36 research outputs found
Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream
Humans recognize visually-presented objects rapidly and accurately. To understand this ability, we seek to construct models of the ventral stream, the series of cortical areas thought to subserve object recognition. One tool to assess the quality of a model of the ventral stream is the Representational Dissimilarity Matrix (RDM), which uses a set of visual stimuli and measures the distances produced in either the brain (i.e. fMRI voxel responses, neural firing rates) or in models (fea-ures). Previous work has shown that all known models of the ventral stream fail to capture the RDM pattern observed in either IT cortex, the highest ventral area, or in the human ventral stream. In this work, we construct models of the ventral stream using a novel optimization procedure for category-level object recognition problems, and produce RDMs resembling both macaque IT and human ventral stream. The model, while novel in the optimization procedure, further develops a long-standing functional hypothesis that the ventral visual stream is a hierarchically arranged series of processing stages optimized for visual object recognition
Measuring and Modeling Physical Intrinsic Motivation
Humans are interactive agents driven to seek out situations with interesting
physical dynamics. Here we formalize the functional form of physical intrinsic
motivation. We first collect ratings of how interesting humans find a variety
of physics scenarios. We then model human interestingness responses by
implementing various hypotheses of intrinsic motivation including models that
rely on simple scene features to models that depend on forward physics
prediction. We find that the single best predictor of human responses is
adversarial reward, a model derived from physical prediction loss. We also find
that simple scene feature models do not generalize their prediction of human
responses across all scenarios. Finally, linearly combining the adversarial
model with the number of collisions in a scene leads to the greatest
improvement in predictivity of human responses, suggesting humans are driven
towards scenarios that result in high information gain and physical activity.Comment: 6 pages, 5 figures, accepted to CogSci 2023 with full paper
publication in the proceeding
Developmental Curiosity and Social Interaction in Virtual Agents
Infants explore their complex physical and social environment in an organized
way. To gain insight into what intrinsic motivations may help structure this
exploration, we create a virtual infant agent and place it in a
developmentally-inspired 3D environment with no external rewards. The
environment has a virtual caregiver agent with the capability to interact
contingently with the infant agent in ways that resemble play. We test
intrinsic reward functions that are similar to motivations that have been
proposed to drive exploration in humans: surprise, uncertainty, novelty, and
learning progress. These generic reward functions lead the infant agent to
explore its environment and discover the contingencies that are embedded into
the caregiver agent. The reward functions that are proxies for novelty and
uncertainty are the most successful in generating diverse experiences and
activating the environment contingencies. We also find that learning a world
model in the presence of an attentive caregiver helps the infant agent learn
how to predict scenarios with challenging social and physical dynamics. Taken
together, our findings provide insight into how curiosity-like intrinsic
rewards and contingent social interaction lead to dynamic social behavior and
the creation of a robust predictive world model.Comment: 6 pages, 5 figures, 2 tables; accepted to CogSci 2023 with full paper
publication in the proceeding