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Research ArticleNew Research, Sensory and Motor Systems

Shared Mechanisms in the Estimation of Self-Generated Actions and the Prediction of Other’s Actions by Humans

Tsuyoshi Ikegami and Gowrishankar Ganesh
eNeuro 18 December 2017, 4 (6) ENEURO.0341-17.2017; https://doi.org/10.1523/ENEURO.0341-17.2017
Tsuyoshi Ikegami
1Center for Information and Neural Networks, National Institute of Information and Communications Technology, 1-4 Yamadaoka, Suita City, Osaka 565-0871, Japan
3Brain Information Communication Research Laboratory Group, ATR, 2-2-2 Hikaridai, Kyoto 619-0288, Japan Seika-Cho, Soraku-gun
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Gowrishankar Ganesh
2CNRS-AIST JRL (Joint Robotics Laboratory), UMI3218/CRT, Intelligent Systems Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba Central 2, 1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, Japan
3Brain Information Communication Research Laboratory Group, ATR, 2-2-2 Hikaridai, Kyoto 619-0288, Japan Seika-Cho, Soraku-gun
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  • Figure 1.
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    Figure 1.

    Experiment. A, The experiment consisted of two motor action tasks, one in which the darts experts threw darts in the presence of visual feedback (VF; where their darts land on the dartboard) and second, in the absence of visual feedback (nVF). After every throw in the nVF condition, the darts experts were asked to self-estimate where their dart had landed on the board by placing a magnet on second dartboard placed behind them. B, In the observation-prediction (OP) tasks, the darts experts watched the video of either a novice dart thrower or a ten-pin bowler (snapshots shown), made a prediction of the outcome of each throw, and were given the feedback of the correct outcome orally by the experimenter. The chance level for the OP tasks for both the bowling and darts observation sessions was 9.09% (1/11 × 100). Each experiment session followed the sequence of blocks as shown in C).

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    Figure 2.

    Darts performance measures and nomenclature. The darts error of each throw in the VF and nVF blocks was defined as the unsigned distance of the dart-landing position (solid-outlined circle) from the board center (closed circle). The self-estimation error of each throw in the nVF blocks was defined as the unsigned distance between the dart-landing position and the self-estimated position (dotted circle).

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    Figure 3.

    Relation between observation prediction and motor action. The darts experts’ outcome prediction of observed darts actions (left red bar) and bowling actions (left blue bar) improved in the trainable observation sessions (Experiment 1). The outcome prediction of observed darts actions did not improve in the untrainable observation session (Experiment 2), although between them the experts watched dart actions by the same novice throwers in Experiments 1 and 2. The experts’ self-estimation error and darts error increased only when they watched the novice’s darts actions and their outcome prediction improved (Exp. 1, middle and right red bars) but not when they watched bowling actions (Exp. 1, middle and right blue bars) or when they failed to improve their outcome prediction of observed darts actions (Exp. 2, middle and right gray bars). Error bars indicate standard error.

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    Figure 4.

    Experimental results of behavioral trends. Increase in dart-landing position bias: The dart-landing position deviated progressively from the board center after outcome prediction of observed darts actions (B, red trace) but not after outcome prediction of bowling actions (A, blue trace) in Experiment 1. Constant self-estimated position bias: The self-estimated position did not change through the experiment, both after outcome prediction of darts actions (B, magenta trace) and after outcome prediction of bowling actions (A, cyan trace). Correlation between dart-landing and self-estimated positions: However, a substantial correlation was observed between dart-landing and their self-estimated positions in both darts (C, D) and bowling (C) observation sessions. Individual data from a representative darts expert in the dart observation session is shown in D, and the correlation coefficient averaged over the x- and y-axis across all darts experts is shown in C. The dashed line in C represents the significance level at p = 0.05. Error bars indicate standard error.

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    Figure 5.

    Proposed model. The model assumes that given a darts goal, a darts expert utilizes his controller to plan an appropriate motor command M, which is then fed to the musculoskeletal system to execute the required dart throw. The error in the throw is observed by the visual system when the room light is on and is used to correct the action in the next trial. The model assumes that in addition, the motor command M can be used to self-estimate the outcome of a throw using the outcome forward model, the output from which is used to correct the subsequent throw even when the room light is off. The model assumes the controller, the outcome forward model, and the muscular skeletal system to be affected by planning noise (δM), self-estimation noise (δSE), and execution noise (δE), respectively.

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    Figure 6.

    Simulation results for behavioral trends. Compare A–D with Fig. 4A–D, which uses the same plots of actual experiment data. The simulations using the C×F+ model can reproduce the evolution of the experts’ behavior: dart-landing position (blue and red traces in A and B, respectively), self-estimated position (cyan and magenta traces in A and B, respectively), and the substantial correlations between these two positions (C) in both the bowling (square) and darts (circle) observation sessions. The dashed line in C represents the significance level at p = 0.05. Error bars indicate standard error.

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    Figure 7.

    Sensitivity analysis of the C×F+ model to change in learning rate. A, The model-fitting accuracy obtained with each learning rate. B, Simulation result of the changes in dart-landing (red) and self-estimated (magenta) position biases for each learning rate. C, Estimated values of βCON (green) and βFOR (orange) for each learning rate. The values averaged over x and y dimensions were plotted. Error bars indicate standard error.

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    Table 1.

    Summary of statistical analysis

    LocationDependent variableType of testStatisticConfidence
    aDarts errors in the VF1 during the darts and bowling observation sessions in Exp. 1Paired t testt(15) = –1.635p = 0.123, CI = –1.08/0.14
    bSelf-estimation errors in the VF1 during the darts and bowling observation sessions in Exp. 1Paired t testt(15) = –1.846p = 0.085, CI = –0.822/0.059
    cChange in the outcome prediction of the observed darts actions in Exp. 1One-sample t testt(15) = 6.544p = 9.245 × 10−6 (corrected p = 0.025), CI = 7.242/16.138
    dChange in the outcome prediction of the observed bowling actions in Exp. 1One-sample t testt(15) = 8.251p = 5.874 × 10−6 (corrected p = 0.025), CI = 5.981/11.150
    eChange in the darts error during the darts observation session in Exp. 1One-sample t testt(15) = 5.096p = 1.315 × 10−4 (corrected p = 0.025), CI = 0.51/1.504
    fChange in the darts error during the bowling observation session in Exp. 1One-sample t testt(15) = –0.110p = 0.914 (corrected p = 0.025), CI = –0.789/0.722
    gChange in the self-estimated error during the darts observation session in Exp. 1One-sample t testt(15) = 2.614p = 0.020 (corrected p = 0.025), CI = 0.033/1.370
    hChange in the self-estimated error during the bowling observation session in Exp. 1One-sample t testt(15) = –0.198p = 0.845 (corrected p = 0.025), CI = –0.710/0.605
    iChanges in the outcome prediction of the observed darts actions in Exps. 1 and 2Two-sample t testt(29) = 3.820p = 6.503 × 10−4, CI = 4.054/13.397
    jChange in the self-estimated error in Exp. 2One-sample t testt(15) = 0.920p = 0.372, CI = –0.231/0.580
    kChange in the darts error in Exp. 2One-sample t testt(15) = 0.107p = 0.917, CI = –0.673/0.744
    l2D standard deviations of the dart-landing position across nVF blocks and observation sessions in Exp. 1Repeated two-way ANOVABlock_F(4,60) = 0.908, Session_F(1,15) = 0.007, Interaction_F(4,60) = 0.822p = 0.465, Block_ηp 2 = 0.057; p = 0.932, Session_ηp 2 = 4.976 × 10−4; p = 0.517, Interaction_ηp 2 = 0.052
    m2D standard deviations of the self-estimated position across nVF blocks and observation sessions in Exp. 1Repeated two-way ANOVABlock_F(4,60) = 0.714, Session_F(1,15) = 0.523, Interaction_F(4,60) = 1.959p = 0.585, Block_ηp 2 = 0.046; p = 0.481, Session_ηp 2 = 0.034; p = 0.112, Interaction_ηp 2 = 0.116
    nSelf-estimated position biases across nVF blocks and observation sessions in Exp. 1Repeated two-way ANOVABlock_F(4,60) = 1.466, Session_F(1,15) = 1.536, Interaction_F(4,60) = 0.483p = 0.224, Block_ηp 2 = 0.089; p = 0.234, Session_ηp 2 = 1.024 × 10−8; p = 0.748, Interaction_ηp 2 = 0.031
    oDart-landing position biases across nVF blocks and observation sessions in Exp. 1Repeated two-way ANOVABlock_F(4,60) = 2.693 Session_F(1,15) = 0.351 Interaction_F(4,60) = 2.247p = 0.039, Block_ηp 2 = 0.1552; p = 0.562, Session_ηp 2 = 0.023; p = 0.075, Interaction_ηp 2 = 0.130
    pDart-landing position biases across nVF blocks during the darts observation session in Exp. 1Repeated one-way ANOVAF(4,60) = 2.926p = 0.028, ηp 2 = 0.163
    qDart-landing position biases across nVF blocks during the bowling observation session in Exp. 1Repeated one-way ANOVAF(4,60) = 1.807p = 0.139, ηp 2 = 0.108
    rDart-landing position biases in nVF1 and nVF3 during the darts observation session in Exp. 1Post hoc Tukey’s testq(k = 5, df = 60) = 4.191p = 0.035
    sCorrelation coefficients between the dart-landing and self-estimation positions during the darts observation session in Exp. 1One-sample t testt(15) = 9.338p = 1.222 × 10−7, CI = 0.282/0.450
    tCorrelation coefficients between the dart-landing and self-estimation positions during the bowling observation session in Exp. 1One-sample t testt(15) = 4.734P = 2.662 × 10−4, CI = 0.144/0.379
    uSimulated position biases across nVF blocks and position metrics during the bowling observation sessionRepeated two-way ANOVABlock_F(4,60) = 0.286, Position_F(1,15) = 107.702, Interaction_F(4,60) = 0.346p = 0.886, Block_ηp 2 = 0.0187; p = 3.060 × 10−8, Position_ηp 2 = 0.878; p = 0.846, Interaction_ηp 2 = 0.023
    vCorrelation coefficients between the simulated dart-landing and self-estimation positions during the bowling observation sessionOne-sample t testt(15) = 71.411p = 2.051 × 10−20, CI = 0.319/0.339
    wModel-fitting accuracies across the deterioration modelsRepeated one-way ANOVAF(3,45) = 12.972p = 3.090 × 10−6, ηp 2 = 0464
    xModel-fitting accuracies in C×F× and C+F× modelsPost hoc Tukey’s testq(k = 4, df = 45) = 3.768p = 0.0511
    yModel-fitting accuracies in C×F× and C×F+ modelsPost hoc Tukey’s testq(k = 4, df = 45) = 6.197p = 0.001
    zModel-fitting accuracies in C×F× and C+F+ modelsPost hoc Tukey’s testq(k = 4, df = 45) = 8.428p = 0.001
    aaModel-fitting accuracies in C+F× and C×F+ modelsPost hoc Tukey’s testq(k = 3, df = 45) = 2.429p = 0.327
    bbModel-fitting accuracies in C×F+ and C+F+ modelsPost hoc Tukey’s testq(k = 4, df = 45) = 2.231p = 0.402
    ccSimulated position biases across nVF blocks and position metrics for C+F× modelRepeated two-way ANOVABlock_F(4,60) = 25.845, Position_F(1,15) = 29.022, Interaction_F(4,60) = 6.243, Simple main effect of blocks: Dart-landing_F(4,60) = 21.436, Self-estimated_F(4,60) = 17.819p = 7.549 × 10−5, Block_ηp 2 = 0.633; p = 1.775 × 10−12, Position_ηp 2 = 0.659; p = 2.872 × 10−4, Interaction_ηp 2 = 0.294; p = 5.099 × 10−11, Dart-landing_ηp 2 = 0.588; p = 1.088 × 10−9, Self-estimated_ηp 2 = 0.543
    ddSimulated position biases across nVF blocks and position metrics for C×F+ modelRepeated two-way ANOVABlock_F(4,60) = 8.033, Position_F(1,15) = 16.645, Interaction_F(4,60) = 5.366, Simple main effect of blocks: Dart-landing_F(4,60) = 7.739, Self-estimated_F(4,60) = 0.212p = 2.962 × 10−5, Block_ηp 2 = 0.349; p = 9.858 × 10−4, Position_ηp 2 = 0.526; p = 9.229 × 10−4, Interaction_ηp 2 = 0.2635; p = 4.258 × 10−5, Dart-landing_ηp 2 = 0.340; p = 0.931, Self-estimated_ηp 2 = 0.014
    eeSimulated position biases across nVF blocks and position metrics for C+F+ modelRepeated two-way ANOVABlock_F(4,60) = 26.752, Position_F(1,15) = 9.673, Interaction_F(4,60) = 2.694, Simple main effect of blocks: Dart-landing_F(4,60) = 16.260, Self-estimated_F(4,60) = 11.320p = 9.295 × 10−13, Block_ηp 2 = 0.641; p = 0.007, Position_ηp 2 = 0.392; p = 0.039, Interaction_ηp 2 = 0.152; p = 4.501 × 10−9, Dart-landing_ηp 2 = 0.520; p = 6.565 × 10−7, Self-estimated_ηp 2 = 0.430
    ffSimulated dart-landing position biases in nVF1 and nVF5 for each of C+F×, C×F+, and C+F+ modelPost hoc Tukey’s testC +F ×_q(k = 5, df = 60) = 11.230, C ×F +_q(k = 5, df = 60) = 6.957, C +F +_q(k = 5, df = 60) = 9.660p = 9.073 × 10−4, p = 9.519 × 10−4, p = 9.073 × 10−4
    ggSimulated self-estimated position biases in nVF1 and nVF5 for each of C+F× and C+F+ modelPost hoc Tukey’s testC +F ×_q(k = 5, df = 60) = 10.719, C +F +_q(k = 5, df = 60) = 8.527p = 9.073 × 10−4, p = 9.076 × 10−4
    hhCorrelation coefficients between the simulated dart-landing and self-estimation positions for C×F+ modelPaired t testt(15) = 68.514p = 3.810 × 10−20, CI = 0.301/0.320
    iiSimulated position biases across nVF blocks and position metrics for F + modelRepeated two-way ANOVABlock_F(4,60) = 5.825, Position_F(1,15) = 16.536, Interaction_F(4,60) = 7.733, Simple main effect of blocks: Dart-landing_F(4,60) = 7.456, Self-estimated_F(4,60) = 0.835p = 4.988 × 10−4, Block_ηp 2 = 0.280; p = 0.001, Position_ηp 2 = 0.524; p = 4.290 × 10−5, Interaction_ηp 2 = 0.340; p = 7.558 × 10−9, Dart-landing_ηp 2 = 0.332; p = 0.508, Self-estimated_ηp 2 = 0.05
    jjCorrelation coefficients between the simulated dart-landing and self-estimation positions for F + modelPaired t testt(15) = 73.051p = 1.460 × 10−20, CI = 0.314/0.333
    kkCorrelation coefficients between the simulated dart-landing and self-estimation positions for C× modelPaired t testt(15) = 35.622p = 6.539 × 10−16, CI = 0.303/0.342
    llSimulated position biases across nVF blocks and position metrics for C× modelRepeated two-way ANOVABlock_F(4,60) = 8.680, Position_F(1,15) = 15.107, Interaction_F(4,60) = 0.733,p = 1.350 × 10−5, Block_ηp 2 = 0.367; p = 0.002, Position_ηp 2 = 0.502; p = 0.573, Interaction_ηp 2 = 0.047
    mmModel fitting accuracies of C×F+ model with values of γ from 0 to –1Repeated one-way ANOVAF(10,150) = 26.078ηp 2 = 0.6348, p = 10−20
    nnModel fitting accuracies of C×F+ model with values of γ from 0 to –1Post hoc Tukey’s testFor any pair of values obtained with γ ≤ –0.3, q(k = 11, df = 150) ≤ 3.714For any pair of values obtained with γ ≤ –0.3, p ≥ 0.235
    ooSelf-estimated position biases of C×F+ model with values of γ from 0 to –1One-sample t testFor γ = 0, t(15) = –3.566, For γ ≤ –0.1, |t(15)| ≤ 1.829For γ = 0, p = 0.003 (corrected p = 0.025); for γ ≤ –0.1, p ≥ 0.087 (corrected p = 0.025)
    ppDart-landing position biases of C×F+ model with values of γ from 0 to –1One-sample t testFor γ ≤ –0.4, |t(15)| ≤ 2.698for γ ≤ –0.4, p ≤ 0.017 (corrected p = 0.025)
    qqCorrelation coefficients between the simulated dart-landing and self-estimation positions of C×F+ model with values of γ from 0 to –1One-sample t testFor all values of γ, t(15) ≥ 70.639For all values of γ, p ≤ 2.413 × 10−19
    rrValues of Embedded Image estimated by C×F+ model with values of γ from 0 to –1Repeated one-way ANOVAF(10,150) = 6.358ηp 2 = 0.298, p = 4.141 × 10−8
    ssValues of Embedded Image estimated by C×F+ model with values of γ from 0 to –1Post hoc Tukey’s testFor any pair of values obtained with γ ≤ –0.3, q(k = 11, df = 150) ≤ 2.714For any pair of values obtained with γ ≤ –0.3, p ≥ 0.707
    ttValues of Embedded Image estimated by C×F+ model with values of γ from 0 to –1Repeated one-way ANOVAF(10,150) = 2.432ηp 2 = 0.1395, p = 0.010
    uuValues of Embedded Image estimated by C×F+ model with values of γ from 0 to –1Post hoc Tukey’s testFor any pair of values obtained with γ ≤ –0.1, q(k = 11, df = 150) ≤ 2.998For any pair of values obtained with γ ≤ –0.1, p ≥ 0.563
    vvLag 1 autocorrelation coefficients of x- and y-axis data of self-estimated positions during the bowling observation session in Exp. 1One-sample t testx-axis_t(15) = –0.975, y-axis_t(15) = –1.784,p = 0.345, CI = –0.088/0.033, p = 0.095, CI = –0.154/0.014
    wwLag 1 autocorrelation coefficients of x- and y-axis data of self-estimated positions during the darts observation session in Exp. 1One-sample t testx-axis_t(15) = –0.429, y-axis_t(15) = –1.833p = 0.674, CI = –0.099/0.066, p = 0.087, CI = –0.134/0.010

Extended Data

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  • Legends for Extended Data

    Extended Data 1. DataSimulation.m. This program selects one of the seven simulation models (model 1–7) used in this study and plots the dart-landing and self-estimated position data simulated by the selected model.

    Extended Data 2. model1.m. This program is an implementation of the simulation algorithm of the model to reproduce the bowling observation session data.

    Extended Data 3. model2.m. This program is an implementation of the simulation algorithm of the C×F× model, which cannot reproduce the darts observation session data.

    Extended Data 4. model3.m. This program is an implementation of the simulation algorithm of the C+F× model, which cannot reproduce the darts observation session data.

    Extended Data 5. model4.m. This program is an implementation of the simulation algorithm of the C×F+ model, which can reproduce the darts observation session data.

    Extended Data 6. model5.m. This program is an implementation of the simulation algorithm of the C×F× model, which cannot reproduce the darts observation session data.

    Extended Data 7. model6.m. This program is an implementation of the simulation algorithm of the C× model, which cannot reproduce the darts observation session data.

    Extended Data 8. model7.m. This program is an implementation of the simulation algorithm of the F+ model, which can reproduce the darts observation session data. Download Extended Data 1, ZIP file

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Shared Mechanisms in the Estimation of Self-Generated Actions and the Prediction of Other’s Actions by Humans
Tsuyoshi Ikegami, Gowrishankar Ganesh
eNeuro 18 December 2017, 4 (6) ENEURO.0341-17.2017; DOI: 10.1523/ENEURO.0341-17.2017

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Shared Mechanisms in the Estimation of Self-Generated Actions and the Prediction of Other’s Actions by Humans
Tsuyoshi Ikegami, Gowrishankar Ganesh
eNeuro 18 December 2017, 4 (6) ENEURO.0341-17.2017; DOI: 10.1523/ENEURO.0341-17.2017
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