High-capacity auditory memory for vocal communication in a social songbird

In species with large vocal repertoires and sophisticated social behaviors, learning to interpret vocal signals requires a large capacity memory system. For example, a high-capacity memory for defining sounds of words is needed to process human language semantics (1). Similarly, humans can recognize a large number of individuals based on the sound of their voices as well as linguistic idiosyncrasies (2, 3) and must therefore have formed memories for those unique acoustic features (4). Young humans form these auditory memories rapidly and retain them for long periods in a process called fast mapping (5)—the formation of these auditory memories with few exposures and their maintenance for long periods of time. While the complexity of animal vocal communication pales in comparison with human spoken language (6), auditory memory also plays an important role in the vocal communication of nonhuman social species. In particular, songbirds demonstrate aptitude in several communicative tasks that require auditory memories for vocal signals (7). For example, young male songbirds imitate the song of a tutor that they have stored as an auditory memory (8); some birds can learn the alarm calls from other species to avoid dangerous situations (9) and can even mimic alarm calls of mammals for deceit purposes (10); and territorial birds learn to recognize their neighbors based on their voice, enabling them to identify and react to unfamiliar intruders at the boundaries of their local territory (11).

Individual recognition based on voice also plays a central role for creating and maintaining bonds in social songbird species such as the zebra finch. In the wild, zebra finches are a gregarious and nomadic species, living and traveling in multifamily colonies sometimes comprising more than 100 individuals (12). Zebra finches also mate for life, making strong pair bonds with their partners that are maintained through vocal communication (12, 13). Laboratory studies have shown that their songs have a strong individual signature and can be used to recognize one’s mate (14), father (15), and peers (16). Individual recognition by vocalizations is not restricted to song; distance calls (DCs) (17), begging calls (18), and soft contact calls (19) are also used for individual recognition in juveniles and adults. In previous work, we have shown that all the call types of the zebra finch repertoire are individualized by distinct individual acoustical cues for each call type and that zebra finches could use those cues to discriminate between two vocalizers, irrespective of the call type (20). Given that zebra finches live in large social groups and that vocal communication plays a key role in the creation and maintenance of their social networks, we hypothesized that they might have a high-capacity auditory memory for the acoustic individual signatures found in their calls. We were also interested in investigating whether zebra finches are capable of fast mapping. To answer these questions, we tested the ability of zebra finches to learn to discriminate the identities of unseen vocalizers based on either their song or DC; the song and the DC are the two loud call types in the zebra finch repertoire with strong individual signatures that birds use to recognize and localize each other often without visual contact (20, 21).

We trained male and female zebra finches to recognize several conspecifics by their songs (n = 19) or DC (n = 19) using a modified go–no go task with food reward (Fig. 1A). To test the birds on a large number of vocalizers, we used a 5-day learning ladder procedure in which subjects began by discriminating one rewarded vocalizer from one nonrewarded vocalizer, while additional vocalizers were added to the test on subsequent days (Fig. 1, B and C). Zebra finches individualize each of their call types, and, although their song and DCs are fairly idiosyncratic and stereotyped, there is also acoustical variability across renditions produced by a single vocalizer (20). Thus, each vocalizer was represented by multiple renditions of its song or DC (Fig. 1B).

The performance of each subject was evaluated on days 4 and 5, after they had had at least 1 day of training on each vocalizer. Overall, task performance was measured using an odds ratio (OR): the odds of interruption for nonrewarded trials (correct responses) divided by the odds of interruption on rewarded trials (incorrect responses). An OR of 1 indicates behavior at chance level, and greater than 1 indicates that the subject successfully distinguished rewarded from nonrewarded trials. Nearly all subjects had ORs significantly greater than 1, indicating that they were successful at this task, both when tested on songs (19 of 19 subjects) and on DCs (18 of 19 subjects) (P < 0.0026, one-sided Fisher’s exact test, Bonferroni corrected; Fig. 1D). There was no difference between males and females on this task as assessed with a mixed effects model, with subject identity as the random effect and call type (DC or song) and subject sex as the fixed effects (fig. S2); the effect of subject sex on the overall log OR was not significant [β = −0.163; 95% confidence interval (CI), −1.012 to 0.687; P = 0.707], and neither was the interaction between subject sex and call type (β = −0.449; 95% CI, −1.315 to 0.416; P = 0.309).

To see whether this performance was driven by memorization of all vocalizers in the test or just recognition of a subset of them, we looked at each subject’s performance in detail by evaluating their behavior per individual vocalizer (Fig. 2). We defined the per-vocalizer OR as the ratio of the odds of interrupting a specific vocalizer by the odds of interrupting a random stimulus sampled equally from rewarded and nonrewarded trials. Using this definition, a vocalizer is memorized if the OR is significantly greater than 1 for nonrewarded vocalizers or less than 1 for rewarded vocalizers. We found that 2 of the 19 subjects were able to memorize the entire set of 16 vocalizers from their songs (12 of 19 learned at least half) and 4 of the 19 subjects were able to memorize the entire set of 12 vocalizers from DCs (15 of 19 learned at least half).

To assess the limits of the auditory memory capacity in these songbirds, for four subjects, we intermixed and doubled the size of the two stimulus sets (song and DCs) in the same session. This resulted in a set of DCs from 24 vocalizers and songs from 32 vocalizers for a total of 56 distinct vocalizers. On the first week after completing the two initial learning ladders and testing (song and DC), subjects were trained on the larger song repertoire (16v16) and DC repertoire (12v12) for 3 days each, thus doubling the total number of vocalizers in 6 days. The following week, subjects were given a single day testing session in which previously learned songs and DCs were intermixed for the first time, with only two vocalizers for each rewarding condition and call type. Under this mixed call type condition, subjects continued to self-initiate trials and interrupt the stimuli at rates seen in previous weeks. We then increased the stimulus set to all vocalizers learned thus far (32 vocalizers on song and 24 vocalizers on DC) and evaluated performance on the next 4 days. The results from these four subjects demonstrated that 40, 52, 30, and 47 (mean, 42) vocalizers could be distinguished successfully.

To assess how quickly stimuli were learned, we generated learning curves showing the interruption probability versus the number of informative trials seen, where an “informative trial” is a trial in which the subject did not interrupt the stimulus, giving the bird an opportunity to learn the reward association (interrupted trials do not give the subject new information about whether the stimulus is rewarded or not) (fig. S4). For both songs and DCs, the probability of interrupting rewarded and nonrewarded stimuli is indistinguishable when no informative trials have been seen (intercepts in Fig. 3, A and B), as one would expect. However, the interruption probabilities for rewarded and nonrewarded vocalizers begin to diverge after only a few informative trials, demonstrating very rapid learning of vocalizer’s identity (Fig. 3, A and B). There is a significant effect of call type on the rate of this divergence (β = 0.155; 95% CI, 0.086 to 0.222; P < 0.001, mixed effects model), suggesting that songs may be learned more quickly and with fewer examples (Fig. 3, C and D, and fig. S5). One can also notice that the default “baseline” interruption rates differed between songs and DCs when no informative trials have been seen [song baseline, 0.08 ± 0.01 (2 SEM); DC baseline, 0.16 ± 0.02; mixed effect models, P < 0.001]. The difference in the baseline interruption rates or in the learning rates between male and female subjects was not significant (mixed effects models, P = 0.563).

As mentioned above, to encourage subjects to use the individual signature and not a particular acoustical feature present in a given rendition, a vocalizer is represented by randomly chosen call renditions. If subjects are identifying the vocalizer and not memorizing the individual recordings, then they should be able to correctly predict to which reward contingency a novel rendition belongs when they have already heard and learned some of the renditions of a vocalizer. Birds are at chance levels for the first few renditions they hear but begin to correctly categorize previously unheard renditions after exposure to other renditions from the same vocalizer (Fig. 4, A and B); post hoc analysis of the order in which renditions were first presented to subjects reveals that the interruption probability of unseen nonrewarded stimuli increases with the rendition presentation order (R²_adj,song = 0.90 and R²_adj,DC = 0.81). In the same vein, the interruption probability of rewarded stimuli decreases with the rendition presentation order for song (R²_adj,song = 0.71), but the same decrease was not apparent for DC (R²_adj,DC = 0.00). The slopes are steeper for the nonrewarded renditions because nonrewarded stimuli are being presented four times more frequently than rewarded stimuli; thus, they are also learned faster. Thus, birds are learning to identify the identity of the vocalizers and do not just memorize the individual sound files.

To test whether these memories are stable over longer times and without any additional reinforcement, we retested two subjects on the largest stimulus set (32 songs and 24 DCs intermixed) after a month during which they were not exposed to any of the vocalizations from the test. While their overall performance slightly decreased from optimal performance during the initial test as measured by the change in log OR [0.12 ± 0.18 (2 SEM) in subject 1 and −0.73 ± 0.23 in subject 2], the overall ORs and OR per vocalizer were still well above chance (P < 0.001), indicating that reward associations were retained after a month. To validate that these responses were remembered and not rapidly relearned, we examined the interruption rates for the first informative trials after 1 month and compared them to the rates found for the first informative trials during initial learning (Fig. 4, C and D). These results indicate that these memories for rewarded and nonrewarded vocalizers are stable and can be recalled a month after learning. This is particularly remarkable given that these memories were acquired rapidly and were only reinforced for a short time.

Zebra finches have exceptional auditory memory abilities for the individual signature found in their communication calls. We found that they are able to quickly learn to recognize the identity of up to ~40 vocalizers and to maintain these auditory memories for a long period of time. The recognition of vocalizers is a nontrivial task since it requires the extraction of the individual signature present in each call while ignoring the variability across call renditions. Thus, these are not auditory memories for specific sounds but for the information bearing invariant features constituting the individual signature of the vocalizer (20). We showed that zebra finches can learn and memorize this individual signature with a very small number of exposures (<5), can simultaneously remember a large number of these vocalizers, and are able to use these memories to classify call renditions that they have not heard before (generalization).

The memory capacity in zebra finches for recognizing individuals from their vocalizations is large and might exceed the limits that could be tested with our experimental design. We found that 16 vocalizers based on song and 12 vocalizers based on DC could be regularly discriminated by our subjects. When subjects were tested on as many vocalizers as could be practically tested in a single session, birds were able to discriminate up to 52 distinct vocalizers. The capacity of this auditory memory is similar to other forms of avian memory that have been well quantified, such as spatial memories in food-caching birds (22) or visual memories in pigeons (23). Auditory memories for object labels have also been shown in parrots (24) and in some mammals (25), including the exceptional example of Rico, the border collie, who could correctly fetch ~200 distinct objects on vocal commands (26). We also found that birds make an efficient use of informative trials during their very rapid learning, as they are able to memorize the individual signature of a vocalizer after only a few examples (<10). This fast mapping for communicative vocal signals has only been shown in humans and dogs and is thought to be a key cognitive ability for language learning (5, 26). Last, this memory was long lasting; birds could still remember which vocalizers were assigned to reward versus nonrewarded groups after 1 month without any reinforcement. While previous experiments had shown that song exposure in zebra finches improves auditory recognition, suggestive of a capacity for long-term auditory memories for conspecific vocalizations (27), this is the first study that quantifies the auditory memory capacity in a songbird for individual signature and demonstrates its remarkable performance. Just as in humans, we postulate that birds use an abstract neural representation of these auditory objects to facilitate both working memory manipulation and long-term memory storage (28).

Since most songbirds are also vocal imitators, one might postulate that the memory mechanisms needed for the song imitation behavior overlap with ones that are needed for individual recognition. The auditory memories could be stored as learned motor programs (29), and the high-level abstract representation could then be a motor code. There are many problems with such a motor theory of perception in songbirds: Individual recognition based on vocalizations is present for calls that are not learned (20); it is equally similar in male and female zebra finches, while only male zebra finches learn to sing; and male zebra finches learn a single song, but, as we have shown, they can remember the individual signature of songs and calls from a much larger number of vocalizers. Therefore, although the motor song nuclei might play a role, we and others (30) postulate that a separate neural mechanism representing high-level auditory features is involved in the formation and use of memories for all auditory objects that are relevant for vocal communication. The second order avian auditory pallial areas NCM (nidopallium caudal medial) and CM (caudal mesopallium) are good candidates for the locus of such an engram. NCM neurons show neural correlates of memories for the tutor song before vocal learning (31), and CM neurons show neural correlates for categories of natural sounds learned in operant conditioning tasks (32, 33). Experiments that have exploited the stimulus-specific habituation observed in NCM neurons also suggest that this auditory area can exhibit a large-capacity memory for conspecific song (34). The identity and the connectivity of neural networks involved for storing and recalling these auditory objects as well as the nature of the neural representation for vocalizations, while an active area of research (35–39), remain relatively unexplored in the birdsong field (7). Just as the neural basis of the song imitation behavior has led to many insights into mechanisms of vocal production and learning (8), we predict that future work on the neural basis of these auditory memories and their rapid formation will reveal core knowledge of the neural circuits and computations needed for recognizing learned meaning in vocal sounds, including in human speech.

The fast-learning and exceptional memory for auditory objects in songbirds is a behavioral trait that is essential for vocal communication in social species. This skill can be added to their well-studied vocal imitation behavior, their ability to learn grammar like rules (40, 41), and their capacity to combine call types to generate complex meaning (42). Individual recognition plays an important role for behaviors in social groups and, in particular, for fission-fusion societies such as those observed in some bird species, including the zebra finch (43), and in mammals such as in the African elephant (44). We suggest that these auditory memories for vocalizers are not only important for mate and kin recognition but also to facilitate group dynamics. Studying vocal communication in gregarious bird species should therefore include the role of higher cognitive functions, such as memory, and take into account the species social dynamics. These vocal and perceptual performances can, in turn, be added to the list of cognitive faculties that have been found in social birds, such as episodic spatial memory (22, 45), social cognition (17, 46), number sense (47), or puzzle solving (48), and that rival the cognitive faculties found in social primates (49, 50).

We thank two undergraduate research apprentices, I. Rice and A. Prasad, who helped train and test the animals. We thank L. Johnston and J. Elie for insightful comments on the manuscript and D. Perkel and A. Leblois for contributing the song stimuli. Funding: This research was funded by NIDCD R01 018321 to F.E.T. and an NSF graduate fellowship DGE 1752814 to K.Y. Author contributions: Experimental design: W.E.W. and F.E.T.; investigation: W.E.W., K.Y., and F.E.T.; data analyses and visualizations: K.Y.; writing, original draft: K.Y. and F.E.T.; writing, review and editing: W.E.W., K.Y., and F.E.T. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Data (trial by trial data and stimulus audio files), code used for analysis, and documentation are available on GitHub at https://github.com/theunissenlab/zebra-finch-memory. Additional data related to this paper may be requested from the authors.