Jeff Vancouver's Research Lab
A major focus of Vancouver’s lab is the role of self-efficacy and expectancy beliefs in motivation.Expectancy beliefs are beliefs about the contingencies between events (e.g.,performance and outcomes). Self-efficacy beliefs are a specific type of expectancy belief regarding (i.e., the belief in one’s capacity to organize and engage in actions necessary to achieve levels of performance or behaviors),though measures of self-efficacy appear to have their predictive power by including several types of expectancies (i.e., include beliefs about the exigencies in the environment that might impact an individuals ability to carryout the actions necessary for performance). Generally self-efficacy and expectancies are hypothesized to be important positive motivators. However,across several studies (Vancouver…),we have demonstrated that self-efficacy beliefs can negatively affect resource allocation. In particularly, high levels of self-efficacy (and expectancy) are likely to lead to reducing the level of resources applied to a performance relative to low levels of self-efficacy. This is the magnitude element of motivation.However, we have also shown that self-efficacy is likely to positively related to decisions to engage in a task. This is the direction element of motivation.Current research focuses on generalizing the effect across various types of tasks and contexts. We are also examining confounds of self-efficacy/expectancy measures and boundary conditions relating to the effect.
Self-regulation approaches to motivation may hold the promise of the integrative theory of motivation. A significant part of work in Dr. Vancouver’s lab involves integrating elements of motivational theory (e.g., self-efficacy beliefs) into a rigorous self-regulation theory of motivation and human behavior (Vancouver,2008). The rigor is found both in terms of the mathematical nature of the theory, which allows us to create computational models that can simulate behavior over time (i.e., provide precise predictions of how a system builtlike we think it is would behavior when exposed to various states, contexts,and conditions), and in terms of the methods we used to see if these models match the behavior of the systems (i.e., undergraduates mostly) we are attempting to understand. Models have been developed that account for the goal-level effect (Vancouver et al, ; Vancouver), information seeking in organizations (Vancouver), and other phenomenon (APS papers). A great deal of research is still needed to confirm the models and to compare them with possible competing models.
HEIDi (Human/Environment Interactional Dynamics initiative)
Understanding human behavior in all its complexity is a daunting task. For some time, scientists have recognized the need to map out the important variables or properties of both individuals and their environments and how those variables influence each other over time and across levels of analysis (from biological to social). In addition, the scientific community has come to recognize that various disciplines have focused on aspects of the general problem, or related problems, often with great success. Yet, the interaction of the scientists across these disciplines, seen as necessary to understand the greater whole, has been less common. There have been concerned efforts (e.g., general systems theory, cognitive science) to foster integration among the relevant disciplines. This is our goal here as well.
Within the OU scientific community, we have formed a group of researchers across several overlapping disciplines focused on the understanding the complexity of human behavior as it unfolds over time and during interactions between the humans and their environments. Specific recognition is given to the notion that a human’s environment often includes other humans (i.e., is social), and that models (both computational, machine, and animal) can be used to represent and test understandings of human-environmental interactions over time.
From this perspective, we include researchers in psychology, computer science and electrical engineering, mathematics, philosophy, anthropology, and biology (including neurological and physiological) into an organized group to meet regularly, and developing mutually interesting projects and collaborations across disciplines that would normally not interact
For more information on HEIDi click here
Download HEIDi Presentations for more detail:
Taxi Drivers / Economic Behavior and Goal-Striving
Our first illustration of the relevance of goal agent subsystems, dynamics, and decision making involves a series of studies by Camerer and colleagues (Camerer et al., 1997). These behavioral economists questioned the established wisdom of labor supply models, which predict that the number of hours individuals work positively correlates with hourly wages or income change. In contrast to this prediction, they found that taxi cab drivers in New York often work longer on days when they were making relatively less income than on days when they were making more. Although financially inefficient, this behavior is cognitively very efficient (i.e., fast and frugal). Indeed, a simple, single goal model focusing on, ironically, money earned can predict it. Specifically,Camerer et al. noted that cab drivers developed daily income goals, which, once met (r = p), allowed them to “exit” (Miller, Galanter, &Pribrum, 1960) the goal subsystem and thus pursue other activities (e.g.,leisure). We call this cognitively efficient because it does not require mental models for calculating rates or the operation of the environment. It simply requires an income goal and the tracking of an inventory (i.e., an income)throughout the day. Notably,the divergence from labor supply models likely emerges from the dynamic quality of the context (a naturalistically common quality). That is, behavior is not a function of some static representation of the situation and an extrapolation regarding what that representation implies in terms of future outcomes.Instead, it is a function of the changing inventory, which requires a simple, though regular observation, and a simple process of inventory control.Moreover, this “heuristic” is adaptive(Gigerenzer, 2008) in that it provides a workable and sometimes even optimal solution when the dynamics of the environment are difficult to predict(Vancouver & Scherbaum, 2008). Of course, this is not the case in the cabdriver context, which is why the finding, which divergences from normative models, is compelling.
Prospect theory is a popular theory in decision making and behavioral economics. They theory describes decisions in terms of curved utility curves that account for robust findings like loss aversion and risk seeking (or avoidance). Yet, the theory is largely static (i.e., does not explain behavior over time) and ambiguous regarding the source of the curves. We have developed computational models of goal-striving agents (i.e., negative feedback control systems) that can account for the curves as well as predict behavior over time. Research is currently testing the model, its implications, and its competitors.