What do across-subject analyses really tell us about neural coding?

Ramírez, Fernando; Revsine, Cambria; Merriam, Elisha P.

doi:10.1016/j.neuropsychologia.2020.107489

Cited by 11 publications

(11 citation statements)

References 83 publications

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“…Critically, regardless of the form of viewpoint selectivity of the underlying neuronal populations, stronger responses for frontal than lateral face views will lead to mirror-symmetry with analysis pipelines that either demean the data prior to RSA or use the Euclidean distance to measure pattern dissimilarities (Ramírez, 2017). We recently also showed that stimuli from previous studies exhibit low-level feature imbalances across face-views consistent with the reported overrepresentation (Ramírez et al, 2020). Building on these observations, geometrical reasoning led us to hypothesize that interhemispheric crossings in the brain might explain common and inconsistent trends observed across neuroimaging studies of viewpoint selectivity (see Figure 2).…”

Section: Introductionsupporting

confidence: 88%

“…Some fMRI voxels exhibit higher BOLD signal levels than others, due to, among many reasons, partial voluming (González Ballester et al, 2002), cortical folding (Polimeni et al, 2010; Gagnon et al, 2015) and specifics of the underlying vasculature (Bandettini and Wong, 1997; Schmid et al, 2019). A static measurement gain field (mGF) can be defined that summarizes the level of signal gain in each voxel (Ramírez et al, 2014; 2020). To model the influence of such gain field on our simulated response patterns, we randomly generated an mGF.…”

Section: Methodsmentioning

confidence: 99%

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A unifying model for discordant and concordant results in human neuroimaging studies of facial viewpoint selectivity

Revsine

Gonzalez-Castillo

Merriam

et al. 2023

Preprint

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Our ability to recognize faces regardless of viewpoint is a key property of the primate visual system. Traditional theories hold that facial viewpoint is represented by view-selective mechanisms at early visual processing stages and that representations become increasingly tolerant to viewpoint changes in higher-level visual areas. Newer theories, based on single-neuron monkey electrophysiological recordings, suggest an additional intermediate processing stage invariant to mirror-symmetric face views. Consistent with traditional theories, human studies combining neuroimaging and multivariate pattern analysis (MVPA) methods have provided evidence of view-selectivity in early visual cortex. However, contradictory results have been reported in higher-level visual areas concerning the existence in humans of mirror-symmetrically tuned representations. We believe these results reflect low-level stimulus confounds and data analysis choices. To probe for low-level confounds, we analyzed images from two popular face databases. Analyses of mean image luminance and contrast revealed biases across face views described by even polynomials—i.e., mirror-symmetric. To explain major trends across human neuroimaging studies of viewpoint selectivity, we constructed a network model that incorporates three biological constraints: cortical magnification, convergent feedforward projections, and interhemispheric connections. Given the identified low-level biases, we show that a gradual increase of interhemispheric connections across network layers is sufficient to replicate findings of mirror-symmetry in high-level processing stages, as well as view-tuning in early processing stages. Data analysis decisions—pattern dissimilarity measure and data recentering—accounted for the variable observation of mirror-symmetry in late processing stages. The model provides a unifying explanation of MVPA studies of viewpoint selectivity. We also show how common analysis choices can lead to erroneous conclusions.

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Section: Introductionsupporting

confidence: 88%

Section: Methodsmentioning

confidence: 99%

A unifying model for discordant and concordant results in human neuroimaging studies of facial viewpoint selectivity

Revsine

Gonzalez-Castillo

Merriam

et al. 2023

Preprint

Self Cite

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“…Thus, while this approach allows for comparisons at the population level both for MEG and fMRI, it is indirect and introduces additional assumptions about the spatial distribution of activity patterns and their representational similarity metric. Specifically, MEG-fMRI fusion based on classical representational similarity analysis (RSA) 37 requires the a priori selection of sensors and/or voxels to include into the computation of an RDM, additionally assumes that all voxels and MEG sensors contribute equally to the representational similarity 61 , and requires the selection of a similarity metric 62, 63 . In contrast, the size of THINGS-data allows using the MEG data directly to predict responses in fMRI ROIs or even individual voxels without having to rely on these assumptions.…”

Section: Resultsmentioning

confidence: 99%

THINGS-data: A multimodal collection of large-scale datasets for investigating object representations in human brain and behavior

Hebart

Contier

Teichmann

et al. 2022

Preprint

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Understanding object representations requires a broad, comprehensive sampling of the objects in our visual world with dense measurements of brain activity and behavior. Here we present THINGS-data, a multimodal collection of large-scale datasets comprising functional MRI, magnetoencephalographic recordings, and 4.70 million similarity judgments in response to thousands of photographic images for up to 1,854 object concepts. THINGS-data is unique in its breadth of richly-annotated objects, allowing for testing countless hypotheses at scale while assessing the reproducibility of previous findings. Beyond the unique insights promised by each individual dataset, the multimodality of THINGS-data allows combining datasets for a much broader view into object processing than previously possible. Our analyses demonstrate the high quality of the datasets and provide five examples of hypothesis-driven and data-driven applications. THINGS-data constitutes the core release of the THINGS initiative (https://things-initiative.org) for bridging the gap between disciplines and the advancement of cognitive neuroscience.

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“…This wealth of prior information provided an important benefit in reducing the number of design and analytical choices that we needed to make, all of which could be considered to be “experimenter degrees of freedom” from a legal perspective. From a technical perspective, the relative simplicity of RS, which allows experimenters to circumvent the difficult problems of accounting for individual differences in the spatial pattern of neural activities ( 63 ) and specifying the functional form of (dis)similarity, may also offer certain advantages in reducing bias in a legal setting over other analytic tools such as MVPA and RSA.…”

Section: Discussionmentioning

confidence: 99%

From scanner to court: A neuroscientifically informed “reasonable person” test of trademark infringement

et al. 2023

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Many legal decisions center on the thoughts or perceptions of some idealized group of individuals, referred to variously as the “average person,” “the typical consumer,” or the “reasonable person.” Substantial concerns exist, however, regarding the subjectivity and vulnerability to biases inherent in conventional means of assessing such responses, particularly the use of self-report evidence. Here, we addressed these concerns by complementing self-report evidence with neural data to inform the mental representations in question. Using an example from intellectual property law, we demonstrate that it is possible to construct a parsimonious neural index of visual similarity that can inform the reasonable person test of trademark infringement. Moreover, when aggregated across multiple participants, this index was able to detect experimenter-induced biases in self-report surveys in a sensitive and replicable fashion. Together, these findings potentially broaden the possibilities for neuroscientific data to inform legal decision-making across a range of settings.

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What do across-subject analyses really tell us about neural coding?

Cited by 11 publications

References 83 publications

A unifying model for discordant and concordant results in human neuroimaging studies of facial viewpoint selectivity

A unifying model for discordant and concordant results in human neuroimaging studies of facial viewpoint selectivity

THINGS-data: A multimodal collection of large-scale datasets for investigating object representations in human brain and behavior

From scanner to court: A neuroscientifically informed “reasonable person” test of trademark infringement

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