Learning strategies in amnesia

Speekenbrink, Maarten; Channon, Shelley; Shanks, David R.

doi:10.1016/j.neubiorev.2007.07.005

Cited by 26 publications

(24 citation statements)

References 44 publications

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“…This corroborates an earlier finding in an experiment with amnesic and control groups (Speekenbrink et al, 2007), where the associative model with a constant learning rate also fitted best (although there, we did not include a Bayesian model with estimated prior variance). The model fits are not informative as to the reason for this better fit.…”

Section: Model Fittingsupporting

confidence: 89%

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Through the looking glass: a dynamic lens model approach to multiple cue probability learning

Shanks¹,

Speekenbrink

2008

The Probabilistic Mind:

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Despite what the somewhat technical name might suggest, multiple cue probability learning (MCPL) problems are commonly encountered in daily life. For instance, we may have to judge whether it will rain from cues such as temperature, humidity, and the time of year. Or, we may have to judge whether someone is telling the truth from cues such as pitch of voice, level of eye contact, and rate of eye blinks. While informative, these cues are not perfect predictors. How do we learn to solve such problems? How do we learn which cues are relevant, and to what extent? How do we integrate the available information into a judgement?Applying a rational analysis (Anderson, 1990; Oaksford & Chater, 1998), we would answer these questions by specifying a rational model, and then compare individuals' judgements to the model-predicted judgements. Insofar as observed behaviour matches the predicted behaviour, we would conclude that people learn these tasks as rational agents. Here, we take a slightly different approach. We still use rational models, but rather than comparing predicted behaviour to actual behaviour (a comparison in what we might call observation space), we make the comparison in parameter space.To be a little less obscure, let's take an agent who must repeatedly predict share price from past share price. A rational agent would make predictions which are optimal given an observed pattern of past share price. The question is whether a real (human) agent makes predictions as the rational agent would. Assume current share price Y t is related to past share price Y t −1 as Y t = β Y t −1 , where β is an unknown constant. In order to make accurate predictions, the agent must infer the value of β from repeated observations of share price. A rational agent, with optimal estimates w t , will make predictionsŷ t = w t Y t −1 . Since the relation is rather simple, and the agent is rational, the inferences w t (and hence predictionsŷ t ) will be accurate quite quickly. Enter the real (rational?) agent, making predictions R t . Rather than assuming these predictions follow from rational estimates w t , we assume they are based on R t = u t Y t −1 , where u t is a coefficient not necessarily equal to w t . Thus, we assume the same structural model for rational and actual predictions, but allow for different parameters u t and w t . By comparing u t to w t , we can see how the real agents' learning compares to the rational agents' learning.

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Section: Model Fittingsupporting

confidence: 89%

“…Finally, it is easy to prove (e.g. Speekenbrink, Channon, & Shanks, 2007) that the assumed response function results in a linear relation between inferred validity and predicted utilization…”

Section: Response Processmentioning

confidence: 99%

Through the looking glass: a dynamic lens model approach to multiple cue probability learning

Shanks¹,

Speekenbrink

2008

The Probabilistic Mind:

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“…Especially when the outcome is a categorical variable, such as in the well-known "Weather Prediction Task" (Gluck, Shohamy, & Myers, 2002;Speekenbrink, Channon, & Shanks, 2008), making a prediction is structurally similar to a decision between multiple arms (possible predictions) that are rewarded (correct prediction) or not (incorrect prediction). Just as in the CMAB, multiplecue probability learning and probabilistic category learning tasks require people to learn a function which maps multiple cues or features to expected outcomes.…”

Section: Contextual Multi-armed Banditsmentioning

confidence: 99%

Putting bandits into context: How function learning supports decision making.

Schulz¹,

Konstantinidis

Speekenbrink³

2018

Journal of Experimental Psychology: Learning, Memory, and Cogni

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We introduce the contextual multi-armed bandit task as a framework to investigate learning and decision making in uncertain environments. In this novel paradigm, participants repeatedly choose between multiple options in order to maximise their rewards. The options are described by a number of contextual features which are predictive of the rewards through initially unknown functions. From their experience with choosing options and observing the consequences of their decisions, participants can learn about the functional relation between contexts and rewards and improve their decision strategy over time. In three experiments, we explore participants' behaviour in such learning environments. We predict participants' behaviour by context-blind (mean-tracking, Kalman filter) and contextual (Gaussian process and linear regression) learning approaches combined with different choice strategies. Participants are mostly able to learn about the context-reward functions and their behaviour is best described by a Gaussian process learning strategy which generalizes previous experience to similar instances. In a relatively simple task with binary features, they seem to combine this learning with a "probability of improvement" decision strategy which focuses on alternatives that are expected to lead to an improvement upon a current favourite option. In a task with continuous features that are linearly related to the rewards, participants seem to more explicitly balance exploration and exploitation. Finally, in a difficult learning environment where the relation between features and rewards is non-linear, some participants are again well-described by a Gaussian process learning strategy, whereas others revert to context-blind strategies.

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“…Thus, under appropriate circumstances (such as elaborated feedback), participants can outperform the elementary cue-tallying (Dawes' rule) heuristic. Other research has similarly highlighted people's capacity to approximate the optimal linear strategy (e.g., Lagnado et al, 2006;Speekenbrink, Channon, & Shanks, 2008;White & Koehler, 2007).…”

Section: Feedback In Mcplmentioning

confidence: 99%

The Effectiveness of Feedback in Multiple-Cue Probability Learning

Newell

Weston

Tunney

et al. 2009

Quarterly Journal of Experimental Psychology

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How effective are different types of feedback in helping us to learn multiple contingencies? This article attempts to resolve a paradox whereby, in comparison to simple outcome feedback, additional feedback either fails to enhance or is actually detrimental to performance in nonmetric multiple-cue probability learning (MCPL), while in contrast the majority of studies of metric MCPL reveal improvements at least with some forms of feedback. In three experiments we demonstrate that if feedback assists participants to infer cue polarity then it can in fact be effective in nonmetric MCPL. Participants appeared to use cue polarity information to adopt a linear judgement strategy, even though the environment was nonlinear. The results reconcile the paradoxical contrast between metric and nonmetric MCPL and support previous findings of people's tendency to assume linearity and additivity in probabilistic cue learning.

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Learning strategies in amnesia

Cited by 26 publications

References 44 publications

Through the looking glass: a dynamic lens model approach to multiple cue probability learning

Through the looking glass: a dynamic lens model approach to multiple cue probability learning

Putting bandits into context: How function learning supports decision making.

The Effectiveness of Feedback in Multiple-Cue Probability Learning

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