Developmental prosopagnosics have normal spatial integration in posterior ventral face-selective regions

  1. Daniel A Stehr
  2. Yiyuan Zhang
  3. Anusha Patgiri
  4. Alexis Kidder
  5. Kendrick Kay
  6. Bradley Duchaine

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    eLife Assessment

    This functional MRI study critically tests the hypothesis that poor face recognition in developmental prosopagnosia in humans is driven by reduced spatial integration and smaller receptive fields in face-selective brain regions. The evidence provided is compelling as it is well-powered, uses state-of-the-art functional brain imaging, eye tracking and computational analyses. The observed lack of difference in population receptive field sizes between face-selective brain regions of individuals with and without prosopagnosia, though a null result, has important implications for the field, and specifically, for theories of face recognition.

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Abstract

Population receptive field (pRF) mapping is an influential neuroimaging technique used to estimate the region of visual space modulating the response of a neuronal population. While pRF mapping has advanced our understanding of visual cortical organization, evidence linking variation in pRF properties to behavioral performance remains limited. One of the most compelling pRF-to-behavior relationships has emerged from research into developmental prosopagnosia (DP). Individuals with DP have severe deficits in facial identity recognition sometimes linked to diminished holistic processing of faces. This perceptual deficit could be explained at the neural level by abnormally small pRFs in face-selective regions that restrict spatial integration of the face information. This hypothesis is supported by data from a small group of DPs but needs to be rigorously evaluated in a larger sample. Here, we measured pRF properties in 20 individuals with DP and 20 controls using a stimulus designed to robustly activate both low– and high-level visual areas. Consistent with previous studies, DPs exhibited weaker face selectivity in core ventral face-selective areas. Crucially, however, across the visual processing hierarchy – from early visual cortex, to intermediate visual areas, and face-selective areas – DPs and controls exhibited remarkable similarity in pRF properties, including pRF size, visual field coverage, and the distribution of pRF centers. Using a larger sample and the latest methods for mapping and modeling pRFs, these results challenge theories attributing DP to reduced spatial integration in face-selective regions. This underscores the need to explore alternative neural mechanisms of DP and to re-evaluate links between pRF properties and behavior more broadly.

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  1. Version published to 10.7554/elife.108657.2 on eLife
  2. Version published to 10.7554/elife.108657 on eLife
  3. eLife

    eLife Assessment

    This functional MRI study critically tests the hypothesis that poor face recognition in developmental prosopagnosia in humans is driven by reduced spatial integration and smaller receptive fields in face-selective brain regions. The evidence provided is compelling as it is well-powered, uses state-of-the-art functional brain imaging, eye tracking and computational analyses. The observed lack of difference in population receptive field sizes between face-selective brain regions of individuals with and without prosopagnosia, though a null result, has important implications for the field, and specifically, for theories of face recognition.

  4. eLife

    Reviewer #1 (Public review):

    Summary:

    The authors examine the neural correlates of face recognition deficits in individuals with Developmental Prosopagnosia (DP; 'face blindness'). Contrary to theories that poor face recognition is driven by reduced spatial integration (via smaller receptive fields), here the authors find that the properties of receptive fields in face-selective brain regions are the same in typical individuals vs. those with DP. The main analysis technique is population Receptive Field (pRF) mapping, with a wide range of measures considered. The authors report that there are no differences in goodness-of-fit (R2), the properties of the pRFs (neither size, location, nor the gain and exponent of the Compressive Spatial Summation model), nor their coverage of the visual field. The relationship of these properties to the visual field (notably the increase in pRF size with eccentricity) is also similar between the groups. Eye movements do not differ between the groups.

    Strengths:

    Although this is a null result, the large number of null results gives confidence that there are unlikely to be differences between the two groups. Together, this makes a compelling case that DP is not driven by differences in the spatial selectivity of face-selective brain regions, an important finding that directly informs theories of face recognition. The paper is well written and enjoyable to read, the studies have clearly been carefully conducted with clear justification for design decisions, and analyses are thorough.

    Issues raised earlier regarding the localisation of face-selective regions between the two groups have been thoughtfully qualified in the revised manuscript. The use of a fixed-window localisation procedure (whereby large group-level ROIs are used to begin, with the top 20% most selective voxels selected in each individual) is a nice touch, ensuring a matched number of voxels in each ROI across groups, despite the clear differences in selectivity. Other clarifications to the results and discussion have also improved the manuscript.

    Weaknesses:

    One small issue concerns the relation between this study and prior work in low-level vision. The authors state in the introduction and discussion that there are "few" studies linking pRF properties to performance, citing only one. This ignores the numerous studies that have been conducted in this area - it would be ideal either to qualify these statements or to cite a greater number of these studies. The revised manuscript also includes mention of correlational analyses in the discussion which would be better placed in the results to begin.

  5. eLife

    Reviewer #2 (Public review):

    This is my review of the revised manuscript, which I initially reviewed in 2025. I thank the authors for their thoughtful responses and additional analyses, including the Bayes factor analyses, tSNR checks, analyses relating pRF properties to behavioral performance, and clarification that PPA scene selectivity did not differ significantly between DPs and controls. I also appreciate the discussion acknowledging that intact pRF properties do not necessarily mean intact holistic processing. I have one remaining suggestion to make this distinction clearer, related to the interpretation of spatial integration.

    Spatial integration and spatial pooling are well-established terms in the pRF literature. However, spatial integration can refer to several processes in the context of developmental prosopagnosia:

    (1) Neural spatial integration: How neural populations combine inputs across visual space, characterized in part by pRF size and spatial summation.

    (2) Perceptual spatial integration: How facial features interact to form a holistic representation, assessed using tasks such as composite-face and part-whole tests.

    (3) Integration across viewing: How information sampled over successive eye movements contributes to a face representation.

    The larger DP sample, consistent results, and Bayesian analyses make a stronger case against the first account that face-recognition difficulties stem from abnormally small pRFs. However, they do not establish that these other aspects of spatial integration are intact. Thus, my remaining concern with "normal spatial integration" in the title & main text is therefore about the semantics of the wording rather than the statistics. I suggest using "preserved population receptive field properties" or "preserved neural spatial integration" throughout the manuscript.

    One minor note:. The merged PDF does not appear to reflect the revisions, so I used the article file with highlighted changes for this review. However, I could not find the following passage from the authors' response in neither version.
    "However, intact pRF properties do not preclude impairments to holistic processing. Such behavioral deficits could instead arise from abnormalities in broader neural networks due to either altered functional connectivity (Avidan et al., 2014; Lohse et al., 2016; Rosenthal et al., 2017) or structural connectivity (Gomez et al., 2015; Song et al., 2015b)."

  6. eLife

    Author response:

    The following is the authors’ response to the original reviews.

    Public Reviews:

    Reviewer #1 (Public review):

    Summary:

    The authors examine the neural correlates of face recognition deficits in individuals with Developmental Prosopagnosia (DP; ’face blindness’). Contrary to theories that poor face recognition is driven by reduced spatial integration (via smaller receptive fields), here the authors find that the properties of receptive fields in face-selective brain regions are the same in typical individuals vs. those with DP. The main analysis technique is population Receptive Field (pRF) mapping, with a wide range of measures considered. The authors report that there are no differences in goodness-of-fit (R2), the properties of the pRFs (neither size, location, nor the gain and exponent of the Compressive Spatial Summation model), nor their coverage of the visual field. The relationship of these properties to the visual field (notably the increase in pRF size with eccentricity) is also similar between the groups. Eye movements do not differ between the groups.

    Strengths:

    Although this is a null result, the large number of null results gives confidence that there are unlikely to be differences between the two groups. Together, this makes a compelling case that DP is not driven by differences in the spatial selectivity of face-selective brain regions, an important finding that directly informs theories of face recognition. The paper is well written and enjoyable to read, the studies have clearly been carefully conducted with clear justification for design decisions, and the analyses are thorough.

    Weaknesses:

    One potential issue relates to the localisation of face-selective regions in the two groups. As in most studies of the neural basis of face recognition, localisers are used to find the face-selective Regions of Interest (ROIs) - OFA, mFus, and pFus, with comparison to the scene-selective PPA. To do so, faces are contrasted against other objects to find these regions (or scenes vs. others for the PPA). The one consistent difference that does emerge between groups in the paper is in the selectivity of these regions, which are less selective for faces in DP than in typical individuals (e.g., Figure 1B), as one might expect. 6/20 prosopagnosic individuals are also missing mFus, relative to only 2/20 typical individuals. This, to me, raises the question of whether the two groups are being compared fairly. If the localised regions were smaller and/or displaced in the DPs, this might select only a subset of the neural populations typically involved in face recognition. Perhaps the difference between groups lies outside this region. In other words, it could be that the differences in prosopagnosic face recognition lie in the neurons that are not able to be localised by this approach. The authors consider in the discussion whether their DPs may not have been ’true DPs’, which is convincing (p. 12). The question here is whether the regions selected are truly the’prosopagnosic brain areas’ or whether there is a kind of survivor bias (i.e., the regions selected are normal, but perhaps the difference lies in the nature/extent of the regions. At present, the only consideration given to explain the differences in prosopagnosia is that there may be ’qualitative’ differences between the two (which may be true), but I would give more thought to this.

    Thank you for the positive assessment of our study and for raising this important theoretical point regarding ROI definition. We agree that face-selective ROIs in DPs, relative to controls, may be smaller, less selective, or altogether missing when traditional methods of localization with fixed thresholds are used (Furl et al, 2011). For this reason - to circumvent potential survivor bias and ensure ROI voxel counts across participants are equated - we used a method of ROI definition whereby each subject’s individual statistical map from the localizer was intersected with a generously-sized group mask for each ROI and the top 20% most category-selective voxels were retained for the pRF analysis (Norman-Haignere et al., 2013; Jiahui et al., 2018). This means that the raw number of voxels per ROI was equal across all participants with respect to the common group space, thereby ensuring a fair comparison even in cases where one group shows diminished category-selectivity. The details of the ROI definition are provided in the Methods at the end of the manuscript. To ensure readers understand our approach, we have also updated the Results section manuscript to highlight this important methodological step.

    “To ensure a fair group comparison because individuals with DP often exhibit category selective responses below typically used thresholds, we adopted the “variable-window” method of ROI definition (Norman-Haignere et al., 2013; Jiahui et al., 2018). In this method, generously-sized group masks for each respective ROI (see Supplementary Figure 11) were intersected with each participant’s individual statistical map and the top 20% most category-selective voxels within the mask were retained for analysis. This method avoids the subjectivity of manually choosing thresholds and ensures that all participants have equally sized ROIs (i.e., identical voxel counts) prior to pRF fittings.”

    With regard to the question of whether face-selective ROIs may be displaced in DPs compared to controls, previous work from the senior author’s lab (Jiahui et al., 2018) shows that, despite exhibiting weaker activations, the peak coordinates of significant clusters in DPs occupy very similar locations to those of controls. And, even if there were indeed slight displacements of face-selective ROIs for some subjects, the group-defined masks used in the present analysis were large enough to capture the majority of the top voxels. In the Supplementary Figure 11, we have included a diagram of the group masks used in our study.

    The reviewer here also points out that more DPs than controls were missing the mFUS region (6/20 DPs vs 2/20 controls; Figure 1C). However, ‘missing’ in this context was not based on face-selectivity but rather a lack of retinotopic tuning. PRFs were fit to all voxels within each ROI - with all subjects starting out with equal voxel counts - and thereafter, voxels for which the variance explained by the pRF model was below 20% were excluded from subsequent analysis. We decided that any ROI with fewer than 10 voxels remaining after thresholding on the pRF fit should be deemed ‘missing’ since we considered the amount of data insufficient to reliably characterize the region’s retinotopic profile. Left mFUS was just one of six face-selective regions under study. The other five regions, many of which evinced strong fits to the pRF model, were represented comparably in DPs and controls and showed high similarity in the pRF parameters. Furthermore, across most participants, mFUS exhibited a low proportion of retinotopically modulated voxels (defined as voxels with pRF R squared greater than 20%, see Figure 1D). A follow-up analysis showed that the count of voxels surviving pRF R squared thresholding in left mFUS was not significantly correlated with mean pRF size (r(30) = 0.23,t = 1.28,p = 0.21) indicating that slightly more exclusion of DPs in this region is unlikely to have biased the group’s average pRF size.

    The discussion considers the differences between the current study and an unpublished preprint (Witthoft et al, 2016), where DPs were found to have smaller pRFs than typical individuals. The discussion presents the argument that the current results are likely more robust, given the use of images within the pRF mapping stimuli here (faces, objects, etc) as opposed to checkerboards in the prior work, and the use of the CSS model here as opposed to a linear Gaussian model previously. This is convincing, but fails to address why there is a lack of difference in the control vs. DP group here. If anything, I would have imagined that the use of faces in mapping stimuli would have promoted differences between the groups (given the apparent difference in selectivity in DPs vs. controls seen here), which adds to the reliability of the present result. Greater consideration of why this should have led to a lack of difference would be ideal. The latter point about pRF models (Gaussian vs. CSS) does seem pertinent, for instance - could the ’qualitative’ difference lead to changes in the shape of these pRFs in prosopagnosia that are better characterised by the CSS model, perhaps? Perhaps more straightforwardly, and related to the above, could differences in the localisation of face-selective regions have driven the difference in prior work compared to here?

    We agree that the use of high-level mapping stimuli (including faces) would be expected to add to the reliability of the present results for DPs and could have further emphasized differences between the groups if true differences did, in fact, exist. In the section of the Discussion titled “What factors may have contributed to the different results for the present study and Witthoft et al. (2016)”, we speculate on the extent to which the type of mapping stimuli and various other methodological factors (e.g. stimulus size, aperture design, pRF model) could have explained the divergent findings in our study versus that of Witthoft et al. (2016). In brief, our use of more colorful, naturalistic stimuli targeting higher-level visual areas elicited better model fits than the black and white checkerboard pattern used by Witthoft et al. (2016). The CSS model we used is better suited for higher-level regions and makes fewer assumptions than the linear pRF model. The field of view of our stimulus was smaller but still representative of the visual angle subtended by faces in the real world. Finally, our aperture design and longer run length likely also improved reliability.

    Regarding the type of pRF model deployed, our use of a non-linear exponent (versus a linear model as in Witthoft et al. (2016)) is unlikely to explain the similarity we observed between the groups in terms of pRF size. Specifically, the groups did not show substantial differences in the exponent across ROIs, as seen in Figure 1G, so the use of a linear model should, in theory, produce similar outcomes for the two groups. We have explicitly mentioned this in the Discussion as follows:

    “Both groups, however, evinced highly similar values for the compressive static nonlinearity parameter across the ROIs studied (Figure 1G). Because this nonlinear exponent parameter did not considerably differ between DPs and controls, the use of a linear model in prior work (Witthoft et al., 2016) is unlikely to explain the massive group differences they reported, as it would be expected to scale pRF estimates similarly for both groups.”

    Overall, these methodological improvements, along with our larger sample size, make us confident in our findings. These are our best attempts to make sense of the divergent findings, but it is not possible to come to a definitive explanation. Examples abound of exaggerated or spurious effects from small-scale studies that ultimately fail to replicate in the related field of dyslexia research (Jednorog et al., 2015; Ramus et al., 2018) and neuroimaging research more broadly (Turner et al., 2018; Poldrack et al., 2017). Sometimes there are clear explanations for a lack of replicability (e.g. software bugs, overly flexible preprocessing methods, etc.), but many times the real reason is difficult to determine.

    Perhaps more straightforwardly, and related to the above, could differences in the localisation of faceselective regions have driven the difference in prior work compared to here?

    The reviewer astutely points out that differences in ROI definition could have played a role in the differences between our work and that of Witthoft et al. (2016). While Witthoft et al. (2016) used a fixed statistical threshold for ROI definition, the authors reported no significant group differences in ROI volume; Nevertheless, it is problematic to confidently establish true equivalence with a sample size of only 7 DPs as they had. If a fixed threshold inadvertently captured slightly different sub-populations of voxels between the groups due to generally weaker face-selectivity among the DPs, it could have heavily skewed the pRF estimates in such a small sample. Our use of the variable-threshold analysis (top 20% of face selective voxels within bounds of a group-defined mask) effectively eliminates this potential spatial sampling artifact.

    Finally, the lack of variations in the spatial properties of these brain regions is interesting in light of the theories that spatial integration is a key aspect of effective face recognition. In this context, it is interesting to note the marked drop in R2 values in face-selective regions like mFus relative to earlier cortex. The authors note in some sense that this is related to the larger receptive field size, but is there a broader point here that perhaps the receptive field model (even with Compressive Spatial Summation) is simply a poor fit for the function of these areas? Could it be that these areas are simply not spatial at all? A broader link between the null results presented here and their implications for theories of face recognition would be ideal.

    The reviewer highlights an excellent point regarding the marked drop in R squared from the early visual cortex to anterior face-selective regions like mFUS. We believe this drop reflects two converging factors. First, it raises the methodological question of whether there is a more effective pRF stimulus for these more anterior regions. For example, it might be possible to obtain higher and more reliable responses there using single isolated faces (Cf. Kay, Weiner, Grill-Spector, 2015).

    Second, we agree that the pRF model might ultimately be an incomplete characterization of the neural function in these higher-level areas. While we would not go so far as to say that these areas are “not spatial at all" (given that they do exhibit reliable visual field coverage), their primary computational role may shift from strict spatial mapping toward position-invariant feature extraction and identity representation. As Reviewer 2 suggests, one possibility is that other brain processes (e.g. functional or structural connectivity between ROIs) may give rise to holistic face processing in ways that are not captured by pRF properties.

    Reviewer #1 (Recommendations for the authors):

    In addition to the points raised in the public review, please address the following issues:

    Related to the question about the localisation of face-selective regions in prosopagnosia (and whether the localised regions differ in the DP vs control analyses), one way to better consider this would be to report the location and size or surface area of the localised regions in typical vs. DP individuals. Perhaps analyses using an averaged or standardised ROI (e.g., averaging control ROIs and applying this to prosopagnosics) would also be of interest to consider how this factor might influence the results.

    As above, a greater discussion of potential reasons for the null results of the present study relative to prior work would be ideal, including consideration of differences in localization of the brain regions and predictions for the way the different pRF models (Gaussian vs. CSS) might have created this (lack of) difference, is my suggestion. Greater discussion of the implications of these findings for theories of face recognition more broadly would also be ideal (e.g., whether the drop in R2 values in face-selective regions suggests a general lack of spatial selectivity).

    As detailed in our responses to the Public Review above, we completely agree that ROI localization and model choices are critical factors when comparing our results to prior work.

    To briefly reiterate: our use of the variable-window analysis explicitly equates ROI sizes across participants, effectively eliminating the spatial sampling artifact that may have arisen had someone used a fixed threshold. We have now explicitly highlighted this methodological strength in both the Methods and Results sections. Furthermore, as shown in Figure 1G, the CSS exponent was highly similar between groups, meaning the model choice alone is unlikely to explain the divergent results (now noted in the Discussion).

    Relatedly, one issue with the interpretation of the null results in the present study is in the statement that ’our results provide strong empirical evidence that pRF properties are highly similar between DPs and controls’. This comes back to the old ’absence of evidence is not evidence of absence’ argument, and I would suggest this statement be more carefully worded - there is certainly a consistent lack of differences between the two groups, which allows one to make the argument that properties are likely similar. But I suggest the authors could be more careful in making this statement.

    We agree that “the absence of evidence is not evidence of absence”, and we appreciate the push to be more precise in our claims.

    To substantiate our claim that pRF properties are indeed similar between the two groups, we have incorporated complementary Bayesian analyses (as also requested by Reviewer 2). As detailed in our response to Reviewer 2 (Point 2) and reported in the new Supplementary Table 6, Bayes Factors using both default and ‘ultrawide’ priors provided consistent, moderate-to-strong evidence explicitly in favor of the null hypothesis across the visual hierarchy. Combined with our frequentist confidence intervals, this allows us to rigorously assert that these spatial properties are highly similar, rather than merely relying on a frequentist failure to reject the null.

    Finally, one typo is present at the bottom of p. 14, which should be ’differentially affected DPs and controls’

    Thank you for catching this typo - it has been corrected.

    Reviewer #2 (Public review):

    Summary:

    This is a well-conducted and clearly written manuscript addressing the link between population receptive fields (pRFs) and visual behavior. The authors test whether developmental prosopagnosia (DP) involves atypical pRFs in face-selective regions, a hypothesis suggested by prior work with a small DP sample. Using a larger cohort of DPs and controls, robust pRF mapping with appropriate stimuli and CSS modeling, and careful in-scanner eye tracking, the authors report no group differences in pRF properties across the visual processing hierarchy. These results suggest that reduced spatial integration is unlikely to account for holistic face processing deficits in DP.

    Strengths

    The dataset quality, sample size, and methodological rigor are notable strengths.

    Weaknesses

    The primary concern is the interpretation of the results.

    (1) Relationship between pRFs and spatial integration While atypical pRF properties could contribute to deficits in spatial integration, impairments in holistic processing in DPs are not necessarily caused by pRF abnormalities. The discussion could be strengthened by considering alternative explanations for reduced spatial integration, such as altered structural or functional connectivity in the face network, which has been reported to underlie DP’s difficulties in integrating facial features.

    We agree that intact pRF properties do not preclude the existence of holistic processing deficits, and that altered structural or functional connectivity within the broader face network is a plausible alternative mechanism for such deficits. To ensure this distinction is clear to the reader, we have updated the Discussion section to highlight these alternative explanations for holistic processing deficits, citing relevant literature.

    “However, intact pRF properties do not preclude impairments to holistic processing. Such behavioral deficits could instead arise from abnormalities in broader neural networks due to either altered functional connectivity (Avidan et al., 2014; Lohse et al., 2016; Rosenthal et al., 2017) or structural connectivity (Gomez et al., 2015; Song et al., 2015b).”

    (2) Beyond the null hypothesis testing framework

    The title claims "normal spatial integration," yet this conclusion is based on a failure to reject the null hypothesis, which does not justify accepting the alternative hypothesis. To substantiate a claim of "normal," the authors would need to provide analyses quantifying evidence for the absence of effects, e.g., using a Bayesian framework.

    To directly address the reviewer’s request to quantify evidence for the absence of an effect, we have added a Bayesian analysis of mean pRF size across all ROIs and hemispheres (Supplementary Table 5).

    Using a default Cauchy prior, the Bayes Factors favored the null hypothesis (BF01 > 1) in 15 out of 16 regions. Only the right OFA showed moderate evidence for the alternative hypothesis. However, upon inspecting this difference, we found that pRFs in the right OFA were actually larger in our DP sample compared to controls (DPs: mean size ± SD = 2.42±0.82; Controls: 1.78±0.75). Because the existing literature explicitly claims that DP is associated with abnormally small pRFs leading to restricted spatial integration (Witthoft et al., 2016; Avidan & Behrmann, 2021), this specific deviation (in the opposite direction) contradicts the alternative hypothesis.

    Because Witthoft et al. (2016) reported that pRFs were approximately three times smaller in DPs, we computed Bayes Factors using an ‘ultrawide’ prior (rscale = 1.414 in function ttestBF from the BayesFactor R package). The ‘ultrawide’ prior, therefore, correctly models the huge effect size expected under this alternative hypothesis. Under these conditions, the evidence strongly favored the null hypothesis across the visual hierarchy. Combined with our frequentist confidence intervals, these Bayesian analyses substantiate our claim that DPs do not suffer from the restricted spatial integration previously theorized to underlie the disorder.

    (3) Face-specific or broader visual processing

    Prior work from the senior author’s lab (Jiahui et al., 2018) reported pronounced reductions in scene selectivity and marginal reductions in body selectivity in DPs, suggesting that visual processing deficits in DPs may extend beyond faces. While the manuscript includes PPA as a high-level control region for scene perception, scene selectivity was not directly reported. The authors could also consider individual differences and potential data-quality confounds (tSNR difference between and within groups, several obvious outliers in the figures, etc). For instance, examining whether reduced tSNR in DPs contributed to lower face selectivity in the DP group in this dataset.

    Thank you for raising these methodological checks. We have addressed each point below to ensure the robustness of our findings.

    (1) Scene Selectivity in PPA: We have now explicitly reported scene selectivity in PPA in Supplementary Table 5. Unlike prior work (Jiahui et al., 2018), we found no significant reduction in scene selectivity in DPs versus controls in our current sample. This finding suggests that the visual deficits in our DP sample are specific to faces rather than reflecting a more domain-general degradation of high-level visual cortex.

    (2) tSNR Between-groups: Independent samples t-tests revealed no significant differences in regional tSNR between DPs and controls across any of the six face-selective regions (all uncorrected, p > 0.085). Bayesian analyses further supported this, with Bayes Factors generally favoring the null hypothesis of equal regional tSNR between groups (bilateral pFUS and mFUS BF01 range: 3.08 to 3.21; bilateral OFA evidence was inconclusive, BF01 range: 0.95 to 1.43). There is therefore no evidence that the DP group suffered from systematically worse signal quality.

    (3) tSNR Within-groups (Correlation): Finally, to address whether variance in signal quality (tSNR) relates to our selectivity measures, we computed Pearson correlations between individual tSNR and face-selectivity magnitude (percent signal change) across the six face regions. Only the left OFA showed a weak, uncorrected correlation (r = 0.32,p = 0.046), which does not survive correction for multiple comparisons. No other regions approached significance. Crucially, for tSNR to act as a confound explaining the group difference in face selectivity, tSNR itself would need to be systematically lower in the DP group. Because our betweengroups analysis established that DPs and controls have equivalent tSNR, any shared variance between tSNR and percent signal change across the entire sample cannot explain the reduction in face selectivity within the DP group.

    (4) Individual Differences and Outliers: Regarding variance and potential outliers in the figures, we intentionally plotted individual data points to provide maximum transparency regarding individual differences. To ensure that extreme values did not unduly influence our group-level conclusions, we utilized Linear Mixed-Effects Models (LMMs) for our primary statistical analyses. LMMs are highly robust to individual variance and non-normality. Furthermore, all data points included in our analyses passed strict, objective quality-assurance thresholds (e.g., stable fixation verified by eye-tracking, and pRF model goodness-of-fit R2 > 0.20). Because these points represent true, high-quality individual variance rather than noise artifacts, we retained them in our analyses.

    (4) Linking pRF properties to behaviour

    The manuscript aims to examine the relationship between pRF properties and behavior, but currently reports only one aspect of pRF (size) in relation to a single behavioral measure (CFMT), without full statistical reporting: "We found no significant association between participants’ CFMT scores and mean pRF size in OFA, pFUS, or mFUS." For comprehensive reporting, the authors could examine additional pRF properties (e.g., center, eccentricity, scaling between eccentricity and pRF size, shape of visual field coverage, etc), additional ROIs (early, intermediate, and category-selective areas), and relate them to multiple behavioral measures (e.g., HEVA, PI20, FFT). This would provide a full picture of how pRF characteristics relate to behavioral performance in DP.

    We agree that a comprehensive reporting of pRF-to-behavior is worthwhile. In our revised manuscript, we extracted participant-by-participant values for the metrics used in our main group comparisons: mean pRF size, mean pRF eccentricity, visual field coverage area at 95% probability contour, pRF center dispersion, and slope relating pRF size to pRF eccentricity. We then correlated these six metrics across all ROIs and hemispheres with scores from both the Cambridge Face Memory Test (CFMT) and Famous Faces Test (FFT). To control for the large number of exploratory correlations, we applied False Discovery Rate (FDR) correction. As shown in newly included Supplementary Table 5, no pRF metrics reliably correlated with behavioral face recognition ability, further cementing our conclusion that these spatial integration mechanisms are intact and decoupled from the recognition deficit in DP. We have reported these new findings in the main text and pointed readers to Supplementary Table 5 for full statistical reporting.

    Reviewer #2 (Recommendations for the authors):

    In addition to the points raised in the public review, please address the following issues:

    (1) Table 1 & Supplementary Table 4

    Caption: "intercept has been mapped to the control group in the left hemisphere".

    Main text: "intercept was mapped to the control group in the right hemisphere."

    These descriptions are directly contradictory. Please correct them and ensure consistency across the main text, tables, and supplementary materials. Supplementary Table 4.

    (2) Supplementary Table 4

    The reported results are identical for V2 and V3, which is highly unlikely. Please double-check and correct any copying errors.

    The caption indicates significance, but the table shows no asterisks.

    (3) Figure 2A

    Missing colorbar (color, units, and label).

    (1) This caption was correct but there was a typo in the main text. The main text has been updated to correctly state that the intercept in the LMM models was mapped to the control group in the left hemisphere.

    (2) The results for V3 had incorrectly been displayed twice. We have corrected the error and updated the table. Also, we have omitted the significance key from the caption since none of the models contain any significant effects.

    (3) A colorbar (with color, units, and label) has been added to Figure 2A. Thank you for your keen eye and attention to detail.

  7. Version published to 10.1101/2025.07.25.666588v2 on bioRxiv
  8. eLife

    eLife Assessment

    This functional MRI study critically tests the hypothesis that poor face recognition in developmental prosopagnosia in humans is driven by reduced spatial integration and smaller receptive fields in face-selective brain regions. The evidence provided is compelling as it is well-powered, uses state-of-the-art functional brain imaging, eye tracking, and computational analyses. The observed lack of difference in population receptive field sizes between face-selective brain regions of individuals with and without prosopagnosia, though a null result, has important implications for the field, and specifically, for theories of face recognition.

  9. eLife

    Reviewer #1 (Public review):

    Summary:

    The authors examine the neural correlates of face recognition deficits in individuals with Developmental Prosopagnosia (DP; 'face blindness'). Contrary to theories that poor face recognition is driven by reduced spatial integration (via smaller receptive fields), here the authors find that the properties of receptive fields in face-selective brain regions are the same in typical individuals vs. those with DP. The main analysis technique is population Receptive Field (pRF) mapping, with a wide range of measures considered. The authors report that there are no differences in goodness-of-fit (R2), the properties of the pRFs (neither size, location, nor the gain and exponent of the Compressive Spatial Summation model), nor their coverage of the visual field. The relationship of these properties to the visual field (notably the increase in pRF size with eccentricity) is also similar between the groups. Eye movements do not differ between the groups.

    Strengths:

    Although this is a null result, the large number of null results gives confidence that there are unlikely to be differences between the two groups. Together, this makes a compelling case that DP is not driven by differences in the spatial selectivity of face-selective brain regions, an important finding that directly informs theories of face recognition. The paper is well written and enjoyable to read, the studies have clearly been carefully conducted with clear justification for design decisions, and the analyses are thorough.

    Weaknesses:

    One potential issue relates to the localisation of face-selective regions in the two groups. As in most studies of the neural basis of face recognition, localisers are used to find the face-selective Regions of Interest (ROIs) - OFA, mFus, and pFus, with comparison to the scene-selective PPA. To do so, faces are contrasted against other objects to find these regions (or scenes vs. others for the PPA). The one consistent difference that does emerge between groups in the paper is in the selectivity of these regions, which are less selective for faces in DP than in typical individuals (e.g., Figure 1B), as one might expect. 6/20 prosopagnosic individuals are also missing mFus, relative to only 2/20 typical individuals. This, to me, raises the question of whether the two groups are being compared fairly. If the localised regions were smaller and/or displaced in the DPs, this might select only a subset of the neural populations typically involved in face recognition. Perhaps the difference between groups lies outside this region. In other words, it could be that the differences in prosopagnosic face recognition lie in the neurons that are not able to be localised by this approach. The authors consider in the discussion whether their DPs may not have been 'true DPs', which is convincing (p. 12). The question here is whether the regions selected are truly the 'prosopagnosic brain areas' or whether there is a kind of survivor bias (i.e., the regions selected are normal, but perhaps the difference lies in the nature/extent of the regions. At present, the only consideration given to explain the differences in prosopagnosia is that there may be 'qualitative' differences between the two (which may be true), but I would give more thought to this.

    The discussion considers the differences between the current study and an unpublished preprint (Witthoft et al, 2016), where DPs were found to have smaller pRFs than typical individuals. The discussion presents the argument that the current results are likely more robust, given the use of images within the pRF mapping stimuli here (faces, objects, etc) as opposed to checkerboards in the prior work, and the use of the CSS model here as opposed to a linear Gaussian model previously. This is convincing, but fails to address why there is a lack of difference in the control vs. DP group here. If anything, I would have imagined that the use of faces in mapping stimuli would have promoted differences between the groups (given the apparent difference in selectivity in DPs vs. controls seen here), which adds to the reliability of the present result. Greater consideration of why this should have led to a lack of difference would be ideal. The latter point about pRF models (Gaussian vs. CSS) does seem pertinent, for instance - could the 'qualitative' difference lead to changes in the shape of these pRFs in prosopagnosia that are better characterised by the CSS model, perhaps? Perhaps more straightforwardly, and related to the above, could differences in the localisation of face-selective regions have driven the difference in prior work compared to here?

    Finally, the lack of variations in the spatial properties of these brain regions is interesting in light of the theories that spatial integration is a key aspect of effective face recognition. In this context, it is interesting to note the marked drop in R2 values in face-selective regions like mFus relative to earlier cortex. The authors note in some sense that this is related to the larger receptive field size, but is there a broader point here that perhaps the receptive field model (even with Compressive Spatial Summation) is simply a poor fit for the function of these areas? Could it be that these areas are simply not spatial at all? A broader link between the null results presented here and their implications for theories of face recognition would be ideal.

  10. eLife

    Reviewer #2 (Public review):

    Summary:

    This is a well-conducted and clearly written manuscript addressing the link between population receptive fields (pRFs) and visual behavior. The authors test whether developmental prosopagnosia (DP) involves atypical pRFs in face-selective regions, a hypothesis suggested by prior work with a small DP sample. Using a larger cohort of DPs and controls, robust pRF mapping with appropriate stimuli and CSS modeling, and careful in-scanner eye tracking, the authors report no group differences in pRF properties across the visual processing hierarchy. These results suggest that reduced spatial integration is unlikely to account for holistic face processing deficits in DP.

    Strengths:

    The dataset quality, sample size, and methodological rigor are notable strengths.

    Weaknesses:

    The primary concern is the interpretation of the results.

    (1) Relationship between pRFs and spatial integration

    While atypical pRF properties could contribute to deficits in spatial integration, impairments in holistic processing in DPs are not necessarily caused by pRF abnormalities. The discussion could be strengthened by considering alternative explanations for reduced spatial integration, such as altered structural or functional connectivity in the face network, which has been reported to underlie DP's difficulties in integrating facial features.

    (2) Beyond the null hypothesis testing framework

    The title claims "normal spatial integration," yet this conclusion is based on a failure to reject the null hypothesis, which does not justify accepting the alternative hypothesis. To substantiate a claim of "normal," the authors would need to provide analyses quantifying evidence for the absence of effects, e.g., using a Bayesian framework.

    (3) Face-specific or broader visual processing

    Prior work from the senior author's lab (Jiahui et al., 2018) reported pronounced reductions in scene selectivity and marginal reductions in body selectivity in DPs, suggesting that visual processing deficits in DPs may extend beyond faces. While the manuscript includes PPA as a high-level control region for scene perception, scene selectivity was not directly reported. The authors could also consider individual differences and potential data-quality confounds (tSNR difference between and within groups, several obvious outliers in the figures, etc). For instance, examining whether reduced tSNR in DPs contributed to lower face selectivity in the DP group in this dataset.

    (4) Linking pRF properties to behavior

    The manuscript aims to examine the relationship between pRF properties and behavior, but currently reports only one aspect of pRF (size) in relation to a single behavioral measure (CFMT), without full statistical reporting:

    "We found no significant association between participants' CFMT scores and mean pRF size in OFA, pFUS, or mFUS."

    For comprehensive reporting, the authors could examine additional pRF properties (e.g., center, eccentricity, scaling between eccentricity and pRF size, shape of visual field coverage, etc), additional ROIs (early, intermediate, and category-selective areas), and relate them to multiple behavioral measures (e.g., HEVA, PI20, FFT). This would provide a full picture of how pRF characteristics relate to behavioral performance in DP.

  11. eLife

    Author response:

    Reviewer #1 (Public review):

    Summary:

    The authors examine the neural correlates of face recognition deficits in individuals with Developmental Prosopagnosia (DP; 'face blindness'). Contrary to theories that poor face recognition is driven by reduced spatial integration (via smaller receptive fields), here the authors find that the properties of receptive fields in face-selective brain regions are the same in typical individuals vs. those with DP. The main analysis technique is population Receptive Field (pRF) mapping, with a wide range of measures considered. The authors report that there are no differences in goodness-of-fit (R2), the properties of the pRFs (neither size, location, nor the gain and exponent of the Compressive Spatial Summation model), nor their coverage of the visual field. The relationship of these properties to the visual field (notably the increase in pRF size with eccentricity) is also similar between the groups. Eye movements do not differ between the groups.

    Strengths:

    Although this is a null result, the large number of null results gives confidence that there are unlikely to be differences between the two groups. Together, this makes a compelling case that DP is not driven by differences in the spatial selectivity of face-selective brain regions, an important finding that directly informs theories of face recognition. The paper is well written and enjoyable to read, the studies have clearly been carefully conducted with clear justification for design decisions, and the analyses are thorough.

    Weaknesses:

    One potential issue relates to the localisation of face-selective regions in the two groups. As in most studies of the neural basis of face recognition, localisers are used to find the face-selective Regions of Interest (ROIs) - OFA, mFus, and pFus, with comparison to the scene-selective PPA. To do so, faces are contrasted against other objects to find these regions (or scenes vs. others for the PPA). The one consistent difference that does emerge between groups in the paper is in the selectivity of these regions, which are less selective for faces in DP than in typical individuals (e.g., Figure 1B), as one might expect. 6/20 prosopagnosic individuals are also missing mFus, relative to only 2/20 typical individuals. This, to me, raises the question of whether the two groups are being compared fairly. If the localised regions were smaller and/or displaced in the DPs, this might select only a subset of the neural populations typically involved in face recognition. Perhaps the difference between groups lies outside this region. In other words, it could be that the differences in prosopagnosic face recognition lie in the neurons that are not able to be localised by this approach. The authors consider in the discussion whether their DPs may not have been 'true DPs', which is convincing (p. 12). The question here is whether the regions selected are truly the 'prosopagnosic brain areas' or whether there is a kind of survivor bias (i.e., the regions selected are normal, but perhaps the difference lies in the nature/extent of the regions. At present, the only consideration given to explain the differences in prosopagnosia is that there may be 'qualitative' differences between the two (which may be true), but I would give more thought to this.

    We acknowledge that face-selective ROIs in DPs, relative to controls, may be smaller, less selective, or altogether missing when traditional methods of localization with fixed thresholds are used (Furl et al, 2011). For this reason - to circumvent potential survivor bias and ensure ROI voxel counts across participants are equated - we used a method of ROI definition whereby each subject’s individual statistical map from the localizer was intersected with a generously-sized group mask for each ROI and the top 20% most category-selective voxels were retained for the pRF analysis (Norman-Haignere et al., 2013; Jiahui et al., 2018). This means that the raw number of voxels per ROI was equal across all participants with respect to the common group space, thereby ensuring a fair comparison even in cases where one group shows diminished category-selectivity. The details of the ROI definition are provided in the Methods at the end of the manuscript. To ensure readers understand our approach, we will also make more explicit mention of this in the main body of the manuscript.

    With regard to the question of whether face-selective ROIs may be displaced in DPs compared to controls, previous work from the senior author’s lab (Jiahui et al., 2018) shows that, despite exhibiting weaker activations, the peak coordinates of significant clusters in DPs occupy very similar locations to those of controls. And, even if there were indeed slight displacements of face-selective ROIs for some subjects, the group-defined masks used in the present analysis were large enough to capture the majority of the top voxels. In the supplemental materials section, we will include a diagram of the group masks used in our study.

    The reviewer here also points out that more DPs than controls were missing the mFUS region (6/20 DPs vs 2/20 controls; Figure 1C). However, ‘missing’ in this context was not based on face-selectivity but rather a lack of retinotopic tuning. PRFs were fit to all voxels within each ROI - with all subjects starting out with equal voxel counts - and thereafter, voxels for which the variance explained by the pRF model was below 20% were excluded from subsequent analysis. We decided that any ROI with fewer than 10 voxels remaining after thresholding on the pRF fit should be deemed ‘missing’ since we considered the amount of data insufficient to reliably characterize the region’s retinotopic profile. While it may be somewhat interesting that four more DPs than controls were ‘missing’ left mFUS, using this particular set of decision criteria, it is important to keep in mind that left mFUS was just one of six face-selective regions under study. The other five regions, many of which evinced strong fits by the pRF model, were represented comparably in DPs and controls and showed high similarity in the pRF parameters. Furthermore, across most participants, mFUS exhibited a low proportion of retinotopically modulated voxels (defined as voxels with pRF R squared greater than 20%, see Figure 1D). A follow-up analysis showed that the count of voxels surviving pRF R squared thresholding in left mFUS was not significantly correlated with mean pRF size (r(30)=0.23, t=1.28, p=0.21) indicating that the greater exclusion of DPs in this region is unlikely to have biased the group’s average pRF size.

    The discussion considers the differences between the current study and an unpublished preprint (Witthoft et al, 2016), where DPs were found to have smaller pRFs than typical individuals. The discussion presents the argument that the current results are likely more robust, given the use of images within the pRF mapping stimuli here (faces, objects, etc) as opposed to checkerboards in the prior work, and the use of the CSS model here as opposed to a linear Gaussian model previously. This is convincing, but fails to address why there is a lack of difference in the control vs. DP group here. If anything, I would have imagined that the use of faces in mapping stimuli would have promoted differences between the groups (given the apparent difference in selectivity in DPs vs. controls seen here), which adds to the reliability of the present result. Greater consideration of why this should have led to a lack of difference would be ideal. The latter point about pRF models (Gaussian vs. CSS) does seem pertinent, for instance - could the 'qualitative' difference lead to changes in the shape of these pRFs in prosopagnosia that are better characterised by the CSS model, perhaps? Perhaps more straightforwardly, and related to the above, could differences in the localisation of face-selective regions have driven the difference in prior work compared to here?

    We agree that the use of high-level mapping stimuli (including faces) adds to the reliability of the present results for DPs and could have further emphasized differences between the groups if true differences did, in fact, exist. We speculate on the extent to which the type of mapping stimuli and various other methodological factors (e.g. stimulus size, aperture design, pRF model) could have explained the divergent findings in our study versus that of Witthoft et al. (2016) in the section of the Discussion titled, “What factors may have contributed to the different results for the present study and Witthoft et al. (2016)”. In brief, our use of more colorful, naturalistic stimuli targeting higher-level visual areas elicited better model fits than the black and white checkerboard pattern used by Witthoft et al. (2016). The CSS model we used is better suited for higher-level regions and makes fewer assumptions than the linear pRF model. The field of view of our stimulus was smaller but still relevant for real-world perception of faces. Finally, our aperture design and longer run length likely also improved reliability. Overall, these methodological improvements, along with our larger sample size, provide stronger evidence for our findings. These are our best attempts to make sense of the divergent findings, but it is not possible to come to a definitive explanation. Examples abound of exaggerated or spurious effects from small-scale studies that ultimately fail to replicate in the related field of dyslexia research (Jednorog et al., 2015; Ramus et al., 2018) and neuroimaging research more generally (Turner et al., 2018; Poldrack et al., 2017). Sometimes there are clear explanations for a lack of replicability (e.g. software bugs, overly flexible preprocessing methods, etc.), but many times the real reason cannot be determined.

    Regarding the type of pRF model deployed, our use of a non-linear exponent (versus a linear model as in the Witthoft et al. (2016) preprint) is unlikely to explain the similarity we observed between the groups in terms of pRF size. Specifically, the groups did not show substantial differences in the exponent by ROI, as seen in Figure 1E, so the use of a linear model should, in theory, produce similar outcomes for the two groups. We will mention this point in the main text.

    Finally, the lack of variations in the spatial properties of these brain regions is interesting in light of the theories that spatial integration is a key aspect of effective face recognition. In this context, it is interesting to note the marked drop in R2 values in face-selective regions like mFus relative to earlier cortex. The authors note in some sense that this is related to the larger receptive field size, but is there a broader point here that perhaps the receptive field model (even with Compressive Spatial Summation) is simply a poor fit for the function of these areas? Could it be that these areas are simply not spatial at all? A broader link between the null results presented here and their implications for theories of face recognition would be ideal.

    The weaker pRF fits found in mFUS, to us, raise the question of whether there is a more effective pRF stimulus for these more anterior regions. For example, it might be possible to obtain higher and more reliable responses there using single isolated faces (Cf. Kay, Weiner, Grill-Spector, 2015). More broadly, though, we agree that it is important to acknowledge that the receptive field model might ultimately be a coarse and incomplete characterization of neural function in these areas. As the other reviewer suggests, one possibility is that other brain processes (e.g. functional or structural connectivity between ROIs) may give rise to holistic face processing in ways that are not captured by pRF properties.

    Reviewer #2 (Public review):

    Summary:

    This is a well-conducted and clearly written manuscript addressing the link between population receptive fields (pRFs) and visual behavior. The authors test whether developmental prosopagnosia (DP) involves atypical pRFs in face-selective regions, a hypothesis suggested by prior work with a small DP sample. Using a larger cohort of DPs and controls, robust pRF mapping with appropriate stimuli and CSS modeling, and careful in-scanner eye tracking, the authors report no group differences in pRF properties across the visual processing hierarchy. These results suggest that reduced spatial integration is unlikely to account for holistic face processing deficits in DP.

    Strengths:

    The dataset quality, sample size, and methodological rigor are notable strengths.

    Weaknesses:

    The primary concern is the interpretation of the results.

    (1) Relationship between pRFs and spatial integration

    While atypical pRF properties could contribute to deficits in spatial integration, impairments in holistic processing in DPs are not necessarily caused by pRF abnormalities. The discussion could be strengthened by considering alternative explanations for reduced spatial integration, such as altered structural or functional connectivity in the face network, which has been reported to underlie DP's difficulties in integrating facial features.

    We agree the Discussion section could benefit from mentioning that alterations to other neural mechanisms, besides pRF organization, could produce deficits in holistic processing. This could take the form of altered functional connectivity (Rosenthal et al., 2017; Lohse et al., 2016; Avidan et al., 2014) or altered structural connectivity (Gomez et al., 2015; Song et al., 2015)

    (2) Beyond the null hypothesis testing framework

    The title claims "normal spatial integration," yet this conclusion is based on a failure to reject the null hypothesis, which does not justify accepting the alternative hypothesis. To substantiate a claim of "normal," the authors would need to provide analyses quantifying evidence for the absence of effects, e.g., using a Bayesian framework.

    We acknowledge that, using frequentist statistical methods, failing to reject the null hypothesis is not sufficient to claim equivalence. For the revision, we will look into additional analyses that could quantify evidence for the null hypothesis. And we will adjust the wording of the title in this regard.

    (3) Face-specific or broader visual processing

    Prior work from the senior author's lab (Jiahui et al., 2018) reported pronounced reductions in scene selectivity and marginal reductions in body selectivity in DPs, suggesting that visual processing deficits in DPs may extend beyond faces. While the manuscript includes PPA as a high-level control region for scene perception, scene selectivity was not directly reported. The authors could also consider individual differences and potential data-quality confounds (tSNR difference between and within groups, several obvious outliers in the figures, etc). For instance, examining whether reduced tSNR in DPs contributed to lower face selectivity in the DP group in this dataset.

    Thank you for this suggestion - we will compare tSNR between the groups as a measure of data quality and we will include these comparisons. A preliminary look indicates that both groups possessed similar distributions of tSNR across many of the face-selective regions investigated here.

    (4) Linking pRF properties to behavior

    The manuscript aims to examine the relationship between pRF properties and behavior, but currently reports only one aspect of pRF (size) in relation to a single behavioral measure (CFMT), without full statistical reporting:

    "We found no significant association between participants' CFMT scores and mean pRF size in OFA, pFUS, or mFUS."

    For comprehensive reporting, the authors could examine additional pRF properties (e.g., center, eccentricity, scaling between eccentricity and pRF size, shape of visual field coverage, etc), additional ROIs (early, intermediate, and category-selective areas), and relate them to multiple behavioral measures (e.g., HEVA, PI20, FFT). This would provide a full picture of how pRF characteristics relate to behavioral performance in DP.

    We will report the full statistical values (r, p) for the (albeit non-significant) relationship between CFMT score and pRF size - thank you for bringing that to our attention. Additionally, we will add other analyses assessing the relationship between a wider array of pRF measures and the other behavioral tests administered to provide a more comprehensive picture of the relation between pRFs and behavior.

    References:

    Avidan, G., Tanzer, M., Hadj-Bouziane, F., Liu, N., Ungerleider, L. G., & Behrmann, M. (2014). Selective Dissociation Between Core and Extended Regions of the Face Processing Network in Congenital Prosopagnosia. Cerebral Cortex, 24(6), 1565–1578. https://doi.org/10.1093/cercor/bht007

    Furl, N., Garrido, L., Dolan, R. J., Driver, J., & Duchaine, B. (2011). Fusiform gyrus face selectivity relates to individual differences in facial recognition ability. Journal of Cognitive Neuroscience, 23(7), 1723–1740. https://doi.org/10.1162/jocn.2010.21545

    Gomez, J., Pestilli, F., Witthoft, N., Golarai, G., Liberman, A., Poltoratski, S., Yoon, J., & Grill-Spector, K. (2015). Functionally Defined White Matter Reveals Segregated Pathways in Human Ventral Temporal Cortex Associated with Category-Specific Processing. Neuron, 85(1), 216–227. https://doi.org/10.1016/j.neuron.2014.12.027

    Jednoróg, K., Marchewka, A., Altarelli, I., Monzalvo Lopez, A. K., van Ermingen-Marbach, M., Grande, M., Grabowska, A., Heim, S., & Ramus, F. (2015). How reliable are gray matter disruptions in specific reading disability across multiple countries and languages? Insights from a large-scale voxel-based morphometry study. Human Brain Mapping, 36(5), 1741–1754. https://doi.org/10.1002/hbm.22734

    Jiahui, G., Yang, H., & Duchaine, B. (2018). Developmental prosopagnosics have widespread selectivity reductions across category-selective visual cortex. Proceedings of the National Academy of Sciences of the United States of America, 115(28), E6418–E6427. https://doi.org/10.1073/pnas.1802246115

    Kay, K. N., Weiner, K. S., Kay, K. N., & Weiner, K. S. (2015). Attention Reduces Spatial Uncertainty in Human Ventral Temporal Cortex Attention Reduces Spatial Uncertainty in Human Ventral Temporal Cortex. Current Biology, 25(5), 595–600. https://doi.org/10.1016/j.cub.2014.12.050

    Lohse, M., Garrido, L., Driver, J., Dolan, R. J., Duchaine, B. C., & Furl, N. (2016). Effective connectivity from early visual cortex to posterior occipitotemporal face areas supports face selectivity and predicts developmental prosopagnosia. Journal of Neuroscience, 36(13), 3821–3828. https://doi.org/10.1523/JNEUROSCI.3621-15.2016

    Norman-Haignere, S., Kanwisher, N., & McDermott, J. H. (2013). Cortical pitch regions in humans respond primarily to resolved harmonics and are located in specific tonotopic regions of anterior auditory cortex. Journal of Neuroscience, 33(50), 19451–19469. https://doi.org/10.1523/JNEUROSCI.2880-13.2013

    Poldrack, R. A., Baker, C. I., Durnez, J., Gorgolewski, K. J., Matthews, P. M., Munafò, M. R., Nichols, T. E., Poline, J. B., Vul, E., & Yarkoni, T. (2017). Scanning the horizon: Towards transparent and reproducible neuroimaging research. Nature Reviews Neuroscience, 18(2), 115–126. https://doi.org/10.1038/nrn.2016.167

    Ramus, F., Altarelli, I., Jednoróg, K., Zhao, J., & Scotto di Covella, L. (2018). Neuroanatomy of developmental dyslexia: Pitfalls and promise. Neuroscience and Biobehavioral Reviews, 84(July 2017), 434–452. https://doi.org/10.1016/j.neubiorev.2017.08.001

    Rosenthal, G., Tanzer, M., Simony, E., Hasson, U., Behrmann, M., & Avidan, G. (2017). Altered topology of neural circuits in congenital prosopagnosia. ELife, 6, 1–20. https://doi.org/10.7554/eLife.25069

    Song, S., Garrido, L., Nagy, Z., Mohammadi, S., Steel, A., Driver, J., Dolan, R. J., Duchaine, B., & Furl, N. (2015). Local but not long-range microstructural differences of the ventral temporal cortex in developmental prosopagnosia. Neuropsychologia, 78, 195–206. https://doi.org/10.1016/j.neuropsychologia.2015.10.010

    Turner, B. O., Paul, E. J., Miller, M. B., & Barbey, A. K. (2018). Small sample sizes reduce the replicability of task-based fMRI studies. Communications Biology, 1(1). https://doi.org/10.1038/s42003-018-0073-z

    Witthoft, N., Poltoratski, S., Nguyen, M., Golarai, G., Liberman, A., LaRocque, K., Smith, M., & Grill-Spector, K. (2016). Reduced spatial integration in the ventral visual cortex underlies face recognition deficits in developmental prosopagnosia. BioRxiv, 1–26.

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