Learning-Induced Changes in Attentional Allocation during Categorization: A Sizable Catalog of Attention Change as Measured by Eye Movements

McColeman, Caitlyn; Barnes, Jordan; Chen, Lihan; Meier, Kimberly; Walshe, R. Calen; Blair, Mark R.

doi:10.1371/journal.pone.0083302

Cited by 17 publications

(48 citation statements)

References 41 publications

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“… Blair et al (2009a) concluded that after learning to correctly categorize the microorganisms, participants’ attention distribution improved: over time, more time was spent looking at relevant features compared to the irrelevant feature. This finding was replicated in further studies with the same stimulus materials ( Blair et al, 2009b ; McColeman et al, 2011 ). More broadly, the idea that learners optimize their attention to look more at relevant information and less at irrelevant information with practice is known as the information reduction hypothesis ( Haider and Frensch, 1999 ).…”

Section: Introductionsupporting

confidence: 63%

Development of Attention and Accuracy in Learning a Categorization Task

et al. 2021

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Being able to categorize objects as similar or different is an essential skill. An important aspect of learning to categorize is learning to attend to relevant features (i.e., features that determine category membership) and ignore irrelevant features of the to-be-categorized objects. Feature variability across objects of different categories is informative, because it allows inferring the rules underlying category membership. In this study, participants learned to categorize fictitious creatures (i.e., aliens). We measured attention to the aliens during learning using eye-tracking and calculated the attentional focus as the ratio of attention to relevant versus irrelevant features. As expected, participants’ categorization accuracy improved with practice; however, in contrast to our expectations, their attentional focus did not improve with practice. When computing the attentional focus, attention to the aliens’ eyes was disregarded, because while eyes attract a lot of attention, they did not vary across aliens (non-informative feature). Yet, an explorative analysis of attention to eyes suggested that participants’ attentional focus did become somewhat more efficient in that over time they learned to ignore the eyes. Results are discussed in the context of the need for instructional methods to improve attentional focus in learning to categorize.

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Section: Introductionsupporting

confidence: 63%

Development of Attention and Accuracy in Learning a Categorization Task

et al. 2021

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“…Facing these challenges, VCL relies on two fundamental processes. The first process is attentional learning, which enables the volitional allocation of attention to relevant features, while ‘filtering out’ distracting salient features that have little relevance for categorization ( Rehder and Hoffman, 2005 ; Sloutsky and Fisher, 2008 ; Blair et al, 2009 ; Hoffman and Rehder, 2010 ; Sloutsky, 2010 ; McColeman et al, 2014 ). The second process is perceptual learning, which enables becoming more sensitive to subtle, initially hard to detect differences between objects from different categories ( Shiffrin and Schneider, 1977 ; Goldstone, 1994 , 1998 ; Goldstone et al, 2001 ; Roelfsema et al, 2010 ).…”

Section: Introductionmentioning

confidence: 99%

Feature saliency and feedback information interactively impact visual category learning

2015

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Visual category learning (VCL) involves detecting which features are most relevant for categorization. VCL relies on attentional learning, which enables effectively redirecting attention to object’s features most relevant for categorization, while ‘filtering out’ irrelevant features. When features relevant for categorization are not salient, VCL relies also on perceptual learning, which enables becoming more sensitive to subtle yet important differences between objects. Little is known about how attentional learning and perceptual learning interact when VCL relies on both processes at the same time. Here we tested this interaction. Participants performed VCL tasks in which they learned to categorize novel stimuli by detecting the feature dimension relevant for categorization. Tasks varied both in feature saliency (low-saliency tasks that required perceptual learning vs. high-saliency tasks), and in feedback information (tasks with mid-information, moderately ambiguous feedback that increased attentional load, vs. tasks with high-information non-ambiguous feedback). We found that mid-information and high-information feedback were similarly effective for VCL in high-saliency tasks. This suggests that an increased attentional load, associated with the processing of moderately ambiguous feedback, has little effect on VCL when features are salient. In low-saliency tasks, VCL relied on slower perceptual learning; but when the feedback was highly informative participants were able to ultimately attain the same performance as during the high-saliency VCL tasks. However, VCL was significantly compromised in the low-saliency mid-information feedback task. We suggest that such low-saliency mid-information learning scenarios are characterized by a ‘cognitive loop paradox’ where two interdependent learning processes have to take place simultaneously.

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“…The third characteristic is the number of categories; data sets we selected either have two or four response options. Each of the four previously-published data sets contained choice and eye-tracking data, and were generously made freely available in Mccoleman et al [47]. We next describe the main details of each data set, but refer to Mccoleman et al [47] for additional details.…”

Section: Data Overviewmentioning

confidence: 99%

“…After making a categorization, each of the 33 participants received corrective feedback. For additional details, we refer readers to original articles [47,48].…”

Section: Data Set 1: Two-alternative Rule-basedmentioning

confidence: 99%

The Quest for Simplicity in Human Learning: Identifying the Constraints on Attention

Galdo¹,

Sloutsky²,

Turner³

2021

Preprint

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For better or worse, humans live a resource constrained existence; only a fraction of the sensations our body experiences ever reach conscious awareness, and we store a shockingly small subset of these experiences in short-term memory for later use. Despite these observations, most theories of learning assume that, given feedback about a new experience, knowledge is updated so as to minimize subsequent errors with minimal consideration of cognitive capacity constraints. Acknowledging that human cognition has clear biological limitations, we explored the degree to which human learning could be better described with sets of biases toward simpler and more parsimonious mental representations (i.e., simplicity biases) relative to an error-driven, accuracy-maximizing normative model. Taking the normative model as a basis, we developed a suite of nested computational models that use various mechanistic simplicity biases to explain learning. We fit these models to four data sets that varied in the type of learning needed to achieve high accuracy. Across all data sets, we found consistent evidence that the best descriptors of human learning were models with mechanisms that instantiated a constrained optimization process, where errors were minimized subject to constraints on both attention and memory. Importantly, whereas normative models failed to account for patterns of attentional deployment over time, models with simplicity biases accounted well for both choice responses and fixation data as participants learned various categorization tasks.

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Learning-Induced Changes in Attentional Allocation during Categorization: A Sizable Catalog of Attention Change as Measured by Eye Movements

Cited by 17 publications

References 41 publications

Development of Attention and Accuracy in Learning a Categorization Task

Development of Attention and Accuracy in Learning a Categorization Task

Feature saliency and feedback information interactively impact visual category learning

The Quest for Simplicity in Human Learning: Identifying the Constraints on Attention

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