Selective and distributed attention in human and pigeon category learning

Castro, Leyre; Savic, Olivera; Navarro, Vı́ctor; Sloutsky, Vladimir M.; Wasserman, Edward A.

doi:10.1016/j.cognition.2020.104350

Cited by 12 publications

(13 citation statements)

References 67 publications

(96 reference statements)

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“…Our peck-tracking experiments (Castro & Wasserman, 2014, 2016, 2017; Sheridan et al, 2019) indicated that pigeons selectively attend to the relevant features of category exemplars. In apparent contrast, the more recent study (Castro et al, 2020) indicated that pigeons distribute their attention among multiple stimulus features. Reconciling these apparently contradictory conclusions requires considering the predictive values of the stimulus features in the different studies.…”

Section: Computational Modeling Of Postacquisition Performancementioning

confidence: 85%

“…To find out, we gave human adults and pigeons a categorization task in which there was a single rule-like deterministic feature that perfectly predicted category membership accompanied by six other features that only probabilistically predicted category membership (Castro et al, 2020). Thus, the task could be learned on the basis of either one deterministic feature (which would encourage selective, focused attention) or multiple probabilistic features (which would encourage distributed attention).…”

Section: Computational Modeling Of Postacquisition Performancementioning

confidence: 99%

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Assessing Attention in Category Learning by Animals

Wasserman

Castro

2021

Curr Dir Psychol Sci

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Appreciating that varied stimuli belong to different categories requires that attention be differentially allocated to relevant and irrelevant features of those stimuli. Such selective attention ought to be definable and measurable in both humans and nonhuman animals. We first discuss the definition of attention and methods of assessing it in animals. We then introduce new experimental and computational tools for assessing attention in pigeons both during and after category learning. Deploying these tools, we have found that, as do humans, pigeons attend more to relevant than to irrelevant stimulus features during category learning. Nonetheless, postacquisition assessment reveals that, compared with human adults, pigeons less selectively attend to deterministic features in preference to probabilistic features of category members, which indicates that pigeons’ attention is more distributed. Fresh opportunities now exist for more effectively understanding the evolution and mechanisms of categorical cognition.

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Section: Computational Modeling Of Postacquisition Performancementioning

confidence: 85%

Section: Computational Modeling Of Postacquisition Performancementioning

confidence: 99%

Assessing Attention in Category Learning by Animals

Wasserman

Castro

2021

Curr Dir Psychol Sci

Self Cite

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“…These category specific stimulus features might be discernable from neuronal population responses in visual associative areas in the pigeon brain (Azizi et al, 2019; Koenen et al, 2016). In addition, Castro et al (2020) found in their experiments that with increasing choice accuracy, pecks directed onto relevant features of the categories increased. Thus, during learning the amount of attention to reward predicting cues increased and was clearly signaled by the pecking behavior of the animals.…”

Section: Discussionmentioning

confidence: 97%

“…Thus, during learning the amount of attention to reward predicting cues increased and was clearly signaled by the pecking behavior of the animals. Based on reward contingencies associated with the resulting responses, the feature that was the best predictor of reward becomes the feature that predominantly, but not exclusively controlled the pigeon’s behavior (Castro et al, 2020). This kind of feature selection does not seem to be based on feature configuration but rather on the additive integration of individual features or common elements (Jitsumori & Yoshihara, 1997; Soto & Wasserman, 2010).…”

Section: Discussionmentioning

confidence: 99%

Digital embryos – A novel technical approach to investigate perceptual categorization in pigeons (Columba livia) using machine learning

Pusch

Packheiser

Koenen

et al. 2021

Preprint

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Pigeons are classic model animals to study perceptual category learning. A theoretical understanding of the cognitive mechanisms of categorization requires a careful consideration of the employed stimulus material. Optimally, stimuli should not consist of real-world objects that might be associated with prior experience. The number of exemplars should be theoretically infinite and easy to produce. In addition, the experimenter should have the freedom to produce 2D- and 3D-versions of the stimuli and, finally, the stimulus set should provide the opportunity to identify the diagnostic elements that the animals use. To this end, we used the approach of "virtual phylogenesis" of "digital embryos" to produce two stimulus sets of objects that meet these criteria. In our experiment pigeons learned to categorize these stimuli in a forced-choice procedure. In addition, we used peck tracking to identify where on the stimulus the animals pecked to signal their choice. Pigeons learned the task and transferred successfully to novel exemplars. Using a k-nearest neighbor classifier, we were able to predict the presented stimulus class based on pecking location indicating that pecks are related to features of interest. We further identified potential strategies of the pigeons through this approach, namely that they were either learning one or two categories to discriminate between stimulus classes. These strategies remained stable during category transfer, but differed between individuals indicating that categorization learning is not limited to a single learning strategy.

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“…Thus, during learning the amount of attention to reward predicting cues increased and was clearly signaled by the pecking behavior of the animals. Based on reward contingencies associated with the resulting responses, the feature that was the best predictor of reward becomes the feature that predominantly, but not exclusively controlled the pigeon's behavior (Castro et al 2020). This kind of feature selection does not seem to be based on feature configuration but rather on the additive integration of individual features or common elements (Jitsumori and Yoshihara 1997;Soto and Wasserman 2010).…”

Section: Discussionmentioning

confidence: 99%

Digital embryos: a novel technical approach to investigate perceptual categorization in pigeons (Columba livia) using machine learning

et al. 2022

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Pigeons are classic model animals to study perceptual category learning. To achieve a deeper understanding of the cognitive mechanisms of categorization, a careful consideration of the employed stimulus material and a thorough analysis of the choice behavior is mandatory. In the present study, we combined the use of “virtual phylogenesis”, an evolutionary algorithm to generate artificial yet naturalistic stimuli termed digital embryos and a machine learning approach on the pigeons’ pecking responses to gain insight into the underlying categorization strategies of the animals. In a forced-choice procedure, pigeons learned to categorize these stimuli and transferred their knowledge successfully to novel exemplars. We used peck tracking to identify where on the stimulus the animals pecked and further investigated whether this behavior was indicative of the pigeon’s choice. Going beyond the classical analysis of the binary choice, we were able to predict the presented stimulus class based on pecking location using a k-nearest neighbor classifier, indicating that pecks are related to features of interest. By analyzing error trials with this approach, we further identified potential strategies of the pigeons to discriminate between stimulus classes. These strategies remained stable during category transfer, but differed between individuals indicating that categorization learning is not limited to a single learning strategy.

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Selective and distributed attention in human and pigeon category learning

Cited by 12 publications

References 67 publications

Assessing Attention in Category Learning by Animals

Assessing Attention in Category Learning by Animals

Digital embryos – A novel technical approach to investigate perceptual categorization in pigeons (Columba livia) using machine learning

Digital embryos: a novel technical approach to investigate perceptual categorization in pigeons (Columba livia) using machine learning

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