1000/1000
Hot
Most Recent
Section | Article | Variable | Effect | Effect p-Value | Robot | Sample Size (ASD) | Mean Age (Standard Deviation) | Functioning/Mean IQ (Standard Deviation) | Duration of Robot Intervention (mn) | Country |
---|---|---|---|---|---|---|---|---|---|---|
Eye contact | Barnes et al. [13] | Eye gaze | robot > human | NA | NAO | 3 | 8.33 (4.04) | NA | 1 session (NA) | USA |
Bekele et al. [14] | Eye gaze | robot > human | 𝑝<0.05 | NAO | 6 | 2.78–4.9 y.o. | NA | 1 session (30–50 mn) | USA | |
Cao et al. [15] | Eye gaze | robot > human | 𝑝<0.05 | NAO | 15 | 4.96 (1.10) | NA | 1 session (NA) | China | |
Costa et al. [16] | Eye contact | robot > human | 𝑝<0.001 | KASPAR | 8 | 6–10 y.o. | NA | 7 sessions (NA) | UK | |
Costa et al. [17] | Eye gaze | robot > human | 𝑝<0.05 | QTRobot | 15 | 9.73 (3.38) | IQ < 80 (n = 8); 80–120 (n = 6); IQ > 120 (n = 1) | 1 session (1.5–4.3 mn) | Luxembourg | |
Damm et al. [18] | Eye gaze | robot > human | 𝑝<0.05 | FLOBI | 9 | 21 (NA) | 112.5 (range: 94–133) | NA | Germany | |
David et al. [19] | Eye contact | robot > human for 2/5 children | 𝑝=0.01 | NAO | 5 | 3–5 y.o. | LF-HF | 8–12 sessions (10 mn/session, 1/day) | Romania | |
Duquette et al. [20] | Eye contact | robot > human | 𝛽=0.35 | TITO | 4 | 5.1 (NA) | LF | 3/week for 7 weeks (NA) | Canada | |
Scassellati et al. [8] | Eye contact with other people | pretest < post-test | 𝑝<0.01; 𝑝<0.05 | JIBO | 12 | 9.02 (1.41) | IQ ≥ 70 | 23 sessions (30 mn/session, 1/day) | USA | |
Shamsuddin et al. [21] | Eye contact | robot > human | NA | NAO | 1 | 10 y.o. | 107 | 1 session (15 mn/session) | Malaysia | |
Simlesa et al. [7] | Eye contact | robot > human | 𝑝<0.05 | NAO | 12 | 5.2 (0.63) | MA: 2–3 y.o. | 1 session (NA) | Croatia | |
Simut et al. [22] | Eye contact | robot > human | 𝑝<0.05 | PROBO | 30 | 6.67 (0.92) | 91.23 (range: 70–119) | 1 session (15 mn/session) | Belgium | |
Tapus et al. [23] | Eye gaze | robot > human | 𝑝<0.05 | NAO | 4 | 4.2 (1.67) | NA | 7–13 sessions (8 mn/session, 2/day) | Romania | |
Wainer et al. [24] | Eye contact | robot > human | 𝑝<0.05 | KASPAR | 6 | 6–8 y.o. | NA | 2 sessions (15 mn/session) | UK | |
Joint attention | Anzalone et al. [25] | Joint attention | robot < human | 𝑝<0.01 | NAO | 16 | 9.25 (1.87) | 73 (14) | 1 session (NA) | France |
Cao et al. [15] | Joint attention | robot = human | 𝑝>0.05 | NAO | 15 | 4.96 (1.10) | NA | 1 session (NA) | China | |
Cao et al. [26] | Joint attention | robot < human | 𝑝<0.001 | NAO | 27 | 46.37 months (4.36) | MA: 42 months max | 2 session (NA) | The Netherlands | |
Ghiglino et al. [6] | Joint attention | robot = human | 𝑝>0.05 | Cozmo | 24 | 5.79 (1.02) | 58.08 (19.39) | 5 weeks (10 mn/session) | Italy | |
Kajopoulos et al. [27] | Joint attention | pretest < post-test | 𝑝<0.05 | CuDDler | 7 | 4–5 y.o. | NA | 6 sessions over 3 weeks (20 mn/session) | Singapore | |
Kumazaki et al. [28] | Joint attention | time +; robot > human | 𝑝<0.01 | CommU | 28 | 70.56 months (6.09) (robot group); 69.00 (4.39) (control) | NA | 1 session (15 mn) | Japan | |
So et al. [29] | Joint attention | pretest < post-test (robot) | 𝑝<0.05 | HUMANE | 38 | 7.51 (0.87) (robot group); 7.91 (0.89) (human group) | LF | 6 sessions (30 mn/session) | China | |
Taheri et al. [30] | Joint attention | time + | 𝑝<0.05 | NAO/ALICE-R50 | 6 | 6–15 y.o. | LF-HF | 12 sessions over 3 months (30 mn/session) | Iran | |
Warren et al. [31] | Joint attention | time + | 𝑝<0.01 | NAO | 6 | 3.46 (0.73) | NA | 4 sessions over 2 weeks (NA) | USA | |
Wiese et al. [32] | Gaze cueing effect | robot > human | 𝑝<0.01 | EDDIE | 18 | 19.67 (1.5) | NA | 1 session (15 mn) | Germany | |
Interaction | Ghiglino et al. [6] | Social interaction initiation | robot > human | 𝑝<0.05 | Cozmo | 24 | 5.79 (1.02) | 58.08 (19.39) | 5 weeks (10 mn/session) | Italy |
Kim et al. [33] | Social behaviors towards peer | robot > human | 𝑝<0.05 | PLEO | 24 | 4–12 y.o. | NA | NA | USA | |
Pop et al. [34] | Collaborative game | robot > human | 𝑝<0.05 | PROBO | 11 | 4–7 y.o. | IQ >70 | 8 sessions (1 mn/session) | Romania | |
Pliasa et al. [35] | Social interaction initiation | robot > human | 𝑝<0.05 | DAISY | 6 | 6–9 y.o. | NA | 2 sessions (20 mn/session) | Bulgaria | |
Rakhymbayeva et al. [36] | Engagement time | tendency time 2 > time 1; familiar > unfamiliar activities | 𝑝=0.05; 𝑝<0.05 | NAO | 7 | 6.1 (2.7) | LF | 7–10 sessions (15 mn/session) | Khazakstan | |
Stanton et al. [37] | Social interaction initiation | robot > human | 𝑝<0.05 | AIBO | 11 | 5–8 y.o. | NA | 1 session (30 mn) | USA | |
Wainer et al. [24] | Cooperation in game | robot > human | 𝑝<0.01 | KASPAR | 6 | 6–8 y.o. | NA | 2 sessions (15 mn/session) | UK | |
Touch | Costa et al. [16] | Spontaneous touch | robot > human | NA | KASPAR | 8 | 6–10 y.o. | NA | 7 sessions (NA) | UK |
Simlesa et al. [7] | Touch | robot > human | 𝑝<0.05 | NAO | 12 | 5.2 (0.63) | MA: 2–3 y.o. | 1 session (NA) | Croatia | |
Communication | Farhan et al. [38] | Verbal and non-verbal communication | time 4 > time 1 | NA | NAO | 4 | 5, 12, 13, 24 y.o. | range: 41–47 | 4 sessions (NA) | Bangladesh |
Huskens et al. [5] | Self initiated questions | pretest < post-test; robot = human | 𝑝<0.05; 𝑝>0.05 | NAO | 6 | 3-14 y.o. | IQ >80 | 4 sessions (10 mn/session) | Netherlands | |
Kim et al. [33] | Speech | robot > human | 𝑝<0.05 | PLEO | 24 | 4–12 y.o. | NA | NA | USA | |
Kumazaki et al. [39] | Posture, gaze, facial expressions | robot > human | 𝑝<0.001 | ACTROID-F | 29 | 29.1 (2.6) | IQ ≥ 70 | 1 session (25 mn) | Japan | |
Simlesa et al. [7] | Vocalization | robot < human | 𝑝<0.01 | NAO | 12 | 5.2 (0.63) | MA: 2–3 y.o. | 1 session (NA) | Croatia | |
Stanton et al. [37] | Speech | robot > human | 𝑝<0.05 | AIBO | 11 | 5–8 y.o. | NA | 1 session (30 mn) | USA | |
Syrdal et al. [40] | Communication | number of interactions + | 𝑝<0.05 | KASPAR | 19 | 2–6 y.o. | NA | NA | UK | |
Taheri et al. [30] | Verbal communication | time + | 𝑝<0.05 | NAO/ALICE-R50 | 6 | 6–15 y.o. | LF-HF | 12 sessions over 3 months (30 mn/session) | Iran | |
Imitation | Conti et al. [41] | Imitation | time + | NA | NAO | 6 | 5 and 10 y.o. | LF | 15 sessions (6–8 mn/session) | Italy |
Costa et al. [17] | Imitation | robot = human | 𝑝>0.05 | QTRobot | 15 | 9.73 (3.38) | IQ < 80 (n = 8); 80–120 (n = 6); IQ > 120 (n = 1) | 1 session (1.5–4.3 mn) | Luxembourg | |
Duquette et al. [20] | Imitation of facial expressions; of words and gestures | robot > human; robot < human | NA | TITO | 4 | 5.1 (NA) | LF | 3/week for 7 weeks (NA) | Canada | |
Pierno et al. [42] | Imitation velocity | robot > human | 𝑝<0.001 | ROBOTIC ARM | 12 | 11.1 (NA) | HF | 1 session (NA) | Italy | |
Simlesa et al. [7] | Imitation | robot = human | 𝑝>0.05 | NAO | 12 | 5.2 (0.63) | MA: 2–3 y.o. | 1 session (NA) | Croatia | |
Soares et al. [43] | Imitation of emotions | robot > human; post-test > pretest (robot), post-test = pretest (human) | 𝑝<0.05; 𝑝<0.05; 𝑝>0.05 | Zeno | 45 | 6.8 (1.5) (robot group); 7.5 (1.4) (human group); 7.8 (1.2) (control) | HF | 2/week for 3 weeks (5–15 mn/session) | Portugal | |
Taheri et al. [44] | Imitation | robot < human | 𝑝<0.001 | NAO | 20 | 4.95 (2.01) | NA | 1 session (NA) | Iran | |
Zheng et al. [45] | Imitation quality | robot > human | 𝑝<0.05 | NAO | 6 | 3.83 (0.54) | NA | 2 sessions (NA) | USA |
Section | Article | Variable | Effect | Effect p-Value | Robot | Sample Size (ASD) | Mean Age (Standard Deviation) | Functioning/Mean IQ (Standard Deviation) | Duration of Robot Intervention (mn) | Country |
---|---|---|---|---|---|---|---|---|---|---|
Appropriate behaviors | Bharatharaj et al. [46] | Touching interaction | NA | NA | KiliRo | 24 | 9.71 (3.24) | NA | 1/day for 7 weeks (60 mn/session) | India |
Costa et al. [16] | Appropriate touch | gentle touch > harsh | 𝑝<0.05 | KASPAR | 8 | 6–10 y.o. | NA | 7 sessions (NA) | UK | |
David et al. [19] | Turn-taking skills | robot = human for 3 children | 𝑝>0.05 | NAO | 5 | 3–5 y.o. | LF-HF | 8–12 sessions (10 mn/session, 1/day) | Romania | |
Ghiglino et al. [47] | Theory of Mind skills | training with humanoid robot > non-anthropomorphic robot and traditional therapy | 𝑝<0.001; 𝑝<0.01 | iCub, COZMO | 43 | 5.8 (1.14) | 71.48 (16.50) (COZMO); 71.14 (15.49) (iCub) | 2/week for 8 weeks (15 mn/session) | Italy | |
Holeva et al. [48] | Theory of Mind skills (NEPSY II) | robot training = human; pretest < post-test | 𝑝>0.05; 𝑝<0.001 | NAO | 44 | 9.48 (1.95) | IQ > 70 | 2/week for 3 months (NA) | Greece | |
Lakatos et al. [49] | Visual Perspective Taking and Theory of Mind skills (Charlie test) | pretest < post-test | 𝑝<0.05 | KASPAR | 13 | 8.11 (1.96) | 79.30 (14.33); range: 60–103 | 1 to 10 sessions (15–20 mn/session) | UK | |
Lee et al. [50] | Proper force of touching | feedback > no feedback | 𝑝<0.05 | Touch pad | 1 | 22 y.o. | 49 | 1 session (NA) | Japan | |
Marino et al. [10] | Recognition and understanding of emotions | pretest < post-test (robot training); pretest = post-test (human training) | 𝑝=0.001; 𝑝>0.05 | NAO | 14 | 73.3 months (16.1) (robot group); 82.1 (12.4) (human) | NA | 10 sessions (90 mn/session, 2/week) | Italy | |
So et al. [51] | Recognition and production of intransitive gestures | robot training > no training | 𝑝<0.05 | NAO | 30 | 5.10 (0.83) (experimental group); 5.8 (0.35) (control) | NA | 4 sessions (30 mn/session, 2/week) | China | |
So et al. [9] | Recognition and production of emotional gestures | robot training > no training | 𝑝<0.001 | NAO | 13 | 8.99 (2.14) (experimental group); 9.50 (2.42) (control) | range: 49–67 | 4 sessions (30 mn/session, 2/week) | China | |
So et al. [52] | Recognition and production of intransitive gestures | robot training = human | 𝑝>0.05 | NAO | 23 | 9.17 (1.29) (robot group); 8.92 (0.93) (human) | range: 46–74 | 5 sessions (30 mn/session, 2/week) | China | |
Takata et al. [53] | Understanding of others’ feelings and behaviors | pretest < post-test | 𝑝<0.01 | Sota, CommU, A-Lab android ST | 14 | 17.57 (3.39) | 89.50 (10.95) | 5 sessions (1 h/session, 1/day) | Japan | |
Wood et al. [54] | Theory of Mind skills (Charlie test) | pretest < post-test for 7/12 children | 𝑝<0.05 | KASPAR | 12 | 11–14 y.o. | MA: 6–14 y.o. | 2–10 sessions (NA) | UK | |
Reducing maladaptive behaviors | Bharatharaj et al. [46] | Stress level | pretest > post-test | 𝑝<0.05 | KiliRo | 24 | 9.71 (3.24) | NA | 1/day for 7 weeks (60 mn/session) | India |
Costa et al. [17] | Stereotyped behaviors | robot < human | 𝑝<0.05 | QTRobot | 15 | 9.73 (3.38) | IQ < 80 (n = 8); 80–120 (n = 6); IQ > 120 (n = 1) | 1 session (1.5–4.3 mn) | Luxembourg | |
Kumazaki et al. [39] | Stress level | robot < human | 𝑝<0.01 | ACTROID-F | 29 | 29.1 (2.6) | IQ ≥ 70 | 1 session (25 mn) | Japan | |
Pop et al. [34] | Stereotyped behaviors | robot < human | 𝑝<0.05 | PROBO | 11 | 4–7 y.o. | IQ > 70 | 8 sessions (1 mn/session) | Romania | |
Shamsuddin et al. [21] | Stereotyped behaviors | robot < human | NA | NAO | 1 | 10 y.o. | IQ = 107 | 1 session (15 mn) | Malaysia | |
Shamsuddin et al. [11] | Stereotyped behaviors | robot < human | NA | NAO | 6 | 8.9 (NA) | range: 46–78 | 5 sessions (15 mn/session) | Malaysia | |
Stanton et al. [37] | Stereotyped behaviors | robot < toy | 𝑝=0.06 | AIBO | 11 | 5–8 y.o. | NA | 1 session (30 mn) | USA |
The benefits for individuals with ASD of interacting with a robot (at least in terms of social development) raise several questions. First, it is worth asking whether this type of interaction is more attractive to individuals with ASD than interaction with a human, as has been proposed in several studies [7][47][58][59]. In the next section, researchers will look at how individuals with ASD perceive robots, and what characteristics they attribute to them.
Several explanations can be proposed based on different theories aimed at explaining the social difficulties observed in autism. Researchers have already observed that people with ASD have social difficulties and show less interest in social stimuli than Typically Developing (TD) individuals. The link between social skills and social motivation seems well established in children with ASD: those with the least social motivation have more severe social difficulties [60][61][62]. Nevertheless, the meaning of this association remains to be determined.
Two main theories have been proposed to explain the association between social difficulties and reduced social interest observed in autism [63]: Social Motivation Theory [64] and Social Cognition Theory [65][66]. Researchers will analyze the two theories that seek to explain the causality between these two characteristics of ASD and build on these theories to explain the benefits brought about by robots.
In conclusion, the use of robots with people with ASD appears to be beneficial in encouraging communication and social interaction, supporting the learning of specific social behaviors, and reducing maladaptive behaviors. Interaction with robots could improve the social skills of individuals with ASD [3]. Children with autism touch and look more at a robot than at a human [7][17][19]. They also show a greater tendency to follow a robot’s gaze than a human’s gaze [32]. Children’s participation in an activity is increased when a robot is present [34][36], thus increasing interaction initiation [6][35] and speech production [33]. During an action imitation task performed by a human or robotic arm, children with autism perform better with the robotic arm, while TD children perform better with the human arm [42]. Furthermore, when ToM training is implemented, children with ASD make more progress with a humanoid robot than with a human [47], and the same results are observable in emotion recognition training with an iconic robot [10]. Finally, studies suggest that the stress felt by individuals with ASD during social interactions could be reduced with a robotic partner [39], leading to a decrease in the occurrence of stereotyped behaviors [17][34]. Further studies are needed to confirm the influence of robots on stereotyped behavior, but the benefits of robots on social development have been confirmed by a recent meta-analysis [3].
Although autistic children may categorize robots in the same way as TD children, they seem more attracted to robots and attribute more human characteristics to them. Some studies even suggest that theyare more interested in robots than in humans. Researchers sought to explain the benefits of robots by drawing on two theories: Social Motivation Theory and Social Cognition Theory. On the one hand, considering Social Motivation Theory, individuals with ASD may show more interest in a non-human agent than in a human one. This increased interest in interaction would then allow them to accumulate social experience, which could consequently reduce their social difficulties. On the other hand, as Social Cognition Theory argues that people with ASD engage less in interactions due to difficulties in understanding the social world, it is conceivable that a simpler, more predictable agent such as a robot could reduce these difficulties. As a result, this type of agent would encourage autistic individuals to engage more fully in social interactions. The robot, as a more attractive, simplified, and predictable social agent than a human, would thus encourage social interactions in autistic individuals. For children with significant social difficulties, robot-assisted training could thus constitute a step of intermediate difficulty compared to training with a human [67].
It therefore seems appropriate to rely on this type of agent when assessing the social skills of individuals with ASD: by reducing the difficulty of the interaction, a robot could enable children to better mobilize their abilities, leading to better estimation of their social skills. This suggests that psychology tests might be more successful if the experimenter is a robot rather than a human, as has already been shown in TD children [68][69][70][71][72]. The robot would then provide a more accurate means of assessing social–cognitive functioning [73][74], which would be particularly relevant for children with ASD [75].