Individual Taste Preferences Predict Cortical Taste Dynamics but Are Modified by Experience
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eLife Assessment
This important study demonstrates how individual taste preferences change over time, how these changes are reflected in cortical activity, and how sensory experience contributes to reshaping both. The evidence is convincing and broadly supports the main conclusions. The findings should be of interest to neuroscientists studying sensory processing and cortical plasticity.
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Abstract
As is true in humans, no two rodents prefer precisely the same tastes. Furthermore, no one rodent keeps precisely the same taste preferences forever. Here, we have made use of this between- and within-animal variability to generate and test novel hypotheses about the stability and malleability of neural perceptual coding. We used a brief-access task (BAT) to reveal both individual differences in taste preferences and shifting preferences within individual rats (quantified in terms of lick bout lengths). We moved on to show that these phenomena are not simply random variation: first, by demonstrating that gustatory cortical (GC) taste response dynamics, the late part of which reflect palatability, match that individual rat’s BAT preferences (evaluated almost 2 weeks prior) better than canonical preference patterns; that is, individual differences in preferences reflect differences in neural taste processing. This match, however, links neural taste processing only to the most recent BAT session—GC palatability processing does not reflect performance in earlier BAT sessions. We hypothesized that any tasting experience might impact taste processing (i.e., not just BAT licking), and tested this hypothesis by adding a second session of GC taste-response recordings; palatability-epoch taste responses in this later session no longer matched the most recent pre-recording BAT session, demonstrating that responses following the first electrophysiology session had changed. Together, these data demonstrate that every tasting experience (regardless of the method of taste delivery) changes the rat’s processing of those tastes.
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eLife Assessment
This important study demonstrates how individual taste preferences change over time, how these changes are reflected in cortical activity, and how sensory experience contributes to reshaping both. The evidence is convincing and broadly supports the main conclusions. The findings should be of interest to neuroscientists studying sensory processing and cortical plasticity.
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Reviewer #1 (Public review):
[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the comments raised in the previous round of review.]
Summary:
Maigler et al. set out to test the hypothesis that individual differences in taste preferences are (in part) due to individual differences in central taste processing. They first tested rats' preferences for a variety of taste stimuli on multiple days. They then recorded responses of neurons in taste cortex to the same tastes on two consecutive days.
Strengths:
The authors collected high-resolution behavioral data from the same animals across multiple days, allowing for a detailed characterization of individual variation in taste preferences. They then performed recordings from the same set of …
Reviewer #1 (Public review):
[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the comments raised in the previous round of review.]
Summary:
Maigler et al. set out to test the hypothesis that individual differences in taste preferences are (in part) due to individual differences in central taste processing. They first tested rats' preferences for a variety of taste stimuli on multiple days. They then recorded responses of neurons in taste cortex to the same tastes on two consecutive days.
Strengths:
The authors collected high-resolution behavioral data from the same animals across multiple days, allowing for a detailed characterization of individual variation in taste preferences. They then performed recordings from the same set of animals in response to the same stimuli, allowing them to draw parallels between behavioral and neural responses.
Weaknesses:
(1) Recordings were performed only after multiple days of preference testing, and preferences were not tested in between/following recording sessions. This design precludes a direct comparison between neural and behavioral responses.
(2) Similarly, correlations between neural responses and behavioral measures are not analyzed/reported on an animal-by-animal basis.
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Reviewer #2 (Public review):
Summary:
The study from Maigler et al investigates how between- and within-animal differences in taste preference relate to differences in neural responsiveness. The experiments rely on an elegant combination of behavioral assays to measure preference (e.g., repeated brief access testing, BAT) and electrophysiological recordings to monitor the activity of ensembles of neurons in the gustatory cortex (GC) of rats.
BAT with distinct batteries of tastants revealed pronounced variability in preference (measured as licking bout size) across individuals. This variability across individuals persisted after repeated testing. Repeated BAT also revealed that each individual rat's preference for different tastants changed across time.
Electrophysiological responses of GC neurons to batteries of tastants showed that …
Reviewer #2 (Public review):
Summary:
The study from Maigler et al investigates how between- and within-animal differences in taste preference relate to differences in neural responsiveness. The experiments rely on an elegant combination of behavioral assays to measure preference (e.g., repeated brief access testing, BAT) and electrophysiological recordings to monitor the activity of ensembles of neurons in the gustatory cortex (GC) of rats.
BAT with distinct batteries of tastants revealed pronounced variability in preference (measured as licking bout size) across individuals. This variability across individuals persisted after repeated testing. Repeated BAT also revealed that each individual rat's preference for different tastants changed across time.
Electrophysiological responses of GC neurons to batteries of tastants showed that firing in the "late epoch" of taste processing (i.e., 500ms post taste delivery) correlated more strongly with the individualized rat's BAT preference rather than with a canonical preference ranking. Importantly, this correlation was stronger for the last BAT session compared to the first. Finally, the authors show that the correlation disappeared in a second, consecutive recording session, indicating that exposure to tastants reconfigure preferences.
Strengths:
(1) The experimental design allows for an unprecedented look at the relationship between individual variability in taste preferences and neural processing.
(2) The study demonstrates that taste preference variability is not mere experimental noise but reflects the dynamic nature of taste. A key strength is the clear evidence that behavioral variability is reflected in neural activity patterns, establishing a strong correlation between brain and behavior.
(3) The evidence that simple exposure to familiar tastes can reconfigure preferences and taste representations is interesting.
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Reviewer #3 (Public review):
Summary:
Maigler & Lin et al present a convincing set of behavioral and electrophysiological experiments and analyses exploring how individual differences in taste preference map onto neural responses in the gustatory cortex (GC). They go on to examine how both preferences and neural responses shift following intervening taste experience. Their experiments are strengthened by examining tastes of distinct identities and palatability (sweet, sour, salty, bitter) and correspond each animal's individual preference to the palatability-related late phase of the neural response.
Strengths:
(1) They demonstrate a relationship between the behavioral expression of taste preference and palatability-related GC neural responses. The direct correlation of expression of taste preference with GC neural responses indicates …
Reviewer #3 (Public review):
Summary:
Maigler & Lin et al present a convincing set of behavioral and electrophysiological experiments and analyses exploring how individual differences in taste preference map onto neural responses in the gustatory cortex (GC). They go on to examine how both preferences and neural responses shift following intervening taste experience. Their experiments are strengthened by examining tastes of distinct identities and palatability (sweet, sour, salty, bitter) and correspond each animal's individual preference to the palatability-related late phase of the neural response.
Strengths:
(1) They demonstrate a relationship between the behavioral expression of taste preference and palatability-related GC neural responses. The direct correlation of expression of taste preference with GC neural responses indicates that taste preference behavior may be less noisy than previously thought, reflecting actual neural activity.
(2) They address the stability of individual taste preference by comparing within and between session expression. This finding indicates that individual preference on any given trial or test session can differ from canonical palatability.
(3) The animal's preferences for various tastes are reflected in the neurophysiological recordings, despite that behavior and physiology sessions are separated by several weeks, with an intervening surgery, across multiple delivery methods (active licking vs passive delivery), and across homeostatic states (thirsty vs sated).
(4) They provide evidence that representational drift in palatability coding may arise from sensory experience rather than from the passive passage of time.
The findings are novel and impactful, and the results are relatively complete.Weaknesses:
(1) The authors state that "no differences in effects were observed between taste batteries" (Methods), but it is not clear which analyses were performed to determine this, especially considering that many of the analyses are within-animal. Without more clarity, it is difficult to evaluate whether the interaction of different tastes within the sets of stimuli bias the main conclusions.
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Author response:
The following is the authors’ response to the previous reviews.
Public Reviews:
Reviewer #1 (Public review):
The authors collect extensive behavioral data and show that preference vary between animals and days, but little insight is provided into what underlies these changes and to what extent they reflect "preference". Two animals drank equal amounts of sucrose and quinine on day one of preference testing, suggesting that behavior does not reflect preference but (lack of) habituation to/proficiency with the testing environment.
Rats underwent several days of habituation and were checked for lick proficiency before testing. A note regarding this fact has been added to the text, and to the fact that day one consumption of NaCl and water was high in the rat that did not lick much for sucrose, making it clear that …
Author response:
The following is the authors’ response to the previous reviews.
Public Reviews:
Reviewer #1 (Public review):
The authors collect extensive behavioral data and show that preference vary between animals and days, but little insight is provided into what underlies these changes and to what extent they reflect "preference". Two animals drank equal amounts of sucrose and quinine on day one of preference testing, suggesting that behavior does not reflect preference but (lack of) habituation to/proficiency with the testing environment.
Rats underwent several days of habituation and were checked for lick proficiency before testing. A note regarding this fact has been added to the text, and to the fact that day one consumption of NaCl and water was high in the rat that did not lick much for sucrose, making it clear that the rat was proficiently licking for tastes that it wanted (lines 102-104). We have also added prose to the Discussion section about what lick data tells us about preferences (lines 360-371).
Recordings were performed only after multiple days of preference testing, and preferences were not tested in between/following recording sessions. This design precludes a direct comparison between neural and behavioral responses.
The proposed experiment is suggested as a future direction (lines 389-393); performing this experiment is currently impossible, as the experimenters involved have moved on.
Similarly, correlations between neural responses and behavioral measures are not analyzed/reported on an animal-by-animal basis.
Although this is an excellent idea, the limitations of the current dataset render the analysis impossible. This is now explained in the Discussion (389-393).
Reviewer #3 (Public review):
The authors state that "no differences in effects were observed between taste batteries" (Methods), but it is not clear which analyses were performed to determine this, especially considering that many of the analyses are within-animal. Without more clarity, it is difficult to evaluate whether the interaction of different tastes within the sets of stimuli bias the main conclusions.
Alas, we are unable to address this concern, although the robustness of our results renders it unlikely that we are observing a phenomenon that is particularly sensitive to specifics of the taste battery.
It is not clear how the analyses in Figure 3 emerge from the behavioral patterns across Figures 2A2 and 2B2. Along these lines, citric acid responses for R1 and R2 and saccharine responses for R7 and R8 are not shown in Figures 2A2 and 2B2, thus, we cannot determine if they change from the first to final BAT sessions. Salt, even at low concentrations, may not be palatable when rats are in a water-restricted state. It would be interesting to see Figure 3's analysis exclusively for sweet or for bitter tastes.
We have clarified the text in the results (line 149), methods (lines 531-548), and figure legend (lines 639-644) to better explain Figure 3. The figure itself was updated such that each taste is now color coded, and an analysis looking exclusively at sweet or bitter tests has been run (and found nonsignificant; lines 157-158).
A potential reason why a single taste experience session causes taste palatability responses in GC neurons to revert to a reflection of canonical palatability ranks, as is seemingly shown in Figure 6, is not discussed.
This prose, which we apologize for failing to include previously, has been added to the Discussion section (lines 364-371).
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eLife Assessment
This important study demonstrates how individual taste preferences change over time, how these changes are reflected in cortical activity, and how sensory experience contributes to reshaping both. The evidence is convincing and broadly supports the main conclusions. The findings should be of interest to neuroscientists studying sensory processing and cortical plasticity.
-
Reviewer #1 (Public review):
Summary:
Maigler et al. set out to test the hypothesis that individual differences in taste preferences are (in part) due to individual differences in central taste processing. They first tested rats' preferences for a variety of taste stimuli on multiple days. They then recorded responses of neurons in taste cortex to the same tastes on two consecutive days.
Strengths:
The authors collected high-resolution behavioral data from the same animals across multiple days, allowing for a detailed characterization of individual variation in taste preferences. They then performed recordings from the same set of animals in response to the same stimuli, allowing them to draw parallels between behavioral and neural responses.
Weaknesses:
(1) The authors collect extensive behavioral data and show that preference vary …
Reviewer #1 (Public review):
Summary:
Maigler et al. set out to test the hypothesis that individual differences in taste preferences are (in part) due to individual differences in central taste processing. They first tested rats' preferences for a variety of taste stimuli on multiple days. They then recorded responses of neurons in taste cortex to the same tastes on two consecutive days.
Strengths:
The authors collected high-resolution behavioral data from the same animals across multiple days, allowing for a detailed characterization of individual variation in taste preferences. They then performed recordings from the same set of animals in response to the same stimuli, allowing them to draw parallels between behavioral and neural responses.
Weaknesses:
(1) The authors collect extensive behavioral data and show that preference vary between animals and days, but little insight is provided into what underlies these changes and to what extent they reflect "preference". Two animals drank equal amounts of sucrose and quinine on day one of preference testing, suggesting that behavior does not reflect preference but (lack of) habituation to/proficiency with the testing environment.
(2) Recordings were performed only after multiple days of preference testing, and preferences were not tested in between/following recording sessions. This design precludes a direct comparison between neural and behavioral responses.
(3) Similarly, correlations between neural responses and behavioral measures are not analyzed/reported on an animal-by-animal basis.
-
Reviewer #2 (Public review):
Summary:
The study from Maigler et al investigates how between- and within-animal differences in taste preference relate to differences in neural responsiveness. The experiments rely on an elegant combination of behavioral assays to measure preference (e.g., repeated brief access testing, BAT) and electrophysiological recordings to monitor the activity of ensembles of neurons in the gustatory cortex (GC) of rats.
BAT with distinct batteries of tastants revealed pronounced variability in preference (measured as licking bout size) across individuals. This variability across individuals persisted after repeated testing. Repeated BAT also revealed that each individual rat's preference for different tastants changed across time.
Electrophysiological responses of GC neurons to batteries of tastants showed that …
Reviewer #2 (Public review):
Summary:
The study from Maigler et al investigates how between- and within-animal differences in taste preference relate to differences in neural responsiveness. The experiments rely on an elegant combination of behavioral assays to measure preference (e.g., repeated brief access testing, BAT) and electrophysiological recordings to monitor the activity of ensembles of neurons in the gustatory cortex (GC) of rats.
BAT with distinct batteries of tastants revealed pronounced variability in preference (measured as licking bout size) across individuals. This variability across individuals persisted after repeated testing. Repeated BAT also revealed that each individual rat's preference for different tastants changed across time.
Electrophysiological responses of GC neurons to batteries of tastants showed that firing in the "late epoch" of taste processing (i.e., 500ms post taste delivery) correlated more strongly with the individualized rat's BAT preference rather than with a canonical preference ranking. Importantly, this correlation was stronger for the last BAT session compared to the first. Finally, the authors show that the correlation disappeared in a second, consecutive recording session, indicating that exposure to tastants reconfigure preferences.
Strengths:
(1) The experimental design allows for an unprecedented look at the relationship between individual variability in taste preferences and neural processing.
(2) The study demonstrates that taste preference variability is not mere experimental noise but reflects the dynamic nature of taste. A key strength is the clear evidence that behavioral variability is reflected in neural activity patterns, establishing a strong correlation between brain and behavior.
(3) The evidence that simple exposure to familiar tastes can reconfigure preferences and taste representations is interesting.
Weaknesses:
The authors appropriately addressed the weaknesses in the revision process.
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Reviewer #3 (Public review):
Summary:
Maigler & Lin et al present a convincing set of behavioral and electrophysiological experiments and analyses exploring how individual differences in taste preference map onto neural responses in the gustatory cortex (GC). They go on to examine how both preferences and neural responses shift following intervening taste experience. Their experiments are strengthened by examining tastes of distinct identities and palatability (sweet, sour, salty, bitter) and correspond each animal's individual preference to the palatability-related late phase of the neural response.
Strengths:
(1) They demonstrate a relationship between the behavioral expression of taste preference and palatability-related GC neural responses. The direct correlation of expression of taste preference with GC neural responses indicates …
Reviewer #3 (Public review):
Summary:
Maigler & Lin et al present a convincing set of behavioral and electrophysiological experiments and analyses exploring how individual differences in taste preference map onto neural responses in the gustatory cortex (GC). They go on to examine how both preferences and neural responses shift following intervening taste experience. Their experiments are strengthened by examining tastes of distinct identities and palatability (sweet, sour, salty, bitter) and correspond each animal's individual preference to the palatability-related late phase of the neural response.
Strengths:
(1) They demonstrate a relationship between the behavioral expression of taste preference and palatability-related GC neural responses. The direct correlation of expression of taste preference with GC neural responses indicates that taste preference behavior may be less noisy than previously thought, reflecting actual neural activity.
(2) They address the stability of individual taste preference by comparing within and between session expression. This finding indicates that individual preference on any given trial or test session can differ from canonical palatability.
(3) The animal's preferences for various tastes are reflected in the neurophysiological recordings, despite that behavior and physiology sessions are separated by several weeks, with an intervening surgery, across multiple delivery methods (active licking vs passive delivery), and across homeostatic states (thirsty vs sated).
(4) They provide evidence that representational drift in palatability coding may arise from sensory experience rather than from the passive passage of time.
The findings are novel and impactful, and the results are relatively complete.Weaknesses:
(1) The authors state that "no differences in effects were observed between taste batteries" (Methods), but it is not clear which analyses were performed to determine this, especially considering that many of the analyses are within-animal. Without more clarity, it is difficult to evaluate whether the interaction of different tastes within the sets of stimuli bias the main conclusions.
(2) It is not clear how the analyses in Figure 3 emerge from the behavioral patterns across Figures 2A2 and 2B2. Along these lines, citric acid responses for R1 and R2 and saccharine responses for R7 and R8 are not shown in Figures 2A2 and 2B2, thus, we cannot determine if they change from the first to final BAT sessions. Salt, even at low concentrations, may not be palatable when rats are in a water-restricted state. It would be interesting to see Figure 3's analysis exclusively for sweet or for bitter tastes.
(3) A potential reason why a single taste experience session causes taste palatability responses in GC neurons to revert to a reflection of canonical palatability ranks, as is seemingly shown in Figure 6, is not discussed.
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Author response:
The following is the authors’ response to the original reviews.
Public Reviews:
Reviewer #1 (Public review):
It is unclear whether there are any systematic changes in preferences over the course of testing that could explain the observed changes in correlation with neural responses, such as changes due to learning (e.g., flavor nutrient conditioning, relief of neophobia), changes in deprivation state, or habituation to/proficiency with the BAT setup.
For the revision, we have added analysis, including a new figure (Figure 3) between what are now Figures 2 & 4, testing the hypothesis that preference changes across testing days are non-random in direction (e.g., that they reflect attenuation of neophobia). This new analysis failed to reveal evidence supporting the hypotheses that: 1) preference for palatable tastes …
Author response:
The following is the authors’ response to the original reviews.
Public Reviews:
Reviewer #1 (Public review):
It is unclear whether there are any systematic changes in preferences over the course of testing that could explain the observed changes in correlation with neural responses, such as changes due to learning (e.g., flavor nutrient conditioning, relief of neophobia), changes in deprivation state, or habituation to/proficiency with the BAT setup.
For the revision, we have added analysis, including a new figure (Figure 3) between what are now Figures 2 & 4, testing the hypothesis that preference changes across testing days are non-random in direction (e.g., that they reflect attenuation of neophobia). This new analysis failed to reveal evidence supporting the hypotheses that: 1) preference for palatable tastes increases with experience (a result that would make sense given research on neophobia; 2) the preference for aversive tastes decrease with experience; or 3) absolute consumption of any particular taste changes in a reliable direction from session to session (lines 142-157 and new Figure 3).
A secondary point is whether any changes in preference are attributed to internal individual versus external contextual factors. Both types of variation (i.e., across individuals and across time within an individual) are mentioned in the introduction, but it is not clear what the authors believe about the nature or neural representation of these sources of variation.
While we assume that differences between rats are due to internal factors (given the controlled home-cage environment), we can’t be sure that some subtle, subthreshold (for us as observers) factor impacts taste preferences. Similarly, while changes across time within an individual is categorically within the individual, we cannot be sure whether some subtle facet of their experiences determines how preferences change (as opposed to it being purely internal). We have added prose to the Discussion session on this topic—including citation of Hilary Schiff’s recent work showing nurture-related preference changes as part of this new prose (lines 387-398).
With respect to neural data analysis, no individual animal/day data are shown, making it difficult to assess the extent to which differences in correlation match individual differences in preferences and/or changes in preference with time within individuals.
The revision now explicitly includes Figure panels (with analysis) showing the relationships between individual neural responses and consumption in the first and last BAT tests for a representative rat (lines 172-198; Figures 4A and 4D). As requested in the non-public comments, we have also added waveforms recorded for the representative neuron in an inset to Figure 4B.
The correlation analysis is also lacking control for the fact that there is a certain degree of "chance" associated with behavioral and neural measures having matching ranks.
Certainly chance cannot explain our results, which consist centrally of within-rat differences in match (that is, regardless of chance match levels, what we observed was specifically an enhancement of that match for the most recent behavioral assessment compared to an earlier assessment in the same rat)—a finding that is all the more surprising given that: 1) 2 weeks separate that behavior test and the electrophysiology session; and that 2) that gap between the ephys test and the (less well-matched) first behavioral test is only 1-3 days longer. Nonetheless, in appreciation of Reviewer 1’s concern, we have added an independent, convergent analysis to the revision, testing whether the observed pattern vanishes when we shuffle the preference ranks between tastes with neighboring ranks in the behavioral data (a more conservative test than complete shuffles among tastes). The results of this analysis, which are in the new Figure 5, provide further proof that our result is not based on chance—that they specifically reflect a match between neuronal activity and behavior (lines 242-251).
Finally, …it is unclear to what extent changes in correlation may be attributed to overall changes in responsiveness of the neural population.
We include several new analyses in the revision that test the hypothesis that the reduction in match between behavioral rankings and neural responses in the second electrophysiology sessions reflects spontaneous or taste-driven changes in neural excitability. These additional analyses reveal no clear between-session differences in baseline and/or taste-evoked responses, or in the percentages of neurons that are taste responsive and/or palatability-related (lines 292-309; Figure 7).
Reviewer #2 (Public review):
The manuscript could use additional corollary analyses to provide a more complete picture of the phenomenon. For instance, how many neurons (per animal and in total) have significant correlations with the final BAT patterns? And with the first BAT? Can a time course of such counts be provided? Can some decoding analyses be performed at a single session level to reconstruct a rat's behavioral preference pattern from its neural activity?
These are all really good ideas. As noted in our response to Reviewer 1, we have implemented all but the last of the suggested analyses, which did not produce evidence suggesting that our results can be explained by changes in neuronal properties between the two recording sessions (lines 292-309; Figure 7). We have also made attempts to apply the decoding analysis; unfortunately, we don’t have large enough samples to obtain stable results such a subtle decoding task (reflecting the last BAT session’s preference pattern is significantly better than the first session’s pattern).
The manuscript could benefit from additional polishing, both in the text as well as in the figures.
An extensive holistic edit has been done, starting with suggestions made by Reviewer 2 in the non-public comments.
Reviewer #3 (Public review):
Without a behavioral measure collected after recording day 1 intraoral exposure, it is not possible to determine whether taste preference was altered by that experience…The authors' conclusion would be strengthened by adding an intervening brief access test between recording days 1 and 2.
We very much appreciate Reviewer 3’s suggestion. Alas, the primary authors involved in data collection on this project have moved on, and we won’t be able to collect the additional dataset that would be required. Instead, we have softened the conclusion that we reached in the last section, and suggested the proposed experiment as a future direction (lines 366-374).
The current experimental design exposes animals to 3 distinct sets of substances … [that] differ in identity … and concentration. Because palatability is known to be comparative depending on the other substances available and concentration-dependent, this introduces challenges to interpretation, [and] without more clarity, it is difficult to evaluate whether the interaction of different tastes within the sets of stimuli biases the main conclusions.”
This is an interesting point. Analyzing each set of batteries separately and performing between-battery comparisons would require a larger number of experimental subjects then we have in our current sample size. That said, while we acknowledge that taste preference ranking is relative, we believe the ranking system used here deviates little, if any, from the 'true' ranking (and is therefore significantly relevant to gustatory activity). This is supported by our newly obtained result in response to Reviewer 1 & Reviewer 2 (see above), where an ancillary shuffle analysis (Figure 5C) showed that swapping adjacent preference orders eliminated the experimental effects across all batteries.
Responses to sweet tastes are not reported in the electrophysiology data. This is seemingly the case because rats given set 1 received no sweet stimulus while rats given set 2 received to 2 distinct sweet tastes. Finally, rats given set 3 did not receive quinine, yet quinine is reported in electrophysiology data.
We are unsure of the source of this confusion—in every case, the rat received the same tastes in the electrophysiology sessions that were delivered in the BAT preference tests—but in appreciation of Reviewer 2’s concern, we have modified the text and table to ensure: 1) that panels reflecting data from single example rats (panels that therefore necessarily include only a subset of possible tastes) are clearly marked as such; and 2) that the nature of which taste batteries were delivered is more explicit (lines 104-112; 172-178).
The choice of reporting average lick cluster size is problematic because the authors use thirsty rats with 10-second-long trials. Thirsty rats are likely to lick in relatively long clusters, especially for neutral and palatable tastes. If the rat is mid-cluster when the trial ends, the final cluster would be cut off prematurely, resulting in shorter overall average lick cluster size, disproportionately affecting neutral and palatable tastes over aversive tastes.
We have ourselves been deeply concerned with this issue, and in fact have recently published a paper that includes within it a direct test demonstrating that calculations of lick bout lengths from 10-sec BAT trials result in taste palatability estimates that are identical to (and less noisy than) those generated from more classically-used 15-min ad lib licking. We now cite this paper (Stone, Lin, et al., 2026) in the Methods section, along with text clarifying how we calculated lick clusters. We also conducted an additional analysis that estimates taste preference after removing these “prematurely ended bouts” without changing the observed pattern of results (lines 494-510).
Of course, even if this last analysis had changed things, the result of clusters being cut short by the end of a trial would be an underestimation of the preference for the palatable tastes (which drive far more licking than aversive tastes and are therefore more likely to be mid-bout at the end of a trial). Such an underestimation would in turn be expected to reduce the observed neural-behavioral correlation. This fact highlights the robustness of our findings.
Canonical palatability rankings may not apply to the concentrations selected in every stimulus set. This is particularly true for set 1, which included two concentrations of citric acid and quinine for the behavior. It is also not clear which concentrations are reported in Figures 3A2 and 3B2. Meanwhile, the concentrations of quinine and citric acid used for electrophysiology are quite low.
In the revised Methods section, we explicitly motivate our reasoning (including citations) behind canonical rankings for each taste battery used (lines 513-522). Every taste used was of agreed-upon preference levels, and in the rare case that two concentrations of the same taste were used, both were known to have distinct palatabilities (e.g., 0.1M NaCl is preferred to 0.05M NaCl). This careful selection of tastes ensured that it was trivial to avoid misordering of canonical palatability rankings.
And even if mistakes in canonical rankings had been made, the impact of these inaccuracies in these rankings would have been minimal. Our findings are primarily driven by high levels of inter-individual (between different rats) and intra-individual (day-to-day fluctuations within the same rat) preference differences. Given this variability, the fact that the brain-behavior correlation were consistently worse using these rankings almost certainly means that the neural activity matches preference behavior—our thesis. This conclusion is further supported by our shuffle analysis, which demonstrated that randomizing the order did not yield superior correlations between taste ranking and GC activity.
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eLife Assessment
This valuable study demonstrates how individual taste preferences shift over time, how these changes relate to cortical activity, and how experience reshapes both. The evidence is largely solid, although additional analyses are needed to strengthen some of the conclusions. The results should be of interest to neuroscientists studying sensory physiology.
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Reviewer #1 (Public review):
Summary:
Maigler et al. set out to test the hypothesis that individual differences in taste preferences are (in part) due to individual differences in central taste processing. The first tested rats' preferences for a variety of taste stimuli on multiple days. They then recorded responses of neurons in the taste cortex to the same tastes on two consecutive days.
Strengths:
The authors collected high-resolution behavioral data from the same animals across multiple days, allowing for a detailed characterization of individual variation in taste preferences. They then performed recordings from the same set of animals in response to the same stimuli, allowing them to draw parallels between behavioral and neural responses. They convincingly show that preference ranks for a variety of basic tastes change over time …
Reviewer #1 (Public review):
Summary:
Maigler et al. set out to test the hypothesis that individual differences in taste preferences are (in part) due to individual differences in central taste processing. The first tested rats' preferences for a variety of taste stimuli on multiple days. They then recorded responses of neurons in the taste cortex to the same tastes on two consecutive days.
Strengths:
The authors collected high-resolution behavioral data from the same animals across multiple days, allowing for a detailed characterization of individual variation in taste preferences. They then performed recordings from the same set of animals in response to the same stimuli, allowing them to draw parallels between behavioral and neural responses. They convincingly show that preference ranks for a variety of basic tastes change over time and that the correlation between neural responses and preferences is not stable, correlating more strongly with more recent measures of preference.
Weaknesses:
Behavioral analysis: Data presentation does not show how preferences change over the course of testing. In particular, it is unclear whether there are any systematic changes in preferences over the course of testing that could explain the observed changes in correlation with neural responses, such as changes due to learning (e.g., flavor nutrient conditioning, relief of neophobia), changes in deprivation state, or habituation to/proficiency with the BAT setup. A secondary point is whether any changes in preference are attributed to internal individual versus external contextual factors. Both types of variation (i.e., across individuals and across time within an individual) are mentioned in the introduction, but it is not clear what the authors believe about the nature or neural representation of these sources of variation.
With respect to neural data analysis, no individual animal/day data are shown, making it difficult to assess the extent to which differences in correlation match individual differences in preferences and/or changes in preference with time within individuals. The correlation analysis is also lacking control for the fact that there is a certain degree of "chance" associated with behavioral and neural measures having matching ranks.
Finally, the conclusion that correlations between final day preferences and neural responses obtained from the second recording session are the result of experience needs more justification; it is unclear to what extent changes in correlation may be attributed to overall changes in responsiveness of the neural population.
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Reviewer #2 (Public review):
Summary:
The study from Maigler et al investigates how between- and within-animal differences in taste preference relate to differences in neural responsiveness. The experiments rely on an elegant combination of behavioral assays to measure preference (e.g., repeated brief access testing, BAT) and electrophysiological recordings to monitor the activity of ensembles of neurons in the gustatory cortex (GC) of rats.
BAT with distinct batteries of tastants revealed pronounced variability in preference (measured as licking bout size) across individuals. This variability across individuals persisted after repeated testing. Repeated BAT also revealed that each rat's preference for different tastants changed across time.
Electrophysiological responses of GC neurons to batteries of tastants showed that firing in the …
Reviewer #2 (Public review):
Summary:
The study from Maigler et al investigates how between- and within-animal differences in taste preference relate to differences in neural responsiveness. The experiments rely on an elegant combination of behavioral assays to measure preference (e.g., repeated brief access testing, BAT) and electrophysiological recordings to monitor the activity of ensembles of neurons in the gustatory cortex (GC) of rats.
BAT with distinct batteries of tastants revealed pronounced variability in preference (measured as licking bout size) across individuals. This variability across individuals persisted after repeated testing. Repeated BAT also revealed that each rat's preference for different tastants changed across time.
Electrophysiological responses of GC neurons to batteries of tastants showed that firing in the "late epoch" of taste processing (i.e., 500ms post taste delivery) correlated more strongly with the individualized rat's BAT preference rather than with a canonical preference ranking. Importantly, this correlation was stronger for the last BAT session compared to the first. Finally, the author shows that the correlation disappeared in a second, consecutive recording session, indicating that exposure to tastants reconfigures preferences.
Strengths:
(1) The experimental design allows for an unprecedented look at the relationship between individual variability in taste preferences and neural processing.
(2) The study demonstrates that taste preference variability is not mere experimental noise but reflects the dynamic nature of taste. A key strength is the clear evidence that behavioral variability is reflected in neural activity patterns, establishing a strong correlation between brain and behavior.
(3) The evidence that simple exposure to familiar tastes can reconfigure preferences and taste representations is interesting.
Weaknesses:
(1) The manuscript could use additional corollary analyses to provide a more complete picture of the phenomenon. For instance, how many neurons (per animal and in total) have significant correlations with the final BAT patterns? And with the first BAT? Can a time course of such counts be provided? Can some decoding analyses be performed at a single session level to reconstruct a rat's behavioral preference pattern from its neural activity?
(2) The manuscript could benefit from additional polishing, both in the text as well as in the figures.
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Reviewer #3 (Public review):
Summary:
Maigler & Lin et al present a compelling set of behavioral and electrophysiological experiments exploring how individual differences in taste preference map onto neural responses in the gustatory cortex (GC). They go on to examine how both preferences and neural responses shift following intervening taste experience. Their experiments are strengthened by examining tastes of distinct identities and palatability (sweet, sour, salty, bitter) and corresponding each animal's individual preference to the palatability-related late phase of the neural response.
Strengths:
(1) They demonstrate a relationship between the behavioral expression of taste preference and palatability-related GC neural responses. The direct correlation of expression of taste preference with GC neural responses indicates that taste …
Reviewer #3 (Public review):
Summary:
Maigler & Lin et al present a compelling set of behavioral and electrophysiological experiments exploring how individual differences in taste preference map onto neural responses in the gustatory cortex (GC). They go on to examine how both preferences and neural responses shift following intervening taste experience. Their experiments are strengthened by examining tastes of distinct identities and palatability (sweet, sour, salty, bitter) and corresponding each animal's individual preference to the palatability-related late phase of the neural response.
Strengths:
(1) They demonstrate a relationship between the behavioral expression of taste preference and palatability-related GC neural responses. The direct correlation of expression of taste preference with GC neural responses indicates that taste preference behavior may be less noisy than previously thought, reflecting actual neural activity.
(2) They address the stability of individual taste preference by comparing within and between session expression. This finding indicates that individual preference on any given test session can differ from canonical palatability.
(3) They provide evidence that representational drift in palatability coding may arise from sensory experience rather than from the passive passage of time. The findings are novel and potentially impactful. The results are relatively complete.
Weaknesses:
Experiments require further clarification. The interpretations would be strengthened by reorganizing the experimental design.
(1) Figures 5-6 show shifts in palatability-related GC responses from recording day 1 to recording day 2. The authors propose that this drift is due to the taste experience during recording day 1, but the study, as designed, does not directly test this idea. Without a behavioral measure collected after recording day 1 intraoral exposure, it is not possible to determine whether taste preference was altered by that experience, nor whether the neural responses collected on recording day 2 represent current or most recent palatability expression vs something else. The authors' conclusion would be strengthened by adding an intervening brief access test between recording days 1 and 2. The authors could then determine whether the behavioral preferences changed after intraoral taste exposure on recording day 1, as well as whether the new set of taste-related palatability responses correlates with the updated taste preferences.
(2) The current experimental design exposes animals to 3 distinct sets of substances. These substances differ in identity (some rats never experienced sweet, while others did not experience bitter during the recording sessions) and concentration (ranging from very aversive to slightly aversive or possibly even neutral). Because palatability is known to be comparative depending on the other substances available and concentration-dependent, this introduces challenges to interpretation.
The authors state that "no differences in effects were observed between taste batteries" (Methods), but it is not clear which analyses were performed to determine the lack of difference, especially considering that many of the analyses are within-animal. Without more clarity, it is difficult to evaluate whether the interaction of different tastes within the sets of stimuli biases the main conclusions.
(3) Responses to sweet tastes are not reported in the electrophysiology data. This is seemingly the case because rats given set 1 received no sweet stimulus while rats given set 2 received to 2 distinct sweet tastes. Finally, rats given set 3 did not receive quinine, yet quinine is reported in electrophysiology data.
(4) The choice of reporting average lick cluster size is problematic because the authors use thirsty rats with 10-second-long trials. Thirsty rats are likely to lick in relatively long clusters, especially for neutral and palatable tastes. If the rat is mid-cluster when the trial ends, the final cluster would be cut off prematurely, resulting in shorter overall average lick cluster size, disproportionately affecting neutral and palatable tastes over aversive tastes.
(5) Canonical palatability rankings may not apply to the concentrations selected in every stimulus set. This is particularly true for set 1, which included two concentrations of citric acid and quinine for the behavior. It is also not clear which concentrations are reported in Figures 3A2 and 3B2. Meanwhile, the concentrations of quinine and citric acid used for electrophysiology are quite low.
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Author response:
Public Reviews:
Reviewer #1 (Public review):
…It is unclear whether there are any systematic changes in preferences over the course of testing that could explain the observed changes in correlation with neural responses, such as changes due to learning (e.g., flavor nutrient conditioning, relief of neophobia), changes in deprivation state, or habituation to/proficiency with the BAT setup.
For the revision, we will add analysis (including either additional panels for Figure 3 or as a new Figure between what are now Figures 3 & 4) testing the hypothesis that preference changes across testing days are non-random. Concretely, we will test: 1) whether the preference for palatable tastes increase with experience (a result that would make sense given research on neophobia; 2) whether the preference for aversive tastes …
Author response:
Public Reviews:
Reviewer #1 (Public review):
…It is unclear whether there are any systematic changes in preferences over the course of testing that could explain the observed changes in correlation with neural responses, such as changes due to learning (e.g., flavor nutrient conditioning, relief of neophobia), changes in deprivation state, or habituation to/proficiency with the BAT setup.
For the revision, we will add analysis (including either additional panels for Figure 3 or as a new Figure between what are now Figures 3 & 4) testing the hypothesis that preference changes across testing days are non-random. Concretely, we will test: 1) whether the preference for palatable tastes increase with experience (a result that would make sense given research on neophobia; 2) whether the preference for aversive tastes decrease with experience; and 3) whether absolute consumption of any particular taste changes in a reliable direction from session to session.
A secondary point is whether any changes in preference are attributed to internal individual versus external contextual factors. Both types of variation (i.e., across individuals and across time within an individual) are mentioned in the introduction, but it is not clear what the authors believe about the nature or neural representation of these sources of variation.
While we assume that differences between rats are due to internal factors (given the controlled home-cage environment), we can’t be sure that some subtle, subthreshold (for us as observers) factor impacts taste preferences. Similarly, while changes across time within an individual is categorically within the individual, we cannot be sure whether some subtle facet of their experiences determines how preferences change (as opposed to it being purely internal). We will add prose to the Discussion session on this topic—including citation of Hilary Schiff’s recent work showing nurture-related preference changes as part of this new prose.
With respect to neural data analysis, no individual animal/day data are shown, making it difficult to assess the extent to which differences in correlation match individual differences in preferences and/or changes in preference with time within individuals.
The revision will include Figure panels (with analysis) showing the relationships between individual neural responses and consumption in the first and last BAT tests for 1-2 representative rats.
The correlation analysis is also lacking control for the fact that there is a certain degree of "chance" associated with behavioral and neural measures having matching ranks.
Certainly chance cannot explain our results, which consist mainly of within-rat differences in match (i.e., specific enhancement of that match for the most recent behavioral assessment)—a finding that is all the more surprising given that: 1) 2 weeks separate that behavior test and the electrophysiology session; and that 2) that 2-week gap is only 1-3 days less than the gap using the first behavioral test (that reliably correlates less well with the neural data). Nonetheless, we will add an independent, convergent analysis to the revision, testing whether the observed pattern vanishes when we shuffle the preference ranks in the behavioral data—if the result is based on chance, this shuffling should have no impact on the neural-behavioral match.
Finally, …it is unclear to what extent changes in correlation may be attributed to overall changes in responsiveness of the neural population.
We will include a new analysis in the revision testing the hypothesis that the reduction in match between the neural and behavioral rankings reflects changes in neural excitability—spontaneous and taste-driven—between the first and second electrophysiology sessions.
Reviewer #2 (Public review):
The manuscript could use additional corollary analyses to provide a more complete picture of the phenomenon. For instance, how many neurons (per animal and in total) have significant correlations with the final BAT patterns? And with the first BAT? Can a time course of such counts be provided? Can some decoding analyses be performed at a single session level to reconstruct a rat's behavioral preference pattern from its neural activity?
These are all really good ideas. We are in the process of implementing all but the last; we will attempt the last as well, but can’t promise that we have large enough ensembles to provide stable results of such a subtle decoding task (reflecting the last BAT session’s preference pattern significantly better than the first session’s pattern).
The manuscript could benefit from additional polishing, both in the text as well as in the figures.
It is being done, on the basis of suggestions made by R2 in the non-public comments.
Reviewer #3 (Public review):
Without a behavioral measure collected after recording day 1 intraoral exposure, it is not possible to determine whether taste preference was altered by that experience…The authors' conclusion would be strengthened by adding an intervening brief access test between recording days 1 and 2.
We very much appreciate Reviewer 3’s suggestion, but the primary authors involved in data collection on this project have moved on, and we won’t be able to collect the additional dataset that would be required. Instead, we will soften the conclusion that we reach in the last section, and suggest this experiment as a future direction.
The current experimental design exposes animals to 3 distinct sets of substances … [that] differ in identity … and concentration. Because palatability is known to be comparative depending on the other substances available and concentration-dependent, this introduces challenges to interpretation, [and] without more clarity, it is difficult to evaluate whether the interaction of different tastes within the sets of stimuli biases the main conclusions.
This is an interesting point. We hope that some of the work that we are undertaking in response to Reviewers 1 & 2 (see above) will shed light on whether there is any non-randomness in between-session preference changes; such non-randomness would imply that we might want to conclude that preferences change more with one battery than another. But we will perform a more direct test of this hypothesis, breaking the dataset apart and asking whether our phenomena are observed more with one battery than another. If it turns out that the magnitude of the impact of experience does depend on the nature of the taste battery (we predict not, for reasons that are in the manuscript), we shall introduce that complexity into our interpretation, and the Discussion thereof.
Responses to sweet tastes are not reported in the electrophysiology data. This is seemingly the case because rats given set 1 received no sweet stimulus while rats given set 2 received to 2 distinct sweet tastes. Finally, rats given set 3 did not receive quinine, yet quinine is reported in electrophysiology data.
We are unsure of the source of this confusion—in every case, the rat received the same tastes in the electrophysiology sessions that were delivered in the BAT preference tests—but we will modify the text to ensure: 1) that panels reflecting data from a single rat (panels that will therefore necessarily include only a subset of possible tastes) are clearly marked as such; and 2) that the nature of which taste batteries were delivered is more explicit.
The choice of reporting average lick cluster size is problematic because the authors use thirsty rats with 10-second-long trials. Thirsty rats are likely to lick in relatively long clusters, especially for neutral and palatable tastes. If the rat is mid-cluster when the trial ends, the final cluster would be cut off prematurely, resulting in shorter overall average lick cluster size, disproportionately affecting neutral and palatable tastes over aversive tastes.
We have ourselves been deeply concerned with this issue; we have recently published a paper that includes within it a direct test demonstrating that calculations of lick bout lengths from 10-sec BAT trials result in taste palatability estimates that are identical to (and less noisy than) those generated from more classically-used 15-min ad lib licking. We will cite this paper (Lin, et al., 2026) in the Methods section of the revision, along with text clarifying how we calculated lick clusters. That said, we are also planning to conduct an additional analysis that estimates taste preference after removing these “premature bouts” and will evaluate how this recalculation affects our results.
Of course, even if 10-sec BAT trial data DIDN’T provide reliable preference measures, the result of clusters being cut short by the end of a trial would be an underestimation of the preference for the palatable tastes (which drive far more licking than aversive tastes and are therefore more likely to be mid-bout at the end of a trial). Such an underestimation would in turn be expected to reduce the observed neural-behavioral correlation. This fact actually highlights the robustness of our findings.
Canonical palatability rankings may not apply to the concentrations selected in every stimulus set. This is particularly true for set 1, which included two concentrations of citric acid and quinine for the behavior. It is also not clear which concentrations are reported in Figures 3A2 and 3B2. Meanwhile, the concentrations of quinine and citric acid used for electrophysiology are quite low.
In the revision Methods section, we will explicitly motivate our reasoning behind canonical rankings for each taste battery used (the added text will include citations). We have also added to the Discussion section prose concerning the possible impact of possibly getting those rankings wrong—i.e., the impact is minimal, given that our results are largely driven by differences between rats (and day-to-day differences within rat), and the resultant fact that almost any choice of canonical rankings would poorly reflect the behavior of individual rats on individual days.
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