Reengineering scientific Ontologies
A new wave of proposals suggests reassessing scientific concepts in light of data. In this project, I am thinking about potential challenges in reengineering scientific ontologies and ways to address them. One of such challenges is theory-ladenness of the data. In particular, scientific concepts affect evidence in various ways, including (i) priming scientists to overemphasize within-concept similarities and between-concept differences, (ii) leading scientists to measure conceptually-relevant dimensions more accurately; (iii) serving as units of scientific experimentation, communication, and theory-building; (iv) affecting the phenomena themselves.
Collaborator: Rob Goldstone
Dubova, M. & Goldstone, R. L. Carving joints into nature: reengineering scientific concepts in light of concept-laden evidence (under review)
Computational epistemology: experimentation strategies
In this project, I am trying to
finally make science work rigorously explore the efficiency of different strategies for making scientific progress. I designed a flexible multi-agent model to serve this purpose! In the current version of the model, the agents (scientists) are searching the "ground truth" space and are building the lower-dimensional explanations for their observations (which, then, influence where they search next). I am simulating the model with different strategies that the agents use to sample, record, explain, and share the data and then compare the results of social learning in these cases. In this project, I am looking at the epistemic success of experimental choice strategies proposed by philosophers of science or executed by scientists themselves (e.g. falsification, confirmation, novelty-seeking, crucial experimentation, random experimentation).
Collaborators: Kevin Zollman, Arseny Moskvichev
Dubova, M., Moskvichev, A., & Zollman, K. Against theory-motivated experimentation in science (submitted)
See the discussion of our preprint at Andrew Gelman's blog
Computational epistemology: simplicity
Simplicity is often viewed as a core cognitive principle. Humans and scientists alike prefer simple explanations. Conventionally, simple representations are associated with a superior generalization ability: they have a capacity to capture the structure in the data and rule out the noise. Representations with more flexibility than required to accommodate the structure of the target phenomenon, on the contrary, risk to catastrophically overfit the observed samples and fail to generalize to new observations. In this project, I computationally probe the effects of simplicity vs. complexity bias in representation learning on agents' generalization ability.
Collaborator: Sabina Sloman
Dubova, M. (2022). Generalizing with overly complex representations. NeurIPS 2022 Workshop on Information-Theoretic Principles in Cognitive Systems.
Poster (presented at the NeurIPS 2022 Workshop on Information-Theoretic Principles in Cognitive Systems)
Augmented intelligence workshop
I am co-organizing an online-workshop on augmented intelligence. The workshop aims to explore the confluence of theoretical, empirical, and technological advances for developing a unified science of intelligent systems that extends beyond the individual person. We hope to coordinate the efforts of computer scientists, psychologists, education researchers, neuroscientists, and biologists to explore how human minds are augmented by other humans (collective intelligence) and by machines they create (human-machine collaboration).
Co-organizers: Rob Goldstone, Mirta Galesic, Gautam Biswas
Image credit: Joe Lee
Dubova, M., Galesic, M., & Goldstone, R. (in press). Cognitive Science of Augmented Intelligence.
Grounded Communicative AI
In this project, I attempted to review main insights of the Embodied, Embedded, Enactive, and Extended cognition research to distinguish the main aspects of naturalistic learning conditions that play causal roles for human language development. I then use this analysis to propose a list of concrete, implementable components for building “grounded” artificial communicative intelligence. These components include embodying machines in a perception-action cycle, equipping agents with active exploration mechanisms so they can build their own curriculum, allowing agents to gradually develop motor abilities to promote piecemeal language development, and endowing the agents with adaptive feedback from their physical and social environment.
Dubova, M. (2021). Building Human-like Communicative Intelligence: A Grounded Perspective. Cognitive Systems Research, 72C, pp. 63-79.
Grounding Relations in perceptual routines
Humans have an exceptional ability to notice relations between different entities, and transfer their relational knowledge across a variety of situations. For example, adults can easily discriminate a pair of identical items from a pair of different items, whatever these items are. It is still unclear how such relational concepts get learnt and grounded in lower-level cognitive architectures. Here, we explore whether many relational concepts can be grounded in simple perception-action routines that allow humans to extract relational information that is invariant to particular entities being compared. We are conducting behavioral experiments to determine the role of active perception in human relational abilities and characterize particular perceptual strategies that people use to infer visual relations. At the same time, we are also experimenting with connectionist models with and without active perception to analyze the strategies that the artificial agents come up with to solve analogous visual relational tasks.
Collaborators: Arseny Moskvichev, Rob Goldstone
Dubova, M. & Moskvichev, A. The role of active perception and naming in sameness comparison (under review)
Poster (presented at the Psychonomic Society 2022)
We are looking at how shared communicative systems can emerge and develop in populations of independently adapting Reinforcement Learning agents. We start with simulations of a "minimal assumptions" multi-agent model (w.r.t. pre-built constraints/architecture/supervision) and then add potentially helpful components one-by-one to probe the necessary and sufficient conditions for the emergence of communicative patterns. To isolate the properties of communicative systems affected by our interventions, we have developed a set of metrics for multi-agent communicative analysis.
Collaborators: Arseny Moskvichev, Rob Goldstone
Dubova, M., Moskvichev, A., & Goldstone, R. (2020). Reinforcement Communication Learning in Different Social Network Structures. ICML 2020 1st Language and Reinforcement Learning Workshop. (see this 5-min video presentation from ICML LaReL)
Dubova, M., & Moskvichev, A. (2020). Effects of supervision, population size, and self-play on multi-agent reinforcement learning to communicate. Artificial Life Conference Proceedings (pp. 678-686).
open-ended evolution of communication
Humans have developed a great variety of complex communicative systems (languages) without any centralized assistance. Therefore, evolution of human communication has often been modeled as a result of distributed learning among agents which are reinforced for successfully transmitting information to each other. These models, however, face two major challenges: 1) even in most successful cases, the agents can only develop a very small number of communicative conventions, whereas humans managed to successfully agree upon thousands of words; 2) after groups of artificial agents converge on a set of communicative conventions, they have no incentive to improve or expand it, whereas the development of human languages is open-ended. Here, I explore whether these two challenges could be resolved by dynamically changing the problem that the agents are learning to solve with communication. I hypothesize that the communicative problem that starts small and gradually increases in difficulty as the agents agree upon new communicative conventions is essential for achieving tractable evolution of rich communicative systems in decentralized multi-agent systems.
Dubova, M. (2021). Growing Opportunities to Grow: Toward Open-Ended Multi-Agent Communication Learning. In ALIFE 2021: The 2021 Conference on Artificial Life. MIT Press.
We are studying how unsupervised and task-dependent perceptual learning mechanisms are supporting adaptive concept learning in humans and artificial neural networks. We formalize multi-task perceptual learning with Bayesian models, and with a convolutional beta-VAE neural network trained to both reconstruct and categorize perceptual inputs. We conduct behavioral experiments to compare model predictions and human learning.
Collaborator: Rob Goldstone
Dubova, M., & Goldstone, R. L. (2021). The Influences of Category Learning on Perceptual Reconstructions. Cognitive Science, 45(5), e12981.
Poster (presented at the 61st Annual Meeting of the Psychonomic Society)
categorical perception meets el greco
It has been commonly assumed that the categorical perception effects should uniformly affect how we perceive different items in the visual field. Here, we adapt the color matching experimental paradigm to test this assumption. Spoiler: we found that only some of the objects in the visual field are biased by categorical color associations. We suspect that the eye movements determine whether perception of a given item is affected by categorical biases.
Collaborator: Rob Goldstone
Dubova, M., & Goldstone, R. L. (2022). Categorical perception meets El Greco: Categories unequally influence color perception of simultaneously present objects. Cognition, 223, 105025.
Dubova, M., & Goldstone, R. (2021). Categories affect color perception of only some simultaneously present objects. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 43, No. 43).
Poster (presented at the Cognitive Science Society 2021)
Semantic similarity detection
We tried to capture between-sentence similarity with a combination of different metrics, starting from simple word overlapping and ending with distance between sentence embedding representations. The work went beyond the initial scope when we obtained unrealistically high performance scores and realized that the evaluation metric used for the algorithms’ selection for many years was biased. Eventually, we did not only develop a new semantic similarity detection method, but also proposed a new evaluation framework for the task.
Collaborator: Anton Belyy
Belyy, A., Dubova, M., & Nekrasov, D. (2018). Improved evaluation framework for complex plagiarism detection. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 157-162).
Belyy, A. V., & Dubova, M. A. (2018). Framework for Russian plagiarism detection using sentence embedding similarity and negative sampling. Dialogue, 1.
We conducted several experimental studies to test the factors that determine the onset of assimilative or contrastive visual adaptation aftereffect. Getting insights from these data, we developed a probabilistic model of the potential common mechanism underlying adaptation aftereffects in the opposite directions. We formalized the alterations of perception which occur after short-term adaptation as a result of Bayesian inference based on learning the perceptual structure of stimuli.
Collaborator: Arseny Moskvichev
Dubova, M., & Moskvichev, A. (2019). Adaptation Aftereffects as a Result of Bayesian Categorization. Proceedings of the 41st Annual Meeting of the Cognitive Science Society (pp. 1669-1675).