Neuroanatomical structures within the medial temporal lobe (MTL) are known to support memory-related behaviors (Scoville and Milner, 1957; Eichenbaum and Cohen, 2004; LaRocque and Wagner, 2015). For decades, experimentalists have also observed MTL-related impairments in tasks designed to test perceptual processing (Suzuki, 2009; Baxter, 2009). These findings centered on perirhinal cortex (PRC), an MTL structure situated at the apex of high-level sensory cortices (Figure 1a). Visual impairments were reported following lesions to PRC in humans and other animals, bolstering a perceptual-mnemonic account of perirhinal function (e.g. Murray and Bussey, 1999; Bussey et al., 2002; Lee et al., 2005; Lee et al., 2006; Barense et al., 2007; Inhoff et al., 2019). However, there were also visual experiments for which no impairments were observed following PRC lesions (e.g. Buffalo et al., 1998a; Buffalo et al., 1998b; Stark and Squire, 2000; Knutson et al., 2012). In this was, decades of evidence resulted in a pattern of seemingly inconsistent experimental outcomes, with no formal method for disambiguating between competing interpretations of the available data.

Figure 1

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One of the central challenges in this experimental literature has been isolating PRC-dependent behaviors from those supported by PRC-adjacent sensory cortex. In the primate, this requires disentangling PRC-dependent performance from visual behaviors supported by the ventral visual stream (VVS; DiCarlo and Cox, 2007; DiCarlo et al., 2012). Lacking more objective metrics, experimentalists had relied on informal, descriptive accounts of perceptual demands; terms such as ‘complexity’ and ‘feature ambiguity’ were intended to characterize those stimulus properties that are necessary to evaluate PRC involvement in visual object perception. However, this informal approach led to conflicting interpretations of the available evidence, without any means to arbitrate between them. For example, the absence of PRC-related deficits in a given study (e.g. Stark and Squire, 2000) has led to the conclusion that PRC is not involved in perception (Suzuki, 2009), while others argue that stimuli in stimuli from these studies are not ‘complex’ enough (i.e. can be represented by canonical visual cortices) and so no perceptual deficits are expected (Bussey and Saksida, 2002).

In recent years, deep learning computational methods have become commonplace in the vision sciences. Remarkably, these models are able to predict neural responses throughout the primate VVS directly from experimental stimuli: given an experimental image as input, these models (e.g. convolutional neural networks, CNNs) are able to predict neural responses. These ‘stimulus-comptable’ methods currently provide the most quantitatively accurate predictions of neural responses throughout the primate VVS (Yamins et al., 2014; Khaligh-Razavi and Kriegeskorte, 2014; Rajalingham et al., 2018; Bashivan et al., 2019). For example, early model layers within a CNN better predict earlier stages of processing within the VVS (e.g. V4; Figure 1b: left, gray) while later model layers better predict later stages of processing within the VVS (e.g. IT; Figure 1b: left, green). We note that there is not a 1–1 correspondence between these models and the primate VVS as they typically lack known biological properties (Zhuang et al., 2021; Doerig et al., 2022). Nonetheless, these models can be modified to evaluate domain-specific hypotheses (Doerig et al., 2022)—for example by adding recurrence (Kubilius et al., 2018; Kietzmann et al., 2019) or eccentricity-dependent scaling (Deza and Konkle, 2020; Jonnalagadda et al., 2021).

Recently, Bonnen et al., 2021 leveraged these ‘VVS-like’ models to evaluate the performance of PRC-intact/-lesioned human participants in visual discrimination tasks. While VVS-like models are able to approximate performance supported by a linear readout of high-level visual cortex (Figure 1b: right, green), human participants are able to out outperform both VVS-like models and a linear readout of direct electrophysiological recordings from the VVS (Figure 1b: right, purple). Critically, VVS-like models approximate PRC-lesioned performance. While these data implicate PRC in visual object processing, there remains experimental data collected from non-human primates which have not been formally evaluated. Like the human literature, non-human primate data have been used to both support and refute PRC involvement in perception. Unlike the naturally occurring lesioned in humans, experiments with non-human primates have unparalleled control over the site and extent of PRC lesions—potentially, providing more incisive tests of competing claims over PRC function. As such, characterizing the discrepancies between human and non-human primate data is a critical step toward developing a more formal understanding of PRC involvement in perception.

In order to resolve this cross-species discrepancy, here we formalize perceptual demands in experiments administered to PRC-intact/-lesioned monkeys (Macaca mulatta). We draw from data collected by Eldridge et al., 2018 which provides striking evidence against PRC involvement in perception: Eldridge et al., 2018 created multiple stimulus sets, allowing for more a fine-grained evaluation of perceptual behaviors than previous, related work (e.g. Bussey et al., 2003). Here, we estimate VVS-supported performance on stimuli from Eldridge et al., 2018 and compare these predictions to PRC-intact and -lesioned choice behaviors. This modeling approach enables us to situate human and macaque lesion data within a shared metric space (i.e. VVS-model performance); as such, previous observations in the human (e.g. Figure 1b: right, green) constrain how data from Eldridge et al., 2018 can be interpreted; critically, to evaluate PRC involvement in perception, the performance of non-lesioned participants must exceed VVS-modeled performance. Given this, supra-VVS performance may be due to PRC-dependent contributions (schematized in Figure 1c: left), or for reasons unrelated to PRC function (schematized in Figure 1c: middle). However, if VVS-supported performance approximates PRC-intact behavior, no perceptual processing beyond the VVS should be necessary (schematized in Figure 1c: right). We refer to stimuli in this category as ‘non-diagnostic’.

We begin with a task-optimized convolutional neural network, pretrained to perform object classification. We estimate the correspondence between this model and electrophysiological responses from high-level visual cortex using a protocol previously reported in Bonnen et al., 2021. We summarize this protocol here, but refer to the previous manuscript for a more detailed account. Using previously collected electrophysiological responses from macaque VVS (Majaj et al., 2015), we identify a model layer that best fits high-level visual cortex: Given a set of images, we learn a linear mapping between model responses and a single electrode’s responses, then evaluate this mapping using independent data (i.e. left-out images). For each model layer, this analysis yields a median cross-validated fit to noise-corrected neural responses, for both V4 and IT. As is consistently reported (e.g. Schrimpf et al., 2020), early model layers (i.e. first half of layers) better predict neural responses in V4 than do later layers (unpaired t-test: ${\displaystyle t(8)=2.70,\mathrm{P}=0.015}$; Figure 1b: left, gray), while later layers better predict neural responses in IT, a higher-level region (unpaired t-test: ${\displaystyle t(8)=3.70,\mathrm{P}=0.002}$; Figure 1b: left, green). Peak V4 fits occur in model layer pool3 (noise-corrected ${\displaystyle \mathrm{r}=0.95\pm 0.30}$ STD) while peak IT fits occur in con5_1 (noise-corrected ${\displaystyle \mathrm{r}=0.88\pm 0.16}$ STD). For ease, in all subsequent analyses we use model responses from a con5_1-adjacent layer, fc6, which has comparable neural fits but a lower-dimensional representation.

Next we compare model, VVS-supported, and human performance within the same metric space: Instead of fitting model responses directly to electrophysiological recordings in high-level visual cortex, as above, here we evaluate the similarity between the performance supported by the model and high-level visual cortex, as well as human performance on these same stimuli. For this comparison, we leverage electrophysiological responses previously collected from macaque IT cortex (Majaj et al., 2015), using a protocol originally detailed in Bonnen et al., 2021. We independently estimate model and VVS-supported performance on a stimulus set composed of concurrent visual discrimination trials, using a modified leave-one-out cross-validation strategy. We then determine the model-VVS fit over the performance estimates, as developed in Bonnen et al., 2021 and outlined in Methods. We can compare model performance with both VVS-supported performance and PRC-intact (human, n = 297) performance on these same stimuli, using data from Bonnen et al., 2021. On this dataset, a computational proxy for the VVS predicts IT-supported performance (${\displaystyle \beta =0.81}$, ${\displaystyle \mathrm{F}(1,30)=13.33}$, ${\displaystyle \mathrm{P}=4\times {10}^{-14}}$; Figure 1b, green), while each are outperformed by (${\displaystyle \beta =0.24}$, $t(31)=9.50$, ${\displaystyle \mathrm{P}=1\times {10}^{-10}}$; Figure 1b: right, purple). These data suggest that while these models are suitable proxies for VVS-supported performance, human performance is able to exceed a linear readout of the VVS.

With these ‘VVS-like’ models, we turn to analyses of macaque lesion data. First, we extract model responses to each stimulus in all four experiments administered by Eldridge et al., 2018. In these experiments, subjects provided a binary classification for each stimulus: ‘cat’ or ‘dog.’ Critically, stimuli were composed not only of cats and dogs, but of ‘morphed’ images that parametrically vary the percent of category-relevant information present in each trial. For example, ‘10% morphs’ were 90% cat and 10% dog. These morphed stimuli were designed to evaluate PRC involvement in perception by creating maximal ‘feature ambiguity,’ a perceptual quality reported to elicit PRC dependence in previous work (Bussey et al., 2002; Norman and Eacott, 2004; Bussey et al., 2006; Murray and Richmond, 2001). On each trial, subjects were rewarded for responses that correctly identify which category best fits the image presented (e.g. 10% = ‘cat’, 90% = ‘dog’, correct response is ‘dog’). We evaluate data from two groups of monkeys in this study: an unoperated control group (n = 3) and a group with bilateral removal of rhinal cortex, which including peri- and entorhinal cortex. We formulate the modeling problem as a binary forced choice (i.e. ‘dog’ = 1, ‘cat’ = 0) and present the model with experimental stimuli. We then extract model responses from a layer that corresponds to ‘high-level’ visual cortex and learn a linear mapping from model responses to predict the category label. For all analyses, we report the results on held-out data (Methods: Determining model performance).

We first evaluate model performance with the aggregate metrics used by the original authors—not on the performance of individual images, but on the proportion of trials within the same ‘bin’ that are correct. With the original behavioral data, we average performance across images within each morph level (e.g. 10%, 20%, etc.) across subjects in each lesion group (PRC-intact Figure 2a, and -lesioned Figure 2b). As reported in Eldridge et al., 2018, there is not a significant difference between the choice behaviors of PRC-lesioned and -intact subjects (no significant difference between PRC-intact/-lesion groups: $R^2=0.00$, $\beta =0.01$, $F(1,86)$ = ${\displaystyle 0.07,\mathrm{P}=0.941}$). For each of these experiments, we extract model responses to all stimuli from a model layer that best corresponds to a high-level visual region, inferior temporal (IT) cortex. Using the model responses from this ‘IT-like’ model layer to each image, we train a linear, binary classification model on the category label of each image (i.e. ‘dog’ or ‘cat’) on 4/5th of the available stimuli. We then evaluate model performance on the remaining 1/5th of those stimuli, repeating this procedure across 50 iterations of randomized train–test splits. A computational proxy for the VVS exhibits the same qualitative pattern of behavior as each subject group (Figure 2c, model performance across multiple train–test iterations in black). Moreover, we observe a striking correspondence between model and PRC-intact behavior (Figure 2b, purple: $R^2=0.98$, $\beta =0.97$, ${\displaystyle t(21)=33.12,\mathrm{P}=6\times {10}^{-19}}$ ) as well as -lesioned subjects (green: $R^2=0.99$, $\beta =0.96$, ${\displaystyle t(21)=57.38,\mathrm{P}=1\times {10}^{-23}}$). Employing the same metric used to claim no significant difference between PRC-lesion/-intact performance, we find no difference between subject and model behavior ($R^2=0.00$, $\beta =-0.01$, ${\displaystyle F(1,86)=-0.11,\mathrm{P}=0.915}$).

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We extend our analysis beyond the aggregate morph- and subject-level analyses used by the original authors, introducing a split-half reliability analysis (Methods: Split-half reliability estimates). This enables us to determine if there is reliable choice behavior, for each subject, at the level of individual images. We restrict our analyses to experiments with sufficient data, as this analysis requires multiple repetitions of each image; we exclude experiments 3 (’Masked Morphs’) and 4 (’Crossed Morphs’) due to insufficient repetitions (which can be seen in Figure 2, rows 3–4). Across both remaining experiments, we find consistent image-level choice behaviors for subjects with an intact (e.g. median ${\displaystyle R_{\text{exp1}}^2=0.94}$, median ${\displaystyle R_{\text{exp2}}^2=0.86}$) and lesioned (e.g. median ${\displaystyle R_{\text{exp1}}^2=0.91}$, median ${\displaystyle R_{\text{exp2}}^2=0.90}$) rhinal cortex (Figure 3a: within-subject reliability on the diagonal; PRC-intact subjects in purple, PRC-lesioned subjects in green). We also observe consistent image-level choice behaviors between subjects (e.g. median ${\displaystyle R_{\text{exp1}}^2=0.86}$, median ${\displaystyle R_{\text{exp2}}^2=0.79}$). These results indicate there is reliable within- and between-subject variance in the image-by-image choice behaviors of experimental subjects (Figure 3a: PRC-intact subjects in purple, PRC-lesioned subjects in green; between-group reliability in gray), suggesting that this behavior is a suitable target to evaluate how well we approximate more granular subject behaviors with a computational proxy for the VVS. We next examine whether the model can predict these more granular, subject- and image-level choice behaviors (see Methods: Consistency estimates).

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Our computational approach is able to predict subject-level choice behavior when aggregated across morph levels, for both PRC-intact (e.g. subject 0; $R^2=0.99\beta =1.01$, ${\displaystyle t(21)=39.30,\mathrm{P}=2\times {10}^{-20}}$) and -lesioned (e.g. subject 4: $R^2=0.99\beta =1.01$, ${\displaystyle t(21)=45.01,\mathrm{P}=1\times {10}^{-21}}$) subjects (Figure 3b). Interestingly, the model’s fit to subject behavior is indistinguishable from the distribution of between-subject reliability estimates (Figure 1a; median of the empirical p(model ${\displaystyle |{\text{reliability}}_{\text{between-subject}})=0.592}$) suggesting that the model exhibits ‘subject-like’ behaviors at this resolution. Our modeling approach is also able to significantly predict image-level choice behaviors for both PRC-lesioned (e.g. subject 3: $R^2=0.86\beta =0.81$, ${\displaystyle \mathrm{F}(1,438)=52.79,\mathrm{P}=5\times {10}^{-192}}$) and -intact subjects (e.g. subject 1: $R^2=0.87\beta =0.88$, ${\displaystyle \mathrm{F}(1,438)=53.24,\mathrm{P}=2\times {10}^{-193}}$). However, the model behavior is unlikely to be observed under the distribution of between-subject reliability estimates (between-subject reliability distributions visualized in Figure 1b; median of the empirical p(model $|{\text{reliability}}_{\text{between-subject}})=0$). That is, the model does not exhibit ‘subject-like’ choice behaviors at the resolution of individual images. This is an important caveat to note when evaluating the correspondence between model performance and animal behavior: as previously reported (Rajalingham et al., 2018), even as these models approximate neural responses and choice behaviors in the aggregate (i.e. across images), they do not necessarily capture the trial-by-trial choice behaviors. We elaborate on this further in the discussion.

There are properties of the experimental design in Eldridge et al., 2018 that encourage a more careful comparison between primate and model behavior. Experimental stimuli contain discrete interpolations between ‘cat’ and ‘dog’ images, such that adjacent stimuli within a morph sequence are highly similar (e.g. see Figure 2—figure supplement 1). The colinearity in this stimulus set is revealed by running a classification analysis over pixels: a linear readout of stimulus category directly from the vectorized (i.e. flattened) images themselves is sufficient to approximate aggregate performance of all experimental groups ($R^2=0.94\beta =0.90$, ${\displaystyle \mathrm{F}(1,42)=26.74}$ , ${\displaystyle \mathrm{P}=5\times {10}^{-28}}$ ; Figure 2—figure supplement 2). To ensure that the VVS modeling approach is not simply a byproduct of the colinearity in the stimuli, we construct a conservative method for model evaluation by restricting training data to images from unrelated morphs sequences (i.e. train on morph sequences A–F, test on morph sequence G). Under this more conservative train–test split, pixels are no longer predictive of primate behavior ($R^2=0.05\beta =0.45$, ${\displaystyle \mathrm{F}(1,42)=1.42}$ , ${\displaystyle \mathrm{P}=0.164}$; Figure 2—figure supplement 3), but there remains a clear correspondence between the model and PRC-lesioned ($R^2=0.87\beta =1.17$, ${\displaystyle \mathrm{F}(1,42)=16.94}$ , ${\displaystyle \mathrm{P}=2\times {10}^{-20}}$ ) and -intact performance ($R^2=0.88\beta =1.16$, ${\displaystyle \mathrm{F}(1,42)=17.39,\mathrm{P}=8\times {10}^{-21}}$; Figure 2—figure supplement 4). That is, although subjects were able to exploit the colinearity in the stimuli to improve their performance with experience, the correspondence between VVS models and primate choice behaviors is not an artifact of these low-level stimulus attributes.

There are properties of the experimental design in Eldridge et al., 2018 that encourage a more careful comparison between primate and model behavior. Experimental stimuli contain discrete interpolations between ‘cat’ and ‘dog’ images, such that adjacent stimuli within a morph sequence are highly similar (e.g. see Figure 2—figure supplement 1). The colinearity in this stimulus set is revealed by running a classification analysis over pixels: a linear readout of stimulus category directly from the vectorized (i.e. flattened) images themselves is sufficient to approximate aggregate performance of all experimental groups ($R^2=0.94\beta =0.90$, ${\displaystyle \mathrm{F}(1,42)=26.74}$ , ${\displaystyle \mathrm{P}=5\times {10}^{-28}}$ ; Figure 2—figure supplement 2). To ensure that the VVS modeling approach is not simply a byproduct of the colinearity in the stimuli, we construct a conservative method for model evaluation by restricting training data to images from unrelated morphs sequences (i.e. train on morph sequences A–F, test on morph sequence G). Under this more conservative train–test split, pixels are no longer predictive of primate behavior ($R^2=0.05\beta =0.45$, ${\displaystyle \mathrm{F}(1,42)=1.42}$ , ${\displaystyle \mathrm{P}=0.164}$; Figure 2—figure supplement 3), but there remains a clear correspondence between the model and PRC-lesioned ($R^2=0.87\beta =1.17$, ${\displaystyle \mathrm{F}(1,42)=16.94}$ , ${\displaystyle \mathrm{P}=2\times {10}^{-20}}$ ) and -intact performance ($R^2=0.88\beta =1.16$, ${\displaystyle \mathrm{F}(1,42)=17.39,\mathrm{P}=8\times {10}^{-21}}$; Figure 2—figure supplement 4). That is, although subjects were able to exploit the colinearity in the stimuli to improve their performance with experience, the correspondence between VVS models and primate choice behaviors is not an artifact of these low-level stimulus attributes.

To evaluate competing claims surrounding PRC involvement in perception, Eldridge et al., 2018 administered a series of visual classification tasks to PRC-lesioned/-intact monkeys. These stimuli were carefully crafted to exhibit a qualitative, perceptual property that had previously been shown to elicit PRC dependence (i.e. ‘feature ambiguity’; Bussey et al., 2002; Norman and Eacott, 2004; Bussey et al., 2006; Murray and Richmond, 2001). The absence of PRC-related deficits across four experiments led the original authors to suggest that perceptual processing is not dependent on PRC. Here, we reevaluate this claim by situating these results within a more formal computational framework; leveraging task-optimized convolutional neural networks as a proxy for primate visual processing (Yamins et al., 2014; Rajalingham et al., 2018; Schrimpf et al., 2020). We first determined VVS-model performance on the experimental stimuli in Eldridge et al., 2018. We then compared these computational results with monkey choice behaviors, including subjects with bilateral lesions to PRC (n = 3), as well as unoperated controls (n = 3). For both PRC-lesioned/-intact monkeys, we observe a striking correspondence between VVS model and experimental behavior at the group (Figure 2d) and subject level (Figure 3b). These results suggest that a linear readout of the VVS should be sufficient to enable the visual classification behaviors in Eldridge et al., 2018; no PRC-related impairments are expected.

In isolation, it is ambiguous how these data should be interpreted. For example, if VVS-modeled accuracy was sufficient to explain PRC-intact performance across all known stimulus sets, this would suggest that PRC is not involved in visual object perception. However, previous computational results from humans demonstrate that PRC-intact participants are able to outperform a linear readout of the VVS (schematized in Figure 1b: right, purple). Because results from these human experiments are in the same metric space as our current results (i.e. VVS-modeled performance), these data unambiguously constrain our interpretation: for a stimulus set to evaluate PRC involvement in visual processing, participants must be able to outperform a linear readout of the VVS. That is, supra-VVS performance must be observed in order to isolate PRC contributions from those of other possible contributors to these behaviors (e.g. prefrontal cortex, schematized in Figure 1c: center). Given that supra-VVS performance is not observed in the current stimulus set (Figure 2d; schematized in Figure 1c: right), we conclude that experiments in Eldridge et al., 2018 are not diagnostic of PRC involvement in perception. Consequently, we suggest that these data do not offer absolute evidence against PRC involvement in perception—revising the original conclusions made from this study.

We note that there is meaningful variance in the trial-level behaviors not captured by the current modeling framework. By conducting a more granular analyses than the original study (i.e. an image-level analysis, instead of averaging across multiple images within the same morph level), we found that image-level choice behaviors are reliable both within and between subjects (Figure 3a). At this image-level resolution, however, the VVS model does not match the pattern of choice behaviors evident in experimental subjects (Figure 3c Figure 3—figure supplement 1). This observation is consistent with previous reports (Rajalingham et al., 2018), suggesting that these VVS-like models are best suited to approximate aggregate choice behaviors, not responses to individual images. Many sources of variance have been identified as possible contributors to these subject–model divergences, such as biologically implausible training data (Zhuang et al., 2021), or lack of known properties of the primate visual system—for example recurrence (Kar and DiCarlo, 2020) or eccentricity-dependent scaling (Jonnalagadda et al., 2021).

While admittedly coarse, these computational proxies for the VVS provide an unprecedented opportunity to understand perirhinal function. Their contribution is, principally, to isolate PRC-dependent behaviors from those supported by the VVS. More generally, however, this is possible because these methods directly interface with experimental data—making predictions of VVS-supported performance directly from experimental stimuli, instead of relying on the discretion of experimentalists. This stimulus-computable property of these models provides a formal ‘linking function’ between theoretical claims with experimental evidence. In turn, this modeling approach creates a unified metric space (in this case, ‘model performance’) that enables us to evaluate experimental outcomes across labs, across studies, and even across species. We believe that a judicious application of these computational tools, alongside a careful consideration of animal behavior, will enrich the next generation of empirical studies surrounding MTL-dependent perceptual processing.

All scripts used for analysis and visualization can be accessed via github at https://github.com/tzler/eldridge_reanalysis (copy archived at Bonnen, 2023). All stimuli and behavioral data used in these analyses can be downloaded via Dryad at https://doi.org/10.5061/dryad.r4xgxd2h7.

1. 1. Bonnen T
   2. Eldridge M

   (2022) Dryad Digital Repository

   Data from: Inconsistencies between human and macaque lesion data can be resolved with a stimulus-computable model of the ventral visual stream.

   https://doi.org/10.5061/dryad.r4xgxd2h7

1. 1. Barense MD
   2. Gaffan D
   3. Graham KS

   (2007) The human medial temporal lobe processes Online representations of complex objects

   Neuropsychologia 45:2963–2974.

   https://doi.org/10.1016/j.neuropsychologia.2007.05.023

   • PubMed
   • Google Scholar
2. 1. Bashivan P
   2. Kar K
   3. DiCarlo JJ

   (2019) Neural population control via deep image synthesis

   Science 364:eaav9436.

   https://doi.org/10.1126/science.aav9436

   • PubMed
   • Google Scholar
3. 1. Baxter MG

   (2009) Involvement of medial temporal lobe structures in memory and perception

   Neuron 61:667–677.

   https://doi.org/10.1016/j.neuron.2009.02.007

   • PubMed
   • Google Scholar
4. 1. Bonnen T
   2. Yamins DLK
   3. Wagner AD

   (2021) When the ventral visual stream is not enough: A deep learning account of medial temporal lobe involvement in perception

   Neuron 109:2755–2766.

   https://doi.org/10.1016/j.neuron.2021.06.018

   • PubMed
   • Google Scholar
5. Software

   1. Bonnen T

   (2023) Analysis and visualization scripts, version swh:1:rev:f04b8ab7a44e297bc94e9ef77c3eb20f80d16b91

   Software Heritage.

   https://archive.softwareheritage.org/swh:1:dir:af4e8e55fbe804078f631cca8abb816b0d563819;origin=https://github.com/tzler/eldridge_reanalysis;visit=swh:1:snp:be1cd4e1f66f55f6b82e97a28e5112cb987fa079;anchor=swh:1:rev:f04b8ab7a44e297bc94e9ef77c3eb20f80d16b91
6. 1. Buffalo EA
   2. Reber PJ
   3. Squire LR

   (1998a) The human Perirhinal cortex and recognition memory

   Hippocampus 8:330–339.

   https://doi.org/10.1002/(SICI)1098-1063(1998)8:4<330::AID-HIPO3>3.0.CO;2-L

   • PubMed
   • Google Scholar
7. 1. Buffalo EA
   2. Stefanacci L
   3. Squire LR
   4. Zola SM

   (1998b) A reexamination of the concurrent discrimination learning task: the importance of anterior Inferotemporal cortex, area Te

   Behavioral Neuroscience 112:3–14.

   https://doi.org/10.1037//0735-7044.112.1.3

   • PubMed
   • Google Scholar
8. 1. Bussey TJ
   2. Saksida LM

   (2002) The organization of visual object representations: a Connectionist model of effects of lesions in Perirhinal cortex

   European Journal of Neuroscience 15:355–364.

   https://doi.org/10.1046/j.0953-816x.2001.01850.x

   • Google Scholar
9. 1. Bussey TJ
   2. Saksida LM
   3. Murray EA

   (2002) Perirhinal cortex resolves feature ambiguity in complex visual discriminations

   European Journal of Neuroscience 15:365–374.

   https://doi.org/10.1046/j.0953-816x.2001.01851.x

   • Google Scholar
10. 1. Bussey TJ
    2. Saksida LM
    3. Murray EA

    (2003) Impairments in visual discrimination after Perirhinal cortex lesions: testing ‘declarative’ vs. ‘perceptual-Mnemonic’ views of Perirhinal cortex function

    The European Journal of Neuroscience 17:649–660.

    https://doi.org/10.1046/j.1460-9568.2003.02475.x

    • PubMed
    • Google Scholar
11. 1. Bussey TJ
    2. Saksida LM
    3. Murray EA

    (2006) Perirhinal cortex and feature-ambiguous discriminations

    Learning & Memory 13:103–105.

    https://doi.org/10.1101/lm.163606

    • Google Scholar
12. Conference

    1. Deng J
    2. Dong W
    3. Socher R
    4. Li LJ

    (2009) ImageNet: A large-scale hierarchical image database

    2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). pp. 248–255.

    https://doi.org/10.1109/CVPR.2009.5206848

    • Google Scholar
13. Preprint

    1. Deza A
    2. Konkle T

    (2020) Emergent Properties of Foveated Perceptual Systems

    arXiv.

    https://arxiv.org/abs/2006.07991

    • Google Scholar
14. 1. DiCarlo JJ
    2. Cox DD

    (2007) Untangling invariant object recognition

    Trends in Cognitive Sciences 11:333–341.

    https://doi.org/10.1016/j.tics.2007.06.010

    • Google Scholar
15. 1. DiCarlo JJ
    2. Zoccolan D
    3. Rust NC

    (2012) How does the brain solve visual object recognition?

    Neuron 73:415–434.

    https://doi.org/10.1016/j.neuron.2012.01.010

    • PubMed
    • Google Scholar
16. Preprint

    1. Doerig A
    2. Sommers R
    3. Seeliger K

    (2022) The Neuroconnectionist Research Programme

    arXiv.

    https://arxiv.org/abs/2209.03718

    • Google Scholar
17. Book

    1. Eichenbaum H
    2. Cohen NJ

    (2004) From Conditioning to Conscious Recollection: Memory Systems of the Brain

    Oxford University Press on Demand.

    https://doi.org/10.1093/acprof:oso/9780195178043.001.0001

    • Google Scholar
18. 1. Eldridge MA
    2. Matsumoto N
    3. Wittig JH
    4. Masseau EC
    5. Saunders RC
    6. Richmond BJ

    (2018) Perceptual processing in the ventral visual stream requires area Te but not Rhinal cortex

    eLife 7:e36310.

    https://doi.org/10.7554/eLife.36310

    • PubMed
    • Google Scholar
19. 1. Inhoff MC
    2. Heusser AC
    3. Tambini A
    4. Martin CB
    5. O’Neil EB
    6. Köhler S
    7. Meager MR
    8. Blackmon K
    9. Vazquez B
    10. Devinsky O
    11. Davachi L

    (2019) Understanding Perirhinal contributions to perception and memory: evidence through the lens of selective Perirhinal damage

    Neuropsychologia 124:9–18.

    https://doi.org/10.1016/j.neuropsychologia.2018.12.020

    • PubMed
    • Google Scholar
20. Preprint

    1. Jonnalagadda A
    2. Wang WY
    3. Manjunath B
    4. Eckstein MP

    (2021) Foveater: Foveated Transformer for Image Classification

    arXiv.

    https://arxiv.org/abs/2105.14173

    • Google Scholar
21. Preprint

    1. Kar K
    2. DiCarlo JJ

    (2020) Fast Recurrent Processing via Ventral Prefrontal Cortex Is Needed by the Primate Ventral Stream for Robust Core Visual Object Recognition

    bioRxiv.

    https://doi.org/10.1101/2020.05.10.086959

    • Google Scholar
22. 1. Khaligh-Razavi SM
    2. Kriegeskorte N

    (2014) Deep supervised, but not Unsupervised, models may explain IT cortical representation

    PLOS Computational Biology 10:e1003915.

    https://doi.org/10.1371/journal.pcbi.1003915

    • PubMed
    • Google Scholar
23. 1. Kietzmann TC
    2. Spoerer CJ
    3. Sörensen LKA
    4. Cichy RM
    5. Hauk O
    6. Kriegeskorte N

    (2019) Recurrence is required to capture the representational Dynamics of the human visual system

    PNAS 116:21854–21863.

    https://doi.org/10.1073/pnas.1905544116

    • PubMed
    • Google Scholar
24. 1. Knutson AR
    2. Hopkins RO
    3. Squire LR

    (2012) Visual discrimination performance, memory, and medial temporal lobe function

    PNAS 109:13106–13111.

    https://doi.org/10.1073/pnas.1208876109

    • PubMed
    • Google Scholar
25. Preprint

    1. Kubilius J
    2. Schrimpf M
    3. Nayebi A
    4. Bear D
    5. Yamins DLK
    6. DiCarlo JJ

    (2018) CORnet: Modeling the Neural Mechanisms of Core Object Recognition

    bioRxiv.

    https://doi.org/10.1101/408385

    • Google Scholar
26. Book

    1. LaRocque K
    2. Wagner AD

    (2015)

    The Medial Temporal Lobe and Episodic Memory

    Elsevier.

    • Google Scholar
27. 1. Lee ACH
    2. Bussey TJ
    3. Murray EA
    4. Saksida LM
    5. Epstein RA
    6. Kapur N
    7. Hodges JR
    8. Graham KS

    (2005) Perceptual deficits in amnesia: challenging the medial temporal lobe ‘Mnemonic’View

    Neuropsychologia 43:1–11.

    https://doi.org/10.1016/j.neuropsychologia.2004.07.017

    • Google Scholar
28. 1. Lee ACH
    2. Buckley MJ
    3. Gaffan D
    4. Emery T
    5. Hodges JR
    6. Graham KS

    (2006) Differentiating the roles of the hippocampus and Perirhinal cortex in processes beyond long-term declarative memory: a double dissociation in dementia

    The Journal of Neuroscience 26:5198–5203.

    https://doi.org/10.1523/JNEUROSCI.3157-05.2006

    • Google Scholar
29. 1. Majaj NJ
    2. Hong H
    3. Solomon EA
    4. DiCarlo JJ

    (2015) Simple learned weighted sums of inferior temporal neuronal firing rates accurately predict human core object recognition performance

    The Journal of Neuroscience 35:13402–13418.

    https://doi.org/10.1523/JNEUROSCI.5181-14.2015

    • PubMed
    • Google Scholar
30. 1. Murray EA
    2. Bussey TJ

    (1999) Perceptual–Mnemonic functions of the Perirhinal cortex

    Trends in Cognitive Sciences 3:142–151.

    https://doi.org/10.1016/S1364-6613(99)01303-0

    • Google Scholar
31. 1. Murray EA
    2. Richmond BJ

    (2001) Role of Perirhinal cortex in object perception, memory, and associations

    Current Opinion in Neurobiology 11:188–193.

    https://doi.org/10.1016/s0959-4388(00)00195-1

    • PubMed
    • Google Scholar
32. 1. Norman G
    2. Eacott MJ

    (2004) Impaired object recognition with increasing levels of feature ambiguity in rats with Perirhinal cortex lesions

    Behavioural Brain Research 148:79–91.

    https://doi.org/10.1016/s0166-4328(03)00176-1

    • PubMed
    • Google Scholar
33. 1. Pedregosa F
    2. Varoquaux G
    3. Gramfort A

    (2011)

    Scikit-learn: machine learning in python

    Journal of Machine Learning Research 12:2825–2830.

    • Google Scholar
34. 1. Rajalingham R
    2. Issa EB
    3. Bashivan P
    4. Kar K
    5. Schmidt K
    6. DiCarlo JJ

    (2018) Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks

    The Journal of Neuroscience 38:7255–7269.

    https://doi.org/10.1523/JNEUROSCI.0388-18.2018

    • PubMed
    • Google Scholar
35. 1. Schrimpf M
    2. Kubilius J
    3. Lee MJ
    4. Ratan Murty NA
    5. Ajemian R
    6. DiCarlo JJ

    (2020) Integrative Benchmarking to advance Neurally mechanistic models of human intelligence

    Neuron 108:413–423.

    https://doi.org/10.1016/j.neuron.2020.07.040

    • PubMed
    • Google Scholar
36. 1. Scoville WB
    2. Milner B

    (1957) Loss of recent memory after bilateral hippocampal lesions

    Journal of Neurology, Neurosurgery, and Psychiatry 20:11–21.

    https://doi.org/10.1136/jnnp.20.1.11

    • PubMed
    • Google Scholar
37. Preprint

    1. Simonyan K
    2. Zisserman A

    (2014) Very Deep Convolutional Networks for Large-Scale Image Recognition

    arXiv.

    https://arxiv.org/abs/1409.1556

    • Google Scholar
38. 1. Stark CEL
    2. Squire LR

    (2000) Intact visual perceptual discrimination in humans in the absence of Perirhinal cortex

    Learning & Memory 7:273–278.

    https://doi.org/10.1101/lm.35000

    • Google Scholar
39. 1. Suzuki WA

    (2009) Perception and the medial temporal lobe: evaluating the current evidence

    Neuron 61:657–666.

    https://doi.org/10.1016/j.neuron.2009.02.008

    • PubMed
    • Google Scholar
40. 1. Yamins DLK
    2. Hong H
    3. Cadieu CF
    4. Solomon EA
    5. Seibert D
    6. DiCarlo JJ

    (2014) Performance-Optimized Hierarchical models predict neural responses in higher visual cortex

    PNAS 111:8619–8624.

    https://doi.org/10.1073/pnas.1403112111

    • PubMed
    • Google Scholar
41. 1. Zhuang C
    2. Yan S
    3. Nayebi A
    4. Schrimpf M
    5. Frank MC
    6. DiCarlo JJ
    7. Yamins DLK

    (2021) Unsupervised neural network models of the ventral visual stream

    PNAS 118:e2014196118.

    https://doi.org/10.1073/pnas.2014196118

    • PubMed
    • Google Scholar

Author details

1. Tyler Bonnen

   Stanford University, Stanford, United States

   Contribution

   Conceptualization, Formal analysis, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   bonnen@stanford.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8709-1651

2. Mark AG Eldridge

   Laboratory of Neuropsychology, National Institute of Mental Health,National Institutes of Health, Bethesda, United States

   Contribution

   Resources, Data curation, Supervision, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   mark.eldridge@nih.gov

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4292-6832

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