Discerning Mouse Trajectory Features With the Drift Diffusion Model

Mouse tracking, a new action-based measure of behavior, has advanced theories of decision making with the notion that cognitive and social decision making is fundamentally dynamic. Implicit in this theory is that people's decision strategies, such as discounting delayed rewards, are stable over task design and that mouse trajectory features correspond to specific segments of decision making. By applying the hierarchical drift diffusion model and the Bayesian delay discounting model, we tested these assumptions. Specifically, we investigated the extent to which the “mouse-tracking” design of decision-making tasks (delay discounting task, DDT and stop-signal task, SST) deviate from the standard “keypress” design of decision making tasks. We found remarkable agreement in delay discounting rates (intertemporal impatience) obtained in the keypress and mouse-tracking versions of DDT (ρ = 0.90) even though these tasks were given about 1 week apart. Rates of evidence accumulation converged well in the two versions (DDT, ρ = .86; SST, ρ = .55). Omission/commission error in SST showed high agreement (ρ = .42, ρ = .53). Mouse-motion features such as maximum velocity and AUC (area under the curve) correlated well with nondecision time (ρ = −.42) and boundary separation (ρ = .44)—the amount of information needed to accumulate prior to making a response. These results indicate that the response time (RT) and motion-based decision tasks converge well at a fundamental level, and that mouse-tracking features such as AUC and maximum velocity do indicate the degree of decision conflict and impulsivity.     

Choosing what to choose from: Preference for inclusion over exclusion when constructing consideration sets from large choice sets

Decision making is a two‐stage process, consisting of, first, consideration set construction and then final choice. Decision makers can form a consideration set from a choice set using one of two strategies: including the options they wish to further consider or excluding those they do not wish to further consider. The authors propose that decision makers have a relative preference for an inclusion (vs. exclusion) strategy when choosing from large choice sets and that this preference is driven primarily by a lay belief that inclusion requires less effort than exclusion, particularly in large choice sets. Study 1 demonstrates that decision makers prefer using an inclusion (vs. exclusion) strategy when faced with large choice sets. Study 2 replicates the effect of choice set size on preference for consideration set construction strategy and demonstrates that the belief that exclusion is more effortful mediates the relative preference for inclusion in large choice sets. Studies 3 and 4 further support the importance of perceived effort, demonstrating a greater preference for inclusion in large choice sets when decision makers are primed to think about effort (vs. accuracy; Study 3) and when the choice set is perceived as requiring more effort because of more information being presented about each alternative (vs. more alternatives in the choice set; Study 4). Finally, Study 5 manipulates consideration set construction strategy, showing that using inclusion (vs. exclusion) in large choice sets leads to smaller consideration sets, greater confidence in the decision process, and a higher quality consideration set.     

What could be My Next Job? Using Flat Information Structures to Generate Creative Future Career Ideas

In a dynamic labor market, it is important to help people combine information and generate creative solutions to cope with complex career challenges. In the present research, we apply the theory of information structure to creative career idea generation and hypothesize that flat information structures—that is, structures in which the information is disorganized—are more conducive to creativity than hierarchical information structures—that is, structures in which information is organized in higher‐order categories. In two experimental studies, participants had to combine career information related to self and work preferences that was presented either in flat or hierarchical structures. We found that flat information structures, compared with hierarchical information structures, led to future career ideas that were more creative on average. Our results suggest that cognitive flexibility explains the effect of information structure on the creativity of career ideas. Theoretical implications and suggestions for career management practices are discussed.     

Learning algorithm for an intelligent decision making system based on multi-agent neurocognitive architectures

The paper presents the formalism of an intelligent decision-making system based on multi-agent neurocognitive architectures, which has an architectural similarity to the human brain. An invariant of the organizational and functional structure of the intellectual decision-making process based on the multi-agent neurocognitive architecture is developed. An algorithm for teaching intelligent decision-making systems based on the self-organization of the invariant of multi-agent neurocognitive architectures is presented. Using this algorithm, an intelligent agent was trained and the architecture of the learning process was built on the basis of an invariant of neurocognitive architecture. Further research is related to training an intelligent agent in more complex behavior and expanding the capabilities of an intelligent decision-making system based on multi-agent neurocognitive architectures.     

问题性网络游戏使用青少年未来取向的助推研究

摘要：以问题性网络游戏使用青少年为研究对象,基于锚定效应助推国民身心健康的理论前提和态度改变理论视角,通过实验1和实验2分别建立未来取向内部锚和未来取向外部锚,检验未来取向内部锚和外部锚对减少问题性网络游戏使用青少年游戏时间的有效性。结果表明,未来取向内部和外部的助推方式,均能够有效促进问题性网络游戏使用青少年游戏时间的减少,提高了决策效用。     

Generating interior search directions for multiobjective linear programming

A new multiobjective linear programming (MOLP) algorithm is presented. The algorithm uses a variant of Karmarkar's interior-point algorithm known as the affine-scaling primal algorithm. Using this single-objective algorithm, interior search directions are generated and used to provide an approximation to the gradient of the (implicitly known) utility function. The approximation is guided by assessing locally relevant preference information for the various interior directions through interaction with a decision maker (DM). The resulting algorithm is an interactive approach that makes its progress towards the solution through the interior of the constraints polytope.     

Decomposition of multivariate utility functions in non-additive expected utility theory

In expected utility many results have been derived that give necessary and/or sufficient conditions for a multivariate utility function to be decomposable into lower-dimensional functions. In particular, multilinear, multiplicative and additive decompositions have been widely discussed. These utility functions can be more easily assessed in practical situations. In this paper we present a theory of decomposition in the context of nonadditive expected utility such as anticipated utility or Choquet expected utility. We show that many of the results used in conventional expected utility carry over to these more general frameworks. If preferences over lotteries depend only on the marginal probability distributions, then in expected utility the utility function is additively decomposable. We show that in anticipated utility the marginality condition implies not only that the utility function is additively decomposable but also that the distortion function is the identity function. We further demonstrate that a decision maker who is bivariate risk neutral has a utility function that is additively decomposable and a distortion function q for which q(½) = ½.     

Dynamic multi-criteria decision making

Most medium-and long-term decision making in industry and government can be viewed as dynamic multi-criteria decision making (MCDM), in which the decision makers are free to alter the emphasis placed on each objective in the light of developing circumstances. In this paper the problem of time-dependent weights in MCDM is discussed and an analysis of empirical data associated with dynamic decision making is presented.     

Pictures and repeated exposure increase perceived accuracy of news headlines

Fake news, deliberately inaccurate and often biased information that is presented as accurate reporting, is perceived as a serious threat. Recent research on fake news has documented a high general susceptibility to the phenomenon and has focused on investigating potential explanatory factors. The present study examined how features of news headlines affected their perceived accuracy. Across four experiments (total N = 659), we examined the effects of pictures, perceptual clarity, and repeated exposure on the perceived accuracy of news headlines. In all experiments, participants received a set of true and false news headlines and rated their accuracy. The presence of pictures and repeated exposure increased perceived accuracy, whereas manipulations of perceptual clarity did not show the predicted effects. The effects of pictures and repeated exposure were similar for true and false headlines. These results demonstrate that accompanying pictures and repeated exposure can affect evaluations of truth of news headlines.