Gender, power and emotions in the collaborative production of knowledge: A large-scale analysis of Wikipedia editor conversations

This paper studies the conversations behind the operations of a large-scale, online knowledge production community: Wikipedia. We investigate gender differences in the conversational styles (emotionality) and conversational domain choices (controversiality and gender stereotypicality of content) among contributors, and how these differences change as we look up the organizational hierarchy. In the general population of contributors, we expect and find significant gender differences, whereby comments and statements from women are higher-valenced, have more affective content, and are in domains that are less controversial and more female-typed. Importantly, these differences diminish or disappear among people in positions of power: female authorities converge to the behavior of their male counterparts, such that the gender gaps in valence and willingness to converse on controversial content disappear. We find greater sorting into topics according to their gender stereotypicality. We discuss mechanisms and implications for research on gender differences, leadership behavior, and conversational phenomena arising from such large-scale forms of knowledge production

Vector Space Semantic Models Predict Subjective Probability Judgments for Real-World Events

We examine how people judge the probabilities of real-world events, such as natural disasters in different countries. We find that the associations between the words and phrases that constitute these events, as assessed by vector space semantic models, strongly correlate with the probabilities assigned to these events by participants. Thus, for example, the semantic proximity of “earthquake” and “Japan” accurately predicts judgments regarding the probability of an earthquake in Japan. Our results suggest that the mechanisms and representations at play in language are also active in high- level domains, such as judgment and decision making, and that existing insights regarding these representations can be used to make precise, quantitative, a priori predictions regarding the probability estimates of individuals

Choice rules and accumulator networks

This article presents a preference accumulation model that can be used to implement a number of different multi-attribute heuristic choice rules, including the lexicographic rule, the majority of confirming dimensions (tallying) rule and the equal weights rule. The proposed model differs from existing accumulators in terms of attribute representation: Leakage and competition, typically applied only to preference accumulation, are also assumed to be involved in processing attribute values. This allows the model to perform a range of sophisticated attribute-wise comparisons, including comparisons that compute relative rank. The ability of a preference accumulation model composed of leaky competitive networks to mimic symbolic models of heuristic choice suggests that these 2 approaches are not incompatible, and that a unitary cognitive model of preferential choice, based on insights from both these approaches, may be feasible. (PsycINFO Database Record (c) 2017 APA, all rights reserved

Measurement Of Angular Coefficients Of Drell-Yan E(+) E(-) Pairs In Ppbar Collisions At Square Root Of Center Of Mass Energy Of 1.96 Tev.

In this paper we present the status of the measurement of angular distributions of final state electrons in ppbar → γ*/Z → e+ e− + X events produced in the Z boson mass region at square root of center of mass energy of 1.96 TeV at the Tevatron. For this analysis, we have used the Run IIb2 dataset collected with the DΦ detector. The angular distributions as a function of the transverse momentum of the electron-positron pair are studied, and the Lam-Tung relation, valid only for a spin-1 description of the gluon is investigated. The final result will also describe the details of the production mechanism of Z bosons via quark anti-quark annihilation or quark-gluon Compton scattering

Naturalistic multiattribute choice

We study how people evaluate and aggregate the attributes of naturalistic choice objects, such as movies and food items. Our approach applies theories of object representation in semantic memory research to large-scale crowd-sourced data, to recover multiattribute representations for common choice objects. We then use standard choice experiments to test the predictive power of various decision rules for weighting and aggregating these multiattribute representations. Our experiments yield three novel conclusions: 1. Existing multiattribute decision rules, applied to object representations trained on crowd-sourced data, predict participant choice behavior with a high degree of accuracy; 2. Contrary to prior work on multiattribute choice, weighted additive decision rules outperform heuristic rules in out-of-sample predictions; and 3. The best performing decision rules utilize rich object representations with a large number of underlying attributes. Our results have important implications for the study of multiattribute choice

Association and response accuracy in the wild

We studied contestant accuracy and error in a popular television quiz show, Jeopardy!. Using vector-based knowledge representations obtained from distributional models of semantic memory, we computed the strength of association between clues and responses in over 5,000 televised games. Such representations have been shown to play a key role in memory and judgment, and consistent with this work, we find that contestants are more likely to provide correct responses when these responses are strongly associated with their clues, and more likely to provide incorrect responses when correct responses are weakly or negatively associated with their clues. This effect is stronger for easier questions with low monetary values, and for questions in which contestants compete to respond quickly. Our results show how distributional models of semantic memory can be used to predict human behavior in naturalistic high-level judgment tasks with skilled participants and significant monetary and social incentives

Event construal and temporal distance in natural language

Construal level theory proposes that events that are temporally proximate are represented more concretely than events that are temporally distant. We tested this prediction using two large natural language text corpora. In study 1 we examined posts on Twitter that referenced the future, and found that tweets mentioning temporally proximate dates used more concrete words than those mentioning distant dates. In study 2 we obtained all New York Times articles that referenced U.S. presidential elections between 1987 and 2007. We found that the concreteness of the words in these articles increased with the temporal proximity to their corresponding election. Additionally the reduction in concreteness after the election was much greater than the increase in concreteness leading up to the election, though both changes in concreteness were well described by an exponential function. We replicated this finding with New York Times articles referencing US public holidays. Overall, our results provide strong support for the predictions of construal level theory, and additionally illustrate how large natural language datasets can be used to inform psychological theory

What I Like Is What I Remember: Memory Modulation and Preferential Choice

Memory is a crucial component of everyday decision making, yet little is known about how memory and choice processesinteract, and whether or not established memory regularities persist during memory-based decision making. In this paper,we introduce a novel experimental paradigm to study the differences between memory processes at play in standard listrecall versus in preferential choice. Using computational memory models, fit to data from two pre-registered experiments,we find that some established memory regularities (primacy, recency, semantic clustering) emerge in preferential choice,whereas others (temporal clustering) are significantly weakened relative to standard list recall. Notably, decision-relevantfeatures, such as item desirability, play a stronger role in guiding retrieval in choice. Our results suggest memory processesdiffer across preferential choice and standard memory tasks, and that choice modulates memory by differentially activatingdecision-relevant features such as what we like

Noisy Parameters in Risky Choice

We examine the effect of variability in model parameters on the predictions of expected utility theory and cumulative prospect theory, two of the most influential choice models in decision making research. We find that zero-mean and symmetrically distributed noise in the underlying parameters of these models can systematically distort choice probabilities, leading to false conclusions. Likewise, differences in choice proportions across decision makers might be due to differences in the amount of noise affecting underlying parameters rather than to differences in actual parameter values. Our results suggest that care and caution are needed when trying to infer the underlying preferences of decision makers, or the effects of psychological, biological, economic, and demographic variables on these preferences

A Notion of Prominence for Games with Natural-Language Labels

We study games with natural-language labels (i.e., strategic problems where options are denoted by words), for which we propose and test a measurable characterization of prominence. We assume that – ceteris paribus – players find particularly prominent those strategies that are denoted by words more frequently used in their everyday language. To operationalize this assumption, we suggest that the prominence of a strategy-label is correlated with its frequency of occurrence in large text corpora, such as the Google Books corpus (“n-gram” frequency). In testing for the strategic use of word frequency, we consider experimental games with different incentive structures (such as incentives to and not to coordinate), as well as subjects from different cultural/linguistic backgrounds. Our data show that frequently-mentioned labels are more (less) likely to be selected when there are incentives to match (mismatch) others. Furthermore, varying one’s knowledge of the others’ country of residence significantly affects one’s reliance on word frequency. Overall, the data show that individuals play strategies that fulfill our characterization of prominence in a (boundedly) rational manner