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.     

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。     

Confidence and Uncertainty in Judgmental Forecasting: Differential Effects of Scenario Presentation

Multiple scenario analysis is used by many organizations for long-term planning, in part because the development of more than one scenario of the future is thought to prevent overconfidence in any one specific forecast. The present study attempts to clarify some of the questions concerning the cognitive effects of scenario presentation by distinguishing between confidence and subjective uncertainty. It examined the impact of different types of scenario information on confidence ratings, credible intervals, and features of forecast series. Subjects (N=186) read either one, two, or no scenario(s) describing potential changes in the level of a stimulus variable, then made forecasts of that variable over the next fifty years. Reading any type of scenario information increased confidence in forecasts; there was no reduction in confidence due to presentation of multiple conflicting scenarios, compared to presentation of only one scenario. Differences in number and in type of scenarios presented did, however, affect subjective uncertainty.     

On the evaluation of one-sided evidence

We examine predictions and judgments of confidence based on one-sided evidence. Some subjects saw arguments for only one side of a legal dispute while other subjects (called ‘jurors’) saw arguments for both sides. Subjects predicted the number of jurors who favored the plaintiff in each case. Subjects who saw only one side made predictions that were biased in favor of that side. Furthermore, they were more confident but generally less accurate than subjects who saw both sides. The results indicate that people do not compensate sufficiently for missing information even when it is painfully obvious that the information available to them is incomplete. A simple manipulation that required subjects to evaluate the relative strength of the opponent's side greatly reduced the tendency to underweigh missing evidence.     

Selecting a new technology strategy using multiattribute,multilevel decision trees

Research indicates that selecting a strategy to best exploit a new technology is a complex decision-making process. The task involves making a series of decisions with multiple alternatives, each to be evaluated by multiple criteria whose values have high levels of uncertainty. This paper presents a methodology for modelling a new technology decision using decision trees and an optimizing algorithm. A problem of a mining company considering the adoption of new technology is used to illustrate the decision-making task and modelling methodology. A numerical solution to the case demonstrates the potential of the optimizing technique in strategy selection.     

Preference structures and threshold models

Preferences are often represented in terms of a function, in the deterministic case as well as in the probabilistic case. In the present paper we develop a new numerical representation of preference structures for which the strict preference relation (P) is without circuit but not necessarily transitive. Moreover, we investigate the consequences of the representation for the usual preference structures. In particular, we propose new formulations for the numerical representation of the interval order structure.