Updates References

Author: felixkoerber

_bibliography/preprints.bib

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+---
+References
+==========
+
+@online{gaglNonHumanRecognitionOrthography2024,
+  title = {Non-{{Human Recognition}} of {{Orthography}}: {{How}} Is It Implemented and How Does It Differ from {{Human}} Orthographic Processing},
+  shorttitle = {Non-{{Human Recognition}} of {{Orthography}}},
+  author = {Gagl, Benjamin and Weyers, Ivonne and Eisenhauer, Susanne and Fiebach, Christian J. and Colombo, Michael and Scarf, Damian and Ziegler, Johannes C. and Grainger, Jonathan and Güntürkün, Onur and Mueller, Jutta L.},
+  date = {2024-06-25},
+  eprinttype = {bioRxiv},
+  eprintclass = {New Results},
+  pages = {2024.06.25.600635},
+  doi = {10.1101/2024.06.25.600635},
+  url = {https://www.biorxiv.org/content/10.1101/2024.06.25.600635v1},
+  urldate = {2024-11-12},
+  abstract = {The ability to robustly recognize strings of letters, a cornerstone of reading, was observed in Baboons and Pigeons despite their lack of phonological and semantic knowledge. Here, we apply a comparative modeling approach to investigate the neuro-cognitive basis of Human, Baboon, and Pigeon orthographic decision behavior, addressing whether phylogenetic relatedness entails similar underlying neuro-cognitive phenotypes. We use the highly transparent Speechless Reader Model (SLR), which assumes letter string recognition based on widely accepted computational principles of predictive coding so that orthographic decisions rely on a prediction error signal emerging from multiple, hierarchically ordered representational levels, i.e., low-level visual, letter, or letter sequence representations. We investigate which representations species use during successful orthographic decision-making. We introduce multiple SLR variants, each including one or multiple prediction error representations, and compare the simulations of each SLR variant to the orthographic decisions from individuals of three species after learning letter strings without meaning. Humans predominantly relied on letter-sequence-level representations, resulting in the highest task performance in behavior and model simulations. Baboons also relied on sequence-based representations but in combination with pixel- and letter-level representations. In contrast, all Pigeons relied on pixel-level representations, partly in combination with letter- and letter-sequence-level representations. These findings suggest that orthographic representations utilized in orthographic decisions reflect the phylogenetic distance between species: Humans and Baboons use more similar representations compared to Pigeons. Overall, the description of orthographic decisions based on a small set of representations and computations was highly successful in describing behavior, even for Humans who mastered reading in its entirety. Significance Statement Baboons and Pigeons show reading-like behavior, suggesting that efficient reading relies partly on neuro-cognitive processes shared across species. Here, we use a computational model to describe, on an individual level, the processes implemented in each Human, Baboon, and Pigeon included in the study. The model allows us to investigate the similarities and differences of how each Human or Animal implemented reading-like behavior on a neuro-cognitive level. We found considerable individual differences in all species, but the processes used by Humans and Baboons were more similar to those implemented by Pigeons. Thus, the neuro-cognitive processes that allow accurate behavioral responses in reading-like tasks reflect the evolutionary distance between species.},
+  langid = {english},
+  pubstate = {prepublished},
+  file = {C:\Users\felix\Zotero\storage\GL8T7ER8\Gagl et al. - 2024 - Non-Human Recognition of Orthography How is it im.pdf}
+}
+
+@online{neamaalkassisFundamentalFrequenciesOur2024,
+  title = {The Fundamental Frequencies of Our Own Voice},
+  author = {Neamaalkassis, Hakam and Boubenec, Yves and Muralikrishnan, R. and Fiebach, Christian and Tavano, Alessandro},
+  date = {2024-02-20},
+  eprinttype = {OSF},
+  doi = {10.31234/osf.io/fm9ed},
+  url = {https://osf.io/fm9ed},
+  urldate = {2024-11-12},
+  abstract = {Own actions send a corollary discharge (CD) signal, that is a copy of the planned motor program, to sensory-specific brain areas to suppress the anticipated sensory response, providing a neural basis for the sense of self. When we speak, the sensory consequences of the fundamental frequency (f0) of our own voice, generated by vocal fold vibrations, are suppressed. However, due to bone/air conduction filtering effects, the f0 we self-generate is measurably different from the f0 we subjectively perceive as defining our own voice. Using an auditory change deafness paradigm, we parametrically tested the sensitivity to auditory change in the frequency neighbourhoods of individual objective and subjective voice f0, and found that participants experience change deafness for both to a similar extent, relative to a control pitch condition. We conclude that when we listen attentively, we are likely to filter out voice pitches in the vicinity of our own objective and subjective voice f0, possibly as a long-term consequence of speaking-induced suppression mechanisms integrated with individual, perceptual bodily priors.},
+  langid = {american},
+  pubstate = {prepublished},
+  file = {C:\Users\felix\Zotero\storage\5XLACTMI\Neamaalkassis et al. - 2024 - The fundamental frequencies of our own voice.pdf}
+}
+
+@online{taylorLettersOptimalTransport2024,
+  title = {Beyond {{Letters}}: {{Optimal Transport}} as a {{Model}} for {{Sub-Letter Orthographic Processing}}},
+  shorttitle = {Beyond {{Letters}}},
+  author = {Taylor, Jack E. and Sinn, Rasmus and Iaia, Cosimo and Fiebach, Christian J.},
+  date = {2024-11-11},
+  eprinttype = {bioRxiv},
+  eprintclass = {New Results},
+  pages = {2024.11.11.622929},
+  doi = {10.1101/2024.11.11.622929},
+  url = {https://www.biorxiv.org/content/10.1101/2024.11.11.622929v1},
+  urldate = {2024-11-12},
+  abstract = {Letter processing plays a key role in visual word recognition. However, word recognition models typically overlook or greatly simplify early perceptual processes of letter recognition. We suggest that optimal transport theory may provide a computational framework for describing letter shape processing. We use representational similarity analysis to show that optimal transport cost (Wasserstein distance) between pairs of letters aligns with neural activity elicited by visually presented letters {$<$}225 ms after stimulus onset, outperforming an existing approach based on shape overlap. We additionally show that optimal transport can capture the emergence of geometric invariances (e.g., to position or size) observed in letter perception. Finally, we demonstrate that Wasserstein distance predicts neural activity similarly well to features from artificial networks trained to classify images and letters. However, whereas representations in artificial neural networks emerge in a computationally unconstrained manner, our proposal provides a computationally explicit route to modeling the earliest orthographic processes.},
+  langid = {english},
+  pubstate = {prepublished},
+  file = {C:\Users\felix\Zotero\storage\SAHIMFH7\Taylor et al. - 2024 - Beyond Letters Optimal Transport as a Model for S.pdf}
+}
+
+@online{wehrheimReliabilityVariabilityComplexity2023,
+  title = {Reliability of {{Variability}} and {{Complexity Measures}} for {{Task}} and {{Task-Free BOLD fMRI}}},
+  author = {Wehrheim, Maren and Faskowitz, Joshua and Schubert, Anna-Lena and Fiebach, Christian},
+  date = {2023-11-15},
+  eprinttype = {OSF},
+  doi = {10.31234/osf.io/ves2t},
+  url = {https://osf.io/ves2t},
+  urldate = {2024-11-12},
+  abstract = {Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, for example with respect to developmental changes of brain function or between-person differences in healthy and clinical populations. However, the psychometric reliability of brain signal variability and complexity measures – which is an important precondition for robust individual differences as well as longitudinal research – is not yet sufficiently studied. We examined reliability (split-half correlations) and test-retest correlations for task-free (resting-state) BOLD fMRI as well as split-half correlations for seven functional task datasets from the Human Connectome Project to evaluate their reliability. We observed good to excellent split-half reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and moderate test-retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (several entropy and dimensionality measures) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time-resolved (dynamic) functional connectivity time series, and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Global (i.e., mean across cortical regions) measures tended to show higher reliability than region-specific variability or complexity estimates. Larger subcortical regions had similar reliability as cortical regions, but small regions showed lower reliability, especially for complexity measures. Lastly, we also show that reliability scores only minorly dependent on differences in scan length and replicate our results across different parcellation and denoising strategies. These results suggest that variability and complexity of BOLD activation time series are robust measures well-suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function.},
+  langid = {american},
+  pubstate = {prepublished},
+  keywords = {BOLD fMRI,complexity,dimensionality,reliability,temporal variability},
+  file = {C:\Users\felix\Zotero\storage\FJPUIELM\Wehrheim et al. - 2023 - Reliability of Variability and Complexity Measures.pdf}
+}