Getting situated: Comparative Analysis of Language models with experimental categorization tasks

Edginer, A., & Goldstone, R. L. (2022). Getting situated: Comparative Analysis of Language models with experimental categorization tasks.  Proceedings of the 44th Annual Conference of the Cognitive Science Society. (pp. 230-236).  Toronto, Canada. Cognitive Science Society.

Common critiques of natural language processing (NLP) methods cite their lack of multimodal sensory information, claiming an inability to learn situated, action-oriented relations through language alone. Barsalou’s (1983) theory of ad hoc categories, which are formed from to achieve goals in real-world scenarios, correspond theoretically to those types of relations with which language models ought to have great difficulty. Recent NLP models have developed dynamic approaches to word representations, where the same word can have different encodings depending on the context in which it appears. Testing these models using categorization tasks with human response data demonstrates that situated properties may be partially captured through semantic analysis. We discuss possible ways in which different notions of situatedness may be distinguished for future development and testing of NLP models.

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The well measured life: Performance, well-being, motivation, and identity in an age of abundant data

Goldstone, R. L. (2022). The well measured life: Performance, well-being, motivation, and identity in an age of abundant data.  Current Directions in Psychological Science, 31(1), 1-9. https://doi.org/10.1177/09637214211053834

Our lives are being measured in rapidly increasing ways and frequency. These measurements have beneficial and deleterious effects at both individual and social levels. Behavioral measurement technologies offer the promise of helping us to know ourselves better and to improve our well-being by using personalized feedback and gamification. At the same time, they present threats to our privacy, self-esteem, and motivation. At the societal level, the potential benefits of reducing bias and decision variability by using objective and transparent assessments are offset by threats of systematic, algorithmic bias from invalid or flawed measurements. Considerable technological progress, careful foresight, and continuous scrutiny will be needed so that the positive impacts of behavioral measurement technologies far outweigh the negative ones.

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Reconstructing Maps from Text

Avery, J. E., Goldstone, R. L, & Jones, M. N. (2021). Reconstructing Maps from Text. Cognitive Systems Research. doi: https://doi.org/10.1016/j.cogsys.2021.07.007

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 . In this paper we investigate the statistical sources required in language to infer maps, and the resulting constraints placed on mechanisms of semantic representation. Study 1 brings word co-occurrence under experimental control to demonstrate that standard DSMs cannot reproduce maps when word co-occurrence is uniform. Specifically, standard DSMs require that direct co-occurrences between city names in a corpus mirror the proximity between the city locations in the map in order to successfully reconstruct the spatial map. 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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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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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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