A generalized sense of number

Arrighi, Roberto; Togoli, Irene; Burr, David

doi:10.1098/rspb.2014.1791

Cited by 130 publications

(175 citation statements)

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“…This result not only shows that numerosity is not derived indirectly from density, texture or other low-level features, but also casts doubts on the idea that density is a primary visual feature: rather, density seems to be derived indirectly from numerosity, reinforcing previous evidence showing that at low numerosities, density judgments are particularly unreliable and are often surrogated by number judgments . Overall our results add to a growing body of literature showing that visual numerosity is perceived directly, rather than being recalculated from area and density (Anobile, Arrighi, et al, 2016;Anobile, Castaldi, Turi, Tinelli, & Burr, 2016;Anobile et al, 2014;Anobile, Cicchini, et al, 2016;Anobile et al, 2015;Arrighi et al, 2014;Burr & Ross, 2008;Cicchini et al, 2016;DeWind et al, 2015;Hurewitz et al, 2006;Kramer, Di Bono, & Zorzi, 2011;Ross & Burr, 2010Stoianov & Zorzi, 2012).…”

Section: Items Connection Affects Ans and Texture-density 140mentioning

confidence: 60%

“…A particularly clear demonstration that numerosity mechanisms need not involve texture density is the crossmodal and cross-format adaptation study of Arrighi, Togoli, and Burr (2014). Adaptation is one of the clearest signatures of the existence of a dedicated system for the encoding of a particular feature (Clifford & Rhodes, 2005;Mollon, 1974;Thompson & Burr, 2009).…”

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confidence: 99%

“…Numerosity is also susceptible to adaptation: viewing large numbers of dots for a few seconds (adapter stimulus) makes subsequent smaller groups of dots seem to contain fewer dots than they actually do (Burr, Anobile, & Turi, 2011;Burr & Ross, 2008;Schwiedrzik, Bernstein, & Melloni, 2016). Recently, Arrighi et al (2014) extended the technique to demonstrate adaptation to temporal numerosity sequences: adapting to a sequence of tones for Items Connection Affects ANS and Texture-Density 134 few seconds changed the apparent numerosity of subsequent streams of visual flashes, and vice-versa, even though no texture was involved (as stimuli were temporarily defined). Importantly, adapting to a series of flashes changes the apparent numerosity of dot arrays: again, there was no texture in the adaptor, yet a spatial pattern seemed to contain fewer dots.…”

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confidence: 99%

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Connecting visual objects reduces perceived numerosity and density for sparse but not dense patterns

et al. 2017

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How is numerosity encoded by the visual system? -directly, or derived indirectly from texture density? We recently suggested that the numerosity of sparse patterns is encoded directly by dedicated mechanisms (which have been described as the "Approximate Number System" ANS). However, at high dot densities, where items become "crowded" and difficult to segregate, "texture-density" mechanisms come into play. Here we tested the importance of item segmentation on numerosity and density perception at various stimulus densities, by measuring the effect of connecting visual objects with thin lines. The results confirmed many previous studies showing that connecting items robustly reduces the apparent numerosity of patterns of moderate density. We further showed that the apparent density of moderatedensity patterns is also reduced by connecting the dots. Crucially, we found that both these effects are strongly reduced at higher numerosities. Indeed for density judgments, the effect reverses, so connecting dots in dense patterns increases the apparent density (as expected from the physical characteristics). The results provide clear support for the three-regime framework of number perception, and suggest that for moderately sparse stimuli, numerosity -but not texture-density -is perceived directly.

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Section: Items Connection Affects Ans and Texture-density 140mentioning

confidence: 60%

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confidence: 99%

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Connecting visual objects reduces perceived numerosity and density for sparse but not dense patterns

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“…Adaptation to so-called causality is also in retinotopic coordinates (25), suggesting that it, too, is quite low level. On the other hand, adaptation to numerosity occurs in spatiotopic coordinates (40). Nevertheless, more work is needed to understand better why some, but not all, types of perceptual function are affected in individuals on the autism spectrum.…”

Section: Discussionmentioning

confidence: 99%

“…As mounting evidence suggests that numerosity adaptation occurs at a high level, across modalities and types of presentation (32,40), it seemed an ideal candidate to test whether reduced adaptation in autism is a general phenomenon affecting much of the perceptual brain or restricted to socially relevant visual stimuli, such as faces.…”

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Children with autism spectrum disorder show reduced adaptation to number

Turi

Burr

Igliozzi

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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Autism is known to be associated with major perceptual atypicalities. We have recently proposed a general model to account for these atypicalities in Bayesian terms, suggesting that autistic individuals underuse predictive information or priors. We tested this idea by measuring adaptation to numerosity stimuli in children diagnosed with autism spectrum disorder (ASD). After exposure to large numbers of items, stimuli with fewer items appear to be less numerous (and vice versa). We found that children with ASD adapted much less to numerosity than typically developing children, although their precision for numerosity discrimination was similar to that of the typical group. This result reinforces recent findings showing reduced adaptation to facial identity in ASD and goes on to show that reduced adaptation is not unique to faces (social stimuli with special significance in autism), but occurs more generally, for both parietal and temporal functions, probably reflecting inefficiencies in the adaptive interpretation of sensory signals. These results provide strong support for the Bayesian theories of autism.utism is a heritable, lifelong neurodevelopmental condition with striking effects on social communication. The condition, however, is also associated with a range of nonsocial symptoms, including both hypersensitivity and hyposensitivity to perceptual stimuli, and sensory-seeking behaviors such as attraction to light, intense looking at objects, and fascination with brightly colored objects. These sensory atypicalities, which now form part of the diagnostic criteria for autism (1), can have debilitating effects on the lives of autistic people (2) and their families (3).We have recently proposed a Bayesian account of autism (4), suggesting that it is not sensory processing itself that is disrupted in individuals with autism, but the interpretation of the sensory input. The Bayesian class of theories, including predictive coding and other generative models (5-7), assumes that perception is an optimized combination of external sensory data (the likelihood) and an internal model (the prior). We suggested that this process may be atypical in autism, in that the internal priors are underweighted and less used than in typical individuals. Our theory has been followed by several others along similar lines (8-11).The suggestion of underutilization of priors leads to several specific predictions. One strong prediction is that autistic individuals should show reduced adaptation aftereffects. Adaptation, which occurs throughout sensory systems, represents a form of experience-dependent plasticity in which our current sensory experience is intimately affected by how we viewed the world only moments before. It is widely held to pose numerous functional advantages (12-16), including serving to autocalibrate perceptual systems to their environment by dynamically tuning its responses to match the distribution of stimuli to make maximal use of the limited working range of the system (12-16). It achieves this by reducing the transm...

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Modality‐independent representations of small quantities based on brain activation patterns

Damarla

Cherkassky

Just

2016

Human Brain Mapping

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Machine learning or MVPA (Multi Voxel Pattern Analysis) studies have shown that the neural representation of quantities of objects can be decoded from fMRI patterns, in cases where the quantities were visually displayed. Here we apply these techniques to investigate whether neural representations of quantities depicted in one modality (say, visual) can be decoded from brain activation patterns evoked by quantities depicted in the other modality (say, auditory). The main finding demonstrated, for the first time, that quantities of dots were decodable by a classifier that was trained on the neural patterns evoked by quantities of auditory tones, and vice-versa. The representations that were common across modalities were mainly right-lateralized in frontal and parietal regions. A second finding was that the neural patterns in parietal cortex that represent quantities were common across participants. These findings demonstrate a common neuronal foundation for the representation of quantities across sensory modalities and participants and provide insight into the role of parietal cortex in the representation of quantity information.

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A generalized sense of number

Cited by 130 publications

References 33 publications

Connecting visual objects reduces perceived numerosity and density for sparse but not dense patterns

Connecting visual objects reduces perceived numerosity and density for sparse but not dense patterns

Children with autism spectrum disorder show reduced adaptation to number

Modality‐independent representations of small quantities based on brain activation patterns

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