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Names for colors vary widely across languages, but color categories are remarkably consistent. Shared mechanisms of color perception help explain consistent partitions of visible light into discrete color vocabularies. But the mappings from colors to words are not identical across languages, which may reflect communicative needs—how often speakers must refer to objects of different color. Here we quantify the communicative needs of colors in 130 different languages by developing an inference algorithm for this problem. We find that communicative needs are not uniform: Some regions of color space exhibit 30-fold greater demand for communication than other regions. The regions of greatest demand correlate with the colors of salient objects, including ripe fruits in primate diets. Our analysis also reveals a hidden diversity in the communicative needs of colors across different languages, which is partly explained by differences in geographic location and the local biogeography of linguistic communities. Accounting for language-specific, nonuniform communicative needs improves predictions for how a language maps colors to words, and how these mappings vary across languages. Our account closes an important gap in the compression theory of color naming, while opening directions to study cross-cultural variation in the need to communicate different colors and its impact on the cultural evolution of color categories.
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What do Biomedical Engineering Faculty Talk About When They Talk About Ethics?
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We examined how people perceive and talk about miscommunication. Participants in study one recalled a miscommunication incident, and then responded to a set of questions regarding their perceptions of the incident. These miscommunications were viewed as relatively unserious, largely the fault of the sender, humorous, confusing and frustrating. Most (76.8%) of the time both interactants were aware of the miscommunication. In a second study we harvested all tweets containing the word “miscommunication” and compared them with tweets containing the word “communication”. Tweets about miscommunication were higher in negative emotionality and certain types of cognitive processing. Hence, the occurrence of miscommunication elicits levels of negative emotions and higher levels of cognition which we interpret as users attempting to make sense of the miscommunication.more » « less
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Endpoint threat detection research hinges on the availability of worthwhile evaluation benchmarks, but experimenters' understanding of the contents of benchmark datasets is often limited. Typically, attention is only paid to the realism of attack behaviors, which comprises only a small percentage of the audit logs in the dataset, while other characteristics of the data are inscrutable and unknown. We propose a new set of questions for what to talk about when we talk about logs (i.e., datasets): What activities are in the dataset? We introduce a novel visualization that succinctly represents the totality of 100+ GB datasets by plotting the occurrence of provenance graph neighborhoods in a time series. How synthetic is the background activity? We perform autocorrelation analysis of provenance neighborhoods in the training split to identify process behaviors that occur at predictable intervals in the test split. Finally, How conspicuous is the malicious activity? We quantify the proportion of attack behaviors that are observed as benign neighborhoods in the training split as compared to previously-unseen attack neighborhoods. We then validate these questions by profiling the classification performance of state-of-the-art intrusion detection systems (R-CAID, FLASH, KAIROS, GNN) against a battery of public benchmark datasets (DARPA Transparent Computing and OpTC, ATLAS, ATLASv2). We demonstrate that synthetic background activities dramatically inflate True Negative Rates, while conspicuous malicious activities artificially boost True Positive Rates. Further, by explicitly controlling for these factors, we provide a more holistic picture of classifier performance. This work will elevate the dialogue surrounding threat detection datasets and will increase the rigor of threat detection experiments.more » « less
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Using mixed-method social network analysis, we explored the discussions happening between instructors within a teaching-related network and how instructional expertise correlated with the content of those discussions. Instructional expertise, defined by the extent to which effective teaching practices were implemented, was measured for 82 faculty teaching at a Midwestern research university in the USA using the Faculty Inventory of Methods and Practices Associated with Competent Teaching (F-IMPACT). Eight instructors from this population were interviewed after being selected from a stratified random sample having varied disciplines, positions, years of teaching experience, number of network alters, and quartile F-IMPACT score. Network Canvas was used to design, capture, and export network data during the interview process, and a deductive qualitative analysis approach was used for coding and analysis. In general, expert instructors had larger networks that also consisted of expert alters and greater frequency of discussions throughout the semester (both formal and informal) and participated in discussions centered around best practices and education research. Inexpert instructors had smaller teaching networks that consisted of other inexpert instructors, lower frequency of interactions, and had discussions that centered around sharing course-specific, surface-level advice.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/FIE58773.2023.10342994
