An official website of the United States government
Goal‐directed behavior is a deeply important part of human psychology. People constantly set goals for themselves and pursue them in many domains of life. In this paper, we develop computational models that characterize how humans pursue goals in a complex dynamic environment and test how well they describe human behavior in an experiment. Our models are motivated by the principle of resource rationality and draw upon psychological insights about people's limited attention and planning capacities. We find that human goal pursuit is qualitatively different and substantially less efficient than optimal goal pursuit in our simulated environment. Models of goal pursuit based on the principle of resource rationality capture human behavior better than both a model of optimal goal pursuit and heuristics that are not resource‐rational. We conclude that the way humans pursue goals is shaped by the need to achieve goals effectively as well as cognitive costs and constraints on planning and attention. Our findings are an important step toward understanding humans' goal pursuit as cognitive limitations play a crucial role in shaping people's goal‐directed behavior.
more »
« less
Are All Steps Equally Important? Benchmarking Essentiality Detection in Event Processes
Natural language often describes events in different granularities, such that more coarse-grained (goal) events can often be decomposed into fine-grained sequences of (step) events. A critical but overlooked challenge in understanding an event process lies in the fact that the step events are not equally important to the central goal. In this paper, we seek to fill this gap by studying how well current models can understand the essentiality of different step events towards a goal event. As discussed by cognitive studies, such an ability enables the machine to mimic human’s commonsense reasoning about preconditions and necessary efforts of daily-life tasks. Our work contributes with a high-quality corpus of (goal, step) pairs from a community guideline website WikiHow, where the steps are manually annotated with their essentiality w.r.t. the goal. The high IAA indicates that humans have a consistent understanding of the events. Despite evaluating various statistical and massive pre-trained NLU models, we observe that existing SOTA models all perform drastically behind humans, indicating the need for future investigation of this crucial yet challenging task.
more »
« less
- Award ID(s):
- 2105329
- PAR ID:
- 10482430
- Publisher / Repository:
- Association for Computational Linguistics
- Date Published:
- Journal Name:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Page Range / eLocation ID:
- 4048 to 4056
- Format(s):
- Medium: X
- Location:
- Singapore
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Automated event detection from news corpora is a crucial task towards mining fast-evolving structured knowledge. As real-world events have different granularities, from the top-level themes to key events and then to event mentions corresponding to concrete actions, there are generally two lines of research: (1) theme detection tries to identify from a news corpus major themes (e.g., “2019 Hong Kong Protests” versus “2020 U.S. Presidential Election”) which have very distinct semantics; and (2) action extraction aims to extract from a single document mention-level actions (e.g., “the police hit the left arm of the protester”) that are often too fine-grained for comprehending the real-world event. In this paper, we propose a new task, key event detection at the intermediate level, which aims to detect from a news corpus key events (e.g., HK Airport Protest on Aug. 12-14), each happening at a particular time/location and focusing on the same topic. This task can bridge event understanding and structuring and is inherently challenging because of (1) the thematic and temporal closeness of different key events and (2) the scarcity of labeled data due to the fast-evolving nature of news articles. To address these challenges, we develop an unsupervised key event detection framework, EvMine, that (1) extracts temporally frequent peak phrases using a novel ttf-itf score, (2) merges peak phrases into event-indicative feature sets by detecting communities from our designed peak phrase graph that captures document cooccurrences, semantic similarities, and temporal closeness signals, and (3) iteratively retrieves documents related to each key event by training a classifier with automatically generated pseudo labels from the event-indicative feature sets and refining the detected key events using the retrieved documents in each iteration. Extensive experiments and case studies show EvMine outperforms all the baseline methods and its ablations on two real-world news corpora.more » « less
-
The emergence of Large Vision-Language Models (LVLMs) has significantly advanced video understanding capabilities. However, existing benchmarks focus predominantly on coarse-grained tasks such as action segmentation, classification, captioning, and retrieval. Furthermore, these benchmarks often rely on entities that can be easily identified verbally, like household objects, animals, human subjects, etc., limiting their applicability to complex, in-the-wild video scenarios. But, many applications such as furniture assembly, cooking, etc., require step-by-step fine-grained spatio-temporal understanding of the video, which is not sufficiently evaluated in current benchmarks.To address this gap, we introduce Flat-Pack Bench, a novel benchmark centered on furniture assembly tasks. Our benchmark evaluates LVLMs on nuanced tasks, including temporal ordering of assembly actions, temporal localization of assembly state, understanding part mating, and tracking, using multiple-choice questions paired with visual prompts highlighting relevant parts as references for fine-grained questions. Our experiments reveal that state-of-the-art LVLMs struggle significantly with fine-grained spatio-temporal reasoning, highlighting their limitations in effectively leveraging temporal information from videos, limited tracking ability, and understanding of spatial interactions like physical contact.more » « less
-
Extreme depositional events in ancient river systems are often overlooked due to the assumption that the sedimentary record is overwritten by average conditions. However, where these high-energy events are preserved, their study is critical for reconstructing past climates—without it, we risk underestimating the true magnitude of environmental change possible in future climate scenarios. In the Uinta Basin (Utah, USA), the Green River Formation preserves the sedimentary record of an ancient river system with highly variable discharge. Because “average” flow conditions lacked the energy to significantly alter the landscape, deposits from more extreme flow events are preferentially preserved. Here we analyze supercritical flow structures formed by fast, inertia-driven flows that capture one such event during the early Paleogene warm period. Nested scour features, interpreted as cyclic step deposits, show depths (x̃ = 2.19 m, min = 0.74 m, max = 16.41 m, n = 40) statistically distinct (Kruskal-Wallis test, p = 0.3852) from paleo-channel depths measured in the same interval (x̃ = 2.75 m, min = 0.95 m, max = 21.00 m, n = 22). A continuous train of nearly-to-fully-preserved scour fills overlying partially-preserved, nested basal scour complexes indicates that large-scale cyclic steps formed during and persisted throughout a catastrophic flood, serving as the dominant sediment transport mechanism. Rivers such as these, that are dominated by deposition during extreme events, operate under fundamentally different sediment transport and flow mechanisms than those with more consistent, perennial hydrographs. These variable discharge river systems are underrepresented in current fluvial models and need greater recognition and study to improve our understanding of Earth’s climate history and future hazards.more » « less
-
Extreme depositional events in ancient river systems are often overlooked due to the assumption that the sedimentary record is overwritten by average conditions. However, where these high-energy events are preserved, their study is critical for reconstructing past climates—without it, we risk underestimating the true magnitude of environmental change possible in future climate scenarios. In the Uinta Basin (Utah, USA), the Green River Formation preserves the sedimentary record of an ancient river system with highly variable discharge. Because “average” flow conditions lacked the energy to significantly alter the landscape, deposits from more extreme flow events are preferentially preserved. Here we analyze supercritical flow structures formed by fast, inertia-driven flows that capture one such event during the early Paleogene warm period. Nested scour features, interpreted as cyclic step deposits, show depths (x̃ = 2.19 m, min = 0.74 m, max = 16.41 m, n = 40) statistically distinct (Kruskal-Wallis test, p = 0.3852) from paleo-channel depths measured in the same interval (x̃ = 2.75 m, min = 0.95 m, max = 21.00 m, n = 22). A continuous train of nearly-to-fully-preserved scour fills overlying partially-preserved, nested basal scour complexes indicates that large-scale cyclic steps formed during and persisted throughout a catastrophic flood, serving as the dominant sediment transport mechanism. Rivers such as these, that are dominated by deposition during extreme events, operate under fundamentally different sediment transport and flow mechanisms than those with more consistent, perennial hydrographs. These variable discharge river systems are underrepresented in current fluvial models and need greater recognition and study to improve our understanding of Earth’s climate history and future hazards.more » « less
