Reasoning across continuous landscapes: A nonlinear dynamical systems theory approach to reasoning

Dual-processing theories of reasoning have gained renewed attention in recent years, particularly in the fields of decision-making under uncertainty, learning, and social judgment. Although the various accounts differ, the common thread is the distinction between two qualitatively different reasoning processes, such as automatic/controlled, fast/slow, and unconscious/conscious. Accordingly, much research is focused on elucidating the nature of the two processes in terms of the kinds of information they process and respond to. Less extensive are attempts to identify mediators that underlie changes between the two reasoning strategies. We argue that nonlinear dynamical systems theory may be able to provide a fresh perspective on reasoning. Nonlinear dynamical systems theory allows us to shift the perspective to the dynamic interactions and transitions among continuous yet qualitatively different reasoning processes. We apply the approach to reasoning in decision-making and judgment under uncertainty. Our primary claim is that using different reasoning strategies is better understood as phase transitions among a landscape of continuous reasoning capacities.     

Effects of task constraint on action dynamics

The actualization of action possibilities (i.e., affordances) can often be accomplished in numerous ways. For instance, an individual could walk over to a rubbish bin to drop an item in or throw the piece of rubbish into the bin from some distance away. The aim of the current study was to investigate the action dynamics that emerge from such under-constrained task or action spaces using an object transportation task. Participants were instructed to transport balls between a starting location and a large wooden box located 9 m away. The temporal interval between the sequential presentation of balls was manipulated as a control parameter and was expected to influence the distance participants moved prior to throwing or dropping the ball into the target box. A two-parameter state space derived from the Cusp Catastrophe Model was employed to illustrate how behavioral variability emerged as a consequence of the under-constrained task context. Two follow-up experiments demonstrated direct correspondence between model predictions and observed action dynamics as a function of increasing task constraints. Implications for modeling, the theory of affordances, and empirical studies more generally are discussed.     

Team adaptability and task cohesion as resources to the non-linear dynamics of workload and sickness absenteeism in firefighter teams

ABSTRACT

The current study builds on the non-linear Dynamic Systems (NDS) perspective to test the assumption that change in sickness absenteeism is non-linear, and that such change is due to workload, team adaptability and task cohesion. Participants were 37 firefighter teams (n = 250 individuals) from a main European capital city. The research hypotheses were tested using SPSS and the “cusp” package, in the statistical software R. The results suggest that change in sickness absenteeism behaviours over time is non-linear, with the cusp catastrophe model predicting such behaviours better than the linear and logistic models. In our model, task cohesion functions as an asymmetry factor (i.e., the independent variable that determines the strength and discrepancy between the two stable states of the dependent variable) leading to a linear change in sickness absenteeism. Interestingly, both workload and team adaptability function as bifurcation (i.e., the independent variable that determines the change between the two stable states of the order parameter) and asymmetry factors leading to non-linear and linear change in sickness absenteeism over time. This study contributes to the growing evidence that incorporating the NDS perspective enables a better understanding of action teams, namely those working in extreme environments.     

An introduction to the Priority‐Pointing Procedure

A qualitative research‐based diagnostic procedure, the Priority‐Pointing Procedure (PPP), is proposed that synthesizes responses to open‐ended questions about the direction an organization should take. It points to a priority for action by measuring imbalances in the numbers of responses in the context of the structure of adjustment decision‐making from nomology. It is shown to be useful for defining variables and structuring criteria for strategic Multi‐Criteria Decision Analysis (MCDA) problems. Copyright © 2000 John Wiley & Sons, Ltd.     

An effective metacognitive strategy: learning by doing and explaining with a computer‐based Cognitive Tutor

Recent studies have shown that self‐explanation is an effective metacognitive strategy, but how can it be leveraged to improve students' learning in actual classrooms? How do instructional treatments that emphasizes self‐explanation affect students' learning, as compared to other instructional treatments? We investigated whether self‐explanation can be scaffolded effectively in a classroom environment using a Cognitive Tutor, which is intelligent instructional software that supports guided learning by doing. In two classroom experiments, we found that students who explained their steps during problem‐solving practice with a Cognitive Tutor learned with greater understanding compared to students who did not explain steps. The explainers better explained their solutions steps and were more successful on transfer problems. We interpret these results as follows: By engaging in explanation, students acquired better‐integrated visual and verbal declarative knowledge and acquired less shallow procedural knowledge. The research demonstrates that the benefits of self‐explanation can be achieved in a relatively simple computer‐based approach that scales well for classroom use.