The study was approved by the Institutional Review Board of the Advanced Telecommunications Institutes International (ATR) in which the current study was conducted.

The result of the fMRI decoder construction stage showed that the highest decoder performance was obtained from the CC ( S1A Fig ). Consistent with this result, previous neuroimaging studies have reported that the CC is highly involved in facial preference [ 15 , 18 , 39 ] as well as preferential decision-making in general [ 21 – 23 ]. Thus, we selected the CC as the target region for the main experiment. Note that the highest decoder performance was also found in the CC when we evaluated decoder performance in the same way using the fMRI signals obtained in the fMRI decoder construction stage of the main experiment ( S1B Fig ). These results indicate robustness of the tendency in which the CC most accurately reflects subjects’ facial preference in the preference-rating task in this study.

Decoder performance for each ROI was defined as the correlation coefficient between actual subjects’ behavioral preference ratings in the preference-rating task and the estimated ratings calculated from activation patterns of the ROI and evaluated by a leave-one-run-out cross validation procedure. In the cross-validation procedure, the pairs of the actual subjects’ behavioral preference ratings and the activation patterns for the ROI measured on one fMRI run were treated as the test data (20 samples), whereas those measured on the remaining runs (220 samples) were used for training the decoder to estimate subjects’ trial-by-trial behavioral preference ratings. Thus, 12 cross-validation sets were generated per subject. For each voxel, activation amplitudes of the training and test data were normalized by mean and variance of activation amplitudes of the training data so that mean and variance of voxel activation amplitudes corresponding to the induction period would be zero and one, respectively. The correlation coefficients for each ROI were first standardized using Fisher’s transformation, averaged over the cross-validation sets, and then averaged across subjects, as shown in S1A Fig .

To construct a preference decoder for each ROI, we used a sparse linear regression algorithm [ 25 ], which automatically selected the relevant voxels within an ROI for decoding. Note that the behavioral preference ratings measured in this study were non-linear. Although they ranged from one to ten, preference measurement on the Likert-type scale cannot be considered strictly linear. Thus, before applying the sparse linear regression for each ROI, the behavioral preference ratings were linearized using an arc hyperbolic tangent function. An estimated rating R decoded , that is, the decoder output calculated based on an activation pattern for a trial, was obtained in each ROI by Here, A voxel represents the activation pattern of voxels in the ROI for the trial. W voxel indicates linear weights for the voxels, which were optimized by the sparse linear regression algorithm based on fMRI data, which was used for training the decoder. b corresponds to the decoder’s constant term, which was determined for each subject as his/her average behavioral preference rating across all faces in the preference-rating task during the fMRI decoder construction stage. A voxel and W voxel are denoted as n-dimensional column vectors with n as the number of voxels in each ROI. T denotes matrix transpose. The inputs to the decoder were subjects’ moment-to-moment brain activations in each ROI, whereas the outputs from the decoder represented the decoder’s best estimate of the corresponding behavioral preference ratings.

A time-course of BOLD signal intensities was extracted from each voxel in each ROI and shifted by 4 s to account for the hemodynamic delay using the Matlab software. A linear trend was removed from the time-course. The time-course was z-score normalized for each voxel using all time points except for those for the initial 10 s in each fMRI run. This normalization was aimed to minimize baseline differences in time-courses of BOLD signal intensities across the fMRI runs. The data samples for computing the decoder were created by averaging the BOLD signal intensities of each voxel for three volumes corresponding to the 6 s rating period.

We specified seven ROIs implicated in facial preference [ 14 – 20 ] according to anatomical data for each subject: the cingulate cortex (CC), lateral prefrontal cortex (LPFC), orbitofrontal cortex (OFC), occipitotemporal cortex, insular cortex, basal ganglia, and amygdala. LPFC was defined as the middle frontal gyrus plus the inferior frontal sulcus. OFC was defined as the orbital gyrus plus the orbital sulci. The occipitotemporal cortex was defined as the lateral occipitotemporal gyrus, the medial occipitotemporal gyrus, plus the occipitotemporal sulcus. The basal ganglia were defined as the caudate, the pallidum, the putamen, plus the nucleus accumbens. Voxels from the left and right hemispheres were merged for each ROI. The cortical regions were specified using an atlas on the BrainVoyager QX software [ 36 ]. A cortical surface for each subject was spatially normalized into a standard cortical surface using a cortex-based alignment method [ 37 ]. Then, the specified regions were projected into a native space for each subject. The subcortical regions were specified for each subject using an automated brain parcellation method [ 38 ] on the Freesurfer software ( http://surfer.nmr.mgh.harvard.edu ).

Each fMRI run for the fMRI decoder construction stage consisted of 20 task trials (one trial = 12 s) plus a 10 s fixation period before the trials and a 2 s fixation period after the trials (one run = 252 s). The fMRI data for the initial 10 s were discarded to allow the longitudinal magnetization to reach equilibrium. Subjects conducted a total of 240 trials in 12 fMRI runs. A presentation order for the abovementioned 240 face pictures was randomized for each subject. Throughout each fMRI run, subjects were asked to fixate on a white bull’s-eye presented at the center of the display. A brief break period was provided after each fMRI run upon a subject’s request.

In the fMRI decoder construction stage, we measured subjects’ blood-oxygen-level dependent (BOLD) signal patterns (see MRI Measurements and Parameters ) while they once again conducted the preference-rating task on the 240 face pictures selected from the pre-test stage (the 100 highest-rated faces, 100 lowest-rated faces, and 40 neutrally rated faces). The measured BOLD signal patterns and behavioral preference ratings were in turn used to compute parameters for a preference decoder for each of the different ROIs (see below). Task procedures were identical to those of the pre-test stage except that an inter-trial period was added to the end of each trial, in which only a white fixation point was presented at the center of the display ( Fig 1B ). Subjects were asked to report their ratings within the reporting period (maximum of 5.5 s). The duration of the inter-trial period varied across trials, depending on subjects’ reporting time, so that the total duration of the reporting and inter-trial periods would be equal to 5.5 s.

Each trial ( Fig 1B ) consisted of a face period (0.5 s), a rating period (6 s), and a reporting period, in the respective order. During the face period, a face picture was presented for 0.5 s at the center of the display. During the rating period, only a fixation point was presented at the center. Subjects were instructed to rate their preference to the previously presented face on a ten-point scale (one for the lowest preference, ten for the highest preference). During the reporting period, subjects were asked to report the preference rating by pressing two buttons (left and right) on a keyboard using the index and middle fingers of their right hand. At the beginning of the reporting period, a random number from one to ten was selected and presented at the center of the display. Subjects were asked to adjust its value to their preference rating, pressing the left button to increment it. Values were “wrapped” so that when subjects attempted to increment past a value of ten, values would start over at one. Subjects completed the reporting period by pressing the right button. After completion of the reporting period, the next trial began.

We used a pool of 400 face pictures (200 males, 200 females, of a variety of races and ages) collected from several open databases [ 30 – 35 ]. A stimulus primarily consisted of a face and usually included some body parts, including hair, a neck, and shoulders, as well as a background scene. Each face picture was 4° square in size. The order of presentation of faces was randomized for each subject.

Only behavioral data were collected from the pre-test stage, during which subjects’ behavioral preference ratings to face pictures were measured. Subjects performed a preference-rating task ( Fig 1B ) for a total of 400 trials across 20 runs. During each run, subjects were asked to fixate on a white bull’s-eye presented at the center of the display. A brief break period was provided after each run upon a subject’s request.

The purpose of the pilot experiment was to determine a target region of interest (ROI) to be used in the main experiment. Three subjects participated in the pilot experiment. The complete experiment consisted of two stages: pre-test (1 d) and fMRI decoder construction (1 d). The two stages were separated by at least 24 h.

Main Experiment

Thirty subjects participated in the main experiment. The main experiment consisted of five stages: pre-test (1 d), fMRI decoder construction (1 d), induction (fMRI DecNef, 3 d), post-test (20 min after the induction stage), and interview (immediately after the post-test stage) stages, in this order (Fig 1A). The pre-test, fMRI decoder construction, and induction stages were separated by at least 24 h. Thirty subjects in the main experiment were randomly assigned to one of the higher-preference (n = 12), lower-preference (n = 12), and control (n = 6) groups. They were not informed about their assigned group.

The procedures of the pre- and post-test stages in the main experiment were identical to those of the pre-test stage in the pilot experiment. As in the pilot experiment, the 100 highest-rated faces, the 100 lowest-rated faces, and 40 neutrally rated faces were selected to be used in the subsequent fMRI decoder construction stage. In addition, out of the 40 neutrally rated faces, 15 were randomly selected for use in the induction stage (“induction faces”) and another set of 15 was also randomly selected as a preference-matched control against the induction faces (“baseline faces”). The baseline faces were not shown during the subsequent induction stage.

For the higher- and lower-preference groups, the procedures of the fMRI decoder construction stage in the main experiment were identical to those in the pilot experiment (see Pilot Experiment). A preference decoder for the CC was computed for each subject for use in the subsequent induction stage. To train the decoder, we used 240 data samples obtained from the 240 trials in the 12 fMRI runs. For each voxel, activation amplitudes of the training data were normalized by the mean and the variance of activation amplitudes of the training data so that the mean and the variance of voxel activation amplitudes would be zero and one, respectively. The mean (± s.e.m.) numbers of voxels selected by the sparse linear regression algorithm to decode the subjects’ preference ratings were 219.8 ± 0.5 across subjects. Note that because the decoder was built based on data samples from all the trials in the fMRI decoder construction stage, a unique set of voxels was selected for each subject.

For the control group, the visual presentations in the fMRI decoder construction stage were identical to those in the pilot experiment while the experiment was conducted outside the MRI scanner without fMRI measurements.

In the induction stage, which consisted of three daily sessions, subjects from the higher- and lower-preference groups were instructed to regulate the activation of their brains, which were controlled by an online fMRI technique [24,40–43]. On each day, subjects participated in up to 12 fMRI runs. The mean (± s.e.m.) number of runs per day was 10.8 ± 0.2. Each fMRI run for the induction stage consisted of 15 trials (one trial = 20 s) preceded by a 30 s fixation period (one run = 330 s). The fMRI data for the initial 10 s were discarded to allow the longitudinal magnetization to reach equilibrium. During each run, subjects were instructed to fixate on a white bull’s-eye presented at the center of the display. After each fMRI run, a brief break period was provided upon a subject’s request.

Each trial in the induction stage (Fig 1C) consisted of a face period (0.5 s), an induction period (6 s), a fixation period (6 s), a feedback period (2 s), and an inter-trial period (5.5 s), in that order. During the face period, one of the 15 induction faces described above was presented for 0.5 s at the center of the display. The order of presentation of the 15 induction faces was randomized for each fMRI run. During the induction period, the color of the fixation point changed from white to green, and no visual stimulus except for the fixation point was presented. Subjects were instructed to regulate activation of their brain, with the goal of making the size of a solid green disk presented in the later feedback period as large as possible. The experimenters provided no further instructions or strategies. During the fixation period, subjects were asked simply to fixate on the central white point. This period was inserted between the induction period and the feedback period to compensate for the known hemodynamic delay, which we assumed lasted 4 s, during which activation patterns in the CC were calculated in time for a green disk to be shown in the subsequent feedback period. The feedback period presented the green disk for 2 s. The size of the disk was determined based on the estimated rating (see below), which is the decoder output value based on the BOLD signal pattern of the CC measured in the prior induction period. The green disk was always enclosed by a larger green concentric circle (10° in diameter), which indicated the disk’s possible maximum size. The feedback period was followed by the inter-trial period, during which subjects were asked to fixate on the central white point. This period was followed by the start of the next trial.

The size of the disk presented during the feedback period was based on the estimated rating from the CC activation pattern, which was computed during the fixation period, as follows. First, measured functional images underwent 3-D motion correction using the Turbo BrainVoyager software. Second, a time-course of BOLD signal intensities was extracted from each of the voxels in the CC identified in the fMRI decoder construction stage and was shifted by 4 s to account for the hemodynamic delay. Third, a linear trend was removed from the time-course for each voxel using a linear regression algorithm based on all time points except for those for the initial 10 s in each fMRI run, and the BOLD signal time-course was z-score normalized for each voxel using BOLD signal intensities measured for 20 s starting from 10 s after the onset of each fMRI run. Fourth, the data sample used to calculate the size of the disk was created by averaging the BOLD signal intensities of each voxel for three volumes corresponding to the 6 s induction period. Finally, the estimated rating was calculated from the data sample using the decoder constructed in the fMRI decoder construction stage. For the higher-preference group, the size of the disk was proportional to the estimated rating (ranging from one to ten). For the lower-preference group, the size of the disk was proportional to 11 minus the estimated rating so that a lower estimated rating resulted in a larger disk. In addition to the fixed amount of the compensation for participation in the experiment, a bonus of up to 3,000 JPY was paid to subjects based on the mean size of the disk during each day.

Induced CC-activation shifts shown in Fig 4 and S4A Fig indicate how far the activation patterns of the CC during the induction stage are from the activation patterns in the CC corresponding to the average behavioral preference rating. The induced CC-activation shift was calculated as follows. First, the estimated rating R decoded from an activation pattern of the CC for a trial was computed by the same method as in the pilot experiment, but only from the CC. That is, R decoded was computed by Here, A voxel represents the activation pattern of voxels in the CC in the induction period. W voxel indicates linear weights for the voxels, which had been computed for each subject in the fMRI decoder construction stage. b corresponds to the decoder’s constant term and had been determined for each subject as her/his average behavioral preference rating in the preference-rating task during the fMRI decoder construction stage. This constant term varied across subjects. A voxel and W voxel are denoted as n-dimensional column vectors with n as the number of voxels in the CC. T denotes matrix transpose.

The induced CC-activation shift for a trial was defined by Based on the following computation, the induced CC-activation shift represents how far the activation patterns in the CC during the induction stage are away from the activation pattern in the CC corresponding to the average behavioral preference rating initially for each subject. As described above, the constant term b was determined for each subject as her/his average behavioral preference rating in the preference-rating task during the fMRI decoder construction stage. The set of 240 faces (the 100 highest-rated faces, the 100 lowest-rated faces, and 40 neutrally rated faces) used in the fMRI decoder construction stage was selected for each subject according to her/his behavioral preference ratings to the 400 faces presented in the pre-test stage. That is, the constant term b represents the subject’s average behavioral preference rating for the population of faces. Thus, the induced CC-activation shift, which is calculated by subtracting the constant term b from the estimated rating R decoded , represents how far the activation pattern in the CC is from the activation patterns in the CC corresponding to the average behavioral preference rating for each subject.

It is necessary to obtain the induced CC-activation shift in order to appropriately evaluate and compare induced activation patterns in the CC across subjects and groups during the induction stage. Note that an induced CC-activation shift being “0” indicates that the activation pattern of the CC was biased in neither the high nor low preference direction. A positive (or negative) value of the induced CC-activation shift indicates that the activation pattern of the CC was biased toward a positive (or negative) preference direction, compared to the activation pattern corresponding to the average behavioral preference rating for each subject. Thus, when the mean-induced CC-activation shift is significantly higher (or lower) than 0, this means that subjects accomplished significant learning to induce the preference-related activation patterns in the CC that correspond to higher (or lower) preference rating.

With the control group, during the induction stage, the induction faces were presented in the same way as with the higher- and lower-preference groups. On the other hand, unlike with the higher- and lower-preference groups, the experiment was conducted outside the MRI scanner without fMRI measurements for the control groups. Subjects from the control group conducted a fixation task (see below) instead of the task given to those from the higher- and lower-preference groups during the induction stage.

In the fixation task for the control group in the “induction” stage, during the 6 s “induction” period, the luminance of the central fixation point slightly decreased (from green to dark green), returning to its original luminance 300 ms later. This luminance change occurred several times in an unpredictable manner during the 6 s period. Subjects from the control group were asked to count the number of luminance changes and report whether the number of the changes was even or odd by pressing one of two buttons using the index or middle finger of their right hand during the fixation period. The task difficulty was controlled by using an adaptive staircase method, so that the overall task difficulty was kept constant throughout the induction stage; the degree of fixation luminance change was slightly increased in the trial following an incorrect answer, and slightly decreased after two consecutive correct answers. Otherwise, luminance was kept around the same. The mean (± s.e.m.) task accuracy for the fixation task was 67.4% ± 5% across subjects. The green “feedback” disk was presented during the 2 s “feedback” period. The size of the disk was determined randomly for each trial. Subjects were instructed to fixate on the center of the display during the feedback period.