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. 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 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.

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.

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.