Grad Application Deadline
PhD applications for Fall 2023 due as well as MA applications for Summer 2023 and Fall 2023 due.
PhD applications for Fall 2023 due as well as MA applications for Summer 2023 and Fall 2023 due.
Investigating compositional visual knowledge through challenging visual tasks ABSTRACT: Human vision manifests remarkable robustness to recognize objects from the visual world filled with a chaotic, dynamic assortment of information. Computationally, our visual system is challenged by the enormous variability in two-dimensional projected images as a function of viewpoint, lighting, material, articulation as well as occlusion. […]
Approximate Analysis by Synthesis: Towards a Computational Theory of Vision ABSTRACT: Vision is humans' underappreciated superpower. It gives us the miraculous ability to perceive the three-dimensional structure of the world from the complex pattern of light rays which are imaged on our retinas. Vision can be conceptualized as Analysis by Synthesis, formalized by Bayesian probability […]
Acquiring syntactic variation: regularization in wh-question production. ABSTRACT: Children are often exposed to language-internal variation. Studying the acquisition of variation allows us to understand more about children’s ability to acquire probabilistic input, their preferences at choice points, and factors contributing to such preference. Using wh-variation as a case study, this dissertation explores the acquisition of […]
Canonical dimensions of vision. For department members, department major/minor undergraduate students, and invited guests. In-person and on Zoom.
Selection in Written Language Production: Evidence from Aphasia ABSTRACT: Most models of word production assume that in the process of producing a target word, multiple distractors also get activated, both other words (at the lexical level) and other phonemes/letters (at the segmental level). Thus, a selection mechanism is needed to select the targets at each […]
Development of relational reasoning: When do children pass the Relational Match-to-Sample task? ABSTRACT: Relational ability—the ability to compare situations or ideas and discover common relations – is a key process in higher-order cognition that underlies transfer in learning and creative problem solving. For this reason, it has generated intense interest both among developmentalists and in […]
Lateralization of Social Interaction Perception. ABSTRACT: Social perception emerges early, occurs automatically, and is used ubiquitously in daily life. Understanding its neural underpinnings is critical to cognitive neuroscience. A region in the right posterior superior temporal sulcus (STS) that selectively supports social interaction perception has been found by contrasting brain responses to interacting and non-interacting […]
Keynote speakers from nearby universities will deliver presentations on aspects of cognitive science in alignment with this year's conference theme. Keynote Speakers: Furthermore, students will have the opportunity to present their accepted research posters, and enter a poster competition to receive an award!
Differentiable Tree Operations Promote Compositional Generalization. ABSTRACT: In the context of structure to structure transformation tasks, sequences of discrete symbolic operations (e.g., op codes or programs) are an important tool but are difficult to learn due to their non-differentiability. To support learning sequences of symbolic operations, we propose a differentiable tree interpreter which compiles high-level […]
Neural representations: From humans to artificial networks and back ABSTRACT: I will discuss various properties of neural representations (dimensionality, spectra, hyperalignments) found in biological brains and show how they can be connected to recent findings in the inner workings of artificial neural networks. I will show results in the context of vision using fMRI data […]
What kind of computation is cognition? Prof. Josh Tenenbaum is a Professor in the Department of Brain & Cognitive Sciences at MIT. Prof. Tenenbaum studies the computational basis of human learning and inference.