David Shanks University College London, UK

1. Learning and Decision Making: An Overview of the Landscape David Shanks University College London, UK General theme: How do people learn to make good choices in decision environments? What are the main research questions in studying decision learning? What factors affect the likelihood of learning an optimal decision strategy?

2. cue 1 choice cue 2 outcome cue 3 cue 4 eg medical symptomsdiseases, weatherprice of orange juice, company data stockmarket changes, etc. Participants receive a payoff for each correct decision or for their overall decision accuracy.

3. Company A Company B Bulk of operations? UK Bulk of operations? US FTSE/Nasdaq? FTSE FTSE/Nasdaq? Nasdaq Established company? No Established company? No Employee turnover? Low Employee turnover? High which company’s shares are more likely to increase?

4. What research questions arise in the study of decision learning? • Search: How do we find and/or constructthe cues and cue values necessary for informing our choices? • Integration: How do we combine cue information? • linear/nonlinear, heuristics • How do different forms of feedback and feedforward affect decision learning? • What form does knowledge take? • connections, exemplars, prototypes, rules • To what extent do people have insight into their decisions? • Can people learn to make optimal decisions? If not, what sort of biases are they prone to?

5. Search: How do we find the cues and cue values necessary for informing our choices? • Very little research on discovering/constructing cues… Klayman (1988): discovery is heavily reliant on outcome feedback much better when the person can intervene and design his/her own experiments. • More research on search amongst available cues, eg process tracing techniques (elimination by aspects, satisficing). • Stopping rule: when should we stop searching for additional cue information? • How do we choose how to allocate our attention across cues?

6. Integration: How do we combine cue information? • Linear/nonlinear • Heuristics • People often thought to have a preference for linear forms and a limit on the number of cues they can combine. Evans et al (1995): doctors say they use more cues than they actually do. • But clearly experts can combine many cues nonlinearly (Ceci & Liker, 1986). • Perhaps sometimes we don’t integrate at all, but use noncompensatory heuristics such as Take-The-Best and other varieties of one-reason decision making? In many environments, such heuristics are optimal or near-optimal.

7. How do different forms of feedback and feedforward affect decision learning? • Feedforward: effects of instructions, task understanding, scale compatibility. • Outcome feedback: knowledge of the value of the outcome. Hard to learn from this, but plenty can be learned. • Cognitive feedback (Balzer et al, 1989): • task information (eg relations between cues and criterion) • cognitive information (eg cue utilization) • FVI (eg achievement) • history

8. What form does knowledge take? • Connections • Exemplars • Linear model (=prototype) • Rules • A huge research area… • Evidence for exemplar-based processes is overwhelming (Nosofsky). • Also much support for connectionist error-driven learning as the fundamental mechanism of human learning • …so perhaps connectionism is a way of implementing exemplar storage (eg McClelland & Rumelhart, 1985)? • Evidence for rules and for multiple strategies more controversial (Johansen & Palmeri, 2002; Juslin et al, 2003).

9. To what extent do people have insight into their decisions? • Important to differentiate between insight into the task vs insight into one’s policy. People often can recognize the policy they used. • People often find it difficult to verbalize their reasons, but can under some circumstance indicate fairly accurately the weight they assigned to each cue (eg Harries & Harvey, 2000). They can also report idealized cue weights (ie insight into the task). • Correlation between subjective and tacit policies is often low. • Are decision strategies employed deliberately or automatically? If the latter, then unlikely to yield insight (eg Bechara et al, 1995; Dienes & Fahey, 1998; Nisbett & Wilson, 1977). • Task properties, eg scale compatibility. Insight is greater when the cue and response dimensions are the same.

10. Can people learn to make optimal decisions? If not, what sort of biases are they prone to? • Certainly people can behave near-optimally in repeated decision environments (eg Shanks et al, 2002; Kelley & Friedman, 2003). • But even in these cases, biases are detectable (eg base-rate neglect: Goodie & Fantino, 1999). • Paradox: classic JDM studies (eg Meehl) indicate that people are outperformed by very simple linear models, yet research in cognitive psychology (eg categorization: Ashby & Maddox, 1992) reveals near-optimal, nonlinear behaviour. • Thus, perhaps people have the competence to make optimal decisions in virtually any domain, and perhaps they often fail to do so because of insufficient or inadequate exposure/motivation/feedback etc?