Models of probabilistic category learning in Parkinson’s disease: Strategy use and the effects of L-dopa

Speekenbrink, Maarten; Lagnado, David A.; Wilkinson, Leonora; Jahanshahi, Marjan; Shanks, David R.

doi:10.1016/j.jmp.2009.07.004

Cited by 19 publications

(11 citation statements)

References 64 publications

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“…Moreover, the present results, in accordance with previous studies using the classic weather prediction task (Gluck et al, 2002;Lagnado et al, 2006;Meeter et al, 2006Meeter et al, , 2008Speekenbrink et al, 2010), highlight the fact that there is likely considerable variability in participants' cognitive strategies in probabilistic decision making scenarios. Previous studies reported that people tend to start with a simple strategy using a single cue (i.e., singleton strategy) but switch to an optimal multicue strategy toward the end of the task.…”

Section: Discussionsupporting

confidence: 92%

“…Taken together, the precise manner in which multiple cues are integrated and how the shift in decision strategy is instantiated computationally and neurally under time pressure represents an exciting challenge for future research. Several previous studies have explored ways of identifying strategies that participants use in solving the classic weather prediction task (Gluck, Shohamy, & Myers, 2002;Lagnado, Newell, Kahan, & Shanks, 2006;Meeter, Myers, Shohamy, Hopkins, & Gluck, 2006;Meeter, Radics, Myers, Gluck, & Hopkins, 2008;Speekenbrink, Lagnado, Wilkinson, Jahanshahi, & Shanks, 2010), which has close parallels with the present protocol. Gluck et al (2002) introduced a method using a least-mean-squared-error measure that compares each participant's data to the ideal response profiles constructed from three different strategy models, the multicue (optimal model), one cue, and singleton strategies.…”

Section: Discussionmentioning

confidence: 97%

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Satisficing in split-second decision making is characterized by strategic cue discounting.

Oh¹,

Beck²,

Zhu³

et al. 2016

Journal of Experimental Psychology: Learning, Memory, and Cogni

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Much of our real-life decision making is bounded by uncertain information, limitations in cognitive resources, and a lack of time to allocate to the decision process. It is thought that humans overcome these limitations through satisficing, fast but "good-enough" heuristic decision making that prioritizes some sources of information (cues) while ignoring others. However, the decision-making strategies we adopt under uncertainty and time pressure, for example during emergencies that demand split-second choices, are presently unknown. To characterize these decision strategies quantitatively, the present study examined how people solve a novel multicue probabilistic classification task under varying time pressure, by tracking shifts in decision strategies using variational Bayesian inference. We found that under low time pressure, participants correctly weighted and integrated all available cues to arrive at near-optimal decisions. With increasingly demanding, subsecond time pressures, however, participants systematically discounted a subset of the cue information by dropping the least informative cue(s) from their decision making process. Thus, the human cognitive apparatus copes with uncertainty and severe time pressure by adopting a "drop-the-worst" cue decision making strategy that minimizes cognitive time and effort investment while preserving the consideration of the most diagnostic cue information, thus maintaining "good-enough" accuracy. This advance in our understanding of satisficing strategies could form the basis of predicting human choices in high time pressure scenarios.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 97%

Satisficing in split-second decision making is characterized by strategic cue discounting.

Oh¹,

Beck²,

Zhu³

et al. 2016

Journal of Experimental Psychology: Learning, Memory, and Cogni

View full text Add to dashboard Cite

show abstract

“…Analysis of the learning strategy of individual subjects in this task has compared single-cue or single feature-based strategies, and the possibility of switching between such strategies (6, 7). Associative or Bayesian learning models that rely on simple stimulus features were used to describe the diversity of individual learning dynamics that subjects exhibited and compare between subjects (8), and reflected differences between healthy subjects and patients (9,10). However, these models were mostly evaluated in terms of their ability to describe subjects' performance, rather than cross-validated predictive power.…”

mentioning

confidence: 99%

High-order feature-based mixture models of classification learning predict individual learning curves and enable personalized teaching

Cohen

Schneidman

2012

Proc. Natl. Acad. Sci. U.S.A.

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Pattern classification learning tasks are commonly used to explore learning strategies in human subjects. The universal and individual traits of learning such tasks reflect our cognitive abilities and have been of interest both psychophysically and clinically. From a computational perspective, these tasks are hard, because the number of patterns and rules one could consider even in simple cases is exponentially large. Thus, when we learn to classify we must use simplifying assumptions and generalize. Studies of human behavior in probabilistic learning tasks have focused on rules in which pattern cues are independent, and also described individual behavior in terms of simple, single-cue, feature-based models. Here, we conducted psychophysical experiments in which people learned to classify binary sequences according to deterministic rules of different complexity, including high-order, multicue-dependent rules. We show that human performance on such tasks is very diverse, but that a class of reinforcement learning-like models that use a mixture of features captures individual learning behavior surprisingly well. These models reflect the important role of subjects' priors, and their reliance on high-order features even when learning a low-order rule. Further, we show that these models predict future individual answers to a high degree of accuracy. We then use these models to build personally optimized teaching sessions and boost learning.inference | information | maximum entropy W e regularly learn to classify sensory stimuli into novel categories, often using an impoverished sampling of the stimulus space and the underlying rule: even a "simple" task of classifying patterns of n bits into two categories, requires an implicit mapping of the 2 n possible patterns, which means there are 2 2 n potential deterministic classification rules. It is clear then that when we learn to classify, we cannot simply explore the space of rules and patterns, but instead must rely on simplifying assumptions.Analysis of human learning of deterministic classification rules has focused on modeling of the average behavior of subjects (1-3), and explored the effect of rule complexity on the average level of success (4). Learning to classify according to a probabilistic rule is inherently ambiguous, and so studies of such tasks have focused on simpler rules than those used in deterministic classification. For example, the weather prediction (WP) task (5) requires learning probabilistic associations between multiple cues and a label, where each cue carries independent information about the correct label. Analysis of the learning strategy of individual subjects in this task has compared single-cue or single feature-based strategies, and the possibility of switching between such strategies (6, 7). Associative or Bayesian learning models that rely on simple stimulus features were used to describe the diversity of individual learning dynamics that subjects exhibited and compare between subjects (8), and reflected differences between healthy sub...

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“…However, patients tested off and on medication were able to discriminate between strong and weak cards as well as HCs. Importantly, critical behavioral differences between those off and on medication were found to be the result of the types of strategies that were implemented (Speekenbrink et al, 2010). HCs and PDs off medication showed hypothesis testing type behavior, partly resulting in more flexibility in switching strategies when experiencing poor outcome feedback, where as PDs on medication switched to poorer strategies.…”

Section: The Effects Of On Vs Off L-dopa Medication In the Wptmentioning

confidence: 98%

“…That is, PDs would be more susceptible to changing their strategies or even show random responding while on medication because they are acting on each discrete change that they experience from trial to trial, and prompted to do so from an amplified error signal. In fact, PDs on medication show more random guessing early in the WPT task (Speekenbrink et al, 2010), and take considerably longer to switch to a single strategy that they can test which enables them to better track cue-outcome associations over trials Speekenbrink et al, 2010). Overall, the studies reviewed suggest that cue-outcome associations learning are preserved in spite of stage of PD, level of medication, and withdrawal from medication.…”

Section: Reinforcement Learning Models and Feedbackmentioning

confidence: 99%

The Role of Feedback in Decision Making

Osman¹

2011

Diagnosis and Treatment of Parkinson's Disease

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Models of probabilistic category learning in Parkinson’s disease: Strategy use and the effects of L-dopa

Cited by 19 publications

References 64 publications

Satisficing in split-second decision making is characterized by strategic cue discounting.

Satisficing in split-second decision making is characterized by strategic cue discounting.

High-order feature-based mixture models of classification learning predict individual learning curves and enable personalized teaching

The Role of Feedback in Decision Making

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