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To ensure the integrity and reliability of structures, it is of great importance to understand the fatigue damage mechanism of materials under different amplitude loading conditions. For this purpose, a nonlinear fatigue damage accumulation model based on material configurational forces is developed in this paper. Specially, since the material configurational force is defined on the material space point and directly associated with the evolution of material configuration, it is introduced as an internal variable to describe fatigue damage accumulation. Additionally, the corresponding relation between configurational force and stress state is presented. Meanwhile, the damage increasing among every loading cycle, that is, the damage rate, is dependent on the stress state and damage state variable. Therefore, the formulation of damage rate is proposed by a function of material configurational force and damage state variable. Further, numerical implementation of this proposed nonlinear fatigue damage accumulation model has been carried out through two notched structures to describe the fatigue damage evolution and predict the fatigue life of materials. The results indicate that the numerical results are consistent with previous experimental data. Highlights: A nonlinear damage accumulation model under uniaxial fatigue is proposed.The configurational force is introduced as an internal variable to describe the damage.The numerical results of the proposed model show adequate accuracy. [ABSTRACT FROM AUTHOR]

When human adults make decisions (e.g., wearing a seat belt), we often consider the negative consequences that would ensue if our actions were to fail, even if we have never experienced such a failure. Do the same considerations guide our understanding of other people's decisions? In this paper, we investigated whether adults, who have many years of experience making such decisions, and 6‐ and 7‐year‐old children, who have less experience and are demonstrably worse at judging the consequences of their own actions, conceive others' actions as motivated both by reward (how good reaching one's intended goal would be), and by what we call "danger" (how badly one's action could end). In two pre‐registered experiments, we tested whether adults and 6‐ and 7‐year‐old children tailor their predictions and explanations of an agent's action choices to the specific degree of danger and reward entailed by each action. Across four different tasks, we found that children and adults expected others to negatively appraise dangerous situations and minimize the danger of their actions. Children's and adults' judgments varied systematically in accord with both the degree of danger the agent faced and the value the agent placed on the goal state it aimed to achieve. However, children did not calibrate their inferences about how much an agent valued the goal state of a successful action in accord with the degree of danger the action entailed, and adults calibrated these inferences more weakly than inferences concerning the agent's future action choices. These results suggest that from childhood, people use a degree of danger and reward to make quantitative, fine‐grained explanations and predictions about other people's behavior, consistent with computational models on theory of mind that contain continuous representations of other agents' action plans. [ABSTRACT FROM AUTHOR]

Although current exemplar models of category learning are flexible and can capture how different features are emphasized for different categories, they still lack the flexibility to adapt to local changes in category learning, such as the effect of different sequences of study. In this paper, we introduce a new model of category learning, the Sequential Attention Theory Model (SAT-M), in which the encoding of each presented item is influenced not only by its category assignment (global context) as in other exemplar models, but also by how its properties relate to the properties of temporally neighboring items (local context). By fitting SAT-M to data from experiments comparing category learning with different sequences of trials (interleaved vs. blocked), we demonstrate that SAT-M captures the effect of local context and predicts when interleaved or blocked training will result in better testing performance across three different studies. Comparatively, ALCOVE, SUSTAIN, and a version of SAT-M without locally adaptive encoding provided poor fits to the results. Moreover, we evaluated the direct prediction of the model that different sequences of training change what learners encode and determined that the best-fit encoding parameter values match learners' looking times during training. [ABSTRACT FROM AUTHOR]

In two recent papers, Rose and Nichols present evidence in favor of the view that humans represent category essences in terms of a telos, such as honey‐making, and not in terms of scientific essences, such as bee DNA. Here, I challenge their interpretation of the evidence and show that it is directly predicted by the main theory they seek to undermine. I argue that their results can be explained as instances of diagnostic reasoning about scientific essences. [ABSTRACT FROM AUTHOR]

This paper develops a theoretical model of optimal schooling levels where ability and family background are the central explanatory variables. We derive schooling demand and supply functions based on individual wealth maximization. Using the National Longitudinal Survey of Youth 1979 data, we stratify our sample into 1-yr full-time equivalent (FTE) work experience cohorts for 1985–1989. The estimated Mincerian “overtaking” cohort (the years of work experience at which individuals’ observed earnings approximately equal what they would have been based on schooling and ability alone) corresponds to 13 FTE years of experience, yielding on average a rate of return of 10.3% and an average (optimal) 11.4 yr of schooling. ( JEL J24, J31, J22) [ABSTRACT FROM AUTHOR]