Reinforcement communication learning in different social network structures

Dubova, M., Moskvichev, A., & Goldstone, R. L. (2020). Reinforcement Communication Learning in Different Social Network Structures. International Conference on Machine Learning, 1st Language and Reinforcement Learning Workshop. 

Social network structure is one of the key determinants of human language evolution. Previous work has shown that the network of social interactions shapes decentralized learning in human groups, leading to the emergence of different kinds of communicative conventions. We examined the effects of social network organization on the properties of communication systems emerging in decentralized, multi-agent reinforcement learning communities. We found that the global connectivity of a social network drives the convergence of populations on shared and symmetric communication systems, preventing the agents from forming many local “dialects”. Moreover, the agent’s degree is inversely related to the consistency of its use of communicative conventions. These results show the importance of the basic properties of social network structure on reinforcement communication learning and suggest a new interpretation of findings on human convergence on word conventions.

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A three-site reproduction of the Joint Simon Effect with the NAO robot

Strait, M., Lier, F., Bernotat, J., Wachsmuth, S., Eyssel, F., Goldstone, R. L., & Sabanovic, S. (2020).  A three-site reproduction of the Joint Simon Effect with the NAO robot.  15th Annual International Conference on Human Robot Interaction.  Cambridge, England.  Association for Computing Machinery (ACM) & Institute of Electrical and Electronics Engineers (IEEE).  (pp. 103-111).  New York: ACM.

The generalizability of empirical research depends on the reproduction of findings across settings and populations. Consequently, generalizations demand resources beyond that which is typically available to any one laboratory. With collective interest in the joint Simon effect (JSE) – a phenomenon that suggests people work more effectively with humanlike (as opposed to mechanomorphic) robots – we pursued a multi-institutional research cooperation between robotics researchers, social scientists, and software engineers. To evaluate the robustness of the JSE in dyadic human-robot interactions, we constructed an experimental infrastructure for exact, lab-independent reproduction of robot behavior. Deployment of our infrastructure across three institutions with distinct research orientations (well-resourced versus resource-constrained) provides initial demonstration of the success of our approach and the degree to which it can alleviate technical barriers to HRI reproducibility. Moreover, with the three deployments situated in culturally distinct contexts (Germany, the U.S. Midwest, and the Mexico-U.S. Border), observation of a JSE at each site provides evidence its generalizability across settings and populations.

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Quantifying Emergent, Dynamic Tonal Coordination in Collaborative Musical Improvisation

Setzler, M., & Goldstone, R. L. (2020).  Quantifying Emergent, Dynamic Tonal Coordination in Collaborative Musical Improvisation.  Proceedings of the 42nd Annual Conference of the Cognitive Science Society. (pp. 461-466).  Toronto, CA. Cognitive Science Society.

Groups of interacting individuals often coordinate in service of abstract goals, such as the alignment of mental representations in conversation, or the generation of new ideas in group brainstorming sessions. What are the mechanisms and dynamics of abstract coordination? This study examines coordination in a sophisticated paragon domain: collaboratively improvising jazz musicians. Remarkably, freely improvising jazz ensembles collectively produce coherent tonal structure (i.e. melody and harmony) in real time performance without previously established harmonic forms. We investigate how tonal structure emerges out of interacting musicians, and how this structure is constrained by underlying patterns of coordination. Dyads of professional jazz pianists were recorded improvising in two conditions of interaction: a ‘coupled’ condition in which they could mutually adapt to one another, and an ‘overdubbed’ condition which precluded mutual adaptation. Using a computational model of musical tonality, we show that this manipulation effected the directed flow of tonal information amongst pianists, who could mutually adapt to one another’s notes in coupled trials, but not in overdubbed trials. Consequently, musicians were better able to harmonize with one another in coupled trials, and this ability increased throughout the course of improvised performance. We present these results and discuss their implications for music technology and joint action research more generally.

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How much to copy from others?

Campbell, C., Izquierdo, E. & Goldstone, R. L. (2020).  How much to copy from others? The role of partial copying in social learning.  Proceedings of the 42nd Annual Conference of the Cognitive Science Society. (pp. 916-922). Toronto, Canada: Cognitive Science Society.

One of the major ways that people engage in adaptive problem solving is by copying the solutions of others. Most of the work on this field has focused on three questions: when to copy, who to copy from, and what to copy. However, how much to copy has been relatively less explored. In the current research, we are interested in the consequences for a group when its members engage in social learning strategies with different tendencies to copy entire or partial solutions and different complexities of search problems. We also consider different network topologies that affect the solutions visible to each member. Using a computational model of collective problem solving, we demonstrate that strategies where social learning involves partial copying outperform strategies where individuals copy entire solutions. We analyze the exploration/exploitation dynamics of these social learning strategies under the different conditions.

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Reconstructing Maps from Text

Avery, J. E., Goldstone, R. L., & Jones, M. N. (2020).  Reconstructing Maps from Text.  Proceedings of the 42nd Annual Conference of the Cognitive Science Society. (pp. 557-563).  Toronto, CA. Cognitive Science Society.

 Previous research has demonstrated that Distributional Semantic Models (DSMs) are capable of reconstructing maps from news corpora (Louwerse & Zwaan, 2009) and novels (Louwerse & Benesh, 2012). The capacity for reproducing maps is surprising since DSMs notoriously lack perceptual grounding (De Vega et al., 2012). In this paper we investigate the statistical sources required in language to infer maps, and resulting constraints placed on mechanisms of semantic representation. Study 1 brings word co-occurrence under experimental control to demonstrate that direct co-occurrence in language is necessary for traditional DSMs to successfully reproduce maps. Study 2 presents an instance-based DSM that is capable of reconstructing maps independent of the frequency of co-occurrence of city names. 

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Becoming cognitive science

Goldstone, R. L. (2019).  Becoming cognitive science.  Topics in Cognitive Science, 1-12.

Cognitive science continues to make a compelling case for having a coherent, unique, and fundamental subject of inquiry: What is the nature of minds, where do they come from, and how do they work? Central to this inquiry is the notion of agents that have goals, one of which is their own persistence, who use dynamically constructed knowledge to act in the world to achieve those goals. An agentive perspective explains why a special class of systems have a cluster of co-occurring capacities that enable them to exhibit adaptive behavior in a complex environment: perception, attention, memory, representation, planning, and communication. As an intellectual endeavor, cognitive science may not have achieved a hard core of uncontested assumptions that Lakatos (1978) identifies as emblematic of a successful research program, but there are alternative conceptions according to which cognitive science has been successful. First, challenges of the early, core tenet of “Mind as Computation” have helped put cognitive science on a stronger foundation—one that incorporates relations between minds and their environments. Second, even if a full cross-disciplinary theoretic consensus is elusive, cognitive science can inspire distant, deep, and transformative connections between pairs of fields. To be intellectually vital, cognitive science need not resemble a traditional discipline with its associated insularity and unchallenged assumptions. Instead, there is strength and resilience in the diverse perspectives and methods that cognitive science assembles together. This interdisciplinary enterprise is fragile and perhaps inherently unstable, as the looming absorption of cognitive science into psychology shows. Still, for many researchers, the excitement and benefits of triangulating on the nature of minds by integrating diverse cases cannot be secured by a stable discipline with an uncontested core of assumptions.

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