Academic Qualifications:
BSc (hons): University of Melbourne, Australia, 1987
PhD: University of California, Los Angeles, 1994
Full Resume (includes list of all publications)
Research and publications
Overall, my work aims to understand how knowledge is represented and used. Towards this aim, I have worked on a number of issues in higher-order cognition. In particular:
(click on a link to go directly to a topic and relevant manuscripts I have authored)
Decision making and streaks
Goal specificity in problem solving
Reasoning and causality (Monty Hall dilemma)
Competition as problem solving
Motivation and problem solving
Decision making and streaks
Recent fMRI evidence has found that specific areas of the brain have increased activation when people experience events producing a streak of the same outcome. This is consistent with what has long been observed, that people's future choices can be influenced by a streak of events. The stock market is a clear example of this, as there have long been "momentum traders."
Burns (in press-a) examined another well-known example of following streaks, the belief in the hot hand in basketball, which leads people to expect a player who has hit a number of shots to be more likely to hit the next shot than would be normally expected. However, there is no evidence that success in shooting is dependent on previous success. From this evidence of independence it has been assumed that belief in the hot hand is an example of irrationality. However, if the goal of basketball is to allocate shots between players so as to maximize team scoring, then streaks are information that can help with this allocation decision (if shots really are independent). Only under very narrow conditions will the information provided by the streak be redundant. Thus the hot hand behavior of giving the ball to players on a streak is adaptive even if the belief that the player has a temporally elevated chance of hitting is false. This distinction between analyzing decision making in terms of behavior that attains goals (as advocated by Gigerenzer and by Anderson), rather than normative validity of beliefs (as advocated by Kahneman and Tversky) is a contrast that the hot hand analysis particularly strongly illustrates.
My current work is focused on the empirical implications of the hot hand analysis, in particular how it can be used to predict when people will follow streaks and when they will go against streaks. Consistent with Burns (in press-a), participants were found to be more likely to follow streaks when they think the generating mechanism is less random (Burns & Corpus, in press, 2002) and when they think it is more variable (Burns, 2003). I am continuing to follow up the implications my analysis has in general for how peoples' decision making will be affected by streaks of events.
Burns, B. D. (2004). Heuristics as beliefs and as behaviors: The adaptiveness of the
"hot hand".
Cognitive psychology, 48, 295-311. (Final submission)
Burns, B. D., & Corpus, B. (2004). Randomness and inductions from streaks: "Gambler's fallacy" versus "Hot hand." Psychonomic Bulletin & Review, 11, 179-184. (Final submission)
Burns, B. D. (2002). Does the law of small numbers explain the gambler's fallacy? Presented at the Forty-Third Annual Meeting of the Psychonomic Society, Kansas City, MO. (Copy of poster)
Burns, B. D. (2003). When it is adaptive to follow streaks: Variability and stocks. In R. Alterman & D. Kirsh (Eds.),
Proceedings of the Twenty-fifth Annual Meeting of the Cognitive Science
Society. Hillsdale, NJ: Lawrence Erlbaum. (Final
submission)
Goal specificity in problem solving
There is greater use of web-based learning, yet how should such systems be designed? One advantage of webpages is that they give learners the chance to explore, yet is it best to simply ask learners to explore or do they require specific goals?
In work with Regina Vollmeyer (Vollmeyer, Burns, & Holyoak, 1996) we addressed the role of goal specificity in problem solving. We had participants learn to control a complex system and found that giving them a specific goal (i.e., the final state they had to bring the system to) from the beginning led to poorer transfer than if they had a nonspecific goal of just exploring, until they were given the final state at the end. Burns and Vollmeyer (2002) showed directly (through protocol analysis) that giving problem solvers a specific goal appeared to harm their performance because it changed their strategy, in that they tried to reach the specific goal prematurely, whereas learners who had not been told what the goal were more likely to try to understand the task. These results suggest that the goals people have during problem solving are critical to what they learn because they change their strategies. We have been extending these ideas and results to learning in multimedia tasks (Vollmeyer & Burns, 2002).
Burns, B. D., & Vollmeyer, R. (2002). Goal specificity and dual-space search theories of problem solving.
The Quarterly Journal of Experimental Psychology, 55A, 241-261. (Final
submission)
Vollmeyer, R., & Burns, B. D. (2002). Goal Specificity and Learning with a Hypermedia Program. Experimental Psychology, 49, 98-108.
Vollmeyer, R., Burns, B. D., & Holyoak, K. J. (1996). The impact of goal specificity and systematicity of strategies on the acquisition of problem structure.
Cognitive Science, 20, 75 - 100.
Reasoning and causality (Monty Hall dilemma)
The well-known Monty Hall dilemma reliably results in people both giving the wrong answer and being resistant to the right answer. Burns and Wieth (under review, 2003) suggest that the barrier to correctly representing the dilemma is that it requires understanding the implications of its causal structure. The problem has a collider structure (two independent influences on a single outcome) which is a causal structure that people often have difficulty reasoning about. Empirically we show that putting the problem into a context in which people tend to reason better about such a causal structures results in better performance, and leads to better reasoning about a counterfactual that would change the causal structure of the dilemma. We are following up this work by investigating whether failure to understand such causal structures may underlie other reasoning failures.
Burns, B. D., & Wieth, M. (in press). The Collider principle in causal
reasoning: Why the Monty Hall Dilemma is so hard. Journal of Experimental Psychology:
General. (Final
submission)
Burns, B. D. & Wieth, M. (2003). Causality and Reasoning: The Monty Hall Dilemma. In R. Alterman & D. Kirsh (Eds.),
Proceedings of the Twenty-fifth Annual Meeting of the Cognitive Science
Society. Hillsdale, NJ: Lawrence Erlbaum. (Final
submission)
Chess skill
The pioneering work by Chase & Simon on chess skill suggested that despite being intellectual, and slow, chess skill critically involves fast processes such as pattern recognition. Do differences in these fast processes have an impact on the results of actual chess games? Burns (in press-b) examined blitz chess tournaments in which players must complete all their moves in 5 minutes each. The performance of players with a wide variety of skill levels was compared with their expected performance in normal (slow) tournament chess. This analysis showed that most of the variance (81%) in players' skill at slow chess can be accounted for by their performance at blitz chess, in which they have on average only seconds to decide on a move. My analysis also indicates that the more skilled players are, the more fast processes distinguish between them rather than slow processes. These results are consistent with the claim that high levels of skill are based on extensive practice.
I am continuing to investigate what factors may mediate the relationship between
blitz chess and normal chess in order to gain insight into expertise.
Burns, B. D. (in press). The effects of speed on skilled chess performance. Psychological
Science. (Final submission)
Analogical reasoning
My thesis work was on analogical reasoning. Analogical reasoning involves transferring an understanding of an old situation to a new one, where typically the former is better understood than the latter. Burns (1996) focused on what is actually transferred between two situations, rather than simply demonstrating that transfer occurs. In particular, I examined why transfer may appear to fail using meta-analogies (analogies between analogies). I presented people with analogies that have different possible answers and showed that the representation for the problem that is chosen largely determines the solutions that can be produced. The representation chosen was then transferred to a subsequent analogy problem and influenced what solution was produced to the new problem. I have also worked on computational modeling of analogical processes (Hummel, Burns, & Holyoak, 1994).
Burns, B. D. (1996). Meta-analogical transfer: Transfer between episodes of analogical reasoning.
Journal of Experimental Psychology: Learning, Memory and Cognition, 22, 1032-1048.
(Final submission)
Hummel, J. E., Burns, B. D., & Holyoak, K. J. (1994). Analogical mapping by dynamic binding: Preliminary investigations. In K. J. Holyoak & J. A. Barnden (Eds.), Advances in connectionist and neural computation theory, Vol. 2: Analogical connections (pp. 416-445). Norwood, NJ: Ablex.
Competition as problem solving
Like most problem solving research, my research has only looked at the performance of an individual against an opponent that is an inanimate object or simply oneself. However, a general theory of problem solving must be able to handle more than static situations. Many interactions between people have the characteristics of complex problem solving exercises, but adversarial situations provide a particularly pure form of social problem
solving. Therefore, competition appears to offer a promising domain in which to apply cognitive principles to interactions. A possible way of dealing with adversarial problem solving (APS) could be forming a mental model of an opponent, thus one of the original motivations for these studies was to examine the nature of a particular mental model that would have testable consequences.
Burns and Vollmeyer (1998) had participants play a two-person, zero-sum, repeated game. We found a relationship between the accuracy of a participant's model of their opponent and performance in the game. In further work, by manipulating how well participants can apply their models, we have shown that participants perform better than their opponent when they can use an appropriate model. Thus we have shown that better modeling leads to better performance, and that this relationship is independent of factors suggested by a game theoretic analysis of the task.
Burns, B. D., & Vollmeyer, R. (1998). Modeling the adversary in competition.
Journal of Personality and Social Psychology, 75, 711-718. (Final
submission)
Burns, B. D., & Vollmeyer, R. (under revision). Competition as adversarial problem solving: A test of modeling the opponent and game
theory. (current
version available on request)
Motivation and problem solving
Cognitive psychologists often ignore the possible effects of motivation on cognitive processes, preferring to treat them as noise. However, there are many reasons to expect motivation to have important, but often unexamined, effects on cognitive processes. I have been collaborating on work investigating the effects that motivation may have on cognition both through planned experiments and by routinely including measures of motivation in my experiments. With Regina Vollmeyer I have been investigating the role of motivation in the problem solving task we used to examine goal specificity which has resulted in
a general model (Rheinberg, Vollmeyer, & Burns, 2000).
In work with Mareike Wieth we have been investigating the effects of motivation on problem solving by examining motivational effects on both insight and incremental problems solving. This work indicates that both forms of problem solving are affected by a manipulation of motivation, even though there are many reasons to expect a simple increase of effort to be less effective in helping solve insight problems. We are continuing to examine the process by which motivation may affect problem solving.
Rheinberg, F., Vollmeyer, R. & Burns, B. D. (2000). Motivation and self-regulated learning. In J. Heckhausen (Ed.),
Motivational psychology of human development (pp. 81-108). North-Holland:
Elsevier.
Wieth, M., & Burns, B. D. (under revision). Influence of motivation on insight and non-insight problem
solving. (current version available
on request)
Classes
I regularly teach the following classes:
Psychology 101 (Honors): Introductory psychology. (Most recent syllabus)
Psychology 200: Cognitive psychology. (Most recent syllabus, Website)
Psychology 803 (Graduate): Higher order cognition. (Most recent syllabus)
I have been developing techniques for using web-browsers to present experiments without the need to interact with a server (all pages are loaded from the hard-drive of the machine the experiment is run on). This has the potential to make it easier for experimenters to exploits the many advantages webpages offer for presenting experiments. A paper describing this approach is under review:
Burns, B. D., & McFarlane (under review). Webpages without a server as general purpose experimental software. (current version available on request)
A mini-experiment illustrating this approach can be downloaded as a zipped file from here: experiment_example
Institut for Psychologie, Universität Potsdam, Germany
Department of Psychology, University of California, Los Angeles, USA
Department of Psychology, University of Melbourne, Australia
Cognitive
science society
A non-psychology website: