A prevailing theory posits that spatial information travels from the postrhinal cortex (POR) to the medial entorhinal cortex (MEC), and nonspatial information is transmitted from the perirhinal cortex (PER) to the lateral entorhinal cortex (LEC), before both types of information merge in the hippocampus (Knierim et al., 2014). An important caveat of this theory is that it does not explain explicitly how visual scene information is processed in these circuits, although it becomes increasingly clear that visual scenes may provide critical contextual information to the hippocampus (Dombeck et al., 2010; Hassabis and Maguire, 2009; Kim et al., 2012; Maguire and Mullally, 2013; Prusky et al., 2004; Wirth et al., 2003; Zeidman et al., 2015).

Recently, we reported that the LEC and MEC can be functionally dissociated in scene-memory tasks (Yoo and Lee, 2017). Interestingly, we found that sensory cues interact with task-specific response types. Specifically, we found that the LEC, but not the MEC, is critical when rats were required to manipulate a common object in different ways (by pushing it or digging sand) using visual scenes in the background, whereas the MEC, but not the LEC, was important when rats made spatial choices in the T-maze using the same visual scenes.

Building on our prior findings, we here sought to address a critical question, namely, whether such dissociation uniquely occurs at the level of the EC or is inherited from its upstream structures. We tested this by pharmacologically manipulating the PER and POR, the direct upstream regions of the LEC and MEC, respectively, and testing rats in the same scene-based testing paradigms applied in the Yoo and Lee study. Furthermore, as a comparison to the null results reported in the LEC and MEC in object-based behavioral tasks in our previous study, we investigated whether the PER and POR play a role in such tasks (Yoo and Lee, 2017).

The following four different behavioral tasks, named based on stimulus and response types, were used in the current study: (i) scene-cued nonspatial response (SCN-NSR) task, (ii) scene-cued spatial response (SCN-SR) task, (iii) object-cued nonspatial response (OBJ-NSR) task, and (iv) object-cued spatial response (OBJ-SR) task. Rats (n = 23) trained to associate different types of stimuli (visual scenes and objects) with responses (spatial and nonspatial responses) were implanted bilaterally with cannulae in both the PER and POR (Figure 1A). Individual tip locations of cannulae were histologically verified (Figure 1B), and data from rats whose cannula-tip locations were misplaced from either area were discarded (n = 2).

Figure 1

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Similar contributions of the PER and POR to visual scene-based nonspatial responses

Rats (n = 7) were trained in the SCN-NSR task as described previously (Yoo and Lee, 2017) (Figure 2A). Injection of muscimol (MUS) into either the PER or POR (0.5 μL per site) resulted in significant performance deficits compared with injection of the same volume of artificial cerebrospinal fluid (ACSF) (Figure 2B; Figure 2—source data 1). A one-way repeated-measures ANOVA showed a significant effect of drug (F_(2,12) = 11.363, p=0.001). A Bonferroni-Dunn post hoc test (corrected α = 0.017) revealed significant differences in performance between ACSF and PER-MUS groups (p<0.01) and between ACSF and POR-MUS groups (p<0.001), but not between PER-MUS and POR-MUS groups (p>0.1) (Figure 2B). These results suggest that, unlike in the LEC and MEC (Yoo and Lee, 2017), both the PER and POR may play some role in nonspatial behavioral choices made using visual background scenes.

Figure 2

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Figure 2—source data 1

Performance in the SCN-NSR task.

Percent correct performance for individual rats under different drug conditions in the SCN-NSR task.

https://doi.org/10.7554/eLife.32657.004

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Figure 2—source data 2

Performance in the SCN-SR task.

Percent correct performance for individual rats under different drug conditions in the SCN-SR task.

https://doi.org/10.7554/eLife.32657.005

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Greater contribution of the PER than the POR to object-based memory tasks

In our previous study (Yoo and Lee, 2017), we reported that the LEC and MEC were not involved in the OBJ-NSR task. To test the involvement of the PER and POR in the same task, we trained the same rats (n = 7) used in the SCN-NSR task in the OBJ-NSR task (Figure 3A). Inactivation of the PER caused a noticeable drop in performance compared with controls (Figure 3B), whereas similar deficits were not found in the POR-MUS group. A one-way repeated-measures ANOVA showed a significant effect of drug (F_(2,12) = 15.548, p<0.001); a significant difference in performance was also found between ACSF and PER-MUS groups (p<0.001) and between PER-MUS and POR-MUS groups (p<0.001; Bonferroni-Dunn) (Figure 3B; Figure 3—source data 1). Interestingly, allowing rats to sample the object only visually in the same task recruited the PER similarly and, to some extent, the POR as well (see Figure 3—figure supplement 1, Figure 3—figure supplement 1—source data 1 for additional details).

Figure 3 with 1 supplement see all

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Figure 3—source data 1

Performance in the OBJ-NSR task.

Percent correct performance for individual rats under different drug conditions in the OBJ-NSR task.

https://doi.org/10.7554/eLife.32657.009

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Figure 3—source data 2

Performance in the OBJ-SR task.

Percent correct performance for individual rats under different drug conditions in the OBJ-SR task.

https://doi.org/10.7554/eLife.32657.010

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We further tested whether the PER was important when spatial choices were required upon recognizing an object, because the PER may be engaged in a task as long as an object is used as a cue, regardless of the response type. For this purpose, we trained another group of rats (n = 6) in the OBJ-SR task, in which rats learned to make spatial choices using a toy object as a cue (Figure 3C). Rats exhibited severe performance deficits in the PER-MUS group, as was the case in the OBJ-NSR task (Figure 3D; Figure 3—source data 2). Compared with the OBJ-NSR task, the POR-MUS group showed a trend toward mild deficits that failed to reach statistical significance. Specifically, a one-way repeated-measures ANOVA showed a significant effect of the drug (F_(2,10) = 7.611, p<0.01) and a significant difference only between ACSF and PER-MUS groups (p<0.005), but not between ACSF and POR-MUS groups (p=0.034, a value that did not reach the corrected α = 0.017 in Bonferroni-Dunn tests) (Figure 3D).

Functional dissociations are less clear in the upstream cortical regions of the EC

To compare the current findings with functional dissociations previously observed in the MEC and LEC (Yoo and Lee, 2017), we plotted the relative performance deficits under MUS conditions (compared to ASCF conditions) in all tasks by subtracting the performance level under MUS from that in control conditions (Figure 4A).

Figure 4 with 1 supplement see all

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In the NSR tasks (SCN-NSR and OBJ-NSR), the PER-MUS group showed large performance deficits in the OBJ-NSR task. The performance deficits in the SCN-NSR task were relatively smaller than those in the OBJ-NSR task. The POR-MUS group did not show any performance deficits in the OBJ-NSR task, but showed significant deficits in the SCN-NSR task (Figure 4A). A comparison of performance between OBJ-NSR and SCN-NSR tasks (two-way repeated measures ANOVA with task and region as within-subject factors) revealed a significant effect of region (F_(1,6) = 30.295, p<0.01), but not task (F_(1,6) = 0.08, p>0.5). The interaction between the two factors was significant (F_(1,6) = 6.561, p<0.05). A post hoc t-test (α = 0.025) showed a significantly larger deficit in the SCN-NSR than in the OBJ-NSR task in the POR (p<0.025), but not in the PER (p>0.1) (Figure 4A). Similar results were also obtained when only visual sampling was allowed in the OBJ-NSR task (Figure 4—figure supplement 1A).

In SR tasks (SCN-SR and OBJ-SR), the PER-MUS group showed larger deficits in the OBJ-SR task than in the SCN-SR task, whereas in the POR-MUS group, the results were similar between the two tasks (Figure 4B). There were no significant effects of region (F_(1,12) = 0.470, p=0.506) or task (F_(1,12) = 0.872, p=0.368), but the interaction between the two factors approached significance (F_(1,12) = 4.338, p=0.059).

Taken together, these findings strongly indicate that whether an object functions as a cue in a task may determine whether the PER plays an important role in the task, whereas the associated type of response may not be so critical. In contrast, the type of response (e.g., spatial navigational response) may play a greater role in recruiting the POR than the type of the cueing stimulus. An important finding in our study is that there is a condition in which the POR may not be necessary, namely the OBJ-NSR task (Figure 4A). One exception might be when a purely visual object is used as a cueing object (see below for further discussion). Most importantly, it is clear that the interaction between the stimulus attribute and response type between the PER and POR is not as strong as between the LEC and MEC (Figure 4C; Figure 4—figure supplement 1B and C).

In the current study, we sought to investigate functional differences between the PER and POR, the upstream regions of the LEC and MEC, respectively. Unlike in the LEC and MEC (Yoo and Lee, 2017), functional dissociations based on the response type (i.e., spatial vs. nonspatial) were less clear in the upstream areas in the scene-based tasks. Instead, the type of stimulus (i.e., object and scene) appears to critically determine the involvement of these areas in a task (Figure 4). Specifically, the PER was important in cases where an object stimulus should be recognized, but less so for scenes, regardless of the response type, whereas the POR was involved when either visual scene recognition or spatial navigation was necessary. Our findings suggest that the PER and POR may provide object and scene information to the LEC and MEC, respectively. In the EC, this information may be associated with task-demand–specific actions involving interaction with an object (LEC) or navigating in space (MEC). That is, the PER-LEC and POR-MEC networks may process ‘what should I do in relation to this object?’ and ‘where should I go from here?', respectively (Figure 4C).

Anatomically, the PER receives multimodal sensory inputs (e.g., auditory, visual, olfactory, and somatosensory) from various sensory-perceptual cortices, whereas the POR receives major inputs largely from visual areas, including the retrosplenial cortex (Agster and Burwell, 2009; Burwell and Amaral, 1998a). In the current study, inactivating the PER indeed produced large deficits compared with controls whenever rats were required to recognize an object before making a spatial or nonspatial response. In contrast, inactivation of the POR resulted in reliable deficits in performance whenever scene was used as a conditional cue before making a spatial or nonspatial choice. Together with prior findings (Epstein and Kanwisher, 1998; Murray and Richmond, 2001), our results suggest that the PER and POR may be specialized in recognizing (and perhaps perceiving) objects (Ahn and Lee, 2017; McTighe et al., 2010) and scenes, respectively.

We previously reported a strong double dissociation in the LEC and MEC between scene-based tasks that required nonspatial responses (i.e., object manipulation) and spatial responses (i.e., navigational turns) (Figure 4—figure supplement 1B) (Yoo and Lee, 2017). Testing rats in the same tasks with the PER or POR inactivated did not result in such strong dissociations in the current study (Figure 4—figure supplement 1C), suggesting that scene-response interactions may uniquely occur in the EC. However, when the stimulus category was extended to include objects, some interactions between stimulus and response types became apparent in the POR, but not necessarily in the PER. Specifically, a comparison of performance deficits between scene- and object-based tasks with the PER inactivated showed similarly larger deficits in object-based tasks (OBJ-NSR and OBJ-SR tasks) than in scene-based tasks (SCN-NSR and SCN-SR tasks), regardless of the response type. However, the navigational task demand in both OBJ-SR and SCN-SR tasks required the POR, irrespective of the type of stimulus, whereas the same region was only necessary when a visual scene (SCN-NSR task), but not an object (OBJ-NSR task), was used as a cue in nonspatial tasks. These results suggest that both scene recognition and spatial navigation constitute important computational components of the POR network (Figure 4C).

It is unclear why the navigational demand affected the POR regardless of the stimulus type, but had a lesser effect in the PER (Figure 4B). One possibility is that the POR indeed processes navigation-related signals in association with objects and scenes, based on the reciprocal connections with the MEC (Figure 4C). Because strong neural correlates for spatial navigation (e.g., grid cells and border cells) have been reported in the MEC (Hafting et al., 2005; Sargolini et al., 2006; Solstad et al., 2008), the MEC may bias the POR network toward processing incoming sensory inputs (e.g., visual object or scene information) in relation to the spatial navigation components of the task. Such a strong bias may not be exerted by the LEC to the PER, because object recognition may lead to many different types of responses in natural settings, as opposed to the case of scene recognition, which is normally associated with spatial navigation. Another possibility is that top-down influences from other navigation-related regions (e.g., retrosplenial cortex) might be stronger in the POR-MEC networks than in the PER-LEC networks. Considering the anatomical finding that the POR-to-PER connection is stronger than the PER-to-POR connection (Burwell and Amaral, 1998b; Furtak et al., 2007; Kealy and Commins, 2011), it is also possible that rats with inactivation in the POR might send disturbing signals to the PER, preventing normal object recognition in the OBJ-SR task (Figure 3D). Nonetheless, our study clearly demonstrates that the POR is not necessary if neither spatial navigation nor visual scene information processing is required.

Visual scenes used in our tasks may be considered as a context, and the PER-POR networks may play significant roles in contextual object recognition (Norman and Eacott, 2005; Heimer-McGinn et al., 2017). In natural situations, an animal never experiences an object completely detached from its background, and a visual context also always involves objects in it (Aminoff et al., 2013). Therefore, PER and POR networks may function in harmony in natural settings, rather than in isolation from each other. For example, an object is recognized efficiently when it appears against a contextually plausible background (Aminoff et al., 2013). Disrupting the POR network may affect such contextual object recognition (Furtak et al., 2012) in our SCN-NSR task because the ‘meaning’ (e.g., push or dig) of the object (i.e., jar) should be disambiguated using visual scenes in the background in that task. Likewise, inactivating the PER as in our study may make it harder for the animal to focus on the target object against its background context. These functional relationships between the PER and POR may underlie the similar deficits between PER-MUS and POR-MUS groups in the SCN-NSR task. Inactivation of the PER may also have some disruptive effects on the POR’s functions in scene information processing through PER-to-POR connections, leading to mild performance deficits (but still >80% correct) in the SCN-SR task in our study. Another possibility is that scene recognition in the POR may require some contribution of the PER, because a scene is normally composed of individual objects (e.g., individual pebble stones in the pebbles scene in our task). On a similar note, inactivation of the POR may make it difficult for an animal to focus on the cueing object in the OBJ-SR task, because contextual disruption, but not facilitation, of object recognition might occur.

In the view of traditional theory, spatial and nonspatial information is processed via separate streams in the medial temporal lobe (Knierim et al., 2014). According to this model, the physical attribute (i.e., spatial or nonspatial) of a stimulus determines the processing stream (i.e., PER-LEC or POR-MEC) to which the information is channeled. However, the current results, together with our previous findings, strongly suggest that such a view may be too simplistic. Our current working model is that the PER-LEC and POR-MEC networks are mainly concerned with object manipulation (or exploration) and spatial navigation (or contextual memory), respectively (Figure 4C). Our model posits that recognition of perceptual stimuli mainly occurs at the level of the PER (for objects) and POR (for scenes), and their task-related actions are associatively represented in the EC networks. The hippocampus, which receives these inputs together, may encode and retrieve various event memories in sequences as an animal navigates through a space to achieve goals.

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Thank you for submitting your article "Interactions between stimulus and response types are uniquely represented in the entorhinal cortex, but not in its upstream cortical regions" for consideration by eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Sabine Kastner as the Senior Editor. The following individual involved in review of your submission has agreed to reveal his identity: Alex Easton (Reviewer #1).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

The current study is a follow-up to work done recently, published in eLife, in which these authors showed a dissociation in the lateral EC (LEC) from the medial EC (MEC) in a scene memory task based on response type. Here the authors test whether a similar dichotomy in processing information exists at the two regions directly upstream from these areas, the perirhinal and postrhinal regions. Each region was inactivated across several tasks using either object or response type. The authors found that the clean dissociation present at EC was not fully reproduced, and that instead there was substantial overlap in the involvement of the two areas across task requirements.

Essential revisions:

Both reviewers were overall positive and felt that the study was a logical and appropriate follow-up to the prior work, and that the experiments were well designed and executed. That the data did not cleanly match predictions nor detract from enthusiasm for the experiment and desire to see it published. However in discussions, it was felt that the authors could be clearer in describing some of the results (see reviews). In addition, and probably more critically, it was felt that the authors need to go beyond their current conclusions and provide a model or proposal of some sort to explain the unexpected results. As one reviewer put it, they should "explain how their results cannot be explained by existing models and therefore which part(s) of the existing models need changing….. what they think might be going on (is it feedback from ERC that means these regions are harder to dissociate further upstream? Is it some anatomical crossover at the PER/POR level that differentiates the effects from the clearer dissociations in ERC? Is it that only in ERC does processing require these components to be separated and before that there is just less going on? Is it a difficulty effect between scenes and objects?)". Basically propose some testable ideas for why the segregation of information is present at one level but not another. These were the essential revisions I think.

Reviewer #1:

This is a concise, but important, follow up study to the authors' 2017 paper. Using spatial and non-spatial tasks in which the instruction cues are either objects (visual or multisensory) or scenes they investigate the role of Perirhinal and Postrhinal cortices through pharmacological inactivation of these regions.

The authors find that inactivation of the perirhinal cortex impairs performance on object cued tasks more than scene cued tasks (for both spatial and non spatial tasks). Postrhinal lesions appear to have a similar effect on both object and scene cued tasks, except where the object is multisensory in nature, in which case for non spatial tasks there is little sign of impairment.

Whilst the paper is clear and the findings of great interest, there is much the authors leave unsaid which is important. They broadly discuss that existing theory needs to be updated – but it seems to me that the authors are in a position with their data now to make suggestions as to what that updated theory might look like. It is important that the authors interpret this data in terms of potential mechanistic explanations. In particular, at the moment they talk in general about POR being critical for recognising visual stimuli and the PER for objects in particular. However, it seems to me this might not reflect the full dissociation the authors suggest. It is possible that both PER and POR could have the same function, but with POR having a more significant effect, meaning that scenes and objects are impaired following POR lesions, but the lesser effects of PER lesions would impair objects but not scenes, where additional information might be able to be used to solve the task. There is no clear double dissociation here to be confident about the independence of these two regions.

In that theme, Figure 4 is extremely helpful, but highlights that scene guided learning is never more impacted by a lesion than object guided learning. This might provide support for the idea that scene guided learning is just easier, and that the apparent dissociation between POR and PER merely reflects this.

Reviewer #2:

The submitted manuscript fits the format of Research Advance papers published in eLife in the sense that it builds upon a Research Article recently published by the same authors in 2017, by using an experimental design directly inspired by the original data. The published paper was about the contribution of lateral and medial entorhinal cortex (LEC and MEC respectively) to spatial and non-spatial response in the context of a scene discrimination. In the present manuscript, the authors ask whether the cortical structures directly upstream LEC and MEC, i.e. perirhinal and postrhinal cortex (PER and POR, respectively) show the same dissociation as that observed in LEC and MEC for the processing of scene-based type of response.

To do so, they use a within-subject design in which rats received local intra-cerebral injections of muscimol in either PER of POR during specific trials of several tasks differing in either the type of stimuli (spatial scene or object) or the type of response required (spatial, i.e. turn left or right, or non-spatial, i.e. push or dig). The results do not reveal a dissociation between PER and POR inactivations with regard to the type of response in the spatial scene discriminations. Both inactivations induce a deficit no matter the response required, contrary to MEC and LEC inactivations. This is the main result shown in Figure 2 and Figure 4—figure supplement 1. In addition, the authors show a complex pattern of results (with stronger effects of PER than of POR inactivations) with regard to object-based discrimination but here again no clear effect of the type of response.

The experiments are very well done. The results are interesting but the way they are summarized in Figure 4 and analyzed in complex two-way and three-way ANOVAs (last section of results) is very complicated and therefore distract the reader from the main message (perfectly understandable from Figure 2 and very nicely summarized in Figure 4—figure supplement 1). That PER and POR inactivations may have different effects on object discrimination in combination with type of response (Figure 3A vs. 3E) is also interesting and may be worth being reported. However, the introduction of the condition "vOBJ-NSR" here makes interpretation of the whole pattern of result even more complicated. If the authors want to carry a clear take-home message with significant impact, I suggest they simplify their presentation as much as possible.

In addition, some of their interpretations of the results are weird. One such example is the section on the results of object-based spatial choice where they say that "A significant difference was found only between ACSF and PER-MUS groups (p < 0.005), and not between ACSF and POR". Not only the statistics they report in this paragraph seem to show that POR are indeed impaired (and do not show a difference with PER), but just looking at the performance points in Figure 3E together with the height of the SEM seems to indicate the existence of an impairment as great in POR than in PER. In contrast it is clear that there is no impairment in PER rats for the same object discrimination if a non-spatial response if required (Figure 3A) while POR rats are clearly impaired. We thus have a dissociation here, though it is not the one that was expected.

Thus overall the paper brings interesting data that complement nicely those of the original paper, but my impression is that the important points are buried in such a way the main message is obscured. What we need here is a model, even sketchy, of how all this circuitry works and a possible interpretation of what makes that the type of response required from the rat is so important in triggering such complex effects in conjunction with the type of stimuli.

Reviewer #1:

[…]

Whilst the paper is clear and the findings of great interest, there is much the authors leave unsaid which is important. They broadly discuss that existing theory needs to be updated – but it seems to me that the authors are in a position with their data now to make suggestions as to what that updated theory might look like. It is important that the authors interpret this data in terms of potential mechanistic explanations. In particular, at the moment they talk in general about POR being critical for recognising visual stimuli and the PER for objects in particular. However, it seems to me this might not reflect the full dissociation the authors suggest. It is possible that both PER and POR could have the same function, but with POR having a more significant effect, meaning that scenes and objects are impaired following POR lesions, but the lesser effects of PER lesions would impair objects but not scenes, where additional information might be able to be used to solve the task. There is no clear double dissociation here to be confident about the independence of these two regions.

We thank the reviewer for this suggestion. In our revised manuscript, we have included our working model to explain the results of the current study and the Yoo and Lee study. Specifically, we have added a model diagram in Figure 4C and, using this model, provided a more detailed discussion of our results. The Discussion section has been rewritten to the model-based accounts of the data and literature. Examples of paragraphs are as follows:

“In the current study, we sought to investigate functional differences between the PER and POR, the upstream regions of the LEC and MEC, respectively. […] That is, the PER-LEC and POR-MEC networks may process “what should I do in relation to this object?” and “where should I go from here?”, respectively (Figure 4C).”

“In the view of traditional theory, spatial and nonspatial information is processed via separate streams in the medial temporal lobe (Knierim et al., 2014). […] The hippocampus, which receives these inputs together, may encode and retrieve various event memories in sequences as an animal navigates through a space to achieve goals.”

In that theme, Figure 4 is extremely helpful, but highlights that scene guided learning is never more impacted by a lesion than object guided learning. This might provide support for the idea that scene guided learning is just easier, and that the apparent dissociation between POR and PER merely reflects this.

We agree with the reviewer that there might be some differences in task difficulty among the behavioral tasks in our current and prior studies. One thing we haven’t observed, however, is a systematic relationship between a certain stimulus type (e.g., scene) and performance level. For example, based on the control’s performance level (ACSF), the easiest task seems to be the SCN-SR task (ACSF: ~95% correct, Figure 2D). However, in the SCN-NSR task, the control’s performance was approximately 10% lower than that in the SCN-SR task even though the same scene stimuli were used (Figure 2B). When objects were used as cues in the OBJ-NSR and OBJ-SR tasks, the control’s performance stayed at ~90% correct level, which was a bit higher than in the SCN-SR task, but lower than in the SCN-SR task. Moreover, performance in the SCN-NSR task of the POR-MUS group was significantly impaired, whereas the same group’s performance was not affected in the OBJ-NSR task. This pattern of results was reversed in the PER-MUS group (Figure 4A). Based on these results, we believe that task difficulty alone may not be able to explain our results.

Reviewer #2:

[…]

The experiments are very well done. The results are interesting but the way they are summarized in Figure 4 and analyzed in complex two-way and three-way ANOVAs (last section of results) is very complicated and therefore distract the reader from the main message (perfectly understandable from Figure 2 and very nicely summarized in Figure 4—figure supplement 1). That PER and POR inactivations may have different effects on object discrimination in combination with type of response (Figure 3A vs. 3E) is also interesting and may be worth being reported. However, the introduction of the condition "vOBJ-NSR" here makes interpretation of the whole pattern of result even more complicated. If the authors want to carry a clear take-home message with significant impact, I suggest they simplify their presentation as much as possible.

We thank the reviewer for the constructive comments. This was one of our concerns as well as we put together our original manuscript. Following the reviewer’s comments, we decided to remove the vOBJ-NSR data from the main text in the revised manuscript (moved to our revised Figure 3—figure supplement 1 and Figure 4—figure supplement 1). We think this makes sense also because rats might rarely experience an object using visual modality alone in natural settings, and the data from the vOBJ-NSR task may be better suited for supplementary information.

In addition, some of their interpretations of the results are weird. One such example is the section on the results of object-based spatial choice where they say that "A significant difference was found only between ACSF and PER-MUS groups (p < 0.005), and not between ACSF and POR". Not only the statistics they report in this paragraph seem to show that POR are indeed impaired (and do not show a difference with PER), but just looking at the performance points in Figure 3E together with the height of the SEM seems to indicate the existence of an impairment as great in POR than in PER. In contrast it is clear that there is no impairment in PER rats for the same object discrimination if a non-spatial response if required (Figure 3A) while POR rats are clearly impaired. We thus have a dissociation here, though it is not the one that was expected.

This is a good point. The performance of the POR-MUS group in the OBJ-SR indeed look lower than that of controls. In this study, we used the Bonferroni/Dunn post-hoc test for all post-hoc comparisons. The significance criterion (α) for the Dunn’s post-hoc test was 0.0167, and only the difference between the ACSF and PER-MUS (p = 0.0037), but not between the ACSF and POR-MUS (p = 0.034) met this criterion. Other conservative tests such as the Scheffe and Tukey/Kramer test also resulted in nonsignificant differences between the ACSF and PER-MUS groups. However, if we use more liberal post-hoc tests such as Fisher’s PLSD and Student Newman-Keuls, the difference became significant. In our prior study (Yoo and Lee, 2017), we used the Bonferroni-Dunn test to interpret the results more conservatively and to minimize the risk of making Type I error. Since the current study examined the same phenomena observed in the EC in the PER and POR, we would like to keep the same post-hoc tests in the current study. In the revised manuscript, we added more information in the graph (Figure 3D) to explicitly show the p-value.

Nonetheless, although the Dunn’s test called the results insignificant, we agree with the reviewer that this is the case where one can see at least the trend of impairment. In the revised manuscript, we reflected this as we develop a working model to explain the results from the current and previous studies. Specifically, our revised manuscript states that the POR could be involved when scene recognition or spatial navigation (separately or together) is required in a task to include the possibility that POR inactivation actually could lead to significant performance deficits in the OBJ-SR task if sampling size increases. We now incorporate this in our newly presented model (see Figure 4C and its associated texts in Discussion) and explicitly states this point in the summary part of the discussion as follows:

“… Unlike in the LEC and MEC (Yoo and Lee, 2017), functional dissociations based on the response type (i.e., spatial vs. nonspatial) were less clear in the upstream areas in the scene-based tasks. Instead, the type of stimulus (i.e., object and scene) appears to critically determine the involvement of these areas in a task (Figure 4). Specifically, the PER was important in cases where an object stimulus should be recognized, but less so for scenes, regardless of the response type, whereas the POR was involved when either visual scene recognition or spatial navigation was necessary…”

The almost complete lack of impairment in the POR-MUS condition in the OBJ-NSR task indeed served as one of the clear results that helped us to build a hypothetical working model in our revised manuscript. That is, as stated above if neither visual scene nor spatial navigation is required, the POR doesn’t seem to be interested in the task (e.g., object-based digging or pushing an object). This point has also been added to our Discussion as we introduce our model and explain it in the revised manuscript.

Thus overall the paper brings interesting data that complement nicely those of the original paper, but my impression is that the important points are buried in such a way the main message is obscured. What we need here is a model, even sketchy, of how all this circuitry works and a possible interpretation of what makes that the type of response required from the rat is so important in triggering such complex effects in conjunction with the type of stimuli.

We thank the reviewer for the suggestion. Please see our responses to reviewer #1.

Author details

1. Eun-Hye Park

   Department of Brain and Cognitive Sciences, Seoul National University, Shillim-dong, Korea

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Visualization, Methodology, Writing—original draft

   Contributed equally with

   Jae-Rong Ahn

   Competing interests

   No competing interests declared

2. Jae-Rong Ahn

   Department of Brain and Cognitive Sciences, Seoul National University, Shillim-dong, Korea

   Contribution

   Data curation, Software, Formal analysis, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Eun-Hye Park

   Competing interests

   No competing interests declared

3. Inah Lee

   Department of Brain and Cognitive Sciences, Seoul National University, Shillim-dong, Korea

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   inahlee@snu.ac.kr

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3760-4257

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