cross-domain scientific analogies

In this project, we are investigating the role of analogy-making in scientific reasoning and creativity. Drawing on cognitive psychology and the history of science, this project explores how analogies can foster creative thinking by connecting seemingly unrelated phenomena. We are experimentally prompting scientists from different fields to think about the phenomena they study using analogies from different domains of science/life (e.g., viewing ‘misinformation’ as a ‘virus’ vs. a ‘canalization system’). The goal is to examine how these analogies influence the design of experiments and the development of theories, hypothesizing that certain analogies will encourage broader exploration and innovative strategies, while others may promote more focused inquiry.

Cognitive & social structure of mathematical discovery

We are exploring the social and cognitive structure of the real-world scientific discovery through a data-driven analysis of the Polymath Project—a large-scale, crowdsourced collaboration of expert mathematicians to solve open mathematical problems. By applying topic modeling and information-theoretic methods, we track how mathematicians transition from broad exploration into focused exploitation of specific subfields of mathematics. The goal of this work is to offer insights into how actual scientific communities explore the space of ideas to solve open scientific problems.

Computational epistemology: Social preferences

In this project, we are looking into how social biases shape the collective development of scientific theories using a computational multi-agent model. This research explores how preferences for learning from successful scholars or those within the same discipline influence the spread of ideas and theory development within the scientific communities. Our preliminary findings indicate that when scholars favor learning from high-performing peers, scientific communities often settle on mid-level theories. In contrast, openness to learning from a broader, more diverse group of colleagues fosters greater theoretical variation, enabling both theoretical breakthroughs and the persistence of outdated theories. This project aims to uncover how scientists should effectively exchange knowledge to collectively build more accurate theories of the world. 

Ontologies and Scientific exploration

How do our scientific conceptual systems (e.g. periodic table in chemistry, taxonomy of mental disorders, ways of partitioning the brain into regions) influence experimentation? I am exploring the question by asking participants to play the role of a neuroscientist, learning the relationship between brain activations and behavior. I prime different participants with different ways of partitioning the brain into areas (e.g. ‘temporal’ and ‘parietal’ lobe) and am looking into the ways in which this priming affects their experimentation and learning results. 

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). 

Reengineering scientific concepts

A new wave of proposals suggests reassessing scientific concepts in light of accumulated 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. (in press, Trends in Cognitive Sciences). Carving joints into nature: reengineering scientific concepts in light of concept-laden evidence. Trends in Cognitive Sciences, S1364-6613.

Dubova. M., Barrett, L. F., Goldstone, R. L., Musslick, S., & Poldrack, R. A. Improving Concepts in Cognitive Science. (workhop proposal)

Workshop on improving cognitive science concepts at CogSci 2024

Also, see this journal cover art by Joe Lee:

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. 

Excess Capacity Learning

I am exploring the implications of the recent insights from statistical learning theory (e.g. double descent of generalization error) for model building in cognitive science, which has so far been primarily focused on information compression as a plausible learning mechanism. I am articulating an alternative paradigm, “excess capacity learning,” in which learners use more representational resources than needed for a task at hand. I review the existing evidence that humans apply more representational resources than needed for a task at hand and articulate specific implications of learning in this excess capacity regime for further empirical testing. These implications include 1) the tendency to both memorize observations and capture higher-level patterns in them, 2) the construction of redundant representations, 3) nonlinear sensitivity to signal-to-noise ratio in the environment, and 4) nonlinear effects of the amount of data on the generalization performance and smoothness of generalization.

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). 

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.

categorical perception

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. 

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.

Language games

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. 

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.

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.

adaptation aftereffects

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.

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.