Semantic and Perceptual Processing of Number Symbols: Evidence from a Cross-linguistic fMRI Adaptation Study

Holloway, Ian D.; Battista, Christian; Vogel, Stephan; Ansari, Daniel

doi:10.1162/jocn_a_00323

Cited by 75 publications

(70 citation statements)

References 45 publications

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“…Effects of semantic priming on brain responses are reliably measured by ERPs (N400 effect) and fMRI (cross-adaptation effect; for review, see Kutas and Federmeier, 2011;Henson and Rugg, 2003). Here we adapted a priming procedure previously used in our group to study object-word interactions in the visual modality (Hurley et al, 2009(Hurley et al, , 2012, but with the inclusion of an olfactory-cued condition. Although previous ERP studies have used odor cues to assess priming effects in response to target pictures (Grigor et al, 1999;Sarfarazi et al, 1999;Castle et al, 2000;Kowalewski and Murphy, 2012), the absence of any direct comparison to nonolfactory primes made it difficult to infer whether the observed findings were specific to the olfactory modality or merely a general property of semantic priming per se.…”

Section: Methodsmentioning

confidence: 99%

“…Such methods have been used successfully with visual (Buckner et al, 1998;Dehaene et al, 2001;Vuilleumier et al, 2005), crosslinguistic (Holloway et al, 2013), and olfactory (Gottfried et al, 2006;Li et al, 2010) stimuli. In the context of this experiment, if the semantic meaning of an object cue is similar to the subsequent target word, then word-evoked fMRI responses should be attenuated in brain areas mediating odor-lexical integration compared with when the cue and target are semantically dissimilar.…”

Section: Methodsmentioning

confidence: 99%

“…Inferential analyses focused on the N400 component, which was measured as the mean EEG amplitude from 300 to 450 ms after word target onset in 15 electrodes where N400 effects were found to be maximal in previous studies (Hurley et al, 2009(Hurley et al, , 2012. To compare the spatial distribution of N400 effects across cue modality (odor-cued vs picturecued), electrodes were grouped into anterior, central, and posterior scalp regions, each containing five electrodes (anterior cluster: AF3, AF4, F3, Fz, F4; central cluster: Fc1, Fc2, C3, Cz, C4); posterior cluster: Cp1, Cp2, P3, Pz, P4).…”

Section: Methodsmentioning

confidence: 99%

“…Significance of N400 effects was examined via a three-factor repeated-measures ANOVA, with factors of cue-target congruency (three levels: match, related nonmatch, unrelated nonmatch), cue modality (two levels: olfactory, visual), and electrode region (three levels: anterior, central, posterior), collapsed across the five electrodes contained within each regional cluster. The N400 effect is characterized as greater negative amplitude on nonmatching compared with matching trials, so the congruency factor was examined with contrast weights of Ϫ1 to both the related nonmatching and the unrelated nonmatching conditions and a contrast weight of ϩ2 to the matching condition, following our prior methods (Hurley et al, 2009(Hurley et al, , 2012.…”

Section: Methodsmentioning

confidence: 99%

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A Designated Odor–Language Integration System in the Human Brain

Olofsson

Hurley

Bowman³

et al. 2014

J. Neurosci.

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Odors are surprisingly difficult to name, but the mechanism underlying this phenomenon is poorly understood. In experiments using event-related potentials (ERPs) and functional magnetic resonance imaging (fMRI), we investigated the physiological basis of odor naming with a paradigm where olfactory and visual object cues were followed by target words that either matched or mismatched the cue. We hypothesized that word processing would not only be affected by its semantic congruency with the preceding cue, but would also depend on the cue modality (olfactory or visual). Performance was slower and less precise when linking a word to its corresponding odor than to its picture. The ERP index of semantic incongruity (N400), reflected in the comparison of nonmatching versus matching target words, was more constrained to posterior electrode sites and lasted longer on odor-cue (vs picture-cue) trials. In parallel, fMRI crossadaptation in the right orbitofrontal cortex (OFC) and the left anterior temporal lobe (ATL) was observed in response to words when preceded by matching olfactory cues, but not by matching visual cues. Time-series plots demonstrated increased fMRI activity in OFC and ATL at the onset of the odor cue itself, followed by response habituation after processing of a matching (vs nonmatching) target word, suggesting that predictive perceptual representations in these regions are already established before delivery and deliberation of the target word. Together, our findings underscore the modality-specific anatomy and physiology of object identification in the human brain.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

A Designated Odor–Language Integration System in the Human Brain

Olofsson

Hurley

Bowman³

et al. 2014

J. Neurosci.

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“…, which is commonly considered responsible for numerical representation (4,(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52)(53), using an established method of functional MRI (fMRI) adaptation for the study of numerosity perception (4). Neural tuning curve shift caused by connectivity in probes.…”

Section: Sulcus (Ips)mentioning

confidence: 99%

Topology-defined units in numerosity perception

Zhou

et al. 2015

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

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What is a number? The number sense hypothesis suggests that numerosity is "a primary visual property" like color, contrast, or orientation. However, exactly what attribute of a stimulus is the primary visual property and determines numbers in the number sense? To verify the invariant nature of numerosity perception, we manipulated the numbers of items connected/enclosed in arbitrary and irregular forms while controlling for low-level features (e.g., orientation, color, and size). Subjects performed discrimination, estimation, and equality judgment tasks in a wide range of presentation durations and across small and large numbers. Results consistently show that connecting/ enclosing items led to robust numerosity underestimation, with the extent of underestimation increasing monotonically with the number of connected/enclosed items. In contrast, grouping based on color similarity had no effect on numerosity judgment. We propose that numbers or the primitive units counted in numerosity perception are influenced by topological invariants, such as connectivity and the inside/outside relationship. Beyond the behavioral measures, neural tuning curves to numerosity in the intraparietal sulcus were obtained using functional MRI adaptation, and the tuning curves showed that numbers represented in the intraparietal sulcus were strongly influenced by topology.W hat is a number? The answer to this age-old and fundamental question of philosophy has increasingly benefited from recent scientific investigation using psychology and neuroscience. The number sense hypothesis (1, 2) suggests that a number is "a basic property of the environment" (3) and particularly, because of its remarkable adaptation effect, "a primary visual property" (2), like color, contrast, or orientation (2-8). However, exactly what attribute in the environment is the primary property and determines numbers in the number sense, or more concretely, what is counted in numerosity perception? Consider the invariant nature of numerosity perception. It is self-evident that numerosity is invariant to specific features (e.g., orientation, size, shape, and color) of individual items to be counted. In other words, the primitive units to be counted must be invariant with variation in form dimensions and other visual features (2, 3, 7, 9-11). Then, the critical question becomes how to define precisely such abstract and invariant attributes. ResultsGeneralizing Connection to a Topological Invariant: Connectivity. We designed arbitrary and irregular shapes of connecting line segments (Fig. 1A, Upper) to test the invariant effect of connection on numerosity judgement independent of the concrete forms or manners of connection (12, 13). Three conditions of connection were constructed: zero, one, and two connected pairs of dots in the test patterns (Fig. 1A). A typical numerosity discrimination task was adopted, in which two visual patterns of dots were briefly presented on opposite sides of fixation (one serving as a reference and the other one serving as a test), and subj...

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Development of Mathematical Reasoning

Iuculano

Menon

2018

Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience

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Mathematical cognition provides a foundation for the development of skills critical for success in the 21st century. The use of mathematics to categorize, visualize, and manipulate information now extends to virtually all domains of human activity, making the necessity for basic mathematical skills more pressing than ever. This chapter summarizes current knowledge about the developmental and neurocognitive basis of mathematical reasoning. We review central cognitive theories and summarize emerging findings on the perceptual and cognitive building blocks of numerical cognition, the functional brain circuits associated with them, and the multiple memory and cognitive‐control systems that play a critical role in scaffolding children's mathematical skill development. We highlight neurodevelopmental models and functional brain circuits that go beyond individual regions involved in number processing and demonstrate that brain systems and circuits engaged by the developing brain are not the same as those engaged in adult brains sculpted by years of learning.

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Semantic and Perceptual Processing of Number Symbols: Evidence from a Cross-linguistic fMRI Adaptation Study

Cited by 75 publications

References 45 publications

A Designated Odor–Language Integration System in the Human Brain

A Designated Odor–Language Integration System in the Human Brain

Topology-defined units in numerosity perception

Development of Mathematical Reasoning

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