HowesA at bham dot ac dot uk
School of Computer Science
University of Birmingham
Ph.D. Opportunities - March 2013
I very much welcome applications from potential Ph.D. students who have a background in Computer Science, Analytics, Psychology, or a related discipline. Please email me if you are interested in studying one of the topics below or any other topic concerning human decision making or cutting edge computer, mobile, or robotic interfaces.
We propose that a person can be said to be computationally rational when the strategies that they choose maximize subjective utility given constraints imposed by their information processing architecture and experience (Lewis, Howes & Singh, in press; Howes, Lewis and Vera, 2009). In an early description of this framework (Howes, Vera, Lewis and McCurdy, 2004) we explored how information processing theories might be formalised so that optimal strategies could be derived. This work yielded predictions of computationally rational strategies given theories concerning the presence or absence of response selection bottlenecks (Howes, Lewis and Vera, 2009). More recently we have developed a generalisation of the framework (Lewis, Howes and Singh, in press) and explored applications to decision making (e.g. Howes, Warren, Farmer, El Deredy and Lewis, submitted). The approach complements normative and evolutionary accounts of rationality and differs from Simon's notion of bounded rationality in its emphasis on the role of optimality in understanding behaviour. It is also a departure from notions of rationality, based on Marr's levels, that abstract entirely from the underlying information processing mechanisms.
Howes, A., Vera, A., Lewis, R.L. & McCurdy, M. (2004). Cognitive constraint modeling: A formal approach to reasoning about behavior. In K. D. Forbus, D. Gentner & T. Regier (Eds.), 26th Annual Meeting of the Cognitive Science Society, CogSci’04. (pp. 595-600). Hillsdale, NJ: Erlbaum. [pdf]
Howes, A., Lewis, R.L. & Vera, A. (2009). Rational adaptation under task and processing constraints: Implications for testing theories of cognition and action. Psychological Review, 116, 4, 717-751. [pdf]
Howes, A., Warren, P.A., Farmer, P., El Deredy, W. & Lewis, R.L. (submitted). Why rational organisms make preference reversals.
Lewis, R. L., Singh, S., & Howes, A. "Rethinking Rationality and its Bounds", The 2013 Marshall M. Weinberg Cognitive Science Symposium on Friday April 5th, Ann Arbor, University of Michigan, with invited presentations by Jonathen Cohen (Princeton), David Danks (Carnegie Mellon), Konrad Körding (Northwestern) and Laura Schulz (MIT). Conference web site.
Lewis, R. L., Singh, S., & Howes, A. (in press). Computational rationality: Linking mechanism and behavior through bounded utility maximization. Topics in Cognitive Science, in press.
My applied interests are in Adaptive Interaction, that is in how people find new and better ways to achieve goals with technology. I study the limits that social and cognitive mechanisms impose on adaptation. I am interested in questions such as how and why people seek and provide information, how they make personal healthcare decisions, and how they choose to stay in touch with colleagues, friends and family. I am particularly interested in computational modeling and I publish in the Cognitive Science and Human-Computer Interaction literature.
Payne, S.J. & Howes, A. (2013). Adaptive Interaction: A utility maximisation approach to understanding human interaction with technology. Morgan Claypool. [Publisher Site] [Amazon]
Integrative models of cognition
Howes, A. (2013). Cognitively bounded rational analysis: A framework for reasoning about integrative models of cognition. Invited presentation. ACT-R Spring School and Workshop, Groningen, Netherlands.
Howes, A. (2013). Unifying rationalistic and mechanistic views of cognition. Invited presentation in G.Gunzelmann (Ed.) Motivations and Goals in Developing Integrative Models of Human Cognition. Cognitive Science 2013 Workshop Programme, Berlin, Germany.
Information seeking and decision making
An important task for many involves deciding what information to gather in support of decision making tasks. With Stelios Lelis I am working on a bounded optimal account of how people make decisions about which reviews to read when they are considering a choice between a set of products. Our computational model inherits ideas from information economics and from Bayesian approaches to understanding optimal data selection. The model is novel in the assumptions concerning the distribution of review values (positive or negative reviews) and the impact of the shape of these distributions on informaking seeking. The model makes a number of predictions that are supported by laboratory studies.
Lelis, S., & Howes, A. (2008). A Bayesian model of how people search online consumer reviews. In B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 553-559). Austin, TX: Cognitive Science Society.
Lelis, S. & Howes, A. (2011). Informing decisions: How people use online rating information to make choices. In the Proceedings of the 29th ACM Conference on Human Factors in Computing Systems CHI'11. ACM Press.
With Yuan-Chi Tseng I am investigating how people adapt visual search strategies in response to the distribution of rewards in the task environment given constraints on the human visual system. The questions asked by Tseng are potentially important to the design of information visualisation technologies.
Strategies for Guiding Interactive Search
Duncan Brumby and I have been investigating the strategies that people use to search web pages. One activity people engage in when using the web is estimating the likelihood that labelled links will lead to their goal. However, they must also decide which items to assess and how to assess them. There are a number of theoretical accounts of this behaviour. The accounts differ in whether it is assumed that people consider all of the items on a page prior to making a selection or whether they make a selection immediately following an assessment of a highly relevant item.
We have conducted experiments designed to discriminate between these accounts. The experiments manipulated the relevance of the target and distracter items, and the location of the target item within the set. The findings suggest that decisions are continually made about whether to select one of the assessed items immediately or whether to make further assessments. Each decision is sensitive to the estimated relevance of all of the items so far assessed, and not just to the most recent item. The findings also suggest that when a goal-relevant item is located participants sometimes choose to check the remaining items in the menu but are more likely to skip some of these items.
With Alonso Vera, Richard Lewis, and Juliet Richardson I have argued that existing languages for representing knowledge for routine cognitive tasks(such as GOMS, UAN, and PDL) can fail either because they demand that task competence is described using serial position to determine temporal order (and they are therefore overly restrictive) or because they demand that partial orderings are specified with temporal dependencies and other logical relationships (and they are therefore under-constrained). We have proposed a theory, called Information-Requirements Grammar (IRG), of how higher-level task knowledge constrains adaptation. The theory formalises a hypothesis about how higher level task adaptation is constrained by the information requirements and resource demands of lower-level tasks. For more information see Howes, Lewis, Vera, Richardson (2005) [pdf].