The quest for simplicity in human learning: Identifying the constraints on attention

Galdo, Matthew; Weichart, Emily R.; Sloutsky, Vladimir M.; Turner, B. E.

doi:10.1016/j.cogpsych.2022.101508

Cited by 7 publications

(24 citation statements)

References 97 publications

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“…By contrast, the right panel of Figure 1D shows that when the learner tries to minimize the total amount of attention, both dimensions are still attended, but neither dimension is attended as much as they would be if the learner only maximized accuracy. In Galdo et al (2022), we performed a "switchboard" analysis (e.g., Turner, 2019;Turner et al, 2018) on four data sets collected by Blair and colleagues (McColeman et al, 2014) and one data set from Mack et al (2016). We found compelling evidence that humans used multiple goals during learning, especially the ones depicted in Figure 1, and the degree to which those goals were prioritized differed across individual participants.…”

Section: Goals 2 and 3: Reducing Attentionmentioning

confidence: 89%

“…One computational model we discuss here is the adaptive attention representation model (AARM; Galdo et al, 2022;Turner, 2019;Weichart, Evans, et al, 2022;, which is derived from other exemplar-based models of categorization (Estes, 1986;Medin & Schaffer, 1978;Nosofsky, 1986). In AARM (as well as other models in this class), attention is represented as a vector containing the amount of attention for each dimension of information.…”

Section: Optimizing For a Learner's Goalsmentioning

confidence: 99%

“…Each location in the space corresponds to a different possible allocation of attention, and yellow-green areas correspond to regions of the space that satisfy the goal(s) used in each panel. We now highlight three potential goals that can independently, or in combination, direct attention during learning (Galdo et al, 2022).…”

Section: Optimizing For a Learner's Goalsmentioning

confidence: 99%

“…Because of the amount of effort that is required to actively maintain a feature-to-category map for every dimension, learners may try to reduce complexity by focusing attention on only a subset of dimensions in a complex learning environment (i.e., when the number of dimensions is large). In Galdo et al (2022), we proposed a few theoretical alternatives that allowed the learner to flexibly allocate attention with the overarching goal of simplifying the learning problem, thereby minimizing cognitive effort. In two examples, we explored the possibility that dimensions of information are "regularized" out, meaning that dimensions would be attended only if their relevance exceeded some type of cost function.…”

Section: Goals 2 and 3: Reducing Attentionmentioning

confidence: 99%

“…The extant theories detailing attention-memory interactions can roughly be distinguished on the basis of how attention is allocated to previous episodic events (i.e., exemplars; Aha & Goldstone, 1992; Nelson & Shiffrin, 2013), clusters of related episodic events (Braunlich & Love, 2022; Love et al, 2004), or individual features of those previous events (Carvalho & Goldstone, 2022; Turner et al, n.d.; Weichart, Galdo, et al, 2022). All of these theories provide evidence that selective attention affects the way the stimulus environment is represented in memory, and the evidence stems from applying computational models to response data from carefully constructed categorization experiments as well as data from neuroimaging (Mack et al, 2013, 2016, 2020) and from eye tracking (Blair et al, 2009; Galdo et al, 2022; Kruschke & Blair, 2000; Rehder & Hoffman, 2005a, 2005b; Weichart, Galdo, et al, 2022). In what follows, we consider the key theoretical issues and potential consequences of the interaction between selective attention and memory.…”

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confidence: 99%

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Cognitive Inertia: Cyclical Interactions Between Attention and Memory Shape Learning

Turner,

Sloutsky

2024

Curr Dir Psychol Sci

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In explaining how humans selectively attend, common frameworks often focus on how attention is allocated relative to an idealized allocation based on properties of the task. However, these perspectives often ignore different types of constraints that could help explain why attention was allocated in a particular way. For example, many computational models of learning are well equipped to explain how attention should ideally be allocated to minimize errors within the task, but these models often assume all features are perfectly encoded or that the only learning goal is to maximize accuracy. In this article, we argue for a more comprehensive view by using computational modeling to understand the complex interactions that occur between selective attention and memory. Our central thesis is that although selective attention directs attention to relevant dimensions, relevance can be established only through memories of previous experiences. Hence, attention is initially used to encode features and create memories, but thereafter, attention operates selectively on the basis of what is kept in memory. Through this lens, deviations from ideal performance can still be viewed as goal-directed selective attention, but the orientation of attention is subject to the constraints of the individual learner.

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Section: Goals 2 and 3: Reducing Attentionmentioning

confidence: 89%

Section: Optimizing For a Learner's Goalsmentioning

confidence: 99%

Section: Optimizing For a Learner's Goalsmentioning

confidence: 99%

Section: Goals 2 and 3: Reducing Attentionmentioning

confidence: 99%

mentioning

confidence: 99%

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Cognitive Inertia: Cyclical Interactions Between Attention and Memory Shape Learning

Turner,

Sloutsky

2024

Curr Dir Psychol Sci

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Cognition: The power of simple associative learning

Sloutsky

Turner

2023

Current Biology

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As within, so without, as above, so below: Common mechanisms can support between- and within-trial category learning dynamics.

Weichart¹,

Galdo²,

Sloutsky³

et al. 2022

Psychological Review

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Two fundamental difficulties when learning novel categories are deciding (a) what information is relevant and (b) when to use that information. Although previous theories have specified how observers learn to attend to relevant dimensions over time, those theories have largely remained silent about how attention should be allocated on a within-trial basis, which dimensions of information should be sampled, and how the temporal order of information sampling influences learning. Here, we use the adaptive attention representation model (AARM) to demonstrate that a common set of mechanisms can be used to specify: (a) How the distribution of attention is updated between trials over the course of learning and (b) how attention dynamically shifts among dimensions within a trial. We validate our proposed set of mechanisms by comparing AARM’s predictions to observed behavior in four case studies, which collectively encompass different theoretical aspects of selective attention. We use both eye-tracking and choice response data to provide a stringent test of how attention and decision processes dynamically interact during category learning. Specifically, how does attention to selected stimulus dimensions gives rise to decision dynamics, and in turn, how do decision dynamics influence which dimensions are attended to via gaze fixations?

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The quest for simplicity in human learning: Identifying the constraints on attention

Cited by 7 publications

References 97 publications

Cognitive Inertia: Cyclical Interactions Between Attention and Memory Shape Learning

Cognitive Inertia: Cyclical Interactions Between Attention and Memory Shape Learning

Cognition: The power of simple associative learning

As within, so without, as above, so below: Common mechanisms can support between- and within-trial category learning dynamics.

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