Self‐Directed Learning Favors Local, Rather Than Global, Uncertainty

Markant, Douglas; Settles, Burr; Gureckis, Todd M.

doi:10.1111/cogs.12220

Cited by 49 publications

(73 citation statements)

References 54 publications

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“…Normative approaches can help address these questions [7], but their computational complexity renders them unlikely candidates for controlling behavior. Instead, these approaches can be better used as a basis for understanding limitations in cognitive processes and why biases emerge in human behavior [8,9]. …”

Section: Introductionmentioning

confidence: 99%

Approach-Induced Biases in Human Information Sampling

et al. 2016

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Information sampling is often biased towards seeking evidence that confirms one’s prior beliefs. Despite such biases being a pervasive feature of human behavior, their underlying causes remain unclear. Many accounts of these biases appeal to limitations of human hypothesis testing and cognition, de facto evoking notions of bounded rationality, but neglect more basic aspects of behavioral control. Here, we investigated a potential role for Pavlovian approach in biasing which information humans will choose to sample. We collected a large novel dataset from 32,445 human subjects, making over 3 million decisions, who played a gambling task designed to measure the latent causes and extent of information-sampling biases. We identified three novel approach-related biases, formalized by comparing subject behavior to a dynamic programming model of optimal information gathering. These biases reflected the amount of information sampled (“positive evidence approach”), the selection of which information to sample (“sampling the favorite”), and the interaction between information sampling and subsequent choices (“rejecting unsampled options”). The prevalence of all three biases was related to a Pavlovian approach-avoid parameter quantified within an entirely independent economic decision task. Our large dataset also revealed that individual differences in the amount of information gathered are a stable trait across multiple gameplays and can be related to demographic measures, including age and educational attainment. As well as revealing limitations in cognitive processing, our findings suggest information sampling biases reflect the expression of primitive, yet potentially ecologically adaptive, behavioral repertoires. One such behavior is sampling from options that will eventually be chosen, even when other sources of information are more pertinent for guiding future action.

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Section: Introductionmentioning

confidence: 99%

Approach-Induced Biases in Human Information Sampling

et al. 2016

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“…Normative approaches can help address these questions (7), but their computational complexity renders them unlikely candidates for controlling behavior. Instead, these approaches can be better used as a basis for understanding limitations in cognitive processes and why biases emerge in human behavior (8,9).…”

Section: Introductionmentioning

confidence: 99%

Approach-induced biases in human information sampling

Hunt

Rutledge

Malalasekera

et al. 2016

Preprint

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2Information sampling is often biased towards seeking evidence that confirms one's prior beliefs. Despite such biases being a pervasive feature of human behavior, their underlying causes remain unclear. Many accounts of these biases appeal to limitations of human hypothesis testing and cognition, de facto evoking notions of bounded rationality, but neglect more basic aspects of behavioral control. Here we demonstrate involvement of Pavlovian approach biases in determining which information humans will choose to sample. We collected a large novel dataset from 32,445 human subjects, making over 3 million decisions, who played a gambling task designed to measure the latent causes and extent of information-sampling biases. We identified three novel approachrelated biases, formalized by comparing subject behavior to a dynamic programming model of optimal information gathering. These biases reflected the amount of information sampled ('positive evidence approach'), the selection of which information to sample ('sampling the favorite'), and the interaction between information sampling and subsequent choices ('rejecting unsampled options'). The prevalence of all three biases was related to a Pavlovian approachavoid parameter quantified within an entirely independent economic decision task. Our large dataset also revealed that individual differences in information seeking are a stable trait across multiple gameplays, and can be related to demographic measures including age and educational attainment. As well as revealing limitations in cognitive processing, our findings suggest information sampling biases reflect the expression of primitive, yet potentially ecologically adaptive, behavioral repertoires. One such behavior is sampling from options that will eventually be chosen, even when other sources of information are more pertinent for guiding future action.

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“…The optimal resolution to this dilemma is tractable only in repeatedly presented object over novel ones (Zajonc, 2001). Similarly, in self-directed learning people tend to choose options with known outcomes (Markant, Settles, & Gureckis, 2016), and in supermarkets consumers are "loyal" to brands, willing to pay a price premium for more familiar products (Ching, Erdem, & Keane, 2013;Keller, 2002).…”

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confidence: 99%

It’s new, but is it good? How generalization and uncertainty guide the exploration of novel options.

Stojić¹,

Schulz²,

Analytis³

et al. 2020

Journal of Experimental Psychology: General

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How do people decide whether to try out novel options as opposed to tried-and-tested ones? We argue that they infer a novel option's reward from contextual information learned from functional relations and take uncertainty into account when making a decision. We propose a Bayesian optimization model to describe their learning and decision making. This model relies on similarity-based learning of functional relationships between features and rewards, and a choice rule that balances exploration and exploitation by combining predicted rewards and the uncertainty of these predictions. Our model makes two main predictions. First, decision makers who learn functional relationships will generalize based on the learned reward function, choosing novel options only if their predicted reward is high. Second, they will take uncertainty about the function into account, and prefer novel options that can reduce this uncertainty. We test these predictions in three preregistered experiments in which we examine participants' preferences for novel options using a feature-based multi-armed bandit task in which rewards are a noisy function of observable features. Our results reveal strong evidence for functional exploration and moderate evidence for uncertainty-guided exploration. However, whether or not participants chose a novel option also depended on their attention, as well as reflecting on the value of the options. These results advance our understanding of people's reactions in the face of novelty.

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Self‐Directed Learning Favors Local, Rather Than Global, Uncertainty

Cited by 49 publications

References 54 publications

Approach-Induced Biases in Human Information Sampling

Approach-Induced Biases in Human Information Sampling

Approach-induced biases in human information sampling

It’s new, but is it good? How generalization and uncertainty guide the exploration of novel options.

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