A unified account of numerosity perception

Cheyette, Samuel J.; Piantadosi, Steven T.

doi:10.1038/s41562-020-00946-0

Cited by 69 publications

(101 citation statements)

References 59 publications

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“…Our results appear broadly in line with the idea that numerical judgments are related to the capacity limits of storing information in working memory when performing a visual task. As recently reported by Cheyette and Piantadosi (2020) , infants and primates have a lower visual memory capacity that limits their accuracy even throughout the small number range, and this might similarly apply to bumblebees.…”

Section: Discussionmentioning

confidence: 67%

“…However, the OFS is thought to depend on object perception and individuation, which depends on working memory and which would also explain the upper limit for this system of three to four items ( Cowan 2001 ). Even though there is also evidence to suggest that performances seen for small versus large numbers might be underpinned by a single system ( Gallistel 1990 ; Dehaene and Brannon 2011 ; Halberda and Odic 2015 ; Cheyette and Piantadosi 2020 ), there is no controversy about the observation that humans and some other animals are exceptionally fast and accurate at assessing numbers of up to four. The ability to process visual information rapidly and in parallel appears to be a general feature of the primate visual system.…”

Section: Introductionmentioning

confidence: 99%

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Bumblebees Use Sequential Scanning of Countable Items in Visual Patterns to Solve Numerosity Tasks

MaBouDi

Dona

Gatto

et al. 2020

Integrative and Comparative Biology

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Synopsis Most research in comparative cognition focuses on measuring if animals manage certain tasks; fewer studies explore how animals might solve them. We investigated bumblebees’ scanning strategies in a numerosity task, distinguishing patterns with 2 items from 4 and 1 from 3, and subsequently transferring numerical information to novel numbers, shapes and colours. Video analyses of flight paths indicate that bees do not determine the number of items by using a rapid assessment of number (as mammals do in “subitizing”); instead, they rely on sequential enumeration even when items are presented simultaneously and in small quantities. This process, equivalent to the motor tagging (“pointing”) found for large number tasks in some primates, results in longer scanning times for patterns containing larger numbers of items. Bees used a highly accurate working memory, remembering which items have already been scanned, resulting in fewer than 1% of re-inspections of items before making a decision. Our results indicate that the small brain of bees, with less parallel processing capacity than mammals, might constrain them to use sequential pattern evaluation even for low quantities.

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Section: Discussionmentioning

confidence: 67%

Section: Introductionmentioning

confidence: 99%

Bumblebees Use Sequential Scanning of Countable Items in Visual Patterns to Solve Numerosity Tasks

MaBouDi

Dona

Gatto

et al. 2020

Integrative and Comparative Biology

View full text Add to dashboard Cite

show abstract

“…Thus, it seems likely that the detection of the largest numerosity was also constrained by the cortical magnification effect of the numerosity map representation. In support of this assumption, Cheyette et al 42 suggested that the limited amount of information processing capability of the underlying neural circuits leads to the inaccurate perception of large numerosity, while a single system represents small and large numerosity.…”

Section: Discussionmentioning

confidence: 99%

Topographic numerosity maps cover subitizing and estimation ranges

et al. 2021

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Numerosity, the set size of a group of items, helps guide behaviour and decisions. Non-symbolic numerosities are represented by the approximate number system. However, distinct behavioural performance suggests that small numerosities, i.e. subitizing range, are implemented differently in the brain than larger numerosities. Prior work has shown that neural populations selectively responding (i.e. hemodynamic responses) to small numerosities are organized into a network of topographical maps. Here, we investigate how neural populations respond to large numerosities, well into the ANS. Using 7 T fMRI and biologically-inspired analyses, we found a network of neural populations tuned to both small and large numerosities organized within the same topographic maps. These results demonstrate a continuum of numerosity preferences that progressively cover both the subitizing range and beyond within the same numerosity map, suggesting a single neural mechanism. We hypothesize that differences in map properties, such as cortical magnification and tuning width, underlie known differences in behaviour.

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“…2f). Selective updating therefore naturally gives rise to diminishing sensitivity towards larger values as is universally observed in Psychophysics 29 , numerical cognition 30,31 , and Behavioural Economics 32 . Going beyond typical studies of transitive inference with deterministic outcomes, we examined whether our simulation results generalize to scenarios where relational outcomes can be variable, as is the case in many real-world domains such as sports, stock markets, or social hierarchies.…”

Section: Mainmentioning

confidence: 88%

Asymmetric learning facilitates human inference of transitive relations

Ciranka

Linde-Domingo

Padezhki

et al. 2021

Preprint

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Humans and other animals are capable of inferring never-experienced relations (e.g., A>C) from other relational observations (e.g., A>B and B>C). The processes behind such transitive inference are subject to intense research. Here, we demonstrate a new aspect of relational learning, building on previous evidence that transitive inference can be accomplished through simple reinforcement learning mechanisms. We show in simulations that inference of novel relations benefits from an asymmetric learning policy, where observers update only their belief about the winner (or loser) in a pair. Across 4 experiments (n=145), we find substantial empirical support for such asymmetries in inferential learning. The learning policy favoured by our simulations and experiments gives rise to a compression of values which is routinely observed in psychophysics and behavioural economics. In other words, a seemingly biased learning strategy that yields well-known cognitive distortions can be beneficial for transitive inferential judgments.

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A unified account of numerosity perception

Cited by 69 publications

References 59 publications

Bumblebees Use Sequential Scanning of Countable Items in Visual Patterns to Solve Numerosity Tasks

Bumblebees Use Sequential Scanning of Countable Items in Visual Patterns to Solve Numerosity Tasks

Topographic numerosity maps cover subitizing and estimation ranges

Asymmetric learning facilitates human inference of transitive relations

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