Is it better to select or to receive? Learning via active and passive hypothesis testing.

Markant, Douglas; Gureckis, Todd M.

doi:10.1037/a0032108

Cited by 132 publications

(190 citation statements)

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“…Work on "active learning" (Cohn et al 1996) in categorization and perceptual estimation tasks has used a related manipulation, effectively allowing participants to design their experiment optimally (Kruschke 2008;Castro et al 2009;Gureckis and Markant 2009;Juni et al 2011;Markant and Gureckis 2014). Some adjustments will likely be needed to apply this model-comparison method to other task structures, though we are optimistic as to the method's wider applicability (Nelson et al 2010).…”

Section: Resultsmentioning

confidence: 99%

Causal Model Comparison Shows That Human Representation Learning Is Not Bayesian

Geana

Niv

2014

Cold Spring Harb Symp Quant Biol

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How do we learn what features of our multidimensional environment are relevant in a given task? To study the computational process underlying this type of "representation learning," we propose a novel method of causal model comparison. Participants played a probabilistic learning task that required them to identify one relevant feature among several irrelevant ones. To compare between two models of this learning process, we ran each model alongside the participant during task performance, making predictions regarding the values underlying the participant's choices in real time. To test the validity of each model's predictions, we used the predicted values to try to perturb the participant's learning process: We crafted stimuli to either facilitate or hinder comparison between the most highly valued features. A model whose predictions coincide with the learned values in the participant's mind is expected to be effective in perturbing learning in this way, whereas a model whose predictions stray from the true learning process should not. Indeed, we show that in our task a reinforcement-learning model could help or hurt participants' learning, whereas a Bayesian ideal observer model could not. Beyond informing us about the notably suboptimal (but computationally more tractable) substrates of human representation learning, our manipulation suggests a sensitive method for model comparison, which allows us to change the course of people's learning in real time.We live in a rich, complex environment, in which we are constantly bombarded with a wide variety of sensory input. Even an action as simple as walking down the street carries with it a large volume of low-quality information in the form of people we see, places we walk by, cars, colors, voices, noises, emotional content, etc. Intuitively, one would imagine that given sufficient resources, it is best to always represent every aspect of the environment so that any detail can potentially be acted upon. However, the "curse of dimensionality" (Bellman 1957) posits that task representations that involve unnecessary stimulus dimensions will not afford efficient learning and decision-making, where efficiency is measured in the number of examples needed to learn the task. In particular, an increase in the number of dimensions of the problem (in our case, the dimensions of the environment that the brain may represent) implies that the learner needs to collect exponentially larger quantities of data to learn to solve the problem. If we want learning to be feasible it is therefore both computationally optimal and a practical imperative to represent tasks with as compact a representation as possible.What are the computational strategies that humans use to learn a representation for a given task? To address this question, we tested participants on a multidimensional trial-and-error choice task, in which only one dimension was relevant to predicting reward (Wilson and Niv 2012;Niv et al. 2015). To test the explanatory power of different models of learning dynamics, we develope...

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Section: Resultsmentioning

confidence: 99%

Causal Model Comparison Shows That Human Representation Learning Is Not Bayesian

Geana

Niv

2014

Cold Spring Harb Symp Quant Biol

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“…Here, the predictions of label entropy and label margin diverge, allowing us to test which of the two models better accounts for sampling behavior. The design of the present experiment capitalizes on this distinction by extending the paradigm used in Markant and Gureckis (2014) to a ternary classification problem, thereby allowing us to separate the predictions of the sampling models presented above. Participants in the experiment collected information by sampling new instances and receiving feedback about their category membership.…”

Section: Empirical Studies Of Information Sampling During Category Lementioning

confidence: 99%

“…One reason for this is that we did not directly measure Markant and Gureckis (2014), which is adapted for the current study. Stimuli were "loop antennas" that varied in diameter and orientation.…”

Section: Empirical Studies Of Information Sampling During Category Lementioning

confidence: 99%

“…In a recent study, we examined the interaction of self-directed information selection and category learning (Markant & Gureckis, 2014). In this experiment, people learned about two categories of "antennas" that varied along two perceptual dimensions (circles that differed in size and the orientation of a central line segment, see Fig.…”

Section: Empirical Studies Of Information Sampling During Category Lementioning

confidence: 99%

“…Clicking the mouse button reveals the category membership of the item. (B) The pattern of sampling behavior observed by self-directed learners in Markant and Gureckis (2014) across eight training blocks. Each dot represents a single stimulus which was selected by a participant and is plotted in the stimulus space.…”

Section: Empirical Studies Of Information Sampling During Category Lementioning

confidence: 99%

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

2015

Self Cite

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Collecting (or "sampling") information that one expects to be useful is a powerful way to facilitate learning. However, relatively little is known about how people decide which information is worth sampling over the course of learning. We describe several alternative models of how people might decide to collect a piece of information inspired by "active learning" research in machine learning. We additionally provide a theoretical analysis demonstrating the situations under which these models are empirically distinguishable, and we report a novel empirical study that exploits these insights. Our model-based analysis of participants' information gathering decisions reveals that people prefer to select items which resolve uncertainty between two possibilities at a time rather than items that have high uncertainty across all relevant possibilities simultaneously. Rather than adhering to strictly normative or confirmatory conceptions of information search, people appear to prefer a "local" sampling strategy, which may reflect cognitive constraints on the process of information gathering.

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Categorization and Concepts

Goldstone

Kersten

Carvalho

2018

Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience

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The building blocks of human cognition are concepts. What we see, hear, interpret, remember, understand, and talk about is crucially shaped by our concepts. They allow us to communicate, categorize objects and events into inductively powerful groups, organize our world, construct complicated thoughts, and conserve memory resources. Alternative theories have proposed that concepts are represented by rules, prototypical category members, many specific exemplars, and structured theories. We consider empirical evidence bearing on these proposals, which, on balance, recommends pluralism, with different representations implicated in different situations. We consider ways in which concepts shape, and are shaped by, perception and language. Concept‐learning research has important educational applications for helping students learn concepts in an efficient and generalizable manner, and we review what is known about how best to improve concept‐learning outcomes. Finally, we consider future directions for research on concepts and categorization, emphasizing links to other fields, such as object recognition and developmental psychology, and the development and testing of computational models.

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Is it better to select or to receive? Learning via active and passive hypothesis testing.

Cited by 132 publications

References 99 publications

Causal Model Comparison Shows That Human Representation Learning Is Not Bayesian

Causal Model Comparison Shows That Human Representation Learning Is Not Bayesian

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

Categorization and Concepts

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