Individual Taste Preferences Predict Cortical Taste Dynamics but Are Modified by Experience

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.

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.

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.

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.