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The relationship between size and performance of collaborative human small groups has been studied broadly across management, psychology, economics, sociology, and engineering disciplines. However, empirical research findings on this question remain equivocal. Many of the earlier studies centered on empirical human-subject experiments, which inevitably involved many confounding factors. To obtain more theory-driven mechanistic explanations of the linkage between group size and performance, we developed an agent-based simulation model that describes the complex process of collaborative group decision-making on problem-solving tasks. To find better solutions to a problem with given complexity, these agents repeatedly explore and share solution candidates, evaluate and respond to the solutions proposed by others, and update their understanding of the problem by conducting individual local search and incorporating others’ proposals. Our results showed that under a condition of ineffective information sharing, group size was negatively related to group performance at the beginning of discussion across each level of problem complexity (i.e., low, medium, and high). However, in the long run, larger groups outperformed smaller groups for the problem with medium complexity and equally well for the problem with low complexity because larger groups developed higher solution diversity. For the problem with high complexity, the higher solution diversity led to more disagreements which in turn hindered larger groups’ collaborative problem-solving ability. Our results also suggested that, in small collaborative team settings, effective information sharing can significantly improve group performance for groups of any size, especially for larger groups. This model provides a unified, mechanistic explanation of the conflicting observations reported in the existing empirical literature.

Effective teamwork in an initially leaderless group requires a high level of collective leadership emerging from dynamic interactions among group members. Leader emergence is a crucial topic in collective leadership, yet it is challenging to investigate as the problem context is typically highly complex and dynamic. Here, we explore leadership emergence and leadership perception by means of computational simulations whose assumptions and parameters were informed by empirical research and human-subject experiments. Our agent-based model describes the process of group planning. Each agent is assigned with three key attributes: talkativeness, intelligence, and credibility. An agent can propose a suggestion to modify the group plan as a speaker or respond and evaluate others’ suggestions and leadership as a listener. Simulation results suggested that agents with high values of talkativeness, intelligence, and credibility tended to be perceived as leaders by their peers. Results also showed that talkativeness may be the most significant and instantaneous predictor for leader emergence of the three investigated attributes: talkativeness, intelligence, and credibility. In terms of group performance, smaller groups may outperform larger groups regarding their problem-solving ability in the beginning, but their performance tends to be of no significant difference in a long run. These results match the empirical literature and offer a mechanistic, operationalized description of the collective leadership processes.

It has long been claimed that certain configurations of facial movements are universally recognized as emotional expressions because they evolved to signal emotional information in situations that posed fitness challenges for our hunting and gathering hominin ancestors. Experiments from the last decade have called this particular evolutionary hypothesis into doubt by studying emotion perception in a wider sample of small-scale societies with discovery-based research methods. We replicate these newer findings in the Hadza of Northern Tanzania; the Hadza are semi-nomadic hunters and gatherers who live in tight-knit social units and collect wild foods for a large portion of their diet, making them a particularly relevant population for testing evolutionary hypotheses about emotion. Across two studies, we found little evidence of universal emotion perception. Rather, our findings are consistent with the hypothesis that people infer emotional meaning in facial movements using emotion knowledge embrained by cultural learning.

Utterance clustering is one of the actively researched topics in audio signal processing and machine learning. This study aims to improve the performance of utterance clustering by processing multichannel (stereo) audio signals. Processed audio signals were generated by combining left- and right-channel audio signals in a few different ways and then by extracting the embedded features (also called d-vectors) from those processed audio signals. This study applied the Gaussian mixture model for supervised utterance clustering. In the training phase, a parameter-sharing Gaussian mixture model was obtained to train the model for each speaker. In the testing phase, the speaker with the maximum likelihood was selected as the detected speaker. Results of experiments with real audio recordings of multiperson discussion sessions showed that the proposed method that used multichannel audio signals achieved significantly better performance than a conventional method with mono-audio signals in more complicated conditions.

A study to determine the feasibility of producing forage for grazing livestock under trees was conducted as a step toward evaluating the potential for silvopasture systems in the northern and central Great Plains. The effects of overstory leaf area index (LAI), percentage understory light transmittance (LT), and soil moisture (SM) on yield and crude protein (CP) of big bluestem [Andropogon gerardii Vitman; (BB)], smooth bromegrass [Bromus inermis Leyss.; (SB)], and mixtures with birdsfoot trefoil [Lotus corniculatus L.; (BFT)] were examined. The study was conducted in both Scotch pine (Pinus sylvestris L.) and green ash (Fraxinus pennsylvancia Marsh.) tree plantations, at the University of Nebraska Agriculture Research and Development Center near Mead, Nebraska. Thirty-six plots representing a wide range of canopy cover were selected at each location and seeded in April 2000 to BB, SB, or mixtures with BFT. Measurements of LAI, LT, and SM were taken throughout the 2001-growing season and plots were harvested in June and September 2001. Soil moisture generally did not explain much of the variability in yield or CP for BB, SB, or BFT. Cumulative LAI or LT averaged over the growing season was the best predictor of yield or CP, particularly under the pine. Yields of BB and SB increased as LAI decreased or LT increased. Conversely, the CP of BB and SB increased as LT decreased for both the June and September harvests. Both BB and SB maintain relatively high productivity under partial shading; however, BFT yields were low at LT levels below 75%.

The current research reports findings from 2 studies assessing the relationship of emotion type, prior leader interaction, and media richness on a leader's emotions to influence subordinate perceptions and performance. Study 1 looked at emotion type (basic vs. blended) and nature of prior leader interaction (positive vs. negative vs. neutral) on perception of negative emotions and message comprehension of negative information conveyed in a leader's e-mail. Results revealed that emotion type and prior interaction interact to influence the perception of positive emotions, where a negative prior interaction followed by negative blended emotions resulted in the most accurate perceptions. Additionally, leader blended emotions increased comprehension for emotionally laden content. Study 2 looked at the effects of emotion type and media richness (rich/video vs. lean/e-mail) on perception of positive and negative emotions, comprehension of positive and negative information, and performance on a creative task. Results showed that emotion type influenced emotion perception, where basic emotions resulted in better accuracy for negative emotions while blended emotions resulted in better accuracy for positive emotions. Also, media richness did not impact perception of positive emotions but did for negative emotions with richer communication channel resulting in better accuracy. For message comprehension, emotion type and media interact to impact understanding for negative content, where highest comprehension was seen when message included blended negative emotions and delivered through a leaner medium (e.g., e-mail). Finally, hierarchical regressions provided initial evidence that emotion type, specifically blended emotions, positively contributes to performance quality. Implications for theory and practice are explored.

Utterance clustering is one of the actively researched topics in audio signal processing and machine learning. This study aims to improve the performance of utterance clustering by processing multichannel (stereo) audio signals. Processed audio signals were generated by combining left- and right-channel audio signals in a few different ways and then extracted embedded features (also called d-vectors) from those processed audio signals. This study applied the Gaussian mixture model for supervised utterance clustering. In the training phase, a parameter sharing Gaussian mixture model was conducted to train the model for each speaker. In the testing phase, the speaker with the maximum likelihood was selected as the detected speaker. Results of experiments with real audio recordings of multi-person discussion sessions showed that the proposed method that used multichannel audio signals achieved significantly better performance than a conventional method with mono audio signals in more complicated conditions.

The accumulation of soil organic matter (SOM) is vital to the agronomic and environmental functioning of agroecosystems, yet the relative influence of inherent soil properties and agricultural management practices on SOM dynamics are not often addressed in individual studies. Using a network of 218 operating farm fields across Wisconsin and southern Minnesota, USA, this research employs single variable analysis (ANOVA and regression) and regression tree analysis to assess the effects of soil properties (texture, drainage class, and pH) and management variables related to crop rotation, tillage, cover cropping, and manure application on SOM, as well as total organic carbon (TOC) and total nitrogen (TN) in the upper 15 cm. Single variable analysis revealed that greater SOM, TOC, and TN were associated with poorly drained soil, tile‐drained fields, high clay content soil, and high biomass crop rotations. SOM and TOC were strongly related (R2 = 0.71), but different regression trees were produced; SOM was most influenced by clay content, while TOC was most influenced by drainage class. Future assessment for the building of SOM or TOC should be conducted with drainage and texture class categories and on a regional basis, given that these factors influence the practices that occur within landscapes. A rapid building of datasets through unstructured sampling, including an abundance of metadata, should be a research priority in agricultural science to identify practices to build SOM on a regional basis.