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REVIEW 3 major objections 4 minor 41 references

Transferring Adaptive Theory of Mind to social robots: insights from developmental psychology to robotics

T0 review · 3 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read This paper argues that social robots need an adaptive Theory of Mind built by integrating teleological reasoning and simulation, drawing on infant development.

desk verdict A clear, honest position paper that makes a plausible but underspecified case for integrating simulation and teleological ToM in robots; worth publishing as a perspective, not as a demonstrated result. read the letter →

arxiv 1909.00197 v1 pith:2KYTQFO7 submitted 2019-08-31 cs.RO cs.AIcs.HC

Francesca Bianco , Dimitri Ognibene This is my paper · ORCID
classification cs.ROcs.AIcs.HC
keywords theoryofmindsocialroboticsdevelopmentalmentalizingteleologicalreasoningsimulationhuman-robotinteractionbelieftracking
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This position paper argues that social robots will keep falling short in everyday, unstructured settings until they can infer hidden mental states—beliefs, desires, intentions—rather than merely recognize actions. The authors build this case by reviewing research on infants' adaptive Theory of Mind and identifying four functional advantages it would give robots: tracking false beliefs, acting proactively, perceiving actively, and learning efficiently. They then argue that the two dominant computational accounts of mentalizing, teleological reasoning and simulation, are not rivals to choose between but complementary processes that should be integrated in future robot architectures. If they are right, the path to flexible social robots runs through developmental psychology, and the same architectures could in turn serve as testbeds for theories of how human mentalizing works.

What carries the argument

The load-bearing object of the paper is the proposed integration loop between two theories of how minds are read. Teleological reasoning (from the rationality principle) infers goals from observable actions against situational constraints; simulation theory instead proposes that we understand others by re-using our own mental states and motor representations. The paper's key move is to treat these not as competing accounts but as complementary components: mirroring or simulation can generate the action trajectories leading to a goal, while teleological inference can choose among candidate goals by rationality, and the same loop can run in reverse. This two-way complementarity is the machinery that is supposed to deliver adaptive mentalizing in robots, with the four functional advantages—belief tracking, proactivity, active perception, and learning—as its downstream effects.

What would settle it

Build three robotic or simulated agents—one using only teleological inference, one using only simulation, and one using the proposed integration—and run them on a false-belief task with unfamiliar agents; if the integrated agent does not consistently beat the better single-model agent in predicting action or belief, the paper's central proposal is not supported.

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Extended reading notes

Core claim

On the paper's own terms, the central claim is that an adaptive Theory of Mind—the capacity to attribute and reason about others' mental states—can and should be built into social robots, and that the way to do it is to combine the teleological and simulation accounts rather than treat them as competing. Teleological reasoning treats action as goal-directed and rational, letting an observer infer intentions from situational constraints; simulation lets an observer use their own mental states as a model of another's. The authors propose a complementary architecture in which simulation supplies the concrete action sequences or trajectory options and teleological inference selects among them using rationality, with the reverse flow also possible: teleological reasoning proposes candidate goals, and simulation chooses between them by internally enacting the resulting experience. This integration, they argue, is what would unlock mentalizing for belief understanding, proactivity and preparation, active perception, and learning, and thereby improve human-robot interaction in unpredictable environments such as disaster response and construction sites.

Load-bearing premise

The argument depends on the assumption that combining teleological reasoning and simulation inside a robot architecture will recreate the adaptive social abilities of human infants, even though the paper does not supply a concrete computational mechanism or demonstration of that transfer.

Editorial extensions

If this is right

  • Robots equipped with an integrated adaptive ToM could understand that a person digging through rubble is searching for survivors, not just recognize the digging motion.
  • Such robots could anticipate a human partner's needs before an action is completed, enabling proactive assistance in collaborative tasks.
  • Reliance on massive human-recorded datasets would shrink, because mental-state inference supplies context that bottom-up action recognition lacks.
  • The same architectures would provide a physical testbed for developmental psychology, letting theories about infant mentalizing be evaluated in an embodied agent.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • An implication left implicit in the paper is that the two models would need to be engaged under different conditions—teleological inference when the context is novel or top-down control is needed, simulation when familiar bottom-up cues dominate—so a workable architecture would also need a meta-level mechanism to choose between them.
  • If the integration claim is right, a concrete testable prediction follows: in false-belief tasks with unfamiliar agents, an integrated model should outperform either a pure simulation or a pure teleological model, especially when the observed agent's behavior is not perfectly rational.
  • The authors' emphasis on infant abilities hints at a developmental curriculum for robots: start with teleological priors, then add simulation-based learning from interaction, rather than attempting adult-level Theory of Mind in one step.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. This paper is a perspective/review article aiming to transfer insights from developmental psychology on infant Theory of Mind (ToM) to social robotics. It argues that current social robots mostly rely on passive perception, prewired knowledge, and deep neural networks that struggle with generalization and online interaction, and that an "adaptive ToM"—the ability to autonomously attribute and reason about mental states—would provide four functional advantages: mentalizing for belief understanding, proactivity and preparation, active perception, and learning. The paper reviews two computational accounts of ToM: the teleological approach (rational-action-based goal inference, including Baker et al.'s Bayesian model) and the simulation approach (motor resonance and predictive activation), and surveys robotic implementations (e.g., Milliez et al., Görür et al., Rabinowitz et al.) to identify current limitations. The central proposal, in Section 3.3, is that simulation and teleological models should be integrated as complementary rather than competing, with simulation providing action trajectories and teleological reasoning providing goal states and candidate inferences. The paper does not present a formal model, experiments, or a concrete computational architecture; it concludes with open questions and a call for future cross-talk between developmental psychology and robotics.

Significance. The paper is a well-referenced synthesis of two research streams and usefully articulates a research agenda. Its strengths include a clear enumeration of functional ToM benefits for robotics, concrete examples of current robotic shortcomings (e.g., hard-coded hypotheses in Milliez et al., the all-observable assumption in Rabinowitz et al.), and a balanced discussion of the limitations of both teleological and simulation accounts. The proposal that these accounts are complementary is plausible and timely, and the developmental-robotics framing is appropriate. However, the significance of the central claim is currently limited by the absence of a mechanistic account or a proof-of-concept demonstration. As a position paper it can stimulate discussion, but as a contribution to a computational journal it needs to specify the integration at a level that could be implemented or at least modeled. The paper makes no falsifiable predictions and ships no code or data, so its value lies in framing rather than demonstration. If the proposed integration were later realized, the contribution could be an important stepping stone for adaptive social robots.

major comments (3)
  1. [Sec. 3.3] The central proposal that simulation and teleological models are complementary is not supported by a concrete computational mechanism. The teleological model described in Sec. 3.1 is a Bayesian inverse planner over beliefs, desires, and actions, whereas the simulation models in Sec. 3.2 operate on motor representations and predictive activation. Section 3.3 does not specify how a simulated motor trajectory becomes a prior or proposal for inverse planning, how an inferred goal posterior modulates simulation, or what update rule closes the loop. Without such an interface, the claim that integration yields improved adaptive ToM is underdetermined and non-testable. The paper also does not address whether the two models' known failure modes are complementary: teleological reasoning struggles with subjective and non-rational mental states, while simulation fails on novel situations and quarantine; no argument is given that combining them overcomes these specific problems rather than inheriting both sets of limitations.
  2. [Secs. 3.1 and 3.3] The paper acknowledges that Baker et al.'s teleological model is "computationally demanding and could not be directly used to support online interactions" (Sec. 3.1), yet the proposed adaptive-robotics applications (proactivity, active perception, learning) require online operation. No approximation scheme, hierarchical inference strategy, or complexity reduction is proposed to bridge this gap. As a result, the claim that integrating the two models will address current robotic limitations is not yet justified.
  3. [Sec. 2.1] The functional advantages listed (mentalizing for belief understanding, proactivity and preparation, active perception, and learning) are presented as benefits that an adaptive ToM would confer, but the paper does not provide evidence that the proposed integration of simulation and teleological models, or any existing architecture, actually delivers these benefits. The examples of Milliez et al. and Görür et al. illustrate current limitations rather than demonstrating the proposed pathway. The claim that these advantages will transfer from human infants to robots is therefore asserted rather than derived.
minor comments (4)
  1. [Sec. 2.1] The term "adaptive ToM" is used throughout, but it is never formally defined beyond "adaptive attribution of mental states." A precise definition would help distinguish it from standard ToM implementations.
  2. [Sec. 3.2] The sentence "Against the simulation theory as a base for mentalizing is also some evidence of its inability to support action understanding in novel situations" is awkwardly phrased and should be reworded for clarity.
  3. [Sec. 4] The two debates (innate versus learned, bottom-up versus top-down) are introduced but not connected explicitly to the proposed integration; making this connection would strengthen the future-directions discussion.
  4. [References] Reference 18 is a self-citation to the authors' prior conference paper; the text should indicate what new material the present contribution adds beyond that paper.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: this is a review and position paper, not a derivation, and the only self-citation is a non-load-bearing pointer.

full rationale

The paper is a narrative review and proposal piece. It surveys teleological and simulation accounts of Theory of Mind and suggests that a future integration of the two approaches might improve adaptive ToM in robots. No equations are derived, no parameters are fitted, and no quantitative claim is made whose output is equivalent to an input by construction. The functional advantages listed in Section 2.1 are argued from external literature and from concrete examples (e.g., Milliez et al.'s belief-tracking robot, Rabinowitz et al.'s machine ToM network), not from the authors' own previous work. The one self-citation (reference 18) is used only as a pointer for further details on functional advantages, while the surrounding text independently summarizes those advantages; it is therefore not load-bearing. The central integration proposal in Section 3.3 is admittedly speculative and under-specified at the representational and algorithmic level, but that is a weakness of support or correctness risk, not circularity: the paper does not claim to derive the benefits of integration from the models it reviews, nor does it reduce the proposal to its inputs by definition. Accordingly, no circular step can be quoted or exhibited, and the appropriate score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The central claim rests on the assumption that psychological theories of ToM can be translated into computational architectures and that combining simulation and teleological models is both possible and beneficial. No free parameters or invented entities are introduced.

assumptions (3)
  • domain assumption Human infant ToM development provides an appropriate blueprint for robotic social intelligence.
    The paper's entire framing in the abstract and Section 2 assumes that insights from infant development can be transferred to robot architectures. This is plausible but not proven.
  • domain assumption The teleological and simulation theories are sufficiently well-defined to be computationally integrated.
    Section 3.3 proposes combining these models, but the paper does not specify how to reconcile their different representations and mechanisms.
  • domain assumption The functional advantages listed in Section 2.1 (belief understanding, proactivity, active perception, learning) will emerge from such integration.
    The paper asserts these benefits without a concrete simulation or proof of concept.

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Cite this review

Pith. "Pith review of Transferring Adaptive Theory of Mind to social robots: insights from developmental psychology to robotics." pith.science (2026). https://pith.science/paper/2KYTQFO7

@misc{pith2026190900197,
  author       = {Pith},
  title        = {Pith review of: Transferring Adaptive Theory of Mind to social robots: insights from developmental psychology to robotics},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2KYTQFO7}},
  note         = {Machine review of arXiv:1909.00197}
}
read the original abstract

Despite the recent advancement in the social robotic field, important limitations restrain its progress and delay the application of robots in everyday scenarios. In the present paper, we propose to develop computational models inspired by our knowledge of human infants' social adaptive abilities. We believe this may provide solutions at an architectural level to overcome the limits of current systems. Specifically, we present the functional advantages that adaptive Theory of Mind (ToM) systems would support in robotics (i.e., mentalizing for belief understanding, proactivity and preparation, active perception and learning) and contextualize them in practical applications. We review current computational models mainly based on the simulation and teleological theories, and robotic implementations to identify the limitations of ToM functions in current robotic architectures and suggest a possible future developmental pathway. Finally, we propose future studies to create innovative computational models integrating the properties of the simulation and teleological approaches for an improved adaptive ToM ability in robots with the aim of enhancing human-robot interactions and permitting the application of robots in unexplored environments, such as disasters and construction sites. To achieve this goal, we suggest directing future research towards the modern cross-talk between the fields of robotics and developmental psychology.

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Works this paper leans on

41 extracted references · 40 canonical work pages

  1. [1]

    Frontiers in psy- chology 8, 1393 (2017)

    Abubshait, A., Wiese, E.: You Look Human, But Act Like a Machine: Agent Appearance and Behavior Modulate Different Aspects of Human -Robot Interaction. Frontiers in psy- chology 8, 1393 (2017)

    work page 2017

  2. [2]

    Child Development Perspectives 12, 183-188 (2018)

    Cangelosi, A., Schlesinger, M.: From Babies to Robots: The Contribution of Developmental Robotics to Developmental Psychology. Child Development Perspectives 12, 183-188 (2018)

    work page 2018

  3. [3]

    Proceedings of the 5th International Workshop on Epigenetic Ro- botics, pp

    Demiris, Y., Dearden, A.: From motor babbling to hierarchical learning by imitation: a robot developmental pathway. Proceedings of the 5th International Workshop on Epigenetic Ro- botics, pp. 31-37 (2005)

    work page 2005

  4. [4]

    Proceedings of IJCAI AAAI, pp

    Ognibene, D., Demiris, Y.: Towards active event recognition. Proceedings of IJCAI AAAI, pp. 2495-2501 (2013)

    work page 2013

  5. [5]

    Frontiers in psychology 8, 1663 (2017)

    Wiese, E., Metta, G., Wykowska, A.: Robots as Intentional Agents: Using Neuroscientific Methods to Make Robots Appear More Social. Frontiers in psychology 8, 1663 (2017)

    work page 2017

  6. [6]

    Advanced Robotics 31, 821-835 (2017)

    Pierson, H., Gashler, M.: Deep learning in robotics: a review of recent research. Advanced Robotics 31, 821-835 (2017)

    work page 2017

  7. [7]

    Online Real-time Multiple Spatiotemporal Action Localisation and Prediction

    Singh, G., Saha, S ., Sapienza, M., Torr, P., Cuzzolin, F.: Online real time multiple spatio- temporal action localisation and prediction on a single platform. arXiv preprint arXiv:1611.08563 (2017)

    work page Pith review arXiv 2017

  8. [8]

    arXiv preprint arXiv:1802.07740 (2018)

    Rabinowitz, N.C., Perbet, F., Song, H.F., Zhang, C., Eslami, S.M.A., Botvinick, M.: Ma- chine Theory of Mind. arXiv preprint arXiv:1802.07740 (2018)

    arXiv 2018

Show all 41 references
  1. [9]

    Advanced Robotics (ICAR) International Conference, pp

    Mariolis, I., Peleka, G., Kargakos, A., Malassiotis, S.: Pose and category recognition of highly deformable objects using deep learning. Advanced Robotics (ICAR) International Conference, pp. 655-662 (2015)

    2015

  2. [10]

    Intelligent Robots and Systems (IROS) IEEE/RSJ International Confer- ence, pp

    Polydoros, A.S., Nalpantidis, L., Kruger, V.: Real-time deep learning of robotic manipulator inverse dynamics. Intelligent Robots and Systems (IROS) IEEE/RSJ International Confer- ence, pp. 3442-3448 (2015)

    2015

  3. [11]

    Nature 550, 354-359 (2017)

    Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., et al.: Mas- tering the game of Go without human knowledge. Nature 550, 354-359 (2017)

    2017

  4. [12]

    in The Oxford Handbook of Philosophy of Cognitive Sci- ence, (Oxford: Oxford University Press) (2012)

    Goldman, A.I.: Theory of mind. in The Oxford Handbook of Philosophy of Cognitive Sci- ence, (Oxford: Oxford University Press) (2012)

    2012

  5. [13]

    Neuron 50, 531-534 (2006)

    Frith, C.D., Frith, U.: The neural basis of mentalizing. Neuron 50, 531-534 (2006)

    2006

  6. [14]

    Devaine, M., Hollard, G., Daunizeau, J.: The social Bayesian brain: does mentalizing make a difference when we learn? PLoS computational biology 10:e1003992 (2014)

    2014

  7. [15]

    Journal of Cognition and Development 17, 683-698 (2016)

    Yott, J., Poulin-Dubois, D.: Are Infants’ Theory-of-Mind Abilities Well Integrated? Implicit Understanding of Intentions, Desires, and Beliefs. Journal of Cognition and Development 17, 683-698 (2016)

    2016

  8. [16]

    Affective theory of mind

    Kosakowski, H.L., Saxe, R.: “Affective theory of mind” and the function of the ventral me- dial prefrontal cortex. Cognitive and Behavioral Neurology 31, 36-50 (2018)

    2018

  9. [17]

    Autonomous Robots 12, 13-24 (2002)

    Scassellati, B.: Theory of mind for a humanoid robot. Autonomous Robots 12, 13-24 (2002)

    2002

  10. [18]

    The 11th Computer Science and Electronic Engineering Conference

    Bianco, F., Ognibene, D.: Functional Advantages of an adaptive Theory of Mind for robotics: a review of current architectures. The 11th Computer Science and Electronic Engineering Conference. IEEE Xplore, University of Essex (2019)

    2019

  11. [19]

    In: Work- shop on Intentions in HRI at ACM/IEEE International Conference on Human -Robot Inter- action (2017)

    Görür, O.C., Rosman, B., Hoffman, G., Albayrak, A.: Toward Integrating Theory of Mind into Adaptive Decision-Making of Social Robots to Understand Human Intention. In: Work- shop on Intentions in HRI at ACM/IEEE International Conference on Human -Robot Inter- action (2017)

    2017

  12. [20]

    The 23rd 10 IEEE International Symposium on Robot and Human Interactive Communication, pp

    Milliez, G., Warnier, M., Clodic, A., Alami, R.: A framework for endowin g an interactive robot with reasoning capabilities about perspective-taking and belief management. The 23rd 10 IEEE International Symposium on Robot and Human Interactive Communication, pp. 1103- 1109 (2014)

    2014

  13. [21]

    The Eleventh ACM/IEEE International Conference on Human Robot In- teration, pp

    Devin, S., Alami, R.: An Implemented Theory of Mind to Improve Human-Robot Shared Plans Execution. The Eleventh ACM/IEEE International Conference on Human Robot In- teration, pp. 319-326 (2016)

    2016

  14. [22]

    Developmental Science 20:e12445 (2017)

    Grosse, W.C., Friederici, A.D., Singer, T., Steinbeis, N.: Implicit and explicit false belief development in preschool children. Developmental Science 20:e12445 (2017)

    2017

  15. [23]

    Bioinspiration & biomimetics 8:035002 (2013)

    Ognibene, D., Chinellato, E., Sarabia, M., Demiris, Y.: Contextual action recognition and target localization with an active allocation of attention on a humanoid robot. Bioinspiration & biomimetics 8:035002 (2013)

    2013

  16. [24]

    Behavioral and Brain Sciences 40, 1–101 (2016)

    Lake, B.M., Ullman, T.D., Tenenbaum, J.B., Gershman, S.J.: Building machines that learn and think like people. Behavioral and Brain Sciences 40, 1–101 (2016)

    2016

  17. [25]

    IEEE Transactions on Im- age Processing 24, 5916-5927 (2015)

    Lee, K., Ognibene, D., Chang, H.J., Kim, T.-K., Demiris, Y.: STARE: Spatio-Temporal At- tention Relocation for Multiple Structured Activities Detection. IEEE Transactions on Im- age Processing 24, 5916-5927 (2015)

    2015

  18. [26]

    Trends in Cognitive Sciences 19, 65–72 (2015)

    Schaafsma, S.M., Pfaff, D.W., Spunt, R.P., Adolphs, R.: Deconstructing and reconstructing theory of mind. Trends in Cognitive Sciences 19, 65–72 (2015)

    2015

  19. [27]

    Cognition 107, 1059-1069 (2008)

    Southgate, V., Johnson , M.H., Csibra, G.: Infants attribute goals even to biomechanically impossible actions. Cognition 107, 1059-1069 (2008)

    2008

  20. [28]

    Trends in Cognitive Sciences 7, 287-292 (2003)

    Gergely, G., Csibra, G.: Teleological reaso ning in infancy: the nai ve theory of rational ac- tion. Trends in Cognitive Sciences 7, 287-292 (2003)

    2003

  21. [29]

    Neu- roImage 161, 9-18 (2017)

    Koster-Hale, J., Richardson, H., Velez, N., Asaba, M., Young, L., Saxe, R.: Mentalizing regions represent distributed, continuous, and abstract dimensions of others' beliefs. Neu- roImage 161, 9-18 (2017)

    2017

  22. [30]

    Brain research 1079, 36-46 (2006)

    Frith, C.D., Frith, U.: How we predict what o ther people are going to do. Brain research 1079, 36-46 (2006)

    2006

  23. [31]

    Current directions in psychological science 19, 301-307 (2010)

    Luo, Y., Baillargeon, R.: Toward a Mentalistic Account of Early Psychological Reasoning. Current directions in psychological science 19, 301-307 (2010)

    2010

  24. [32]

    Nature Human Behaviour 1:0064 (2017)

    Baker, C.L., Jara-Ettinger, J., Saxe, R., Tenenbaum, J.B.: Rational quantitative attribution of beliefs, desires and percepts in human mentalizing. Nature Human Behaviour 1:0064 (2017)

    2017

  25. [33]

    Dev Sci 16, 209-226 (2013)

    Hamlin, J.K., Ullman, T., Tenenbaum, J., Goodman, N., Baker, C.: The mentalistic basis of core social cognition: experiments in preverbal infants and a computational model. Dev Sci 16, 209-226 (2013)

    2013

  26. [34]

    Trends in Cognitive Sciences 2, 493-501 (1998)

    Gallese, V., Goldman, A.: Mirror neurons and the simulation theory of mind-reading. Trends in Cognitive Sciences 2, 493-501 (1998)

    1998

  27. [35]

    Biology letters 5, 769-772 (2009)

    Southgate, V., Johnson, M.H ., Osborne, T., Csibra, G.: Predictive motor activation during action observation in human infants. Biology letters 5, 769-772 (2009)

    2009

  28. [36]

    Current biology 17, 2117-2121 (2007)

    Brass, M., Schmitt, R.M., Spengler, S., Gergely, G.: Investigating action understanding: in- ferential processes versus action simulation. Current biology 17, 2117-2121 (2007)

    2007

  29. [37]

    Trends in Cognitive Sciences 11, 194-196 (2007)

    Keysers, C., Gazzola, V.: Integrating simulation and theory of mind: From self to social cognition. Trends in Cognitive Sciences 11, 194-196 (2007)

    2007

  30. [38]

    Current biology 17, 724-732 (2007)

    Frith, C.D., Frith, U.: Social cognition in humans. Current biology 17, 724-732 (2007)

    2007

  31. [39]

    Science 330, 1830-1834 (2010)

    Kovacs, A.M., Teglas, E., Endress, A.D.: The social sense: susceptibility to others' beliefs in human infants and adults. Science 330, 1830-1834 (2010)

    2010

  32. [40]

    Journal of Anthropological Psychology 17, 26-27 (2006)

    Baron-Cohen, S.: Mindreading: Evidence for both innate and acquired facto rs. Journal of Anthropological Psychology 17, 26-27 (2006)

    2006

  33. [41]

    Journal of the Royal Society, Interface 13 (2016)

    Bhat, A.A., Mohan, V., Sandini, G., Morasso, P.: Humanoid infers Archimedes' principle: understanding physical relations and object affordances through cumulative lea rning expe- riences. Journal of the Royal Society, Interface 13 (2016)

    2016

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