Introduction

Rats and related rodents emit a large number of ultrasonic vocalizations in the 50-kHz range [1]. There have been two major hypotheses advanced to account for these calls: the movement byproduct hypothesis and the affective communication hypothesis. The movement byproduct hypothesis posits that the 50-kHz vocalizations emitted by rodents are largely produced by the biomechanical forces on the thoracic area generated during movement [2]. In contrast, the affective communication hypothesis posits that these vocalizations convey the animal’s affective state to surrounding animals. The fact that 50-kHz calls are highly varied, with some authors recognizing as many as 14 different kinds involving distinct patterns of frequency modulation (FM) [3] raises questions about how the two hypotheses can account for such variability.

Thiessen and collaborators pioneered the movement byproduct hypothesis after finding a strong correlation between locomotion and the production of some ultrasonic vocalizations (USV) in gerbils [4]. Particularly striking was that the calls were emitted most often when the animal landed on its forefeet when jumping, when the lungs are presumably compressed and air expelled. Similarly, vocalizations have been linked to the landing phase of hops and darts in female rats during mating [2]. Vocalizations necessarily occur on the expiratory phase of respiration and hence, behaviors which modulate breathing could also modulate USVs [5]. For example, USVs are frequently seen during sniffing behavior and show periodicity in the 6–8 Hz range, synchronous with the sniffing respiratory rhythm [6]. Locomotor movements are known to entrain respiration in many quadrupeds, including rats [7, 8]. Hence, it is plausible that vocalizations are entrained to locomotion.

Several lines of evidence argue against the movement byproduct hypothesis. First, although sniffing and walking entrain respiration, rats can sniff and move without vocalizing [6, 8]. In fact, electromyographic studies suggest that vocalizations require the active contraction of the larynx muscles during the exhalation [9, 10]. Concordantly, rats with transected laryngeal nerves do not vocalize even though they actively move about [11, 12]. Second, respiration and locomotion can be decoupled during vocalizations as the rat prolongs the expiratory phase to produce a call [8]. Third, the periodic frequency modulations produced during trills require active oscillatory adduction of the laryngeal muscles, suggesting that complex calls are not produced by accident [5, 9]. Fourth, a recent study reported that vocalizations often occur at the onset of locomotion, not when the animal is stopping [13]. Finally, the presence of another rat can influence vocalization rate independent of the rate of movement [13]. Despite this evidence, it is clear that vocalizations are strongly associated with movements, leaving open the possibility that at least some vocalizations are motion byproducts [8, 13].

The alternate hypothesis is that USVs are indicators of affective state [14], with 22 kHz calls indicating negative affect and 50-kHz calls indicating positive affect. The 22kHz calls are emitted in situations where the rat may experience anxiety [1]. For example, 22 kHz calls are elicited by predators (such as a cat) [15], foot shock [16], fear conditioning [17–19], a dominant conspecific [20–22], acoustic startle [23], handling by an unfamiliar handler [24, 25], and social defeat [21, 22]. In contrast, 50-kHz calls are present during anticipation of rewards such as an addictive drug (amphetamine or methamphetamine) [26], sexual encounters [27], stimulation of brain reward pathways [28], social play [29] and “tickling” by human handlers [30]. Further distinctions have been drawn between different categories of 50-kHz calls, such as flat and frequency modulated (FM) calls [25–28]. Flat 50-kHz calls have been proposed as a social coordination signal based on the finding that rats in their home cage emit these calls when a partner is removed [31]. These calls are also associated with aggressive interactions and, therefore, could be an indicator of social ambivalence [29]. In contrast, FM calls have been associated with a variety of positive situations such as social interactions or rewards [26–28]. However, the affective communication hypothesis does not predict nor offer a particularly compelling account for why there should be as many as 14 categories of vocalizations.

One of the issues complicating the understanding of ultrasonic calls is that it is not yet clear where to draw categorical boundaries between calls. As mentioned, a distinction is often made between flat and FM calls [3, 29, 31, 32]. FM calls are sometimes further subdivided. For example, Takahashi et al. [33] divided FM calls by the frequencies around which they range (25kHz, 40kHz or 60kHz). The most extensive categorization scheme is that proposed by Wright et al. [3] who identified 14 distinct categories of 50-kHz calls based on acoustic parameters and call profile. Similarly detailed schemes have been devised for mouse vocalizations [34, 35]. However, it has been argued that these categorization schemes are more detailed than necessary [36]. Exactly which call categories are needed to capture the essential features of rat communication remains an open question.

In this experiment, we sought to discriminate between the movement byproduct and affective communication hypotheses by examining the correspondence between specific subtypes of 50-kHz calls and specific movements. We examined behavior and vocalizations while juvenile rats awaited the arrival of a play partner, a period associated with a large number of 50-kHz calls [37] which, owing to the lack of a partner, can be uniquely ascribed to one animal. The movement byproduct hypothesis predicts that vocalizations should be tightly correlated with actions involved in locomotion, not those in which the rat is stationary. Further, because each movement produces its own characteristic airflow pattern, there should be a one-to-one correspondence between specific movements and vocalization types. The affective communication hypothesis, on the other hand, simply predicts a high number of positively valenced calls, presumably intended to elicit play from the expected partner. In this view, calls need not be associated with any particular behavior. Hence, a finding that 50-kHz vocalizations are decoupled from behaviors would strongly favor the affective communication hypothesis over the movement byproduct hypothesis.

Methods

Scoring

Behavior.

For each animal the type of behavior and duration of that particular behavior were scored during the 2-minute testing session. Importantly, we assigned a behavioral category at every time point, so that no time was left unaccounted for. Knowing the rats’ behavior at every time point was important because in our analysis, that lack of a vocalization during a specific behavior was informative (e.g., a call which occurs for 8 of 8 instances of a behavior is more tightly coupled to that behavior than one which occurs only 3 times out of 8). Behaviors were coded via frame-by-frame analysis of video taken either at a 45-degree angle relative to vertical or via an overhead camera. A switch from a camcorder (45 degree angle) to an infrared webcam (overhead) occurred mid-way through data collection due to the greater ease-of-use of the webcam. We have found that either view is sufficient to distinguish the behaviors we studied here.

The behaviors included in the analyses are described in Table 1, which also describes the criteria used in categorization. Separate coders performed behavioral (and vocal) coding for each of the analyses. In the first analysis, overlapping behaviors were allowed (e.g., if the animal turned while walking, that time interval would be coded as both a turn and a walk). In the second analysis, each time interval was assigned only to one behavior, thus simplifying our vocal-behavioral correlation analysis. In the case of turning and walking simultaneously, the behavior was coded as a walk. Most other behaviors were mutually exclusive by their nature.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 1. Description of Rat Behaviors.

https://doi.org/10.1371/journal.pone.0175841.t001

Vocalizations.

Acoustic data was analyzed using Raven Pro 1.4 software (Bioacoustics Research Program, Cornell Lab of Ornithology, Ithaca, NY). The Raven Pro software generated spectrograms with a 256-sample Hann window from which the experimenter manually selected 50-kHz vocalizations. 50-kHz vocalizations were included if they followed Wright et al.’s [3] call criteria, specifically: 1) temporal continuity, 2) fundamental frequencies between 35- and 90-kHz, and 3) a local peak in sound intensity distinct from background noise that exhibited patterned temporal structure. Some putative vocalizations were not perceptually distinct from the background noise and were hence removed from further analyses. After manual scoring, the Raven Pro software provided the beginning and end times of each vocalization as well as the manually determined category of each call. The call classifications were done according to the 14 categories identified by Wright et al. [3]. Although these calls were identified from slightly older rats (60–70 days of age), most of our calls were highly similar to at least one of the 14 adult call types, suggesting that the call structure of Long-Evans rats does not change much between 1 and 2 months of age. Examples of vocalizations in each category are shown in Fig 1. As with behavioral coding, categorization of vocalizations for each experiment was by one experimenter and the coders differed for analysis 1 and 2.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 1. Examples of categorized 50-kHz calls (as originally described by Wright and colleagues [3]) taken from our data.

https://doi.org/10.1371/journal.pone.0175841.g001

Results

Vocalization counts during anticipation of play

To investigate the behavioral correlates of vocalizations, we ran juvenile rats in an anticipation of play paradigm, analyzing vocalizations during the two-minute period before the introduction of a play partner. Animals were tested over the course of 7 anticipation periods, one per day, each followed by pairing with the same play partner (see Methods for details). In the first analysis, we used data from 9 animals and a total of 14 play anticipation sessions (some animals contributed two sessions recorded on different days). To test the robustness of our results, a second analysis was conducted, using data from 12 rats and a total of 12 test sessions, all from testing day 7.

Previous studies have shown that FM 50-kHz calls are most common during appetitive situations. For the purposes of comparison with previous studies, we counted the frequency of occurrence of each of the 14 call sub-types identified in this study, using categories previously suggested by Wright et al. [3]. As shown in Fig 2, calls containing trills (trills, trills with jumps, and composites) were by far the most frequent types of calls. In comparison, flats were the least frequent call. These data contrast with those of Wright et al, who found that almost half of the calls emitted by young adults tested one at a time were flats, but the data agrees with Himmler et al. [48], who found that the most frequent vocalizations during play by juveniles were trill type calls, accounting for 77% of the total calls.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 2. Total number of vocalizations by call category.

Categories correspond to those shown in Fig 1. Bars indicate the total raw counts of vocalizations across Analysis 1 and Analysis 2 combined.

https://doi.org/10.1371/journal.pone.0175841.g002

Specific correspondences between vocalizations and behaviors

To further address the locomotor byproduct and affective state hypothesizes, we counted the number of co-occurrences of each vocalization type with each of the coded behavioral categories. A Monte-Carlo shuffling technique was then used to estimate how likely the observed frequency of coincidence between a behavior and a vocalization was to have occurred by chance, expressed as a z-score (a z-score above 2.3 is significant at p < .01). Negative z-scores indicated that the call was much less likely to occur during the specified behavior than if calls were randomly distributed.

As shown in Fig 3, some actions are clearly more likely to be associated with certain vocalizations than others. Our first analysis was a preliminary study to establish the feasibility of the method, and hence the behavioral sessions were taken from different days during testing. Despite this variability, the z-scores from this study show clear evidence of certain calls being tied to certain behaviors. As shown in Fig 3A, jumping behavior is consistently associated with the composite, split, and complex call categories with z-values of 7.9, 3.7, and 3.7 respectively. Running appears to be consistently associated with the composite and trill-with-jumps calls. Walking was associated with the trill-with-jump, trill, complex and flat-with-trill calls. Further, several calls were associated with turning (invert U, trill-with-jumps, complex and ramp) and shaking (complex, flat-with-trill, and composite). Finally, rearing, resting, and exploring are among the least likely behaviors to be coupled with any type of vocalizations. Z-scores for the most statistically significant vocalization-behavior correspondences are shown in Table 2.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 3. Z-scores showing the probability of occurrence of each of the vocalization-behavioral combinations are graphically presented.

(A) Data from Analysis 1. (B) Data from Analysis 2. (C) Grand average of the results from Analysis 1 and 2. Colors indicate the z-score and the same color scheme is used for all subplots. Note that vocalizations and behaviors with very low rates of occurrence have been omitted, as it was not possible to run statistical analysis for these low-frequency events.

https://doi.org/10.1371/journal.pone.0175841.g003

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 2. Ten strongest associations between behaviors and vocalizations are shown for Analysis 1.

Values are z-scores.

https://doi.org/10.1371/journal.pone.0175841.t002

A second analysis was conducted to test the reliability of our methodology and to insure that our results actually reflected calls emitted during the anticipation of a play partner. While analysis 1 focused on a variety of testing days, the second analysis used data from 12 rats on their 7^th day of testing. As the vocal and behavioral scoring for each analysis involved a different coder, an inter-observer reliability analysis was conducted. To do this, the two experimenters coded three data sets in common. The raw behavioral counts showed a 92% agreement between coders. For vocalizations, the raw vocalization counts showed only a 36% agreement. Upon closer inspection, it was determined that the discrepancies were due to disagreement over how to categorize four highly similar call types: trills, complex, composite and trill-with-jumps. Accounting for this confusion gave 98% agreement in call categorization. Despite the less than complete agreement between coders, the data from analysis 2 (Fig 3B) look strikingly similar to those from analysis 1 (Fig 3A). Again, jumps were highly correlated with composite and split calls, but now also with multistep calls. Runs were now associated with a much wider range of call types, including multi-step, split, flat-trill, step up, and trill-with-jumps. Walking was now more associated with trill and step-up but not flat-trill calls. Turning was now associated with trill but not up ramp calls. Some splits occurred during rest, but again, resting, exploring and rearing were either unrelated to most calls or even anti-correlated. Z-scores for the most statistically significant vocalization-behavior correspondences are shown in Table 3.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 3. Ten strongest associations between behaviors and vocalizations are shown for Analysis 2.

Values are z-scores.

https://doi.org/10.1371/journal.pone.0175841.t003

Given the gross similarities between the results of analysis 1 and 2 we averaged the results (Fig 3C) to present a summary of our findings. Speaking broadly, slower locomotion (turning and walking) is associated with calls that include trills or warbles (trill, trill with jump, and complex) as well as up ramps. As locomotion becomes more vigorous (running and jumping), vocalizations shift to composite and sometimes split and multi-step calls. Again, less active states such as resting, exploration and rearing are associated with a marked reduction in the probability of calls, suggesting that these are receptive states for the rat (i.e., they are quiet, perhaps listening, rather than emitting calls).

The highly significant statistical relationship between jumps and composite calls is consistent with the idea that composite calls are a byproduct of jumping. Similarly, calls with trills might be byproducts of walking and running. To investigate these possibilities further, we examined the consistency of the coupling between calls and behaviors, collapsing across data from Analyses 1 and 2. If a call was caused by a particular movement, then we’d expect a one-to-one correspondence between that call (or group of similar calls) and that movement. To address this, we first computed the proportion of times that each behavior was associated with any call. We focused on calls either within 100 ms of the onset or offset of the movement because these are the times of the most dramatic postural change and hence the most likely to lead to thoracic compression and involuntary calls. As shown in Fig 4A, there were three behaviors which were frequently accompanied by calls: runs, accompanied by calls 96% of the time, jumps, accompanied by calls 93% of the time, and shakes, accompanied by calls 83% of the time. In comparison, with the possible exception of shakes, the offset of behaviors was less frequently accompanied by a call (Fig 4B). Note that the relatively high number of calls after a rest, explore and rear are likely onset responses to the subsequent movement, which are often locomotive.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 4. Proportion of each behavior associated with vocalizations.

(A) Bars show the proportion of times each behavior type was accompanied by any vocalization within 100 ms of the onset of that behavior. (B) Same as A but vocalizations are counted within 100 ms of the offset of the behavior. (C) The proportion of times each behavior was accompanied by vocalization, broken down by the specific type of vocalization.

https://doi.org/10.1371/journal.pone.0175841.g004

The locomotor byproduct hypothesis also predicts that a given movement should elicit the same (or highly similar) call. As seen in Fig 4C, this is not the case, even for running, which is associated with composite calls 13% of the time, trill with jumps 23% of the time, and trills 30% of the time. As shown in Fig 1, these calls are fairly distinct. Similarly, shakes are accompanied by trills, trills with jumps, complex, step-up and composite calls. These findings are hence not consistent with the idea that the movement obligatorily causes a specific type of call.

Similarities between calls based on behavioral correlates

It is apparent from Fig 3, that certain calls have similar behavioral correlates. For example, trill and trill with jump calls are almost equally likely to be emitted during turning and walking, whereas split, composite and multi-step calls are all associated with jumping. To assess this further, we performed a clustering analysis, based on the pair-wise correlations of the behavioral z-scores shown in Fig 3C for each vocalization. The results, shown in Fig 5 indicate that the ten most frequent calls types can be grouped into three or perhaps four larger categories. The first consists of composite, split, and multi-step, calls that are generally associated with vigorous activities such as jumping. The second group consists of calls with trills (trill, trill-with-jumps, complex and flat-trill calls), which are generally associated with slower locomotion. A third group consists of the inverted U and up ramp calls, whose behavioral correlates were not well defined. The step-up calls may form a fourth group that seems to be solely associated with walking. As we discuss below, it is important to bear in mind that these results can be influenced by the perceptual similarity of the calls. Two calls that are often confused by coders will end up having highly similar behavioral correlates. However, they also point the way to a more objective method for determining a categorization scheme that reflects the natural communicative purposes used by rats.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 5. Dendrogram showing the relative similarity of different call types based on behavioral correlates.

The x-axis shows the distance between pairs of vocalizations based on the correlation between their behavioral z-scores (i.e., the rows of Fig 3). The dendrogram was generated using the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) algorithm. As with Figs 3 and 4, data from the 4 least frequent vocalizations have been omitted.

https://doi.org/10.1371/journal.pone.0175841.g005

Discussion

Our results show that specific sub-types of rat 50-kHz vocalizations are strongly associated with specific behaviors during play anticipation. Vocalizations frequently co-occurred with active behaviors, such as running, walking and jumping. Less active behaviors, such as exploratory sniffing and rearing were strongly predictive of a lack of any vocalizations. Our findings shed light on two prominent hypothesizes thought to account for the emission of 50-kHz calls: the locomotion byproduct hypothesis [2] and the affect state hypothesis [1, 36, 49].

The motor byproduct hypothesis predicts a much more specific relationship between movements and vocalizations than we have observed. Certainly, locomotor behaviors were far more likely to be accompanied by a call than stationary behaviors. Further, a closer inspection showed that three vigorous movements, runs, jumps and shakes, were almost always (83–96%) associated with a call and these calls occurred at the onset of the movement. However, if the calls are a consequence of the movement, the similar mechanism of production should cause all calls associated with a particular movement to be highly similar. This was not the case. Runs, for example, were strongly associated with trills, trills with jumps, and composite calls. If calls are a physiological by-product the relationship between body movements and vocalization must be very complex. Our data strengthen the arguments made by Laplange et al. [13], discussed in the Introduction, against the notion that vocalizations are physiological coupled to movement.

Consistent with the idea that 50-kHz calls communicate positive mood, rats emitted more of these calls when prior experience led them to expect a play partner. This is consistent with previous reports [13, 37]. Rats also exhibited more runs and jumps under these conditions. The relationship between calls and movement in this case might arise not because the locomotion is causally producing the calls, but because both locomotion and calling are indicative of elevated positive affective. This explanation is consistent with the effects of amphetamine and cocaine, which elicit robust increases in 50-kHz vocalizations and activity levels [50, 51], with high frequencies of calls being emitted whether the rats are moving or not [52]. On the other hand, the affective communication hypothesis offers no explanation for the specific relationships between calls and actions shown in Fig 3. It might be the case that rats shift from trills to composite calls to communicate both increasing speed and positive mood. But ultimately, this is a post hoc explanation.

If neither the motion byproduct nor the affective communication hypotheses can fully account for our observed results, what are the alternatives? It seems plausible that the different call types have specific meaning. Indeed, several studies have shown a distinction between 50-kHz flat and frequency-modulated calls, based on both behavioral context and pharmacological treatment [3, 29, 31, 32]. Further, vocalizations may be temporally locked to on-going behavior on a sub-second time scale. Specifically, during juvenile play, Himmler et al. [48] showed that 50-kHz calls were more frequent in a 300 ms window before contact is made compared to a 300 ms window after contact is terminated. Our results show that there is far more detail in both the specificity of calls and to their temporal coupling to behavior than has previously been suspected. The finding that rats emit specific calls tied to their ongoing behavior raises the possibility that such calls may be used to coordinate moment-by-moment social interactions (i.e., to warn a playmate of an impending attack, communicate that the contact is friendly, beg for mercy, etc.). An important caveat is that our findings come from single animals awaiting a play partner, which undermines the idea that the observed vocalizations serve a social role. However, the number of vocalizations increased significantly over 7 days of repeated reunions with a play partner in the test chamber, suggesting that vocalizations may be intended for the soon-to-arrive play partner. To pursue the possible communicative role of specific vocalizations, the positive and negative associations between specific call types and behaviors found in the present study in the absence of a partner need to be compared with the associations that are produced when a social partner is present.

How do our results compare with previous studies? Our results are consistent with the high association between movements and 50-kHz vocalizations reported by Laplagne and Costa [13]. They showed a striking increase in what they called “step” calls during high-speed locomotion. Closer inspection of their examples suggests that their “step” includes Wright et al.’s step, split and complex types. This is entirely consistent with our finding of a strong association between these same three calls and jumps. Takahashi et al. [33] also found a strong relationship between movement and high-frequency calls. In their case, calls above 60 kHz, which included trills, upward ramp and part of the step call were strongly associated with locomotion. Our results agree, but also include several other 50-kHz calls associated with movement including trills-with-jumps, complex, composite and multi-step calls. Contrasting with previous reports in gerbils [53] and rats [2] which claim that vocalizations were frequently tied to the landing of the forepaws after a jump, we find that vocalizations are most often associated with the onset of a movement. Our results agree with the more recent observations of Laplagne and Costa [13], who found a dramatic increase in call density peaking 140 ms before movement onset.

An important issue is whether the results obtained from our analysis method are stable across repeated observations. To this end, we replicated our analysis twice using different testing days and different coders. Based on a subset of data coded by both coders, we found that agreement for behavioral categories was quite high (92%) while agreement on vocal categories was much lower (36%). The issue was that several of the most frequent call types (trills, complex, composite and trill-with-jumps) are highly similar, leading to different decisions by the two coders. By temporarily treating these four categories as identical, we found a much higher agreement between coders (98%). Despite the less-than-perfect agreement between coders, the overall pattern of results is strikingly similar between analyses (compare Fig 3A to 3B), helping to establish that our results are not due to sampling or statistical errors. The slight differences between analysis 1 and analysis 2 may be due to coding differences but also suggest that slightly more observation time may be necessary to obtain truly stable correspondence between vocalizations and behaviors. The limitation here is not minutes of observation, of which we had an easily manageable 24–28 minutes per analysis, but the time it takes to manually identify and categorize behaviors and calls (2946 behaviors and 4272 vocalizations for the combined analyses). Our best guess as to the true call-behavior correspondence is provided by the averaged data from our two analyses (Fig 3C).

As mentioned in the Introduction, it is currently unclear how many call categories should be used in the analysis of rat USVs. Several researchers have broken 50-kHz calls into flat and FM calls [3, 29, 31, 32]. Takahashi et al. [33] found evidence for two categories within the 50-kHz range, a group of 60 kHz calls broadly associated with movement and another of 40 kHz calls associated with feeding. Brudzynski [36] has suggested that four categories are probably sufficient to capture the important distinctions between 50-kHz calls (flats, trills, step-trills or “other”) while Wright et al. [3] found evidence for 14 distinct calls within the 50-kHz range. Our study offers a possible answer to this question. A clustering algorithm was used to identify groups of calls with similar behavioral correlates among the call categories originally identified by Wright et al. [3]. It is perhaps worth emphasizing that our categorization is based on the way the calls are used, not their acoustic similarity. As shown in Fig 5, this clustering algorithm identifies three categories of calls: Multi-part calls (including composite, split, and multi-step) were all associated with running and jumping; calls with trills were associated with slower locomotion; and inverted U and up ramp calls had similar, though less well-defined, behavioral correlates. Step-up calls may form a fourth distinct category associated with walking. The similarities in the usage of the calls we’ve identified are based on objective criteria, although admittedly it is not clear what level of similarity should be used as a criterion. An important caveat is that certain distinctions between calls may become relevant in other behavioral circumstances. For example, flat calls are likely to form a separate behavioral category, such as re-establishing social contact with cage mates [54], but we did not even include them in our clustering because they were so infrequent in our paradigm. Another caveat is that the perceptual similarities among certain call types may have artificially increased our estimates of their behavioral similarities. For example, as revealed by our coder reliability data, calls with trills (trill, trill-with-jumps, complex and flat-trill) were easily confused, reducing our ability to discriminate any differences in their usage. Studies of categorical perception in human speech show that acoustic parameters of a sound do not necessarily predict their perceptual similarity [55]. Hence, we cannot assume that calls that we perceive as similar based on a spectrogram are, in fact, similar to the rat. It remains possible that trills, trills-with-jumps, and complex calls have different meanings that will manifest once a more objective method of vocal categorization is implemented. In sum, our study provides a method to identify groupings of calls but further studies using different behavioral contexts and, perhaps, automated recognition of vocalizations, will be necessary to definitively identify the behaviorally relevant call categories.

Conclusions

We have presented novel methods for identifying the behavioral correlates between behavioral actions and vocalizations during a behavioral test paradigm in rats (Figs 3 and 4). Our findings provide new clues about how and why rats emit 50kKz vocalizations. The lack of a clear one-to-one relationship between one specific action and one specific call casts doubt on the proposition that FM 50-kHz calls are a byproduct of locomotion. On the other hand, it is difficult to explain why calls meant to convey an affective state would be specifically linked to certain behaviors. If the tight coupling between vocalizations and behavior persist during social interactions, they could be used to signal specific actions, allowing animals to anticipate such things as nose-to-nape contact and react accordingly. The methodology outlined in the present paper could be used to more fully explore the social dimension of 50-kHz calls. In addition, once these findings are validated in different social and non-social contexts, the vocalization-behavior associations may also have practical applications. For example, if particular behavior-vocalization profiles are robust, they could be used to more thoroughly evaluate neurobehavioral disruptions produced by psychotropic drugs and models of mania or depression.

Acknowledgments

We kindly thank the animal care staff and veterinarian for their help with the care of the rats.