The ability to perceive similarities and differences between objects plays an integral role in cognition and behaviour. Perceived similarities are important, for example, for categorizing a fruit at the grocery store as an apple rather than a pear. The appreciation of more fine-grained similarities between exemplars of a category also shapes behaviour, such as when comparing different apples in order to select one for purchase. Indeed, experimental work in psychology has confirmed that the similarity of objects influences performance in numerous behavioural contexts, including but not limited to categorization, object discrimination, recognition memory, and prediction (see Medin et al., 1993; Goldstone and Son, 2012; Hebart et al., 2020, for review). Yet, despite the well-established links to behaviour, how the brain represents these similarities between objects is only beginning to be understood. A central question that has received limited investigation so far is how fine-grained visual similarities that observers subjectively perceive among subordinate category exemplars map onto neural object representations. Answering this question can provide insight as to what brain regions provide the ‘read-out’ for such subjective reports. Moreover, this endeavor holds promise for understanding how differences in the way in which observers perceive their visual environment are reflected in variations in functional brain organization (Charest and Kriegeskorte, 2015).

Functional neuroimaging, combined with pattern analysis techniques, provides a powerful tool for examining the mapping between similarity relationships in visual perception of objects and similarities in corresponding neural representations (Kriegeskorte and Kievit, 2013). A significant body of research addressing this issue has focused on the characterization of category structure and other coarse object distinctions, such as animacy. Findings from this research indicate that activity patterns in a large expanse of the ventral visual stream (VVS), often referred to as inferotemporal (IT) cortex or ventral temporal cortex, capture much of this structure in the environment (Kriegeskorte et al., 2008; Connolly et al., 2012; Mur et al., 2013; Proklova et al., 2016; Cichy et al., 2019). For example, numerous studies have revealed a similarity-based clustering of response patterns for objects in IT that is tied to category membership (e.g., Kriegeskorte et al., 2008; Proklova et al., 2016; see Grill-Spector and Weiner, 2014 for review). In these studies, and in most related work, the primary focus has been on similarity in relation to object distinctions that are defined in objective terms, and on the characterization of neural representations that is shared by observers. As such, they do not address whether activity patterns in IT also capture similarity relationships among objects that characterize subjective aspects of visual perception that may vary across individuals’ reports. When neural activity that corresponds to subjectively perceived visual similarities has been examined, extant research has mostly focussed on specific object features, such as shape or size (Op de Beeck et al., 2008; Haushofer et al., 2008; Schwarzkopf et al., 2011; Moutsiana et al., 2016) rather than on similarities between complex real-world objects that differ from each other on multiple dimensions. A notable exception to this research trend is an fMRI study that focussed on perceived similarities among select real-world objects that are personally meaningful (e.g., images of observers’ own car, their own bicycle; Charest et al., 2014), which demonstrated links between observer-specific perceived similarity and the similarity structure embedded in activity patterns in IT.

In order to reveal the mapping between fine-grained perceived visual similarities among category exemplars and similarities in neural activity, it may not be sufficient to focus on activity in IT but critical to consider VVS regions situated downstream on the medial surface of the temporal lobe. Perirhinal cortex (PrC), and the primary region to which it projects, that is, lateral entorhinal cortex, are of particular interest when such perceived similarity relationships concern entire objects. The representational–hierarchical model of object processing in the VVS asserts that structures in the medial temporal lobe constitute the apex of its processing hierarchy (Murray and Bussey, 1999; Bussey and Saksida, 2007; Cowell et al., 2010; Kent et al., 2016). It proposes that there is a progressive integration of object features along the VVS that allows for increasing differentiation in the representation of objects from posterior to more anterior regions in the service of perception as well as other cognitive domains (e.g., recognition memory). Visual objects are thought to be represented in PrC in their most highly integrated form based on complex feature conjunctions (Murray and Bussey, 1999; Buckley and Gaffan, 2006; Bussey and Saksida, 2007; Graham et al., 2010; Kent et al., 2016), which support perceptual discrimination of even highly similar exemplars from the same object category (e.g., O’Neil et al., 2013; O’Neil et al., 2009). Lateral entorhinal cortex (or its human homologue anterolateral entorhinal cortex [alErC; Maass et al., 2015; Yeung et al., 2017]) has been suggested to extend this role in visual object discrimination through integration with additional spatial features (Connor and Knierim, 2017; Yeung et al., 2017). Taken together, these properties make PrC and alErC ideally suited for providing the read-out for subjective reports of perceived visual similarity among the subordinate exemplars of object categories. Although more posterior VVS regions may also predict perceived similarity, the representational–hierarchical model asserts that they do not provide the same level of differentiation as regions at the apex in the medial temporal lobe. Moreover, they may also not capture those aspects of perceived similarity structure that are observer specific.

There is some evidence from human lesion studies suggesting that the integrity of regions in the medial temporal lobe is critical for perceiving the similarity among complex objects when it is high and objects cannot be easily discriminated from each other. This evidence comes from experiments that used variants of the oddity-discrimination task. In this task, participants view multiple objects (a minimum of three) on a computer screen and are asked to report the item that is least similar to the others. A finding documented in multiple reports (Buckley et al., 2001; Barense et al., 2007; Bartko et al., 2007; Inhoff et al., 2019; cf., Stark and Squire, 2000; Levy et al., 2005; Hales and Clark, 2015) is that patients with medial temporal lobe lesions that include PrC and alErC show deficits in oddity-discrimination tasks when complex objects with substantial feature overlap are compared, but not when oddity judgements require comparison of simple visual stimuli that can be distinguished based on a single feature such as colour or luminance. While the results of this lesion research have critically informed theoretical arguments that PrC plays a role in perceptual discrimination of objects (see Bonnen et al., 2021, for a recent computationally focused review), it is important to note that they do not provide a characterization of similarity structure of neural representations in PrC and alErC, nor a characterization of the transformation of representations from more posterior VVS regions to these regions in the medial temporal lobe. Moreover, lesion findings do not speak to whether neural representations in the medial temporal lobe capture the perceived similarity structure that is unique to individual observers.

In the current fMRI study, we tested the idea that the visual similarity structure among exemplars of real-world categories that observers subjectively perceive is predicted with higher precision by the similarity structure of neural activity in PrC and alErC than in the more posterior VVS regions traditionally considered in neuroscience investigations of visual object perception, including IT. During scanning, we administered a novel experimental task that required visual discrimination between consecutively presented exemplars from real-world categories as well as categorization (see Figure 1). In addition, we obtained ratings of perceived visual similarities among these exemplars from each observer offline (see Figure 2), as well as estimates of similarity derived from an influential computational model that describes objects at their intermediate visual feature level (HMAX, Riesenhuber and Poggio, 1999; Cadieu et al., 2007; Serre et al., 2007; Khaligh-Razavi and Kriegeskorte, 2014). At the behavioural level, we found that discrimination performance was highly sensitive to fine-grained perceived similarity among exemplars, including those aspects that were observer specific. Representational similarity analyses (RSAs) of ultra-high-resolution fMRI data revealed, in line with our hypotheses, that activation patterns in PrC and alErC predict this fine-grained structure within categories with higher precision than any other VVS region, and that they predict even those aspects of similarity structure that are unique to individual observers.

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Vision neuroscience has made great strides in understanding the hierarchical organization of object representations along the VVS. How VVS representations capture fine-grained differences between objects that observers subjectively perceive has received limited examination so far. In the current study, we addressed this question by focussing on perceived similarities among exemplars of real-world categories. Using a novel Category-Exemplar 1-Back Task, we found that visual discrimination performance is highly sensitive to the visual similarity structure that is reflected in observers’ subjective reports. Combining this task with fMRI scanning at ultra-high-resolution allowed us to show, in line with our general hypotheses, that activity patterns in PrC and alErC predict perceived visual similarities among exemplars with higher precision than any other VVS region, including prediction of those aspects of similarity structure that are unique to individual observers.

Research that has aimed to characterize the nature of object representations in human PrC with fMRI has shown that the degree of feature overlap between objects is captured by activation patterns in this region. Such a relationship has been revealed in multiple task contexts, with images of real-world objects and with words denoting such objects; moreover, it has been observed for feature overlap at the perceptual as well as the semantic level (Clarke and Tyler, 2014; Erez et al., 2016; Bruffaerts et al., 2013; Martin et al., 2018a). These findings, in combination with work from neurophysiology in nonhuman animals and from computational modelling, have been interpreted to suggest that PrC integrates features of objects with complex conjunctive coding into representations of whole objects, and that the resulting conjunctive representations allow for differentiation of objects even when they are highly similar due to a high degree of feature overlap (Bussey et al., 2002; Murray and Bussey, 1999; Cowell et al., 2010). Indeed, it is this type of conjunctive coding that has motivated the central notion of the representational–hierarchical model of VVS organization that PrC, together with alErC (Connor and Knierim, 2017; Yeung et al., 2017), can be considered the pinnacle of the VVS object-processing hierarchy. Using a metric that was rooted in participants’ subjective reports of perceived visual similarity and that showed a direct relationship to behavioural discrimination performance, the current fMRI findings provide new support for this hierarchical model by revealing increased differentiation of subordinate category exemplars in PrC and alErC, as compared to more posterior VVS regions.

Findings from lesion studies conducted with oddity-discrimination tasks support the idea that medial temporal lobe structures downstream from IT play a critical role in processes required for the appreciation of fine-grained visual similarities between complex real-world objects that are expressed in perceptual reports (see Bonnen et al., 2021, for review). Numerous studies conducted in humans and in other species have shown that performance on such tasks relies on the integrity of PrC when objects with high visual feature overlap must be judged (Buckley et al., 2001; Barense et al., 2007; Bartko et al., 2007; Inhoff et al., 2019; cf., Stark and Squire, 2000; Levy et al., 2005; Hales and Clark, 2015). For example, Barense et al., 2007 compared performance on multiple visual oddity tasks between individuals with lesions in the medial temporal lobe that largely spared PrC and ErC, versus individuals with more widespread damage in the medial temporal lobes that included PrC and ErC. Most notably, individuals in the latter but not in the former group showed impairments in identifying the odd-one out item in sets of images of real-world objects that shared a high number of overlapping visual features. While the results of these prior lesion studies are compatible with the conclusions we draw in the current study, they do not allow for characterization of similarity structure of neural representations in PrC and alErC, and their direct comparison with representations in other medial temporal and posterior VVS regions, as provided here.

The anatomical specificity of our fMRI findings in the medial temporal lobe is striking. While activity patterns that reflected the similarity structure among category exemplars were present in PrC and alErC, they were absent in medial temporal regions that have previously been implicated in visual discrimination of scenes, specifically pmErC and PhC cortex (see Schultz et al., 2015, for review). This specificity is noteworthy in light of documented differences in functional connectivity between these regions that have been linked to object versus scene processing, with PrC being connected to alErC, and PhC being connected to pmErC, respectively (Maass et al., 2015; see Schröder et al., 2015 for more broadly distributed differences in functional connectivity between ErC subregions and other cortical structures).

The higher precision we observed for the representation of perceived similarity relationships in the medial temporal lobe, as compared to more posterior VVS regions, is of particular theoretical interest for the representational–hierarchical model of VVS organization (Murray and Bussey, 1999; Bussey and Saksida, 2007; Cowell et al., 2010; Kent et al., 2016). Most revealing, in this context, is the comparison between PrC and ErC versus LOC, a region that is part of the large swath of cortex that is often referred to as IT in neuroimaging research and that has been linked to processing of object shape in many prior fMRI studies (e.g., Grill-Spector et al., 2001; Kriegeskorte et al., 2008; Connolly et al., 2012; Mur et al., 2013; Proklova et al., 2016; Cichy et al., 2019). While activity patterns in LOC predicted some aspects of similarity structure among subordinate category exemplars in the current work, this relationship was observed at a coarser grain than in PrC and alErC; it only held when perceived visual similarity was low, and when performance in behavioural discrimination revealed that objects were easily distinguishable. Indeed, complementary pattern classification analyses revealed that activity patterns associated with exemplars of high perceived similarity were not sufficiently separable in LOC so as to allow for classification as distinct items. By contrast, this classification could be successfully performed based on activity patterns in PrC and alErC. Indeed, our searchlight analyses showed that these two regions in the medial temporal lobe were the only ones in which activity was related to perceived visual similarity at a fine-grained level.

Our analyses of the relationship between activity patterns and aspects of perceived similarity among exemplars that are tied to objective image characteristics, as estimated by the computational HMAX model, offer support for the central claim of the representational–hierarchical model that the transformation of object representations from IT (specifically, LOC) to medial temporal-lobe structures involves further integration. Notably, the component of perceived similarity structure that was shared by observers showed a statistical relationship to the estimates of the HMAX model, which describes objects at the intermediate feature level and which has been linked to LOC representations in prior work (Riesenhuber and Poggio, 1999; Cadieu et al., 2007; Serre et al., 2007; but see Khaligh-Razavi and Kriegeskorte, 2014, Kubilius et al., 2016 for limitations as compared to deep convolutional neural network models). In the present study, we also found that HMAX estimates of the similarity among the exemplars we employed were correlated with activity patterns in LOC, but only at low and medium levels of similarity. The fine-grained similarity structure among exemplars that led to the largest number of confusion errors in behavioural discrimination on the Category-Exemplar 1-Back Task during scanning was predominantly observer specific, and this fine-grained observer-specific structure was solely predicted by activity patterns in PrC and alErC. The coding of objects in PrC and alErC as fully integrated entities based on complex feature conjunctions arguably affords the flexibility that is required to capture the fine-grained differentiation among exemplars that characterizes the perception of individual observers. .

Our study was not designed to directly address what factors might drive the variability in perceived similarity structure across observers that were present in their subjective reports, their discrimination performance, and in corresponding activity patterns in PrC and alErC. Prior evidence from fMRI research on neural representations in other VVS regions suggests that past experience and object familiarity may play an important role. Charest et al., 2014 revealed observer-specific effects in the similarity structure of activity patterns in IT that were tied to participants' reports for highly familiar real-world objects with unique personal meaning (e.g., images of observers’ own car, their own bicycle). Notably, this observer-specific mapping between similarity in activity patterns and reports was not present for unfamiliar objects. There is also evidence from behavioural training studies indicating that prior experience with categories has an impact on how the similarity among its exemplars is perceived. In a recent study by Collins and Behrmann, 2020, for example, it was shown that just a few days of repeated exposure can lead to increased differentiation among exemplars, and that these changes are most pronounced at the level of fine-grained similarity structure when observers have had some prior experience with the category in question. This change in similarity structure based on training occurred in the absence of any apparent opportunity to gain new sematic knowledge about the exemplars in question, suggesting it could reflect an increase in perceptual expertise. Indeed in recent behavioural research from our laboratory, we have found that the degree of self-reported exposure to real-world object categories, but not corresponding semantic knowledge, predicts observers’ perceived visual similarity structure among exemplars, and that this relationship is most notable at the level of fine-grained similarity structure (Minos et al., 2021). It is possible that the observer specificity in fine-grained differentiation among exemplars that was predicted by activity patterns in PrC and alErC in the current study is tied to similar factors at work; it may reflect interindividual differences in perceptual expertise across observers and categories. Although speculative at present, such an account would be in line with a large body of evidence revealing learning-related plasticity in object representations in these medial temporal lobe structures (see Banks et al., 2014, for review). This account can be directly tested with training paradigms that target specific real-world object categories in future fMRI research. Regardless of the outcome of such future research, the current findings highlight the critical value of probing the subjective appreciation of visual object similarities, and their variability across observers, for a complete understanding of the transformation of neural representations from posterior regions to those at the apex of the VVS.

All data generated or analyzed during this study are included in the manuscript and supporting fields. Source data files have been provided for Figures 1, 2, 3, 4, 6,7.

1. 1. Banks PJ
   2. Warburton EC
   3. Brown MW
   4. Bashir ZI

   (2014) Mechanisms of synaptic plasticity and recognition memory in the perirhinal cortex

   Progress in Molecular Biology and Translational Science 122:193–209.

   https://doi.org/10.1016/B978-0-12-420170-5.00007-6

   • PubMed
   • Google Scholar
2. 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
3. 1. Bartko SJ
   2. Winters BD
   3. Cowell RA
   4. Saksida LM
   5. Bussey TJ

   (2007) Perceptual functions of perirhinal cortex in rats: zero-delay object recognition and simultaneous oddity discriminations

   The Journal of Neuroscience 27:2548–2559.

   https://doi.org/10.1523/JNEUROSCI.5171-06.2007

   • PubMed
   • Google Scholar
4. 1. Behzadi Y
   2. Restom K
   3. Liau J
   4. Liu TT

   (2007) A component based noise correction method (CompCor) for BOLD and perfusion based fMRI

   NeuroImage 37:90–101.

   https://doi.org/10.1016/j.neuroimage.2007.04.042

   • PubMed
   • Google Scholar
5. 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
6. 1. Bruffaerts R
   2. Dupont P
   3. Peeters R
   4. De Deyne S
   5. Storms G
   6. Vandenberghe R

   (2013) Similarity of fMRI activity patterns in left perirhinal cortex reflects semantic similarity between words

   The Journal of Neuroscience 33:18597–18607.

   https://doi.org/10.1523/JNEUROSCI.1548-13.2013

   • PubMed
   • Google Scholar
7. 1. Buckley MJ
   2. Booth MC
   3. Rolls ET
   4. Gaffan D

   (2001) Selective perceptual impairments after perirhinal cortex ablation

   The Journal of Neuroscience 21:9824–9836.

   https://doi.org/10.1523/JNEUROSCI.21-24-09824.2001

   • PubMed
   • Google Scholar
8. 1. Buckley MJ
   2. Gaffan D

   (2006) Extent of MTL lesions in animals and human patients

   Trends in Cognitive Sciences 3:100–107.

   https://doi.org/10.1016/j.tics.2006.01.008

   • PubMed
   • 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

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

    (2007) Memory, perception, and the ventral visual‐perirhinal‐hippocampal stream: thinking outside of the boxes

    Hippocampus 17:898–908.

    https://doi.org/10.1002/hipo.20320

    • PubMed
    • Google Scholar
11. 1. Cadieu C
    2. Kouh M
    3. Pasupathy A
    4. Connor CE
    5. Riesenhuber M
    6. Poggio T

    (2007) A model of V4 shape selectivity and invariance

    Journal of Neurophysiology 98:1733–1750.

    https://doi.org/10.1152/jn.01265.2006

    • PubMed
    • Google Scholar
12. 1. Charest I
    2. Kievit RA
    3. Schmitz TW
    4. Deca D
    5. Kriegeskorte N

    (2014) Unique semantic space in the brain of each beholder predicts perceived similarity

    PNAS 111:14565–14570.

    https://doi.org/10.1073/pnas.1402594111

    • PubMed
    • Google Scholar
13. 1. Charest I
    2. Kriegeskorte N

    (2015) The brain of the beholder: honouring individual representational idiosyncrasies

    Language, Cognition and Neuroscience 30:367–379.

    https://doi.org/10.1080/23273798.2014.1002505

    • Google Scholar
14. 1. Cichy RM
    2. Kriegeskorte N
    3. Jozwik KM
    4. van den Bosch JJF
    5. Charest I

    (2019) The spatiotemporal neural dynamics underlying perceived similarity for real-world objects

    NeuroImage 194:12–24.

    https://doi.org/10.1016/j.neuroimage.2019.03.031

    • PubMed
    • Google Scholar
15. 1. Clarke A
    2. Tyler LK

    (2014) Object-specific semantic coding in human perirhinal cortex

    The Journal of Neuroscience 34:4766–4775.

    https://doi.org/10.1523/JNEUROSCI.2828-13.2014

    • PubMed
    • Google Scholar
16. 1. Collins E
    2. Behrmann M

    (2020) Exemplar learning reveals the representational origins of expert category perception

    PNAS 117:11167–11177.

    https://doi.org/10.1073/pnas.1912734117

    • PubMed
    • Google Scholar
17. 1. Connolly AC
    2. Guntupalli JS
    3. Gors J
    4. Hanke M
    5. Halchenko YO
    6. Wu Y-C
    7. Abdi H
    8. Haxby JV

    (2012) The representation of biological classes in the human brain

    The Journal of Neuroscience 32:2608–2618.

    https://doi.org/10.1523/JNEUROSCI.5547-11.2012

    • PubMed
    • Google Scholar
18. 1. Connor CE
    2. Knierim JJ

    (2017) Integration of objects and space in perception and memory

    Nature Neuroscience 20:1493–1503.

    https://doi.org/10.1038/nn.4657

    • PubMed
    • Google Scholar
19. 1. Cowell RA
    2. Bussey TJ
    3. Saksida LM

    (2010) Components of recognition memory: dissociable cognitive processes or just differences in representational complexity

    Hippocampus 20:1245–1262.

    https://doi.org/10.1002/hipo.20865

    • PubMed
    • Google Scholar
20. 1. Epstein RA
    2. Baker CI

    (2019) Scene perception in the human brain

    Annual Review of Vision Science 5:373–397.

    https://doi.org/10.1146/annurev-vision-091718-014809

    • PubMed
    • Google Scholar
21. 1. Erez J
    2. Cusack R
    3. Kendall W
    4. Barense MD

    (2016) Conjunctive coding of complex object features

    Cerebral Cortex (New York, N.Y 26:2271–2282.

    https://doi.org/10.1093/cercor/bhv081

    • PubMed
    • Google Scholar
22. 1. Esteban O
    2. Markiewicz CJ
    3. Blair RW
    4. Moodie CA
    5. Isik AI
    6. Erramuzpe A
    7. Kent JD
    8. Goncalves M
    9. DuPre E
    10. Snyder M
    11. Oya H
    12. Ghosh SS
    13. Wright J
    14. Durnez J
    15. Poldrack RA
    16. Gorgolewski KJ

    (2019) fMRIPrep: a robust preprocessing pipeline for functional MRI

    Nature Methods 16:111–116.

    https://doi.org/10.1038/s41592-018-0235-4

    • PubMed
    • Google Scholar
23. 1. Fischl B

    (2012) FreeSurfer

    NeuroImage 62:774–781.

    https://doi.org/10.1016/j.neuroimage.2012.01.021

    • PubMed
    • Google Scholar
24. Book

    1. Goldstone RL
    2. Son JY

    (2012) Similarity

    Oxford University Press.

    https://doi.org/10.1093/oxfordhb/9780199734689.013.0010

    • Google Scholar
25. 1. Gorgolewski KJ
    2. Auer T
    3. Calhoun VD
    4. Craddock RC
    5. Das S
    6. Duff EP
    7. Flandin G
    8. Ghosh SS
    9. Glatard T
    10. Halchenko YO
    11. Handwerker DA
    12. Hanke M
    13. Keator D
    14. Li X
    15. Michael Z
    16. Maumet C
    17. Nichols BN
    18. Nichols TE
    19. Pellman J
    20. Poline JB
    21. Rokem A
    22. Schaefer G
    23. Sochat V
    24. Triplett W
    25. Turner JA
    26. Varoquaux G
    27. Poldrack RA

    (2016) The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments

    Scientific Data 3:160044.

    https://doi.org/10.1038/sdata.2016.44

    • PubMed
    • Google Scholar
26. 1. Graham KS
    2. Barense MD
    3. Lee ACH

    (2010) Going beyond LTM in the MTL: a synthesis of neuropsychological and neuroimaging findings on the role of the medial temporal lobe in memory and perception

    Neuropsychologia 48:831–853.

    https://doi.org/10.1016/j.neuropsychologia.2010.01.001

    • PubMed
    • Google Scholar
27. 1. Grill-Spector K
    2. Kourtzi Z
    3. Kanwisher N

    (2001) The lateral occipital complex and its role in object recognition

    Vision Research 41:1409–1422.

    https://doi.org/10.1016/s0042-6989(01)00073-6

    • PubMed
    • Google Scholar
28. 1. Grill-Spector K
    2. Weiner KS

    (2014) The functional architecture of the ventral temporal cortex and its role in categorization

    Nature Reviews. Neuroscience 15:536–548.

    https://doi.org/10.1038/nrn3747

    • PubMed
    • Google Scholar
29. Book

    1. Hales JB
    2. Clark RE

    (2015) Organization and structure of brain systems supporting memory

    In: Hales JB, editors. In The Maze Book. Humana Press. pp. 143–164.

    https://doi.org/10.1007/978-1-4939-2159-1_6

    • Google Scholar
30. 1. Haushofer J
    2. Livingstone MS
    3. Kanwisher N

    (2008) Multivariate patterns in object-selective cortex dissociate perceptual and physical shape similarity

    PLOS Biology 6:e187.

    https://doi.org/10.1371/journal.pbio.0060187

    • PubMed
    • Google Scholar
31. 1. Hebart MN
    2. Zheng CY
    3. Pereira F
    4. Baker CI

    (2020) Revealing the multidimensional mental representations of natural objects underlying human similarity judgements

    Nature Human Behaviour 4:1173–1185.

    https://doi.org/10.1038/s41562-020-00951-3

    • PubMed
    • Google Scholar
32. 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
33. 1. Kent BA
    2. Hvoslef-Eide M
    3. Saksida LM
    4. Bussey TJ

    (2016) The representational–hierarchical view of pattern separation: Not just hippocampus, not just space, not just memory

    Neurobiology of Learning and Memory 129:99–106.

    https://doi.org/10.1016/j.nlm.2016.01.006

    • PubMed
    • Google Scholar
34. 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
35. 1. Kriegeskorte N
    2. Mur M
    3. Ruff DA
    4. Kiani R
    5. Bodurka J
    6. Esteky H
    7. Tanaka K
    8. Bandettini PA

    (2008) Matching categorical object representations in inferior temporal cortex of man and monkey

    Neuron 60:1126–1141.

    https://doi.org/10.1016/j.neuron.2008.10.043

    • PubMed
    • Google Scholar
36. 1. Kriegeskorte N
    2. Mur M

    (2012) Inverse MDS: Inferring dissimilarity structure from multiple item arrangements

    Frontiers in Psychology 3:245.

    https://doi.org/10.3389/fpsyg.2012.00245

    • PubMed
    • Google Scholar
37. 1. Kriegeskorte N
    2. Kievit RA

    (2013) Representational geometry: integrating cognition, computation, and the brain

    Trends in Cognitive Sciences 17:401–412.

    https://doi.org/10.1016/j.tics.2013.06.007

    • PubMed
    • Google Scholar
38. 1. Kubilius J
    2. Bracci S
    3. Op de Beeck HP

    (2016) Deep neural networks as a computational model for human shape sensitivity

    PLOS Computational Biology 12:e1004896.

    https://doi.org/10.1371/journal.pcbi.1004896

    • PubMed
    • Google Scholar
39. 1. Levy DA
    2. Shrager Y
    3. Squire LR

    (2005) Intact visual discrimination of complex and feature-ambiguous stimuli in the absence of perirhinal cortex

    Learning & Memory (Cold Spring Harbor, N.Y.) 12:61–66.

    https://doi.org/10.1101/lm.84405

    • PubMed
    • Google Scholar
40. 1. Maass A
    2. Berron D
    3. Libby LA
    4. Ranganath C
    5. Düzel E

    (2015) Functional subregions of the human entorhinal cortex

    eLife 4:e06426.

    https://doi.org/10.7554/eLife.06426

    • PubMed
    • Google Scholar
41. 1. Martin CB
    2. Douglas D
    3. Newsome RN
    4. Man LL
    5. Barense MD

    (2018a) Integrative and distinctive coding of visual and conceptual object features in the ventral visual stream

    eLife 7:e31873.

    https://doi.org/10.7554/eLife.31873

    • PubMed
    • Google Scholar
42. 1. Martin CB
    2. Sullivan JA
    3. Wright J
    4. Köhler S

    (2018b) How landmark suitability shapes recognition memory signals for objects in the medial temporal lobes

    NeuroImage 166:425–436.

    https://doi.org/10.1016/j.neuroimage.2017.11.004

    • PubMed
    • Google Scholar
43. 1. Medin DL
    2. Goldstone RL
    3. Gentner D

    (1993) Respects for similarity

    Psychological Review 100:254–278.

    https://doi.org/10.1037/0033-295X.100.2.254

    • Google Scholar
44. 1. Migo EM
    2. Montaldi D
    3. Mayes AR

    (2013) A visual object stimulus database with standardized similarity information

    Behavior Research Methods 45:344–354.

    https://doi.org/10.3758/s13428-012-0255-4

    • PubMed
    • Google Scholar
45. 1. Minos AN
    2. Ferko KM
    3. Köhler S

    (2021) Expertise predicts perceived visual similarity between exemplars of real-world object categories

    Journal of Vision 21:2985.

    https://doi.org/10.1167/jov.21.9.2985

    • Google Scholar
46. 1. Misaki M
    2. Kim Y
    3. Bandettini PA
    4. Kriegeskorte N

    (2010) Comparison of multivariate classifiers and response normalizations for pattern-information fMRI

    NeuroImage 53:103–118.

    https://doi.org/10.1016/j.neuroimage.2010.05.051

    • PubMed
    • Google Scholar
47. 1. Moutsiana C
    2. de Haas B
    3. Papageorgiou A
    4. van Dijk JA
    5. Balraj A
    6. Greenwood JA
    7. Schwarzkopf DS

    (2016) Cortical idiosyncrasies predict the perception of object size

    Nature Communications 7:1–12.

    https://doi.org/10.1038/ncomms12110

    • PubMed
    • Google Scholar
48. 1. Mur M
    2. Bandettini PA
    3. Kriegeskorte N

    (2009) Revealing representational content with pattern-information fMRI—an introductory guide

    Social Cognitive and Affective Neuroscience 4:101–109.

    https://doi.org/10.1093/scan/nsn044

    • PubMed
    • Google Scholar
49. 1. Mur M
    2. Meys M
    3. Bodurka J
    4. Goebel R
    5. Bandettini PA
    6. Kriegeskorte N

    (2013) Human object-similarity judgments reflect and transcend the primate-IT object representation

    Frontiers in Psychology 4:128.

    https://doi.org/10.3389/fpsyg.2013.00128

    • PubMed
    • Google Scholar
50. 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

    • PubMed
    • Google Scholar
51. 1. Oosterhof NN
    2. Connolly AC
    3. Haxby JV

    (2016) CoSMoMVPA: multi-modal multivariate pattern analysis of neuroimaging data in Matlab/GNU Octave

    Frontiers in Neuroinformatics 10:27.

    https://doi.org/10.3389/fninf.2016.00027

    • PubMed
    • Google Scholar
52. 1. Op de Beeck HP
    2. Torfs K
    3. Wagemans J

    (2008) Perceived shape similarity among unfamiliar objects and the organization of the human object vision pathway

    The Journal of Neuroscience 28:10111–10123.

    https://doi.org/10.1523/JNEUROSCI.2511-08.2008

    • PubMed
    • Google Scholar
53. 1. O’Neil EB
    2. Cate AD
    3. Köhler S

    (2009) Perirhinal cortex contributes to accuracy in recognition memory and perceptual discriminations

    The Journal of Neuroscience 29:8329–8334.

    https://doi.org/10.1523/JNEUROSCI.0374-09.2009

    • PubMed
    • Google Scholar
54. 1. O’Neil EB
    2. Barkley VA
    3. Köhler S

    (2013) Representational demands modulate involvement of perirhinal cortex in face processing

    Hippocampus 23:592–605.

    https://doi.org/10.1002/hipo.22117

    • PubMed
    • Google Scholar
55. 1. Proklova D
    2. Kaiser D
    3. Peelen MV

    (2016) Disentangling representations of object shape and object category in human visual cortex: The animate–inanimate distinction

    Journal of Cognitive Neuroscience 28:680–692.

    https://doi.org/10.1162/jocn_a_00924

    • PubMed
    • Google Scholar
56. 1. Riesenhuber M
    2. Poggio T

    (1999) Hierarchical models of object recognition in cortex

    Nature Neuroscience 2:1019–1025.

    https://doi.org/10.1038/14819

    • PubMed
    • Google Scholar
57. 1. Schröder TN
    2. Haak KV
    3. Jimenez NIZ
    4. Beckmann CF
    5. Doeller CF

    (2015) Functional topography of the human entorhinal cortex

    eLife 4:e06738.

    https://doi.org/10.7554/eLife.06738.001

    • Google Scholar
58. 1. Schultz H
    2. Sommer T
    3. Peters J

    (2015) The role of the human entorhinal cortex in a representational account of memory

    Frontiers in Human Neuroscience 9:628.

    https://doi.org/10.3389/fnhum.2015.00628

    • PubMed
    • Google Scholar
59. 1. Schwarzkopf DS
    2. Song C
    3. Rees G

    (2011) The surface area of human V1 predicts the subjective experience of object size

    Nature Neuroscience 14:28–30.

    https://doi.org/10.1038/nn.2706

    • PubMed
    • Google Scholar
60. 1. Serre T
    2. Oliva A
    3. Poggio T

    (2007) A feedforward architecture accounts for rapid categorization

    PNAS 104:6424–6429.

    https://doi.org/10.1073/pnas.0700622104

    • PubMed
    • Google Scholar
61. 1. Smith SM
    2. Nichols TE

    (2009) Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference

    NeuroImage 44:83–98.

    https://doi.org/10.1016/j.neuroimage.2008.03.061

    • PubMed
    • Google Scholar
62. 1. Stark CE
    2. Squire LR

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

    Learning & Memory (Cold Spring Harbor, N.Y.) 7:273–278.

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

    • PubMed
    • Google Scholar
63. 1. Wang L
    2. Mruczek REB
    3. Arcaro MJ
    4. Kastner S

    (2015) Probabilistic maps of visual topography in human cortex

    Cerebral Cortex (New York, N.Y 25:3911–3931.

    https://doi.org/10.1093/cercor/bhu277

    • PubMed
    • Google Scholar
64. 1. Wisse LEM
    2. Kuijf HJ
    3. Honingh AM
    4. Wang H
    5. Pluta JB
    6. Das SR
    7. Wolk DA
    8. Zwanenburg JJM
    9. Yushkevich PA
    10. Geerlings MI

    (2016) Automated hippocampal subfield segmentation at 7T MRI

    AJNR. American Journal of Neuroradiology 37:1050–1057.

    https://doi.org/10.3174/ajnr.A4659

    • PubMed
    • Google Scholar
65. 1. Yeung L-K
    2. Olsen RK
    3. Bild-Enkin HEP
    4. D’Angelo MC
    5. Kacollja A
    6. McQuiggan DA
    7. Keshabyan A
    8. Ryan JD
    9. Barense MD

    (2017) Anterolateral entorhinal cortex volume predicted by altered intra-item configural processing

    The Journal of Neuroscience 37:5527–5538.

    https://doi.org/10.1523/JNEUROSCI.3664-16.2017

    • PubMed
    • Google Scholar

Author details

1. Kayla M Ferko

   1. Brain and Mind Institute, University of Western Ontario, London, Canada
   2. Robarts Research Institute Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Visualization, Writing – original draft, Writing – review and editing

   Contributed equally with

   Anna Blumenthal

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4362-7295

2. Anna Blumenthal

   1. Brain and Mind Institute, University of Western Ontario, London, Canada
   2. Cervo Brain Research Center, University of Laval, Quebec, Canada

   Contribution

   Conceptualization, Data curation, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Kayla M Ferko

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8768-0189

3. Chris B Martin

   Department of Psychology, Florida State University, Tallahassee, United States

   Contribution

   Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7014-4371

4. Daria Proklova

   Brain and Mind Institute, University of Western Ontario, London, Canada

   Contribution

   Data curation, Formal analysis, Methodology, Software

   Competing interests

   No competing interests declared

5. Alexander N Minos

   Brain and Mind Institute, University of Western Ontario, London, Canada

   Contribution

   Data curation, Investigation

   Competing interests

   No competing interests declared

6. Lisa M Saksida

   1. Brain and Mind Institute, University of Western Ontario, London, Canada
   2. Robarts Research Institute Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada
   3. Department of Physiology and Pharmacology, University of Western Ontario, London, Canada

   Contribution

   Conceptualization, Writing – review and editing

   Competing interests

   No competing interests declared

7. Timothy J Bussey

   1. Brain and Mind Institute, University of Western Ontario, London, Canada
   2. Robarts Research Institute Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada
   3. Department of Physiology and Pharmacology, University of Western Ontario, London, Canada

   Contribution

   Conceptualization, Writing – review and editing

   Competing interests

   No competing interests declared

8. Ali R Khan

   1. Brain and Mind Institute, University of Western Ontario, London, Canada
   2. Robarts Research Institute Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada
   3. School of Biomedical Engineering, University of Western Ontario, London, Canada
   4. Department of Medical Biophysics, University of Western Ontario, London, Canada

   Contribution

   Data curation, Funding acquisition, Methodology, Software, Supervision

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0760-8647

9. Stefan Köhler

   1. Brain and Mind Institute, University of Western Ontario, London, Canada
   2. Department of Psychology, University of Western Ontario, London, Canada

   Contribution

   Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, Writing – original draft, Writing – review and editing

   For correspondence

   stefank@uwo.ca

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1905-6453

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