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Published in Second Workshop on Representational Alignment at ICLR 2025, 2025
A hallmark of human vision is to recognize objects in complex naturalistic scenes. This study proposes a tool to investigate human perception by using a computer graphics approach, using three-dimensional object meshes to render synthetic scenes and study how these scenes are represented in the brain.
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Published in arXiv preprint arXiv:2505.09662, 2025
This study compares the persuasiveness of large language models (Claude 3.5 Sonnet and DeepSeek v3) against incentivized human persuaders in real-time conversations. Results show that LLM persuasive superiority is context-dependent, varying with the truthfulness of persuasion attempts, the model used, and diminishing with repeated interactions.
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Published in SIGBOVIK 2024, 2024
This paper addresses the contentious issue of author ordering in scientific publications. The authors propose AMOR, a probabilistic system that randomly shuffles the author list each time a paper is viewed, eliminating distinctions such as co-first or co-middle authorship. Presented at SIGBOVIK 2024.
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Published in Journal of Biological Psychiatry, 2023
Imaging genetics provides an opportunity to discern associations between genetic variants and brain imaging phenotypes. This review summarizes findings from imaging genetics studies spanning from early infancy to early childhood, with a focus on studies examining genetic risk for neuropsychiatric disorders.
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Published in Computational Life Sciences - Data Engineering and Data Mining for Life Sciences, 2023
This chapter describes the process of building an Image Database with searchable metadata. Searching for an image greatly benefits from having useful metadata to accompany it. The proposed solution allows the user to save the picture together with its online location (url) and suitable metadata. The application then saves both the picture and its metadata in a SQLite database. This database can be queried and is accessible via a RESTful API service.
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Published in Cognitive Science Society, 2023
A hallmark of human vision is the ability to rapidly recognize objects in a complex naturalistic scene. However, the exact mechanisms behind the computational invariance of object recognition remain unknown. In this study, we investigate object constancy by estimating how the ventral visual stream processes shading, shadows, textures, and specularities.
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Published in Cognitive Science Society, 2023
Humans can successfully interpret images even when significant image transformations have distorted them. Such images aid in differentiating existing computational models for perception because models that predict similar results for typical stimuli may diverge when confronted with atypical stimuli. We propose an explanation of people’s ability to perceive a specific class of degraded stimuli that require extreme generalization capabilities: Mooney, or two-tone images of faces.
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Published in Developmental Science Journal, 2023
Mental rotation, the cognitive process of moving an object in mind to predict how it looks in a new orientation, is coupled to intelligence, learning, and educational achievement. On average, adolescent and adult males solve mental rotation tasks slightly better (i.e., faster and/or more accurate) than females. When such behavioral differences emerge during development, however, remains poorly understood. Here we analyzed effect sizes derived from 62 experiments conducted in 1705 infants aged 3–16 months. We found that male infants recognized rotated objects slightly more reliably than female infants. This difference survives correction for small degrees of publication bias. These findings indicate that gender differences in mental rotation are small and not robustly detectable in the first months of postnatal life
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