Iannetti, GD

Novembre, G

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feature listing solutions are higher dimen-sional [3]. Those solutions also containcategory-specific features, such ‘has abeak’, that are largely absent in the authors'solution. Moreover, many listed features,even for everyday objects, are relational orextrinsic in nature rather than intrinsic tothe object [6], such as features related tofunction. People view thematically relatedobjects (e.g., predator and prey, a manand his tie, etc.) as similar [6,7]. Likewise,embeddings based on people's real-world choices reveal that most dimensionsare goal relevant [8]. One interesting ques-tion is how different embedding spaces re-late [9].One particularly impressive aspect ofthe paper by Hebart et al. is the scale ofthe endeavour. To push even farther,judgements will need to be samplednonrandomly to focus on the informativejudgments that reduce uncertainty in thelocation of objects within the embedding.We adopted such an approach usingactive learning to create an embeddingspace more than an order of magnitudelarger using fewer similarity judgments[10]. We believe such ideas, combinedwith the types of contribution exemplifiedHebart et al., will both help elucidatehow humans represent concepts and pro-vide valuable resources to support alliedendeavours.1Department of Experimental Psychology, University CollegeLondon, 26 Bedford Way, London, WC1H 0AP, UK*Correspondence:b.love@ucl.ac.uk (B.C. Love).https://doi.org/10.1016/j.tics.2020.12.003© 2020 Elsevier Ltd. All rights reserved.References1. Hebart, M.N. et al. (2020) Revealing the multidimen-sional mental representations of natural objects under-lying human similarity judgements. Nat. Hum. Behav. 4,1173–11852. Deng, J. et al. (2009) ImageNet: a large-scale hierarchicalimage database. In 2009 IEEE Conference on ComputerVision and Pattern Recognition, pp. 248–255, IEEE3. McRae, K. et al. (2005) Semantic feature productionnorms for a large set of living and nonliving things.Behav. Res. Methods 37, 547–5594. Shepard, R.N. (1962) The analysis of proximities: multidi-mensional scaling with an unknown distance function.Part 1. Psychometrika 1, 125–1405. Hebart, M.N. et al. (2019) THINGS: a database of 1,854object concepts and more than 26,000 naturalistic objectimages. PLoS One 14, e02237926. Jones, M. and Love, B.C. (2007) Beyond common features:the role of roles in determining similarity.Cognit. Psychol. 55,196–2317. Bassok, M. and Medin, D.L. (1997) Birds of a feather flocktogether: similarity judgments with semantically-rich stimuli.J. Mem. Lang. 36, 311–3368. Hornsby, A.N. et al. (2020) Conceptual organization is revealedby consumer activity patterns. Comput. Brain Behav. 3,162–1739. Roads, B.D. and Love, B.C. (2020) Learning as the unsu-pervised alignment of conceptual systems. Nat. Mach.Intell. 2, 76–8210. Roads, B.D. and Love, B.C. (2020) Enriching ImageNet withhuman similarity judgments and psychological embeddings.arXiv Published online November 22, 2020 https://arxiv.org/abs/2011.11015ForumHyperscanning AloneCannot Prove Causality.Multibrain StimulationCanGiacomo Novembre1,2,* andGian Domenico Iannetti 1,2,*Brains that work together, coupletogether through interbrain syn-chrony. Does interbrain synchronycausally facilitate social interac-tion? This question cannot beanswered by simply recording frommultiple brains (hyperscanning). Itinstead requires causal protocolsentailing their simultaneous stimu-lation (multibrain stimulation). Wehighlight promising findings andfuture horizons of this nascent field.Hyperscanning and SocialInteractionThe simultaneous recording from multiplebrains – hyperscanning – has led to manyreports of interbrain synchrony among so-cially interacting individuals. Such states ofsynchrony appear to facilitate socialbehaviors such as interpersonal coordina-tion, cooperation and communication[1,2]. This phenomenon has deep ontoge-netic and phylogenetic roots. It is observ-able in early mother–child interactions [1],and it is well conserved across a numberof social species [2].Social interactions, by definition, entailinformation transfer between two or moreindividuals, typically through acoustic orvisual signals (for the sake of conciseness,from here onwards we only refer to two indi-viduals). It follows that nearly all hyper-scanning studies record from multiplebrains while they are exposed to similarsensory input. This consideration raises afundamental question concerning the natureof interbrain synchrony. Can it be conceptu-alized as a neural mechanism that causallyfacilitates social interaction, or is it an epiphe-nomenon that by itself has no direct effecton social interaction but simply emerges asa consequence of two brains encoding asimilar sensory environment?Interbrain Synchrony: Mechanismor Epiphenomenon?Themechanistic perspective postulates thattwo individuals achieve interbrain synchronyto better function in a social context. Thiscould be considered a generalization of thewidely accepted principle that two distinctareas of the same brain communicatethrough coherent neural activity [3], althoughapplied across two separate individuals.That is, two areas, each in a different brain,would optimize social behavior when theirrhythms are synchronized. Indeed, as thebrain samples information from the environ-ment rhythmically rather than continuously,synchronizing two neural rhythms acrosstwobrains could effectively facilitate interper-sonal information flow [4].Alternatively, the epiphenomenal perspec-tive postulates that interbrain synchronyarises as a mere consequence of the factthat two individuals share the same sensoryTrends in Cognitive Sciences96 Trends in Cognitive Sciences, February 2021, Vol. 25, No. 2Trends in Cognitive SciencesFigure 1. MBS: Theory and Practice. (A) Hyperscanning versus MBS. In Hyperscanning, inter-brainsynchrony is measured during social interaction (e.g., with electroencephalography, functional magneticresonance imaging, or functional near-infrared spectroscopy). In MBS, interbrain synchrony is exogenouslymodulated (e.g., with transcranial stimulation, sensory stimulation, or optogenetics), to test its causal effectson social interaction. Causal effects of interbrain synchrony cannot be proved with hyperscanning, due to itsintrinsic correlational nature. (B) Present and future applications of MBS. MBS can be achieved usingnoninvasive neurostimulation (e.g., tACS in humans; left), rhythmic sensory stimulation (e.g., acousticstimulation causing neural entrainment outlasting the stimulation offset; middle), or invasive neurostimulation(e.g., optogenetics in rats; right). For illustrative purposes, we depict only in-phase and anti-phaserelationships between the stimuli delivered to two brains, although several other stimulus properties could bemanipulated. Abbreviations: MBS, multibrain stimulation.environment or perform the same task.Indeed, two brains receiving the sameinput (or performing the same movements)would simply display similar neural re-sponses at comparable latencies. Thiswould in turn lead to spurious synchrony,purely epiphenomenal in nature and nothaving a role in causally determining orfacilitating the motor output underlying thesocial behavior. Although this perspectivehas been discussed as plausible in themost recent reviews of the field [1,2], con-clusive empirical evidence is lacking.The Importance of CausalEvidenceHow can we distinguish between themechanistic and epiphenomenal interpre-tations of interbrain synchrony? Here, weargue that hyperscanning alone, while pro-viding important correlational evidence,cannot produce any substantial leaptowards addressing this fundamentalquestion. Obtaining causal evidence istherefore necessary (see Box 1 for a dis-cussion on how to infer causality inneuroscience).To illustrate the difference between corre-lational and causal evidence, imagine ascientist who needs to reverse engineeran electronic device. Observing that acertain pattern of current flow (A) within acircuit is always followed by a light turningon (B) implies that A and B are somehowrelated to one another. Yet, this alonedoes not imply that A is causing B. Toprove a causal relationship, one needs tomanipulate the pattern (A), and examinethe effects on the light (B).Along the same line, scientists should notsolely report correlations between interbrainsynchrony (assessed by hyperscanning) andsocial behavior. They should also exog-enously manipulate interbrain synchronyand carefully assess its causal effects on so-cial interaction (Figure 1A). Such exogenousmanipulation of interbrain synchrony can beachieved using multibrain stimulation (MBS).MBS Can Prove CausalityMBS entails the simultaneous stimulationof multiple brains engaged in a social inter-action, in order to manipulate interbrainsynchrony. This reverses the dominanthyperscanning approach, according towhich one manipulates social interaction(independent variable) and measuresinterbrain synchrony (dependent variable).In MBS, it is interbrain synchrony that ismanipulated (as the independent variable),while changes in social interaction aremeasured (as the dependent variable)(Figure 1A). Hence, using MBS, it is possi-ble to establish whether interbrainsynchrony causally modulates socialinteraction, and eventually confront themechanistic and epiphenomenal explana-tions of interbrain synchrony.Evidence from three different laboratorieshas shown that this approach is feasible,for example by using transcranial alternatingcurrent stimulation simultaneously in twoindividuals (hyper-tACS; [5–7]). One studydemonstrated that pairs’ accuracy inestablishing interpersonal coordinationTrends in Cognitive SciencesTrends in Cognitive Sciences, February 2021, Vol. 25, No. 2 97Box 1. Inferring Causality in NeuroscienceDissecting causal from correlational relationships is a central problem in neuroscience. When measuring twovariables simultaneously, such as brain activity from two distinct regions, or brain activity and behavior, it isoften important to infer whether one variable causes another. Merely observing that one variable precedesanother is not sufficient to infer causality: for example, a barometer indicating bad weather is not the causeof a subsequent thunderstorm.To overcome this problem, two distinct approaches are typically used. A first, interventional approach requiresbrain stimulation: a wide range of techniques that are used to interfere with brain activity of a given region, andmonitor the effects upon a second region or behavior. Alternatively, a second, analytical approach entails theuse of algorithms such as Granger causality [12]: brain activity recorded from a given region is used to predictthe activity of a second region or various forms of behavior.While both approaches can undeniably contribute to understanding the neurophysiology of social interactions,we argue that only the interventional approach can provide direct evidence of causality, whereas the analyticalone can only indirectly infer it – also considering that when there are conflicting causal models that cannot beteased apart with a particular set of variables, no fancy analysis can overcome the problem. Therefore, whenpossible, interventional studies should be preferred over analytical ones.in a finger-tapping task is augmentedwhen their motor cortices are stimulatedwith beta band (20 Hz) in-phase currents[6]. Another study has shown that MBSimproves learning outcome when the infe-rior frontal cortices of a student and aninstructor are simultaneously stimulatedwith theta band (6 Hz) in-phase currents [7].Future Perspectives for MBSMBS is an experimental approach still inits infancy. For example, hyper-tACS para-digms have so far addressed a limitednumber of homologous brain regions, inter-action tasks, and signal manipulations [5–7].Relative to the latter, experimenters havemostly delivered constant frequency andamplitude signals, with limited explorationof phase differences (besides perfect in-phase or anti-phase). However, it is wellknown that neural networks operate in amore complex manner [4], especially inthe context of naturalistic social interac-tions. As the most recent tACS technologypermits to (i) control several signal proper-ties; (ii) easily target a number of different(including heterologous) brain regions; and(iii) do this in the context of different tasks, itis clear that this approach has been under-exploited. In particular, hyper-tACS couldbe further sophisticated to induce neuralsignals simulating the interbrain synchroni-zation observed during naturalistic socialinteractions.tACS is by no means the sole way toachieve MBS. In our opinion, at least twoviable alternatives have high potential andwill be widely used in the future (Figure 1B).First, carefully controlled sensory stimulationcould be used to induce and manipulateinterbrain synchrony [8]. This approachcapitalizes on the crucial evidence that theentraining effects of rhythmic sensory stimulioften outlast sensory stimulation [4,9]. Itfollows that this property could be exploitedto induce coupled neural rhythms in twobrains simultaneously, for example present-ing two different sensory stimuli (one toeach brain), which nonetheless elicit similarneural entrainment. Once such entrainmentis reached and the stimuli have stopped,social interaction could be implementedand assessed (notably, following andnot during sensory stimulation).Additionally, more-invasive neurostimulationtechniques such as intracerebral electricalstimulation or optogenetics can be usedto deliver MBS, especially considering thatinterbrain synchrony is not unique tohumans, but also occurs in other species[10,11]. This approach would lead to asignificant gain of spatial specificity withrespect to the targeted neural network andcellular type, thus overcoming a well-known limitation of noninvasive transcranialstimulation protocols such as tACS. Ofnote, one recent study demonstratedthat separate cellular populations within therodent prefrontal cortex, preferentiallyencoding either one’s own behavior andthat of a social partner, differentially contrib-ute to interbrain synchrony [11].Combining Hyperscanning withMBSA final exciting prospect that we wish tohighlight is the possibility to combine MBSand hyperscanning. This approach is partic-ularly fruitful because MBS andhyperscanning offer complementary ratherthan alternative advantages. Specifically,hyperscanning is necessary to identifythe social behaviors associated withinterbrain synchrony in the context of natu-ralistic and unrestricted social interactions.Next, MBS should be used to simulatesuch interbrain synchrony exogenously,and thereby quantitatively measure itseffects upon social behavior. Intriguingly,hyperscanning and MBS might be usedsimultaneously if one wished to assesswhether interbrain synchrony has reallyoccurred following MBS.Concluding RemarksAlthough the use of hyperscanning iscentral to investigate the neurophysiologyof social interactions, MBS offers the onlyvalidated empirical approach capable ofteasing apart the mechanistic from theepiphenomenal interpretation of interbrainsynchrony. Turning the insights achievedthrough hyperscanning from correlationalto causal, MBS is likely to lead to a para-digm shift in social neuroscience. Wehave described multiple ways how thiscould be achieved. We forecast that the im-plementation of MBS, alone or combinedwith hyperscanning, will yield ground-breaking discoveries in the coming years.AcknowledgmentsWe thank R. Somervail, R.J. Bufacchi, Y. Guo, A.Fitzpatrick, A. Koul, and D. Ahmar for useful commentson a previous version of this manuscript. GDI acknowl-edges the support of the Wellcome Trust (COLLJLARAXR) and of the European Research Council(Consolidator Grant PAINSTRAT).Trends in Cognitive Sciences98 Trends in Cognitive Sciences, February 2021, Vol. 25, No. 21Neuroscience and Behaviour Laboratory, Italian Institute ofTechnology, Rome, Italy2Department of Neuroscience, Physiology and Pharmacology,University College London, UK*Correspondence:giacomo.novembre@iit.it (G. Novembre) andgiandomenico.iannetti@iit.it (G.D. Iannetti).https://doi.org/10.1016/j.tics.2020.11.003© 2020 The Authors. Published by Elsevier Ltd. This is an openaccess article under the CC BY license(http://creativecommons.org/licenses/by/4.0/).References1. Wass, S.V. et al. (2020) Interpersonal neural entrainment dur-ing early social interaction. Trends Cogn. Sci. 24, 329–3422. Kingsbury, L. and Hong, W. (2020) A multi-brain frame-work for social interaction. Trends Neurosci. 43,651–6663. Fries, P. (2005) A mechanism for cognitive dynamics:Neuronal communication through neuronal coherence.Trends Cogn. Sci. 9, 474–4804. Lakatos, P. et al. (2019) A new unifying account of theroles of neuronal entrainment. Curr. Biol. 29, 1–165. Szymanski, C. et al. (2017) Hyper-transcranial alternatingcurrent stimulation: Experimental manipulation of inter-brain synchrony. Front. Hum. Neurosci. 11, 1–156. Novembre, G. et al. (2017) Interpersonal synchronyenhanced through 20 Hz phase-coupled dual brainstimulation. Soc. Cogn. Affect. Neurosci. 12,662–6707. Pan, Y. et al. (2020) Dual brain stimulation enhancesinterpersonal learning through spontaneous movementsynchrony. Soc. Cogn. Affect. Neurosci. Publishedonline June 15, 2020. https://doi.org/10.1093/scan/nsaa0808. Hoehl, S. et al. (2020) Interactional synchrony: signals,mechanisms and benefits. Soc. Cogn. Affect. Neurosci.Published online March 3, 2020. https://doi.org/10.1093/scan/nsaa0249. Guo, Y. et al. (2020) Ultralow-frequency neural entrainmentto pain. PLoS Biol. 18, 1–2710. Zhang, W. and Yartsev, M.M. (2019) Correlated neuralactivity across the brains of socially interacting bats. Cell178, 413–428.e2211. Kingsbury, L. et al. (2019) Correlated neural activity andencoding of behavior across brains of socially interactinganimals. Cell 178, 429–446.e1612. Weichwald, S. and Peters, J. (2020) Causality in cognitiveneuroscience: concepts, challenges, and distributional ro-bustness. J. Cogn. Neurosci. Published online August 19,2020. https://doi.org/10.1162/jocn_a_01623Trends in Cognitive SciencesTrends in Cognitive Sciences, February 2021, Vol. 25, No. 2 99

Hyperscanning Alone Cannot Prove Causality. Multibrain Stimulation Can

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Hyperscanning Alone Cannot Prove Causality. Multibrain Stimulation Can

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