An improved probabilistic account of counterfactual reasoning

When people want to identify the causes of an event, assign credit or blame, or learn from their mistakes, they often reflect on how things could have gone differently. In this kind of reasoning, one considers a counterfactual world in which some events are different from their real-world counterparts and considers what else would have changed. Researchers have recently proposed several probabilistic models that aim to capture how people do (or should) reason about counterfactuals. We present a new model and show that it accounts better for human inferences than several alternative models. Our model builds on the work of Pearl (2000), and extends his approach in a way that accommodates backtracking inferences and that acknowledges the difference between counterfactual interventions and counterfactual observations. We present six new experiments and analyze data from four experiments carried out by Rips (2010), and the results suggest that the new model provides an accurate account of both mean human judgments and the judgments of individuals

Non-Compositionality in Sentiment: New Data and Analyses

When natural language phrases are combined, their meaning is often more than the sum of their parts. In the context of NLP tasks such as sentiment analysis, where the meaning of a phrase is its sentiment, that still applies. Many NLP studies on sentiment analysis, however, focus on the fact that sentiment computations are largely compositional. We, instead, set out to obtain non-compositionality ratings for phrases with respect to their sentiment. Our contributions are as follows: a) a methodology for obtaining those non-compositionality ratings, b) a resource of ratings for 259 phrases -- NonCompSST -- along with an analysis of that resource, and c) an evaluation of computational models for sentiment analysis using this new resource.Comment: Published in EMNLP Findings 2023; 13 pages total (5 in the main paper, 3 pages with limitations, acknowledgments and references, 5 pages with appendices

A short response-time atomic source for trapped ion experiments

Ion traps are often loaded from atomic beams produced by resistively heated ovens. We demonstrate an atomic oven which has been designed for fast control of the atomic flux density and reproducible construction. We study the limiting time constants of the system and, in tests with $^{40}\textrm{Ca}$, show we can reach the desired level of flux in 12s, with no overshoot. Our results indicate that it may be possible to achieve an even faster response by applying an appropriate one-off heat treatment to the oven before it is used.Comment: 5 pages, 7 figure

Patent semantics : analysis, search and visualization of large text corpora

Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (leaves 47-48).Patent Semantics is system for processing text documents by extracting features capturing their semantic content, and searching, clustering, and relating them by those same features. It is set apart from existing methodologies by combining a visualization scheme that integrates retrieval and clustering, providing a variety of ways to find and relate documents depending on their goals. In addition, the system provides an explanatory mechanism that makes the retrieval an understandable process rather than a black box. The domain in which the system currently works is biochemistry and molecular biology patents but it is not intrinsically constrained to any document set.by Christopher G. Lucas.M.Eng.and S.B

Flower Detection Using Object Analysis: New Ways to Quantify Plant Phenology in a Warming Tundra Biome

Rising temperatures caused by global warming are affecting the distributions of many plant and animal species across the world. This can lead to structural changes in entire ecosystems, and serious, persistent environmental consequences. However, many of these changes occur in vast and poorly accessible biomes and involve myriad species. As a consequence, conventional methods of measurement and data analysis are resource-intensive, restricted in scope, and in some cases, intractable for measuring species changes in remote areas. In this article, we introduce a method for detecting flowers of tundra plant species in large data sets obtained by aerial drones, making it possible to understand ecological change at scale, in remote areas. We focus on the sedge species E. vaginatum that is dominant at the investigated tundra field site in the Canadian Arctic. Our system is a modified version of the Faster R-CNN architecture capable of real-world plant phenology analysis. Our model outperforms experienced human annotators in both detection and counting, recording much higher recall and comparable level of precision, regardless of the image quality caused by varying weather conditions during the data collection. (K. Stanski, GitHub - karoleks4/flower-detection: Flower detection using object analysis: New ways to quantify plant phenology in a warming tundra biome. GitHub. Accessed: Sep. 17, 2021. [Online]. Available: https://github.com/karoleks4/flower-detection.

Dissecting causal asymmetries in inductive generalization

Suppose we observe something happen in an interaction be- tween two objects A and B. Can we then predict what will hap- pen in an interaction between A and C, or between B and C? Recent research, inspired by work on the “causal asymmetry”, suggests that people use cues to causal agency to guide object- based generalization decisions, even in relatively abstract set- tings. When object A possesses cues to causal agency (e.g. it moves, remains stable throughout the interaction), people tend to predict that what happened will probably also occur in an interaction between A and C, but not between B and C. Here we replicate and extend this work, with the goal of identify- ing the cues that people use to determine that an object is a causal agent. In four experiments, we manipulate three prop- erties of the agent and recipient objects. We find that people anchor their inductive generalizations around the agent object when that object possesses all three cues to causal agency, but removing either cue abolishes the asymmetry

A rational model of spatial neglect

Spatial neglect has been a phenomenon of interest for perceptual and neuropsychological researchers for decades. However, the underlying cognitive processes remain unclear. We provide a Bayesian framework for the classic line bisection task in spatial neglect, regarding it as rational inferences in the face of uncertain information. A Bayesian observer perceives the left and right endpoints of a line with uncertainty, and leverages prior expectations about line lengths to compensate for this uncertainty. This Bayesian model provides a basis for characterizing different patterns of behavior. Our model also captures the paradoxical cross-over effect observed in earlier studies as a natural outcome when uncertainty is high and the observer falls back on priors. It provides measures that correlate well with measures from other neglect tests, and can accurately distinguish stroke patients from healthy controls. It has the potential to facilitate spatial neglect studies and inform clinical decisions