- Email: [email protected]
A measuring stick for other minds
JID:PLREV AID:938 /DIS
[m3SC+; v1.272; Prn:6/11/2017; 12:29] P.1 (1-3)
Available online at www.sciencedirect.com
ScienceDirect Physics of Life Reviews ••• (••••) •••–••• www.elsevier.com/locate/plrev
Comment
A measuring stick for other minds Comment on ‘Seeing mental states: An experimental strategy for measuring the observability of other minds’ by Cristina Becchio et al. Imme Christina Zillekens a,∗ , Leonhard Schilbach a,b a Independent Max Planck Research Group for Social Neuroscience and International Max Planck Research School for Translational Psychiatry
(IMPRS-TP), Max Planck Institute of Psychiatry, Munich, Germany b Department of Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany
Received 2 November 2017; accepted 3 November 2017 Communicated by J. Fontanari
In their compelling article ‘Seeing mental states: An experimental strategy for measuring the observability of other minds’ Becchio et al. [1] tackle a long-standing and controversial issue, namely the perennial question of whether we can access or even quite literally see other minds. Much of the relevant interdisciplinary literature is built on the premise that one’s access to others’ minds is indirect and inferential in nature [e.g. [4,5]]. Recent insights from phenomenology, however, suggest that in some cases of social interaction we may have an immediate perceptual access to others’ minds [e.g. [2,7]]. In other words, theories of ‘mind-reading’ have often omitted the discussion of perceptual access to mental states due to the assumption that mental states are not expressed in observable bodily behavior. Yet, the phenomenological tradition points towards an experiential difference in the perception of embodied intentionality as compared to non-intentionality. Building upon this insight, it has been argued that this experiential difference might also be related to a ‘genuine, non-trivial difference in the informational content of the perception of embodied intentionality as compared to non-intentionality’ [3]. Furthermore, access to such an informational difference might guide the skilled human observer in reacting to social signals, helping us to skillfully manage social interactions without a constant need for mental state attribution [6]. In our understanding, Becchio et al.’s approach bears strong resemblance to the above described notion in that they argue congently for the observability of mental states. What is more, they actually provide a much needed operational approach to define the degree of observability of mental states. In fact, they present an empirical 4-step procedure that relies on a quantification of the informational utility and validity of kinematic features in order to successfully identify the intention of an actor. Notably, the aim of their approach is to objectively measure to what extent two intentional movements differ in their kinematic features, whether participants are able to differentiate between the intentions and the predictive value of specific motion features. More specifically, step 1 of the procedure proposed DOI of original article: https://doi.org/10.1016/j.plrev.2017.10.002. * Corresponding author.
E-mail address: [email protected] (I.C. Zillekens). https://doi.org/10.1016/j.plrev.2017.11.007 1571-0645/© 2017 Elsevier B.V. All rights reserved.
JID:PLREV AID:938 /DIS
2
[m3SC+; v1.272; Prn:6/11/2017; 12:29] P.2 (1-3)
I.C. Zillekens, L. Schilbach / Physics of Life Reviews ••• (••••) •••–•••
by Becchio and colleagues addresses the fundamental question of whether an intention can be ‘specified’ in terms of a unique motion pattern. Importantly, the authors stress that objectively differing kinematic features need to be validated in terms of their practical usability. Hence, the objective would be to determine whether a kinematic analysis of actions could contain significant information to discriminate the underlying intentions. The authors put forward a Linear Classification Analysis (LCA) to compare the spatiotemporal patterns of different intentions. A classification score then indicates the degree of specificity of the movement information for an intention. Choosing the well known Drift Diffusion Model (DDM) to examine whether the participants are capable of discriminating between different motion intentions and to validate the prognostic value of a stimulus feature by looking at the changes in the speed of evidence accumulation, Becchio et al. are further able to capture inter-individual variances and draw conclusions about the generalizability of the informational content of stimuli features. In step 3, a second model, the Classification and Regression Tree (CART) model, is applied to the experimental test data. On each layer, another motion feature as well as different feature manifestations are added to form branches of a decision tree that helps to differentiate intentions. Subsequently, each composition of motion features can be examined in terms of how well it predicts one intention or the other. Additionally, one can deduce the likelihood for choosing intention A over intention B given a specific set of kinematic information. This approach allows to elegantly test features and feature combinations in their predictive power and to further assess whether features were actually used by participants, i.e. whether they contribute to perceptual efficiency. Finally, the authors validate the predictive ‘rules’ derived from the CART by investigating changes in drift rate of the DDM. The assumption here is that a perceptually efficient kinematic feature, a feature that is both informative and whose kinematic information is also accessible to the observer, should be reflected in an increased drift rate. If this can be confirmed, so the authors argue, then the features evidently seem to contribute to perceptual efficiency and consequently, can be considered as key players in immediate access to social information, i.e. to mental states. In this seminal paper, the authors, thus, manage to turn the long-standing question of the observability of mental states into an empirically tractable issue and suggest a formal characterization thereof. This is a major achievement and we applaud the authors for doing so. What appears to be missing in the paper, we believe, is an equally impressive characterization of how different kinematic features might not only lead to or trigger perception, but also how they might bring about responses in a human observer and how a perceived state may afford certain kinds of responses (rather than others). We believe that this might constitute an important extension of their approach. The authors also provocatively state that their proposal of a data-driven and classificatory approach that relies on kinematic data should be capable of capturing ‘any mental state instantiated into specific pattern of behavior’. However, the approach does not deny that some mental states might turn out to be ‘invisible’ when assessed by means of their kinematics. Importantly, the 4-step procedure also allows to determine and to quantify this degree of observability, which appears to be an important clinical as well as epistemological consideration to us. The authors suggest that an implementation of their approach might be particularly useful for the design of ‘social’ robots without the need to endow robots with a fully-fledged mechanism for mental state attribution, i.e. a Theory of Mind, and, thus, indicate a wide range of implications of their proposal. From a psychiatric point of view, the proposal of a quantitative approach to the human ability of seeing others’ minds in their behavior promises to shed new light onto conditions, which have been associated with differential social impairments such as autism spectrum disorder and schizophrenia, but also chronic depression and social anxiety disorder. Taken together, Becchio et al. provide not only a fresh and new look onto the perennial problem of other minds, but actually provide a much needed and highly promising empirical approach to it, which is likely to significantly advance our mechanistic understanding of what it means to see (and interact with) other minds. References [1] Becchio C, Koul A, Ansuini C, Bertone C, Cavallo A. Seeing mental states: an experimental strategy for measuring the observability of other minds. Phys Life Rev 2017. https://doi.org/10.1016/j.plrev.2017.10.002 [in this issue]. [2] Gallagher S, Zahavi D. The phenomenological mind: an introduction to philosophy of mind and cognitive science. London: Routledge; 2007. [3] Gangopadhyay N, Schilbach L. Seeing minds: a neurophilosophical investigation of the role of perception–action coupling in social perception. Soc Neurosci 2012;7(4):410–23. https://doi.org/10.1080/17470919.2011.633754. [4] Goldman AI. Simulating minds. Oxford University Press; 2006.
JID:PLREV AID:938 /DIS
[m3SC+; v1.272; Prn:6/11/2017; 12:29] P.3 (1-3)
I.C. Zillekens, L. Schilbach / Physics of Life Reviews ••• (••••) •••–•••
3
[5] Gopnik A, Wellman HM. Why the child’s theory of mind really is a theory. Mind Lang 1992;7(1–2):145–71. https://doi.org/10.1111/j.14680017.1992.tb00202.x. [6] Schilbach L, Timmermans B, Reddy V, Costall A, Bente G, Schlicht T, et al. Toward a second-person neuroscience. Behav Brain Sci 2013;36(04):393–414. https://doi.org/10.1017/S0140525X12000660. [7] Zahavi D. Complexities of self. Autism 2010;14(5):547–51. https://doi.org/10.1177/1362361310370040.
[m3SC+; v1.272; Prn:6/11/2017; 12:29] P.1 (1-3)
Available online at www.sciencedirect.com
ScienceDirect Physics of Life Reviews ••• (••••) •••–••• www.elsevier.com/locate/plrev
Comment
A measuring stick for other minds Comment on ‘Seeing mental states: An experimental strategy for measuring the observability of other minds’ by Cristina Becchio et al. Imme Christina Zillekens a,∗ , Leonhard Schilbach a,b a Independent Max Planck Research Group for Social Neuroscience and International Max Planck Research School for Translational Psychiatry
(IMPRS-TP), Max Planck Institute of Psychiatry, Munich, Germany b Department of Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany
Received 2 November 2017; accepted 3 November 2017 Communicated by J. Fontanari
In their compelling article ‘Seeing mental states: An experimental strategy for measuring the observability of other minds’ Becchio et al. [1] tackle a long-standing and controversial issue, namely the perennial question of whether we can access or even quite literally see other minds. Much of the relevant interdisciplinary literature is built on the premise that one’s access to others’ minds is indirect and inferential in nature [e.g. [4,5]]. Recent insights from phenomenology, however, suggest that in some cases of social interaction we may have an immediate perceptual access to others’ minds [e.g. [2,7]]. In other words, theories of ‘mind-reading’ have often omitted the discussion of perceptual access to mental states due to the assumption that mental states are not expressed in observable bodily behavior. Yet, the phenomenological tradition points towards an experiential difference in the perception of embodied intentionality as compared to non-intentionality. Building upon this insight, it has been argued that this experiential difference might also be related to a ‘genuine, non-trivial difference in the informational content of the perception of embodied intentionality as compared to non-intentionality’ [3]. Furthermore, access to such an informational difference might guide the skilled human observer in reacting to social signals, helping us to skillfully manage social interactions without a constant need for mental state attribution [6]. In our understanding, Becchio et al.’s approach bears strong resemblance to the above described notion in that they argue congently for the observability of mental states. What is more, they actually provide a much needed operational approach to define the degree of observability of mental states. In fact, they present an empirical 4-step procedure that relies on a quantification of the informational utility and validity of kinematic features in order to successfully identify the intention of an actor. Notably, the aim of their approach is to objectively measure to what extent two intentional movements differ in their kinematic features, whether participants are able to differentiate between the intentions and the predictive value of specific motion features. More specifically, step 1 of the procedure proposed DOI of original article: https://doi.org/10.1016/j.plrev.2017.10.002. * Corresponding author.
E-mail address: [email protected] (I.C. Zillekens). https://doi.org/10.1016/j.plrev.2017.11.007 1571-0645/© 2017 Elsevier B.V. All rights reserved.
JID:PLREV AID:938 /DIS
2
[m3SC+; v1.272; Prn:6/11/2017; 12:29] P.2 (1-3)
I.C. Zillekens, L. Schilbach / Physics of Life Reviews ••• (••••) •••–•••
by Becchio and colleagues addresses the fundamental question of whether an intention can be ‘specified’ in terms of a unique motion pattern. Importantly, the authors stress that objectively differing kinematic features need to be validated in terms of their practical usability. Hence, the objective would be to determine whether a kinematic analysis of actions could contain significant information to discriminate the underlying intentions. The authors put forward a Linear Classification Analysis (LCA) to compare the spatiotemporal patterns of different intentions. A classification score then indicates the degree of specificity of the movement information for an intention. Choosing the well known Drift Diffusion Model (DDM) to examine whether the participants are capable of discriminating between different motion intentions and to validate the prognostic value of a stimulus feature by looking at the changes in the speed of evidence accumulation, Becchio et al. are further able to capture inter-individual variances and draw conclusions about the generalizability of the informational content of stimuli features. In step 3, a second model, the Classification and Regression Tree (CART) model, is applied to the experimental test data. On each layer, another motion feature as well as different feature manifestations are added to form branches of a decision tree that helps to differentiate intentions. Subsequently, each composition of motion features can be examined in terms of how well it predicts one intention or the other. Additionally, one can deduce the likelihood for choosing intention A over intention B given a specific set of kinematic information. This approach allows to elegantly test features and feature combinations in their predictive power and to further assess whether features were actually used by participants, i.e. whether they contribute to perceptual efficiency. Finally, the authors validate the predictive ‘rules’ derived from the CART by investigating changes in drift rate of the DDM. The assumption here is that a perceptually efficient kinematic feature, a feature that is both informative and whose kinematic information is also accessible to the observer, should be reflected in an increased drift rate. If this can be confirmed, so the authors argue, then the features evidently seem to contribute to perceptual efficiency and consequently, can be considered as key players in immediate access to social information, i.e. to mental states. In this seminal paper, the authors, thus, manage to turn the long-standing question of the observability of mental states into an empirically tractable issue and suggest a formal characterization thereof. This is a major achievement and we applaud the authors for doing so. What appears to be missing in the paper, we believe, is an equally impressive characterization of how different kinematic features might not only lead to or trigger perception, but also how they might bring about responses in a human observer and how a perceived state may afford certain kinds of responses (rather than others). We believe that this might constitute an important extension of their approach. The authors also provocatively state that their proposal of a data-driven and classificatory approach that relies on kinematic data should be capable of capturing ‘any mental state instantiated into specific pattern of behavior’. However, the approach does not deny that some mental states might turn out to be ‘invisible’ when assessed by means of their kinematics. Importantly, the 4-step procedure also allows to determine and to quantify this degree of observability, which appears to be an important clinical as well as epistemological consideration to us. The authors suggest that an implementation of their approach might be particularly useful for the design of ‘social’ robots without the need to endow robots with a fully-fledged mechanism for mental state attribution, i.e. a Theory of Mind, and, thus, indicate a wide range of implications of their proposal. From a psychiatric point of view, the proposal of a quantitative approach to the human ability of seeing others’ minds in their behavior promises to shed new light onto conditions, which have been associated with differential social impairments such as autism spectrum disorder and schizophrenia, but also chronic depression and social anxiety disorder. Taken together, Becchio et al. provide not only a fresh and new look onto the perennial problem of other minds, but actually provide a much needed and highly promising empirical approach to it, which is likely to significantly advance our mechanistic understanding of what it means to see (and interact with) other minds. References [1] Becchio C, Koul A, Ansuini C, Bertone C, Cavallo A. Seeing mental states: an experimental strategy for measuring the observability of other minds. Phys Life Rev 2017. https://doi.org/10.1016/j.plrev.2017.10.002 [in this issue]. [2] Gallagher S, Zahavi D. The phenomenological mind: an introduction to philosophy of mind and cognitive science. London: Routledge; 2007. [3] Gangopadhyay N, Schilbach L. Seeing minds: a neurophilosophical investigation of the role of perception–action coupling in social perception. Soc Neurosci 2012;7(4):410–23. https://doi.org/10.1080/17470919.2011.633754. [4] Goldman AI. Simulating minds. Oxford University Press; 2006.
JID:PLREV AID:938 /DIS
[m3SC+; v1.272; Prn:6/11/2017; 12:29] P.3 (1-3)
I.C. Zillekens, L. Schilbach / Physics of Life Reviews ••• (••••) •••–•••
3
[5] Gopnik A, Wellman HM. Why the child’s theory of mind really is a theory. Mind Lang 1992;7(1–2):145–71. https://doi.org/10.1111/j.14680017.1992.tb00202.x. [6] Schilbach L, Timmermans B, Reddy V, Costall A, Bente G, Schlicht T, et al. Toward a second-person neuroscience. Behav Brain Sci 2013;36(04):393–414. https://doi.org/10.1017/S0140525X12000660. [7] Zahavi D. Complexities of self. Autism 2010;14(5):547–51. https://doi.org/10.1177/1362361310370040.