Recursive hierarchical embedding in vision is impaired by posterior middle temporal gyrus lesions

Martins, Maurício; Krause, Carina Denise; Neville, David A.; Pino, Danièle; Villringer, Arno; Obrig, Hellmuth

doi:10.1093/brain/awz242

Cited by 12 publications

(11 citation statements)

References 55 publications

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“…We developed a new experimental paradigm consisting of two tasks which involved hierarchical (HIER) and iterative (ITER) processing. We confirmed behaviorally, that these new tasks correlated specifically with previously validated visuo-spatial recursive and iteration tasks (19, 54), thus showing that they are suitable for segregating hierarchical from non-hierarchy related processing. In fMRI we found that a left lateralized network encompassing lateral prefrontal and posterior temporal cortices was involved in the processing of hierarchies confirming previous studies in the domains of language and vision (5, 6, 19, 55).…”

Section: Discussionsupporting

confidence: 76%

“…We confirmed behaviorally, that these new tasks correlated specifically with previously validated visuo-spatial recursive and iteration tasks (19, 54), thus showing that they are suitable for segregating hierarchical from non-hierarchy related processing. In fMRI we found that a left lateralized network encompassing lateral prefrontal and posterior temporal cortices was involved in the processing of hierarchies confirming previous studies in the domains of language and vision (5, 6, 19, 55). We also confirmed previous findings demonstrating that IFG activity in the processing of hierarchical structures becomes stronger with increased automaticity (56, 57).…”

Section: Discussionsupporting

confidence: 76%

“…Overall, activity within IFG seems to increase with processing automaticity. Moreover, recent studies which control for the effects of automaticity also highlight the role of pSTS in the processing of hierarchies in the visual and musical domains (18,19) , thus suggesting that pSTS activity is involved in the representation of hierarchical structures more broadly, both for low and high levels of automaticity.…”

Section: Introductionmentioning

confidence: 97%

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Distinct hippocampal and cortical contributions in the representation of hierarchies

Scholz

Villringer

Martins

2022

Preprint

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Humans generate complex hierarchies across a variety of domains, including language and music, and this capacity is often associated with activity in inferior frontal gyrus (IFG). Non-human animals have also been shown to represent simple hierarchies in spatial navigation, and human neuroimaging work has implicated the hippocampus in the encoding of items-in-contexts representations, which constitute 2-level hierarchical dependencies. These fields of research use distinct paradigms, leading to disjoint models and precluding cross-species comparisons. Here, we designed a paradigm to integrate both literature strands and show that anterior hippocampus and medial prefrontal cortex encode hierarchical context, mimicking findings from animal spatial navigation. Additionally, we replicated classic neurolinguistic findings of 1) left IFG and posterior temporal cortex in the representation of hierarchies and 2) the association between IFG and processing automaticity. We propose that mammals share an evolutionary ancient system to generate simple hierarchies which is complemented in humans by additional capacities.

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Section: Discussionsupporting

confidence: 76%

Section: Discussionsupporting

confidence: 76%

Section: Introductionmentioning

confidence: 97%

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Distinct hippocampal and cortical contributions in the representation of hierarchies

Scholz

Villringer

Martins

2022

Preprint

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“…The roles of the pMTG in semantic retrieval, verbal memory, and multiple advanced network systems have been well documented in imaging (Davey et al, 2016;Karnath, 2001;Mesgarani, Cheung, Johnson, & Chang, 2014;Xu et al, 2015;Xu et al, 2019) and lesion (Leff et al, 2009;Martins et al, 2019) studies. Verbal learning and semantic retrieval are abstract cognitive functions in humans.…”

Section: A Path Potentially Mediating the Relationship Between Wmh And Recall Memorymentioning

confidence: 99%

Gray matter asymmetry in asymptomatic carotid stenosis

Gao

Xiao

2021

Human Brain Mapping

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Even clinically "asymptomatic" carotid stenosis is associated with multidomain cognitive impairment, gray matter (GM) atrophy, and silent lesion. However, the links between them remain unclear. Using structural MRI data, we examined GM asymmetry index (AI) and white matter hyperintensity (WMH) in 24 patients with severe asymptomatic carotid stenosis (SACS), 24 comorbidity-matched controls, and independent samples of 84 elderly controls and 22 young adults. As compared to controls, SACS patients showed worse verbal memories, higher WMH burden, and rightlateralized GM in posterior middle temporal and mouth-somatomotor regions. These clusters extended to pars triangularis, lateral temporal, and cerebellar regions, when compared with young adults. Further, a full-path of WMH burden (X), GM volume (atrophy, M1), AI (asymmetry, M2), and neuropsychological variables (Y) through a serial mediation model was analyzed. This analysis identified that left-dominated GM atrophy and right-lateralized asymmetry in the posterior middle temporal cortex mediated the relationship between WMH burden and recall memory in SACS patients. These results suggest that the unbalanced hemispheric atrophy in the posterior middle temporal cortex is crucial to mediating relationship between WMH burden and verbal recall memories, which may underlie accelerated aging and cognitive deterioration in patients with SACS and other vascular cognitive impairment.

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“…In order to anticipate the environment in which it evolves, the cognitive system has to infer from the surface properties of the signal its underlying organization. The human brain is surprisingly good at this task and nested structure extraction has been demonstrated in multiple domains [1][2][3][4][5][6][7]. However, since the hierarchical structure is not present in the signal itself but must be inferred from it, the cognitive system cannot determine a priori if a signal has an underlying hierarchical organization [8,9].…”

Section: Introductionmentioning

confidence: 99%

Uncovering hierarchical structure through statistical dependency

Schmid¹,

Saddy²,

Franck³

2023

Preprint

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In this paper, we explore the extraction of recursive nested structure in the processing of self-similar binary sequences generated by two Lindenmayer grammars: the Fibonacci grammar and the Skip grammar. In each of these grammars only sequential order information marks the hierarchical structure. Although closely related, these grammars differ from a formal point of view: the Fibonacci grammar is perfectly scale-free and presents an isomorphism between its surface and derivational properties while the Skip grammar, although also self-similar, does not present this isomorphism. Our goal was to explore the influence of these formal differences on the extraction of hierarchical structure by the participants. To this end, we implemented these grammars in a serial reaction time task. The results show that in both the Fibonacci grammar and the Skip grammar, participants elaborated a hierarchical structure from the signal. This suggests the involvement of at least partially similar mechanisms during processing. However, some processing differences remained that cannot be explained by the hypotheses proposed so far regarding the processing of strings generated by L-systems. We hypothesize that these effects would be due to the self-similarity of the signal which would act as a reinforcement of the structure elaborated by the participants.

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Recursive hierarchical embedding in vision is impaired by posterior middle temporal gyrus lesions

Cited by 12 publications

References 55 publications

Distinct hippocampal and cortical contributions in the representation of hierarchies

Distinct hippocampal and cortical contributions in the representation of hierarchies

Gray matter asymmetry in asymptomatic carotid stenosis

Uncovering hierarchical structure through statistical dependency

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