious Gabor at the transfer orientation in half the trials with flashing noise presented to the other eye (50 trials per block for four blocks). In the other half trials a blank screen was presented. The result showed a chance-level accuracy at 0.51 ± 0.01. Therefore, the transfer results in [Figure 1](#fig1){ref-type="fig"}--[3](#fig3){ref-type="fig"} were not contaminated by the leakage of the Gabor perception.

Location specificity and transfer: The effects of bottom-up and top-down influences at the untrained locations {#s2-2}
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Vernier learning is highly location specific. In our previous study ([@bib29]) that used the same stimulus configuration as here in [Figure 4](#fig4){ref-type="fig"}, Vernier learning at one visual quadrant location showed zero transfer to a diagonal location, with the transfer index TI = -0.1 ± 0.16. However, additional training at the transfer location with an orientation discrimination task successfully enabled Vernier learning transfer (TI = 0.98 ± 0.22). The strong location specificity as well as the complete learning transfer after double training formed the baselines for the current experiments.10.7554/eLife.14614.009Figure 4.The effects of bottom-up stimulation of the transfer location on Vernier learning transfer.(**a**) CFS configurations for the simultaneous Vernier training and bottom-up stimulation of the diagonal transfer location. Instruction stimuli: The observer was instructed before the experiment to perform the Vernier task in the dominant eye, while flashing noise patterns covered the opposite visual hemifield across the horizontal meridian. A blank screen was shown to the non-dominant eye. Actual stimuli: A horizontal Gabor was simultaneously flashed in the non-dominant eye at the diagonal transfer location. The observers were neither told, nor were they aware of, the presence of the Gabor stimulus. (**b**) The mean and individual learning and transfer data with training and bottom-up stimulation of the transfer location. (**c**) Control experiment. Same as 4b except that there was no Vernier training. (**d**). Control experiment. Same as 4b except that there was no presence of the Gabor stimulus at the transfer location. (**e**). A summary of learning and transfer in the training plus bottom-up stimulation condition, the bottom-up stimulation alone condition, and the training plus noise-only condition. (**f**). A summary of the transfer indices in the training plus bottom-up stimulation condition and the training plus noise-only condition. Error bars indicate ± 1 standard error of the mean. DE - dominant eye. \*p\<0.05; \*\*p\<0.01; \*\*\*p\<0.001. See [Figure 4---source data 1](#SD4-data){ref-type="supplementary-material"} for raw data.**DOI:** [http://dx.doi.org/10.7554/eLife.14614.009](10.7554/eLife.14614.009)10.7554/eLife.14614.010Figure 4---source data 1.The first data sheet summarizes the mean and individual data presented in figure panels 4b, 4c, and 4d.The other data sheets contain raw staircase data for 4b, 4c, and 4d.**DOI:** [http://dx.doi.org/10.7554/eLife.14614.010](10.7554/eLife.14614.010)

### The effects of bottom-up exposure of the untrained location on learning transfer {#s2-2-1}

To investigate the effect of bottom-up stimulation of the transfer location on Vernier learning transfer, we asked the observers to perform the Vernier task while a Gabor was simultaneously presented at a diagonal quadrant location subconsciously. Specifically, the observers practiced the Vernier task in one visual quadrant of the dominant eye, while the opposite visual hemifield across the horizontal meridian was covered by flashing noise ([Figure 4a](#fig4){ref-type="fig"} instruction stimuli). In the actual experiment a horizontal Gabor was simultaneously presented at the diagonal location in the non-dominant eye ([Figure 4a](#fig4){ref-type="fig"} actual stimuli). Because of the near-threshold Vernier discrimination at the trained location and the noise suppression, this horizontal Gabor bottom-up stimulated the transfer location without the observers' awareness.

Eight observers received five sessions of Vernier training and simultaneous subconscious bottom-up stimulation of the diagonal transfer location. This procedure reduced Vernier thresholds by 32.4% ± 4.3% (t~7~ = 7.54, p\<0.001, 95% CI = 22.2% to 42.5%, Cohen's d = 2.32) at the trained location, as well as by 35.3% ± 6.4% (t~7~ = 5.53, p\<0.001, 95% CI = 20.2% to 50.4%, Cohen's d = 1.95) at the transfer location ([Figure 4b](#fig4){ref-type="fig"}). The two improvements were comparable (t~7~ = 0.54, p = 0.60, 95% CI = −15.6% to 9.8%, Cohen's d = 0.19). The TI = 1.13 ± 0.17 (t~7~ = 6.54, p\<0.001, 95% CI = 0.72 to 1.55, Cohen\'s d = 2.31; [Figure 4f](#fig4){ref-type="fig"}), indicating complete transfer of Vernier learning. This learning transfer was not caused by the bottom-up stimulation alone. Without Vernier training (eight observers in a control condition instead reported the red/green color of a peripheral dot at the original Vernier stimulus location), mere bottom-up stimulation of the diagonal location produced insignificant change of Vernier performance at the stimulated location (4.1 ± 9.1%, t~7~ = 0.45, p = 0.67, 95% CI = −17.5% to 25.7%, Cohen's d = 0.16) ([Figure 4c](#fig4){ref-type="fig"}).

We also tested whether the bottom-up location stimulation by the Gabor stimulus was necessary for the learning transfer to the untrained location, since a large noise field covering two visual quadrants also included the untrained location. The experimental conditions were unchanged as in [Figure 4a](#fig4){ref-type="fig"} except that the horizontal Gabor was absent at the transfer location. The training reduced Vernier thresholds in eight new observers at the trained location by 32.3 ± 3.4% (t~7~ = 9.42, p\<0.001, 95% CI = 24.2% to 40.5%, Cohen's d = 3.33) ([Figure 4d](#fig4){ref-type="fig"}). However, training reduced Vernier thresholds at the transfer location insignificantly by 6.0 ± 4.3% (t~7~ = 1.40, p = 0.21, 95% CI = −4.1% to 16.1%, Cohen's d = 0.49). The improvement was significantly lower at the transfer location than at the trained location (t~7~ = 8.01, p\<0.001, 95% CI = 18.6% to 34.2%, Cohen's d = 2.83). The transfer index TI = 0.13 ± 0.11 (t~7~ = 1.16, p = 0.28, 95% CI = −13.5% to 39.7%, Cohen\'s d = 0.41; [Figure 4f](#fig4){ref-type="fig"}).

An independent samples Mann-Whitney U test revealed a significant difference of transfer indices between the training plus bottom-up stimulation condition ([Figure 4b](#fig4){ref-type="fig"}) and the training plus noise-only condition (p\<0.001). The above results together indicated that bottom-up stimulation of the untrained location was indeed necessary as well as sufficient for the complete learning transfer shown in [Figure 4b](#fig4){ref-type="fig"}.

### The effects of top-down attention to the untrained location on learning transfer {#s2-2-2}

To create a top-down stimulus configuration, the observers were instructed before the experiment to report/guess a Gabor or an uppercase C at the transfer location in the non-dominant eye while the noise was flashed in the dominant eye ([Figure 5a](#fig5){ref-type="fig"} instruction stimuli. The observers needed to close the dominant eye to see the Gabor or letter C). But the actual stimuli contained no Gabor or letter C ([Figure 5a](#fig5){ref-type="fig"} actual stimuli), which the observer was not aware of due to flashing noise suppression. Therefore during these trials, which alternated with Vernier discrimination trials in the same blocks, the observers top-down attended to the transfer location without actual bottom-up stimulation. Fake feedback was provided to give the observers an impression that their responses were 70--80% correct in a specific block. Prior to each block, a black square and a white square were shown to indicate the training and transfer locations, respectively.10.7554/eLife.14614.011Figure 5.The effect of top-down spatial attention to the transfer location on Vernier learning transfer.(**a**) CFS configurations for a top-down spatial attention trial. The observers were instructed through a demo to report a Gabor or an uppercase C in the non-dominant eye at the diagonal transfer location, which was also dichoptically covered by the flashing noise in the dominant eye. In actual experiment the Gabor/C stimulus was absent. The Vernier trials and the Gabor/C trials alternated within the same block of trials. (**b**) The mean and individual learning and transfer data with training and top-down attention to the transfer location. (**c**) Control experiment. Same as 5b except that there was no Vernier training. (**d**) A summary of learning and transfer in the training plus top-down attention condition and the top-down attention alone condition. (**e**) A summary of the transfer indices in the training plus top-down attention condition and the previous training plus noise-only condition (replotted from [Figure 4d](#fig4){ref-type="fig"}). Error bars indicate ± 1 standard error of the mean. DE - dominant eye. \*p\<0.05; \*\*p\<0.01; \*\*\*p\<0.001. See [Figure 5---source data 1](#SD5-data){ref-type="supplementary-material"} for raw data.**DOI:** [http://dx.doi.org/10.7554/eLife.14614.011](10.7554/eLife.14614.011)10.7554/eLife.14614.012Figure 5---source data 1.The first data sheet summarizes the mean and individual data presented in figure panels 5b and 5c.The other data sheets contain raw staircase data for 5b and 5c.**DOI:** [http://dx.doi.org/10.7554/eLife.14614.012](10.7554/eLife.14614.012)

The Vernier training and top-down attention to the transfer location improved Vernier thresholds at the trained location by 28.4 ± 4.0% (t~7~ = 6.86, p\<0.001, 95% CI = 18.6% to 38.2%, Cohen's d = 2.43), as well as at the transfer location by 28.9 ± 4.9% (t~7~ = 6.42, p\<0.001, 95% CI = 18.3% to 40.0%, Cohen's d = 2.27) ([Figure 5b](#fig5){ref-type="fig"}). The two improvements were nearly identical (t~7~ = 0.22, p = 0.83, 95% CI = −5.3% to 6.4%, Cohen's d = 0.08), and the TI = 1.04 ± 0.08 (t~7~ = 11.80, p\<0.001, 95% CI = 0.83 to 1.25, Cohen\'s d = 4.17; [Figure 5e](#fig5){ref-type="fig"}), indicating complete learning transfer. The learning transfer was greater than that in the training plus noise-only condition in [Figure 4d](#fig4){ref-type="fig"} when the transfer indices were compared (p\<0.001, independent-samples Mann-Whitney U test), suggesting that top-down attention was necessary for the learning transfer.

In addition, a control condition with top-down attention trials only (without Vernier training) failed to improve the Vernier threshold at the transfer location (-1.1 ± 6.1%, t~7~ = −0.19, p =0.85, 95% CI = −14.3% to 12.2%, Cohen's d = 0.07) ([Figure 5c](#fig5){ref-type="fig"}), ruling out the possibility that the learning transfer was a result of top-down attention alone. The results in [Figure 5](#fig5){ref-type="fig"} together indicated that, like bottom-up stimulation, top-down spatial attention to the transfer location is also sufficient to enable complete transfer of Vernier learning.

Finally, we checked the effectiveness of flashing noise suppression on Gabor perception at the transfer location. We tested twenty-three observers (eight from [Figure 4b](#fig4){ref-type="fig"}, five from [Figure 4e](#fig4){ref-type="fig"}, six from [Figure 5b](#fig5){ref-type="fig"}, and four from [Figure 5c](#fig5){ref-type="fig"}) for four blocks, 50 trials per block, after they completed their training experiments. They were asked to report whether a sub-conscious Gabor (50% of the trials) was presented at the transfer location with flashing noise suppression. The Gabor stimulus was absent in the other 50% of the trials. The mean report rate was 0.49 ± 0.01. Therefore, the results in [Figures 4](#fig4){ref-type="fig"},[5](#fig5){ref-type="fig"} were not contaminated by the observers' likely perception of the Gabor stimulus at the transfer location.

Discussion {#s3}
==========

A major focus of perceptual learning research has been on the links between learning specificity and training-induced neural changes in the brain. The common explanations include learning specificity as a product of neural plasticity in early visual areas that are most feature selective and retinotopic ([@bib10]; [@bib25]; [@bib17]), or of improved readout of inputs from early visual areas ([@bib7]; [@bib16]; [@bib14]; [@bib12]). Here our results paint a completely opposite picture: It is the actions (or the absence of actions) with the untrained conditions that decide the learning specificity and transfer. We show that for orientation and location specificity, the absence of bottom-up stimulation of neurons representing the untrained conditions, as well as top-down attention to these neurons, prevent perceptual learning from transferring to untrained conditions.

In one ERP study ([@bib33]), we discovered that Vernier learning and its transfer to an untrained hemisphere accompanies significant occipital P1-N1 changes when the Vernier task is performed at either the trained or the untrained location after training. However, if learning does not transfer, as shown in about half the observers, the P1-N1 changes are limited to the trained location. We interpret P1-N1 changes as possible indications of top-down connections between high-level Vernier learning and visual neurons at the trained location, as well as at untrained locations when learning transfers. We also interpret learning specificity as a result of absent functional connections. Our current data suggest that both bottom-up stimulation of, and top-down attention to, untrained conditions may activate visual neurons representing the untrained conditions, so as to foster the connections to enable learning transfer.

In another ERP study ([@bib34]), we also found that orientation discrimination learning and its transfer to an untrained hemisphere accompanies significant occipital C1 changes when the orientation discrimination task is performed at the trained and untrained locations, respectively, after training ([@bib34]). Such C1 changes are not evident with an untrained shape-discrimination task ([@bib34]) or with passive viewing ([@bib3]). These results further indicate that the functional connections are task specific, consistent with the observations that perceptual learning is task specific ([@bib21]; [@bib1]; [@bib6]). The task-specific functional connections associated with learning and its transfer are also supported by fMRI evidence, in that the generalized orientation discrimination learning is accompanied with task-specific enhancement of orientation-selective responses in the early visual areas including V1, V2 and V3 ([@bib5]). These results together may also explain fMRI results that orientation specificity is accompanied with refined representation of the trained orientation in early visual areas ([@bib9]). This is because top-down modulation by high-level orientation learning may not reach the early cortical representations of untrained orientations as a result of absent functional connections.

As we pointed out earlier, when a near-threshold task is practiced, most brain resources are devoted to the training orientation and retinal location. The untrained orientations and retinal locations, and thus the relevant visual neurons, are neither bottom-up stimulated nor top-down attended. At least in the case of location specificity, there is evidence that spatial attention could suppress other unattended locations even with no presence of competing stimuli ([@bib23]; [@bib22]), as is typical in a perceptual learning study. We suspect that some or all of these factors could contribute to under-activations of neurons at untrained conditions, which in turn could lead to missing functional connections of these neurons to high-level learning to prevent learning transfer.

Our previous double training studies have revealed significant and often complete learning transfer to untrained conditions ([@bib31]; [@bib36]). These transfer results prompted us to propose a rule-based perceptual learning theory. Specifically, we suggest that visual perceptual learning is a high-level process. The brain learns the rules of reweighting visual inputs to achieve better visual performance, and these rules are potentially applicable to new orientations and retinal locations. More recent evidence also suggests that these rules are conceptual, in that learning can transfer between physically distinct stimuli that are initially encoded by different neural mechanisms (e.g., between local and global orientations defined by gratings and symmetric dot patterns, or between first- and second-order motion directions) ([@bib27]). The current findings add to this theory by elucidating why perceptual learning, if high-level and rule-based, can be specific in the first place. These results also suggest that double training schemes function by providing bottom-up and top-down forces to activate untrained neurons, which in turn initiate functional connections for learning transfer. A recent computational model explains how such functional connections can be built with double training ([@bib24]). In the model, the secondary training task activates the untrained neurons, which could be recalled when the brain is off-task, so that high-level \'concept neurons\' that have learned the task can connect to these untrained neurons in an off-task self-organization manner for learning transfer. Apparently the activations of untrained neurons due to unconsciousness stimulation, or top-down attention without actual stimulation, can also be recalled off-task in the context of this computational account.

We once had observers practice a peripheral Vernier task identical to the current one, while flashing a Gabor simultaneously at a diagonal location (without noise suppression) for the purpose of stimulating neurons at this latter location ([@bib28]). However, we found no evidence for learning transfer. A more recent psychophysical study also replicated the null-transfer results ([@bib13]). Similarly, an earlier monkey-recording study ([@bib17]) reported that orientation discrimination learning does not transfer to a different location where a same grating stimulus is simultaneously presented. These null-transfer effects may be caused by attentional competition, in that concentrated attention to the training location would suppress the high-contrast stimuli at a different location ([@bib30]). The attentional suppression effect is avoided in double training when the primary training and the secondary training that stimulates the untrained location are performed in alternating blocks of trials ([@bib31]), or when the stimulation of the untrained locations is below awareness as in our current study. In the latter case the bottom-up stimulation of transfer location through one eye may create an input contrast in the eye-of-origin feature, and this contrast, although invisible perceptually, has been shown to be very salient because it more strongly activates V1 neurons than the surrounding stimuli ([@bib38]).

Perceptual learning can also be task irrelevant. Watanabe and colleagues ([@bib30]; [@bib20]) reported that training improves discrimination of a nearby feature that is task irrelevant and sub-threshold (to avoid attentional suppression), and that the learning occurs only when the irrelevant feature is temporally paired with rewards with the trained task. They explained this task irrelevant perceptual learning (TIPL) as a result of interactions between spatially diffusive rewards and bottom-up exposure of the task irrelevant feature ([@bib19]). Note that our double-training enabled learning transfer are distinct from TIPL in two important ways. First, unlike TIPL, the training and exposure do not require temporal pairing and spatial proximity of two stimuli. Indeed the training phase can precede the exposure phase with a time gap of up to 8 weeks ([@bib36]), and the training and exposure locations can be at diagonal quadrants of the visual field separated by 10 degrees ([@bib28]), while double training is still effective. In the current study, the training and sub-threshold exposure are performed in separate blocks of trials, rather than paired, in [Figure 1](#fig1){ref-type="fig"}, and are spatially separated by 10 degrees in [Figure 4](#fig4){ref-type="fig"}. Second, our recent evidence suggests that double-training enabled learning transfer is still task specific ([@bib6]). We found that orientation discrimination learning cannot transfer to a contrast discrimination task using the same Gabor stimulus, even after the observers receive additional exposure of the transfer task through easy trials in separate blocks of trials. The same is true at a reverse direction when the observers learn contrast discrimination and receive exposure of orientation discrimination, the transfer task, through easy trials.

Materials and methods {#s4}
=====================

Observers and apparatus {#s4-1}
-----------------------

One hundred and thirteen (113) undergraduate students with normal or corrected-to-normal vision participated in this study. All were inexperienced in psychophysical observations and were naïve to the purpose of the study. Informed consent, and consent to publish was obtained from each observer before testing. This study was approved by the Peking University Institution Review Board.

The stimuli were generated by a Matlab-based WinVis program (Neurometrics Institute, Oakland, CA) and presented on a 21-inch Sony G520 CRT monitor (1600 × 1200 pixel, 0.25 × 0.25 mm per pixel, 75 Hz frame rate, 43.5 cd/m^2^ mean luminance). The luminance of the monitor was linearized by an 8-bit look-up table. A chin-and-head rest helped stabilize the head of the observer. Experiments were run in a dimly lit room.

Stimuli {#s4-2}
-------

The Gabor stimuli (Gaussian-windowed sinusoidal gratings) for foveal orientation discrimination ([Figure 1](#fig1){ref-type="fig"}--[3](#fig3){ref-type="fig"}) had a standard deviation at 0.48°, a spatial frequency at 1.5 cpd, a contrast at 0.47, a base orientation at 36° or 126°, and a phase randomized for every presentation. The CFS configuration consisted of a central flashing white noise pattern in the dominant eye, and sometimes a Gabor stimulus orthogonal to the trained orientation in the non-dominant eye ([Figure 1b](#fig1){ref-type="fig"}). The noise pattern, refreshed at 9.4 Hz, consisted of 25 × 25 randomly generated black or white blocks (0.17° × 0.17° each) for a total size of 4.30° × 4.30°. The dichoptic stimulus presentations were realized with a stereoscope. The noise pattern was presented in the dominant eye to suppress the perception of the Gabor stimulus presented in the non-dominant eye ([@bib26]).

The Vernier stimuli for peripheral Vernier discrimination ([Figure 4](#fig4){ref-type="fig"}--[5](#fig5){ref-type="fig"}) were identical to those used in a previous study ([@bib29]), which consisted of an upper and a lower vertical Gabor on a mean luminance screen background. The two Gabors had an identical standard deviation at 0.29°, a spatial frequency at 3 cycles per degree, a contrast of 0.47, a phase fixed at 90°, and a center to center distance at 4λ ([Figure 4a](#fig4){ref-type="fig"}). The vertical position of each Gabor shifted away in opposite directions to form a specific Vernier offset. The Vernier stimuli were presented in the upper left quadrant (or lower right quadrant, balanced among observers) at 5° retinal eccentricity.

The CFS configuration consisted of a flashing white noise pattern (10 Hz), which covered either the lower or upper visual hemifield opposite to the Vernier stimulus location in the dominant eye, and a horizontal Gabor in the non-dominant eye in a quadrant diagonal to the Vernier quadrant. The latter Gabor was identical to those forming the Vernier stimuli. The noise pattern consisted of 50 × 38 randomly generated black or white blocks (0.25° × 0.25° each) for a total size of 12.44° × 9.46^°^. The viewing distance was 1 m.

Procedures {#s4-3}
----------

In an orientation discrimination trial, the fixation cross was first presented for 320 ms, and was then followed by a blank gap of 267 ms before the onset of the first stimulus interval. The reference orientation Gabor (36° or 126°, counterbalanced among observers) and the test orientation Gabor (reference + ∆ori) were presented in two stimulus intervals (106 ms each) in a random order, which were separated by a 533 ms inter-stimulus interval. The observers judged in which stimulus interval the Gabor was more clockwise. Auditory feedback was given on incorrect responses.

In a Vernier discrimination trial, the fixation cross was first presented for 200 ms, and was then followed by a blank gap of 200 ms before the onset of the first stimulus interval. The Vernier stimuli were then presented at one visual quadrant for 200 ms. The observers' task was to judge whether the lower Gabor was shifted to the left or the right in comparison to the upper Gabor. Auditory feedback was given on incorrect responses.

Orientation and Vernier discrimination thresholds were measured with a 2AFC staircase procedure using a classical 3-down-1-up staircase rule that resulted in a 79.4% convergence level. Each staircase consisted of four preliminary reversals and six experimental reversals (approximately 50--60 trials). The step size of the staircase was 0.05 log units. The geometric mean of the experimental reversals was taken as the threshold for each staircase run.

Each experiment consisted of seven sessions including the pre- and post-training sessions on seven different days. The pre-training session measured the orientation thresholds at the training and transfer orientations ([Figures 1](#fig1){ref-type="fig"}--[3](#fig3){ref-type="fig"}), or Vernier thresholds at the training and transfer locations ([Figures 4](#fig4){ref-type="fig"}--[5](#fig5){ref-type="fig"}), for six staircases each. The post-training session measured the same thresholds for five staircases each. The geometric mean was taken as the pre- or post-training threshold with each condition. The five training sessions each consisted of 10 staircases of orientation discrimination training ([Figures 1](#fig1){ref-type="fig"}--[3](#fig3){ref-type="fig"}) or Vernier discrimination ([Figures 4](#fig4){ref-type="fig"}--[5](#fig5){ref-type="fig"}), as well as 10 blocks of bottom-up and/or top-down trials with the transfer condition (50 trials per block in [Figures 1](#fig1){ref-type="fig"}--[3](#fig3){ref-type="fig"}, or the same number of trials as in 10 training staircases in [Figures 4](#fig4){ref-type="fig"}--[5](#fig5){ref-type="fig"}), with the training and bottom-up/top-down tasks switched every five blocks of trials. Each training session lasted about 1.5 hr.

Funding Information
===================

This paper was supported by the following grant:

-   http://dx.doi.org/10.13039/501100001809National Natural Science Foundation of China 31230030 to Cong Yu.

This research was supported by a Natural Science Foundation of China Grant 31230030 (CY). We thank the helpful comments and suggestions from Fang Fang, Sheng He, Stanley Klein, Wu Li, Shin Shimojo, Rufin Vogels, and Li Zhaoping during various stages of this study.

Additional information {#s5}
======================

The authors declare that no competing interests exist.

Y-ZX, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article.

J-YZ, Conception and design, Analysis and interpretation of data, Drafting or revising the article.

CY, Conception and design, Analysis and interpretation of data, Drafting or revising the article.

Human subjects: Informed consent, and consent to publish was obtained from each observer before testing. This study was approved by the Peking University Institution Review Board.

10.7554/eLife.14614.013

Decision letter

Gold

Joshua I

Reviewing editor

University of Pennsylvania

,

United States

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

\[Editors' note: this article was originally rejected after discussions between the reviewers, but the authors were invited to resubmit after an appeal against the decision.\]

Thank you for submitting your work entitled \"Bottom-up and top-down influences at untrained conditions determine perceptual learning specificity and transfer\" for consideration by *eLife*. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and David Van Essen as the Senior Editor. Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that this submission will not be considered further for publication in *eLife*.

Although the reviewers and the Reviewing Editor were in general agreement that the topic of the study is quite interesting and could potentially advance our understanding of specificity and transfer in perceptual learning, they also felt that the study lacked critical controls that are needed to validate the main findings. Specifically, it would be important to show if and how much learning occurs in the bottom-up and top-down conditions for subjects that do not also go through the training sequence. If you are able to carry out these controls and address the concerns, we invite you to resubmit this work to *eLife*.

The reviewers also noted the following other major concerns that you may want to keep in mind for possible revisions:

1\) Given that the authors\' previous work has already shown that some activity is needed to induce transfer, it is important to more clearly describe the novelty of the current results to a general-audience journal.

2\) Across the experiments, the number of subjects is quite variable, with E1 having double the subjects as some other studies (N=10 vs N=8 vs N=5, etc.). This is an important issue, especially since transfer effects are compared across subject groups and the number of subjects for individual groups is relatively low to begin with. What criteria were used to determine how many subjects were run in each study and when data collection in each study should be stopped?

3\) There are a number of issues with the statistical methods that should be addressed, including: a) specifying the meaning of error bars in the figures and reported measurement/sampling variability in the text (SEM? STD? Confidence intervals?); b) the transfer index is used in the text but not in the figures to summarize the findings, and a significance test of the value of the transfer index is reported only for the data presented in [Figure 1](#fig1){ref-type="fig"} and not the other Figures, and no transfer index is reported for the spatial experiments; c) many non-pairwise t-tests were performed (non-parametric tests would probably be more appropriate) without correction for multiple comparisons -- most were highly significant, but this procedure should still be implemented; and d) Do any of the statistical tests take into account the uncertainty in the estimates of the various values measured per subject (e.g., the error bars apparent in the dim points in [Figure 1a, d, f](#fig1){ref-type="fig"}, etc.)?

4\) In the CSF experiments, the authors mention that the subjects were \'neither told nor aware of\' the untrained stimuli in the non-dominant eye (NDE). Were any queries made of the subjects to confirm that the stimulus presented to the NDE was actually \'subconsciously\' perceived?

*Reviewer \#1:*

In this study, the authors investigated effects of bottom-up and top-down stimulation of the untrained condition in determining specificity and transfer in perceptual learning. The continuous flash suppression (CFS) technique was used to present subconscious bottom-up or top-down stimuli to the transfer orientation or location. They found that either bottom-up stimulation or top-down attention is sufficient to enable significant learning transfer, and conclude that learning specificity may result from under activation of untrained visual neurons due to insufficient bottom-up stimulation and/or top-down attention.

The topic of the study is quite interesting and could potentially advance our understanding of specificity and transfer in perceptual learning. However, it seems to me that the study may have missed a critical control condition -- how much learning was induced by the bottom-up and top-down stimulation of the transfer orientation or location? Although the results are presented as transfer of learning from the trained condition, stimulation of the transfer condition could by itself lead to improved performance.

*Reviewer \#2:*

In this paper, Xiong et al. show that both bottom-up and top-down factors can amplify transfer effects to untrained stimulus orientations and locations. Their results provide one possible explanation regarding why transfer effects have been inconsistently reported in the past. I think the experiments are generally well designed and well controlled. However, I have several concerns that I would like to see addressed. Moreover, the authors' previous work has already shown that some activity is needed to induce transfer. So the novelty of the present demonstration that either bottom-up or top-down activation is sufficient should be clarified for a general audience journal.

Specific comments

1\) Number of subjects: Across the experiments, the number of subjects is quite variable, with E1 having double the subjects as some other studies (N=10 vs N=8 vs N=5, etc.). This is an important issue, especially since transfer effects are compared across subject groups and the number of subjects for individual groups is relatively low to begin with. This left me wondering what criteria were used to determine how many subjects were going to be run in each study and when data collection in each study should be stopped.

2\) Statistical methods. The authors used a transfer index for the baseline experiment to compare the transfer effects between experimental conditions (which is fine). The authors then provide statistical results for [Figure 1g](#fig1){ref-type="fig"}. But for other comparisons, statistical results comparing transfer indexes between the baseline ([Figure 1a](#fig1){ref-type="fig"}) experimental conditions in [Figures 2](#fig2){ref-type="fig"}--[3](#fig3){ref-type="fig"} were not reported. Also many non-pairwise t-tests were performed without correction for multiple comparisons -- true most were highly significant, but this procedure should still be implemented. For the spatial experiments, no formal transfer indexes were established.

3\) In the CSF experiments, the authors mention that the subjects were \'neither told nor aware of\' the untrained stimuli in the non-dominant eye (NDE). Were any queries made of the subjects to confirm that the stimulus presented to the NDE was actually \'subconsciously\' perceived?

\[Editors' note: what now follows is the decision letter after the authors submitted for further consideration.\]

Thank you for resubmitting your work entitled \"Bottom-up and top-down influences at untrained conditions determine perceptual learning specificity and transfer\" for further consideration at *eLife*. Your revised article has been favorably evaluated by David Van Essen (Senior editor), a Reviewing editor, and two reviewers.

The manuscript has been much improved, but there are some remaining minor issues that should be addressed before acceptance, as outlined below:

1\) [Figure 4E](#fig4){ref-type="fig"} appear to be mislabeled. The last panel should be (d) not (e) -- so in the summary figures I think it would be better to indicate in all bar graphs which figures they derive from. For example, in [Figure 4E](#fig4){ref-type="fig"} -- the label should be (4b) (4c) and (4d) instead of (b) (c) and (e). Another example is [Figure 3E](#fig3){ref-type="fig"} -- the label could be (3b) (1a) and (1f) instead of (b) (1a) (1f).

2\) The authors should consider discussing the Jehee J. Neuro and Byers J. Neurophys papers given the relevance of top down attention during training. Another potentially relevant paper is Szpiro and Carrasco (2015) in Psych Sci, which is seemingly both consistent and inconsistent with the present findings -- they showed that exogenous attention induces transfer in spatial frequency but not an orientation task.

3\) All raw source data should be uploaded. Currently it appears that only a summary file is provided in. xls format.

10.7554/eLife.14614.014

Author response

\[Editors' note: the author responses to the first round of peer review follow.\]

*Although the reviewers and the Reviewing Editor were in general agreement that the topic of the study is quite interesting and could potentially advance our understanding of specificity and transfer in perceptual learning, they also felt that the study lacked critical controls that are needed to validate the main findings. Specifically, it would be important to show if and how much learning occurs in the bottom-up and top-down conditions for subjects that do not also go through the training sequence. If you are able to carry out these controls and address the concerns, we invite you to resubmit this work to eLife.*

These control data are now presented in [Figures 1e](#fig1){ref-type="fig"},[2c](#fig2){ref-type="fig"},[3c](#fig3){ref-type="fig"},[4c](#fig4){ref-type="fig"},[5c](#fig5){ref-type="fig"}. Among these control conditions, [Figures 1e](#fig1){ref-type="fig"},[2c](#fig2){ref-type="fig"},[4c](#fig4){ref-type="fig"},[5c](#fig5){ref-type="fig"} show no significant effects of the bottom-up or top-down conditions on learning at the transfer condition when no training is carried out. [Figure 3c](#fig3){ref-type="fig"} shows a small learning effect that is significantly lower than the improvement with the training sequence (14.6% vs. 45.1%, p = 0.001). These control results thus validate the main findings in this study.

*The reviewers also noted the following other major concerns that you may want to keep in mind for possible revisions:*

*1) Given that the authors\' previous work has already shown that some activity is needed to induce transfer, it is important to more clearly describe the novelty of the current results to a general-audience journal.*

We describe the novelty and significant of this study to the general audience more clearly at the end of the Introduction:

"These results provide a solution to the mystery of learning specificity that has dominated the history of perceptual learning research. With learning specificity considered as a by-product of training, the field should move on to study the brain mechanisms of perceptual learning without much of specificity-related constraints. Moreover, more efficient training paradigms can be designed to generate perceptual learning without the unwanted specificity in practical settings."

2\) Across the experiments, the number of subjects is quite variable, with E1 having double the subjects as some other studies (N=10 vs N=8 vs N=5, etc.). This is an important issue, especially since transfer effects are compared across subject groups and the number of subjects for individual groups is relatively low to begin with. What criteria were used to determine how many subjects were run in each study and when data collection in each study should be stopped?

We did a power analysis in a recent JOV paper (Cong et al., 2016) and found that if only the pre- and post-training thresholds are compared with a t-test, 5-6 subjects is sufficient to obtain high statistical power in a perceptual learning experiment. However, this number is inadequate when multiple comparisons are performed. We thus increased the number of subjects in each experiment to 8-10 to deal with the power issue.

*3) There are a number of issues with the statistical methods that should be addressed, including: a) specifying the meaning of error bars in the figures and reported measurement/sampling variability in the text (SEM? STD? Confidence intervals?);*

SEM is specified in the legend of each figure; SEM and 95% confidence levels are specified in the text.

*b) The transfer index is used in the text but not in the figures to summarize the findings, and a significance test of the value of the transfer index is reported only for the data presented in [Figure 1](#fig1){ref-type="fig"} and not the other Figures, and no transfer index is reported for the spatial experiments;*

The transfer indices are now summarized in each figure. A significance test is also reported for each transfer index.

*c) Many non-pairwise t-tests were performed (non-parametric tests would probably be more appropriate) without correction for multiple comparisons -- most were highly significant, but this procedure should still be implemented;*

Independent-samples Kruskal-Wallis test with correction for multiple comparisons ([Figure 1](#fig1){ref-type="fig"}--[3](#fig3){ref-type="fig"}) and Mann-Whitney U test ([Figure 4](#fig4){ref-type="fig"}--[5](#fig5){ref-type="fig"}) are now performed to compare transfer indices among different conditions.

*d) Do any of the statistical tests take into account the uncertainty in the estimates of the various values measured per subject (e.g., the error bars apparent in the dim points in [Figure 1a, d, f](#fig1){ref-type="fig"}, etc.)?*

Our statistical tests do not consider the error bars of individual data. We consulted an expert in statistics on this issue, and the response is: if the mean is calculated when each individual datum is weighted by its standard error, there is a risk that when one datum has very small error bars, the calculated mean will be strongly biased by this datum.

*4) In the CSF experiments, the authors mention that the subjects were \'neither told nor aware of\' the untrained stimuli in the non-dominant eye (NDE). Were any queries made of the subjects to confirm that the stimulus presented to the NDE was actually \'subconsciously\' perceived?*

Yes, we not only asked the observers after the experiments, but also tested some of them to confirm their oral reports. These tests are described in two occasions of the text:

"In Figure 3b-c the Gabor/C judgments were near chance (0.80), suggesting effective suppression of the perception of the orthogonal Gabor by the continuous flashing noise. We further confirmed this CFS effect with 18 observers (10 from [Figure 1d](#fig1){ref-type="fig"}, 3 from [Figure 2b](#fig2){ref-type="fig"}, and 5 from [Figure 3b](#fig3){ref-type="fig"}) after they completed their experiments. Each observer was presented a sub-conscious Gabor at the transfer orientation in half the trials with flashing noise presented to the other eye (60 trials per block for 4 blocks). In the other half trials a blank screen was presented. The result showed a chance-level accuracy at 0.51 ± 0.01. Therefore, the transfer results in [Figure 1](#fig1){ref-type="fig"}--[3](#fig3){ref-type="fig"} were not contaminated by the leakage of the Gabor perception."

"Finally, we checked the effectiveness of flashing noise suppression on Gabor perception at the transfer location. We tested observers (13 from [Figure 4b](#fig4){ref-type="fig"}, 4 from [Figure 4e](#fig4){ref-type="fig"}, and 6 from [Figure 5b](#fig5){ref-type="fig"}) for 4 blocks, 50 trials per block, after they completed their training experiments. They were asked to report whether a sub-conscious Gabor (50% of the trials) was presented at the transfer location with flashing noise suppression. The Gabor stimulus was absent in the other 50% of the trials. The mean report rate was 0.49 ± 0.01. Therefore, the results in [Figure 5](#fig5){ref-type="fig"} were not contaminated by the observers' likely perception of the Gabor stimulus at the transfer location."

*Reviewer \#1:*

*In this study, the authors investigated effects of bottom-up and top-down stimulation of the untrained condition in determining specificity and transfer in perceptual learning. The continuous flash suppression (CFS) technique was used to present subconscious bottom-up or top-down stimuli to the transfer orientation or location. They found that either bottom-up stimulation or top-down attention is sufficient to enable significant learning transfer, and conclude that learning specificity may result from under activation of untrained visual neurons due to insufficient bottom-up stimulation and/or top-down attention.*

The topic of the study is quite interesting and could potentially advance our understanding of specificity and transfer in perceptual learning. However, it seems to me that the study may have missed a critical control condition -- how much learning was induced by the bottom-up and top-down stimulation of the transfer orientation or location? Although the results are presented as transfer of learning from the trained condition, stimulation of the transfer condition could by itself lead to improved performance.

These control data are now presented in [Figures 1e](#fig1){ref-type="fig"},[2c](#fig2){ref-type="fig"},[3c](#fig3){ref-type="fig"},[4c](#fig4){ref-type="fig"},[5c](#fig5){ref-type="fig"}. Among these control conditions, [Figures 1e](#fig1){ref-type="fig"},[2c](#fig2){ref-type="fig"},[4c](#fig4){ref-type="fig"},[5c](#fig5){ref-type="fig"} show no significant effects of the bottom-up or top-down conditions on learning at the transfer condition when no training is carried out. [Figure 3c](#fig3){ref-type="fig"} shows a small learning effect that is significantly lower than the improvement with the training sequence (14.6% vs. 45.1%, p = 0.001). These control results thus validate the main findings in this study.

*Reviewer \#2:*

*In this paper, Xiong et al. show that both bottom-up and top-down factors can amplify transfer effects to untrained stimulus orientations and locations. Their results provide one possible explanation regarding why transfer effects have been inconsistently reported in the past. I think the experiments are generally well designed and well controlled. However, I have several concerns that I would like to see addressed. Moreover, the authors' previous work has already shown that some activity is needed to induce transfer. So the novelty of the present demonstration that either bottom-up or top-down activation is sufficient should be clarified for a general audience journal.*

This issue is addressed in the revision. See our responses above.

*Specific comments*

1\) Number of subjects: Across the experiments, the number of subjects is quite variable, with E1 having double the subjects as some other studies (N=10 vs N=8 vs N=5, etc.). This is an important issue, especially since transfer effects are compared across subject groups and the number of subjects for individual groups is relatively low to begin with. This left me wondering what criteria were used to determine how many subjects were going to be run in each study and when data collection in each study should be stopped.

This issue is addressed in the revision. See our responses above.

*2) Statistical methods. The authors used a transfer index for the baseline experiment to compare the transfer effects between experimental conditions (which is fine). The authors then provide statistical results for [Figure 1g](#fig1){ref-type="fig"}. But for other comparisons, statistical results comparing transfer indexes between the baseline ([Figure 1a](#fig1){ref-type="fig"}) experimental conditions in [Figures 2](#fig2){ref-type="fig"}--[3](#fig3){ref-type="fig"} were not reported. Also many non-pairwise t-tests were performed without correction for multiple comparisons -- true most were highly significant, but this procedure should still be implemented. For the spatial experiments, no formal transfer indexes were established.*

These issues are addressed in the revision. See our responses above.

*3) In the CSF experiments, the authors mention that the subjects were \'neither told nor aware of\' the untrained stimuli in the non-dominant eye (NDE). Were any queries made of the subjects to confirm that the stimulus presented to the NDE was actually \'subconsciously\' perceived?*

This issue is addressed in the revision. See our responses above.

\[Editors' note: the author responses to the re-review follow.\]

*The manuscript has been much improved, but there are some remaining minor issues that should be addressed before acceptance, as outlined below:*

*1) [Figure 4E](#fig4){ref-type="fig"} appear to be mislabeled. The last panel should be (d) not (e) -- so in the summary figures I think it would be better to indicate in all bar graphs which figures they derive from. For example, in [Figure 4E](#fig4){ref-type="fig"} -- the label should be (4b) (4c) and (4d) instead of (b) (c) and (e). Another example is [Figure 3E](#fig3){ref-type="fig"} -- the label could be (3b) (1a) and (1f) instead of (b) (1a) (1f).*

Thanks a lot for pointing out the mistakes. The labels have been updated as suggested.

2\) The authors should consider discussing the Jehee J. Neuro and Byers J. Neurophys papers given the relevance of top down attention during training. Another potentially relevant paper is Szpiro and Carrasco (2015) in Psych Sci, which is seemingly both consistent and inconsistent with the present findings -- they showed that exogenous attention induces transfer in spatial frequency but not an orientation task.

We added the following sentences to an existing paragraph in the Discussion section to discuss the Jehee paper (Jehhe et al., 2012) and the Byers paper (Byers and Serences, 2014):

"In another ERP study (Zhang et al., 2015), we also found that orientation discrimination learning and its transfer to an untrained hemisphere accompanies significant occipital C1 changes when the orientation discrimination task is performed at the trained and untrained locations, respectively, after training (Zhang et al., 2015). Such C1 changes are not evident with an untrained shape-discrimination task (Zhang et al., 2015) or with passive viewing (Bao et al., 2010). These results further indicate that the functional connections are task specific, consistent with the observations that perceptual learning is task specific (Shiu and Pashler, 1992; Ahissar and Hochstein, 1993; Cong et al., 2016). The task-specific functional connections associated with learning and its transfer are also supported by fMRI evidence, in that the generalized orientation discrimination learning is accompanied with task-specific enhancement of orientation-selective responses in the early visual areas including V1, V2 and V3 (Byers and Serences, 2014). These results together may also explain fMRI results that orientation specificity is accompanied with refined representation of the trained orientation in early visual areas (Jehee et al., 2012). This is because top-down modulation by high-level orientation learning may not reach the early cortical representations of untrained orientations as a result of absent functional connections."

Szpiro and Carrasco (2015, Psych Sci) reported that three days of training (800 trials per day) failed to improve the performance of an orientation comparison task, unless exogenous attention was added. However, 2400 trials in 3 days is a lot of trials for perceptual learning to occur in many tasks. So we double-checked their non-learning baseline results with nearly identical procedures. All observers we trained showed consistent and significant orientation learning, as much as when exogenous attention was introduced. We showed the data to Carrasco recently when she was visiting us in Beijing. It would be better that we solve the discrepancies with Carrasco in a different occasion.

*3) All raw source data should be uploaded. Currently it appears that only a summary file is provided in. xls format.*

Now we upload five Excel files, each containing data for one figure. Each data sheet in an Excel file contains data for one specific subpanel of the figure. Each observer's raw data (individual staircase data with values of reversals), as well as the calculations for the individual means and group means are listed.
