Hostname: page-component-7479d7b7d-q6k6v Total loading time: 0 Render date: 2024-07-14T14:27:10.610Z Has data issue: false hasContentIssue false

Feeling signs: motor encoding enhances sign language learning in hearing adults

Published online by Cambridge University Press:  29 April 2024

Laura M. Morett*
University of Missouri
Mathew Cieśla
Northumbria University
Mary E. Bray
University of Alabama
Karen Emmorey
San Diego State University
Corresponding author: Laura M. Morett; Email:
Rights & Permissions [Opens in a new window]


Manual production enhances learning and recall of signs by hearing second language learners; however, the mechanisms enabling this effect are unclear. We examined whether the motor encoding (somatosensory feedback) that occurs during sign production benefits learning and whether it interacts with sign iconicity, which also enhances learning. American Sign Language (ASL) signs varying in iconicity were learned either via production (repetition) with the eyes closed or via observation without production. Signs learned via production were recalled more accurately than signs learned via observation, indicating that motor encoding from manual production enriches the representations of signs. Moreover, the effect of motor encoding interacted with iconicity, suggesting that motor encoding may particularly enhance the recall of signs low in iconicity. Together, these results reveal the importance of somatosensory feedback as a key mechanism underlying the beneficial effect of production on sign learning, demonstrating that feeling one’s own signing promotes learning and recall of signs.

Research Report
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (, which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
© The Author(s), 2024. Published by Cambridge University Press


When hearing individuals learn a sign language, they acquire a second language (L2) in a second modality (M2) (Chen Pichler & Koulidobrova, Reference Chen Pichler, Koulidobrova, Marschark and Spencer2016). Despite growing interest, M2L2 acquisition is understudied relative to spoken L2 acquisition (Schönström, Reference Schönström2021). In addition to broadening the diversity of languages studied within the field of L2 acquisition, research on M2L2 acquisition can provide unique insight into the role of motor encoding in L2 learning and representation. Several studies suggest that deaf signers rely primarily on motor (somatosensory) rather than visual input from their own signing as a means of self-monitoring and error detection (Emmorey, Bosworth, et al., Reference Emmorey, Bosworth and Kraljic2009). Moreover, producing actions and signs similarly improves memory for action phrases and nouns in hearing non-signers and deaf signers (Zimmer & Engelkamp, Reference Zimmer and Engelkamp2003). In addition, there is evidence that relative to observing hand gestures conveying the meanings of words from an unfamiliar spoken L2, producing such hand gestures enhances the learning of these words, suggesting that meaningful motor encoding during learning enriches L2 lexical representations (Garcia-Gamez & Macizo, Reference Garcia-Gamez and Macizo2019; Morett, Reference Morett2018). These findings lead us to hypothesize that motor encoding may play an important role in sign learning during M2L2 acquisition. The current study tests this hypothesis by comparing sign learning via production without visual encoding vs. observation without production, providing insight into how somatosensory feedback enriches representations of M2L2 signs.

Embodied theories of language processing posit that the body plays an integral role in language comprehension, production, and acquisition (e.g., Atkinson, Reference Atkinson2010). Although much of this research has focused on how conceptual representations are grounded in embodied experience (e.g., Dijkstra & Post, Reference Dijkstra and Post2015), some studies have examined how the body’s involvement in language production affects language comprehension. For example, some evidence suggests that speech is comprehended by mentally simulating articulation (D’Ausilio et al., Reference D’Ausilio, Pulvermüller, Salmas, Bufalari, Begliomini and Fadiga2009; Iacoboni, Reference Iacoboni2008; but see Hickok et al., Reference Hickok, Costanzo, Capasso and Miceli2011) and that writing words by hand facilitates their subsequent recognition (Longcamp et al., Reference Longcamp, Boucard, Gilhodes, Anton, Roth, Nazarian and Velay2008). With respect to sign language, evidence suggests that similar to speech, comprehension may entail mentally simulating articulation (Corina & Gutierrez, Reference Corina and Gutierrez2016; Watkins & Thompson, Reference Watkins and Thompson2017; but see Brozdowski & Emmorey, Reference Brozdowski and Emmorey2020) and that producing signs during M2L2 learning enhances their subsequent recall (Morett, Reference Morett2015). Taken together, these findings provide evidence that language comprehension is influenced by motor encoding from language production.

A key mechanism of embodied language processing is iconicity, which facilitates the association of symbols with their referents via form-meaning resemblance (Perniss & Vigliocco, Reference Perniss and Vigliocco2014). Traditionally, the form of language and its underlying representations have been considered largely amodal and abstract, and iconicity has been considered marginal and largely irrelevant to language processing and acquisition (e.g., Newmeyer, Reference Newmeyer1992). Although this view was originally derived from spoken languages, it has been extended to sign languages to affirm their status as languages rather than gestural systems (Klima & Bellugi, Reference Klima and Bellugi1979). Contemporary research with a more nuanced and expansive view of iconicity has refuted the traditional view by providing evidence of iconicity’s pervasiveness in both spoken and signed languages from around the world (Dingemanse et al., Reference Dingemanse, Blasi, Lupyan, Christiansen and Monaghan2015; Perniss et al., Reference Perniss, Thompson and Vigliocco2010). Consistent with this view, iconicity facilitates the acquisition of L1 signs by deaf children from a variety of languages, including American Sign Language (ASL; Caselli & Pyers, Reference Caselli and Pyers2017), British Sign Language (BSL; Thompson et al., Reference Thompson, Vinson, Woll and Vigliocco2012), Turkish Sign Language (Sümer et al., Reference Sümer, Grabitz and Küntay2017), Israeli Sign Language (Novogrodsky & Meir, Reference Novogrodsky and Meir2020), and the acquisition of L2 signs from German Sign Language by hearing children (Goppelt-Kunkel et al., Reference Goppelt-Kunkel, Stroh and Hänel-Faulhaber2023).

For adults, iconic gestures conveying the meanings of words from an unfamiliar L2 facilitate learning (Kelly et al., Reference Kelly, McDevitt and Esch2009; Morett, Reference Morett2014). Moreover, iconic gestures may influence the degree to which M2L2 signs are perceived as iconic, for better or worse. On the one hand, sign-gesture resemblance may facilitate sign-referent association, as evidenced by increased guessability and recall of the meanings of signs that closely resemble gestures used to depict the same concepts. For example, the sign “to cut with scissors” in the Sign Language of the Netherlands resembles a pantomimic gesture (Karadöller et al., Reference Karadöller, Peeters, Manhardt, Özyürek and Ortegain press; Ortega et al., Reference Ortega, Schiefner and Özyürek2019). On the other hand, sign-gesture resemblance may interfere with the phonological representation of signs, as evidenced by the less accurate imitation of iconic signs resembling gestures such as BSL to brush, which resembles (but is not identical to) pantomiming brushing, compared with non-iconic signs with no resemblance to gestures, such as BSL sister, which is articulated by tapping a curled index finger extended from a fist to the nose twice (Ortega & Morgan, Reference Ortega and Morgan2015a, Reference Ortega and Morgan2015b). Thus, iconicity has been proposed to facilitate conceptual-semantic associations in M2L2 learning but interferes with phonological representations (Ortega, Reference Ortega2017). Although iconicity is related to transparency—the ability to infer the meanings of signs based on their form—signs may be considered iconic but may not be transparent. For example, in ASL, ball is articulated by bringing the hands together, depicting the shape of a ball, and is rated as highly iconic, but its meaning is not transparent (easily guessed) for non-signers (Sehyr & Emmorey, Reference Sehyr and Emmorey2019). Thus, inferring the meanings of signs from gestures may not always benefit M2L2 learners. Experience with gestures may be transferred to representations of the phonological forms of similar signs in M2L2 acquisition, just as L1 speech sounds and syntactic structures are transferred to similar counterparts in spoken L2 acquisition (Morett & MacWhinney, Reference Morett and MacWhinney2013; Yang et al., Reference Yang, Chen and Xiao2022). However, gesture-sign transfer may be somewhat limited in early-stage M2L2 learners. For example, a pilot study eliciting signs from hearing non-signers revealed that handshapes used in gestures (e.g., a raised fist with index finger extended to express wait) were sometimes, but not always, transferred to signs with a similar handshape (Chen Pichler, Reference Chen Pichler, Mathur and Napoli2011). Finally, the results of a recent experimental study suggest that although L2 sign iconicity positively affects meaning inference by hearing adult non-signers after 1-2 exposures, similarity to iconic gestures does not (Hofweber et al., Reference Hofweber, Aumônier, Janke, Gullberg and Marshallin press).

In a previous study (Morett, Reference Morett2015), we investigated the impacts of embodied action and iconicity on M2L2 sign learning by showing videos of signs from ASL that were high or low in iconicity and then prompting the production of either the signs or meaningless hand movements. We found that the production of signs during learning led to more accurate subsequent recall of learned signs than the production of meaningless hand movements. This finding is consistent with the encoding specificity principle (Tulving & Thompson, Reference Tulving and Thompson1973), which posits that, relative to encoding contexts mismatching retrieval contexts, encoding contexts matching retrieval contexts facilitate recall. Furthermore, iconic signs were recalled more accurately than non-iconic signs, particularly following longer delays; however, this effect was not influenced by the meaningfulness of the embodied action (production of signs vs. meaningless hand movements). Although these findings suggest that motor encoding from sign production may facilitate M2L2 learning, visual encoding also occurred during sign production and may have affected learning. Moreover, although there was no evidence of an interaction between learning condition and iconicity, categorical variation in iconicity and a limited sample size (n = 26) may have precluded its detection. If such an interaction exists, the effects of iconicity and motor encoding may be (super-) additive, with motor encoding amplifying the effect of iconicity on M2L2 sign learning. Another possibility is that the effect of motor encoding may be compensatory, such that it is larger for more difficult-to-learn signs low in iconicity than for easier-to-learn signs high in iconicity. Alternatively, the effects of motor encoding and iconicity may not interact, indicating their independence. Thus, additional investigation is needed to understand how motor encoding affects M2L2 sign learning and whether—and how—it interacts with iconicity within this context.

In the current study, we manipulated motor encoding during learning by presenting ASL signs that varied continuously in iconicity (using ratings from the ASL-LEX database; Caselli et al., Reference Caselli, Sehyr, Cohen-Goldberg and Emmorey2017; Sehyr et al., Reference Sehyr, Caselli, Cohen-Goldberg and Emmorey2021), and then either prompting the repetition of these signs with the eyes closed (production condition) or presenting the signs for an additional time without production (observation condition). In the latter condition, we presented videos of the signs produced by the model rather than asking participants to imagine themselves signing as there was no way to verify whether participants complied with the instructions or whether the imagined signs were correct. Because our goal was to disentangle the impact of motor encoding from that of visual encoding, we did not include a condition incorporating both motor and visual encoding in the current study. We probed the recall of learned signs five minutes and one week after learning by asking learners to produce the signs given their English translation. On the basis of our previous finding that production during learning facilitates subsequent recall of learned ASL signs (Morett, Reference Morett2015), we predicted that, relative to the observation condition, motor encoding without visual encoding would enhance subsequent recall of learned signs. Moreover, on the basis of previous work (e.g., Campbell et al., Reference Campbell, Martin and White1992), we predicted THAT iconicity would enhance sign recall, such that more iconic signs would be recalled more accurately than less iconic signs. Finally, we predicted that motor encoding would interact with iconicity, with the results distinguishing between a larger effect of motor encoding on the recall of more vs. less iconic signs.



Fifty-two participants enrolled in a university in the southeastern US volunteered to participate in this experiment in return for partial course credit or pro bono. Nine were excluded due to technical issues (e.g., experimental control script crashing), one was excluded due to failure to follow instructions, and one was excluded due to correctly responding to less than half of the catch trials, yielding a final sample of 41 participants (4 males, 37 females; age: M = 19.28, SD = 1.32). This sample size exceeds by three the minimum sample size of 38 participants computed via a between-factors repeated measures power analysis based on Morett (Reference Morett2015) with 85% power to detect f =. 436 with α =. 05. All participants were L1 English speakers, with three reporting knowledge of a spoken second language. Thirty-three participants reported no knowledge of a sign language and eight reported minimal exposure to or knowledge of ASL (e.g., a few signs or the fingerspelled alphabet).Footnote 1 This experiment was approved by the institutional review board of the university at which data were collected, and informed consent was obtained from all participants.


Prior to the first session of the experiment, the participants completed a pre-experimental questionnaire that elicited information about their demographics (e.g., age and gender) and exposure to and proficiency in signed and spoken L2s. Following the first session of the experiment, the participants completed a post-experimental questionnaire that asked them to report their previous knowledge of any signs learned in the experiment and whether they accurately followed the instructions.


Twenty ASL sign videos representing common nouns were selected from the ASL-LEX 2.0 database (Sehyr et al., Reference Sehyr, Caselli, Cohen-Goldberg and Emmorey2021; see Appendix A). The iconicity ratings from ASL-LEX were provided by 21 to 37 hearing English L1 non-signers and on the basis of a Likert scale ranging from one (not iconic) to seven (highly iconic) (for details see Caselli et al., Reference Caselli, Sehyr, Cohen-Goldberg and Emmorey2017). To ensure an even distribution of iconicity ratings across the 20 signs, the signs were chosen within three-tenth increments.

Twenty English words representing common nouns were selected for inclusion as catchwords during the learning phase of the first session of the experiment (see Appendix B). Catchwords were selected based on their comparable frequency and weak semantic relationship to the English translations of the signs learned in the experiment.


This experiment was conducted using the Zoom videoconferencing application. Stimuli were presented via screen sharing using PsychoPy (Peirce, Reference Peirce2022; Peirce et al., Reference Peirce, Gray, Simpson, MacAskill, Höchenberger, Sogo, Kastman and Lindeløv2019). Two experimental sessions were administered one week apart. In the first session, participants first learned the signs, and recall was assessed after five minutes. The second session assessed the recall of signs learned in the first session after one week. The experimenter monitored participants throughout both sessions to ensure that instructions were followed.

The first session consisted of a learning phase and a testing phase. The learning phase consisted of three blocks, during which all the signs to be learned were presented in random order. At the beginning of each learning block, the participants were informed that twenty ASL signs would be presented and that they should try to learn them as best they could. In each trial, a fixation cross appeared for one second, followed by a video of the sign with its English translation underneath. This was followed by a five-second delay to assist in the learning process (Kapnoula & Samuel, Reference Kapnoula and Samuel2022). Participants were then instructed either to close their eyes and produce the sign they just saw (production condition) or to be presented with the sign video again without its English translation (observation condition; see Figure 1). This process was repeated for all twenty signs in the set, with the order of presentation randomized in each block for each participant. To eliminate the possible transfer of production to the observation condition, the learning condition was varied between participants such that they learned all signs in either the production condition (n = 19) or the observation condition (n = 22). To ensure that participants were attending to the experimental task, they were asked to indicate whether they had seen a specific English word in catch trials occurring at four random intervals in each block during the learning phase. This word was either the translation of a sign they had seen or a catchword that they had not seen. After the learning phase, the participants were given a five-minute break before proceeding to the testing phase.

Figure 1. Sample learning trials from (A) production and (B) observation conditions.

The testing phase comprised a sign recall task that tested memory for learned signs via forward translation (English to ASL; see Figure 2). The on-screen instructions stated that English translations of the signs would be presented as text and that participants should produce the corresponding signs as accurately as possible. If a participant did not remember a sign, they were instructed to say so. Knowledge of all twenty ASL signs presented in the preceding learning phase was assessed, and the order of presentation was randomized for each participant. The first session of the experiment lasted approximately thirty minutes.

Figure 2. Sample sign recall trial.

The second session (one week later) consisted only of the testing phase. All twenty ASL signs learned in the learning phase of the first session were again tested via the sign recall task. The order of presentation was randomized for each participant. The second session of the experiment lasted approximately fifteen minutes.

Sign recall coding

Sign recall accuracy in the testing phase was coded using three categories: correct (2), partially correct (1), and incorrect (0). ASL signs for which all three phonological parameters (handshape, movement, and location) were produced correctly were coded as correct. Signs for which two of the three phonological parameters were produced correctly (e.g., handshape and location, but not movement) were coded as partially correct. Signs that were forgotten or for which one or zero phonological parameters were produced correctly were coded as incorrect. If a sign was self-corrected, the final production was coded.

The primary coder (a hearing non-signer), who was not involved in data collection and was blind to the learning condition, coded all signs produced by the participants. To establish interrater reliability, a second coder (also a hearing non-signer), who was blind to the learning condition, independently coded all signs produced by eleven randomly selected participants (26.83% of the data). The agreement between the primary and secondary coders was 81%, indicating very good agreement.

Data analysis

Prior to analysis, any signs that participants reported knowing were excluded for those participants (eight observations), as were signs that participants in the production condition reported watching themselves produce without experimenter knowledge (six observations) and that participants in the observation condition reported producing without experimenter knowledge (two observations). Following these exclusions, sign recall in the testing phase was analyzed in R using a linear mixed effect model via the lme4 package (Bates et al., Reference Bates, Mächler, Bolker and Walker2015), and p-values were obtained using the lmerTest package (Kuznetsova et al., Reference Kuznetsova, Brockhoff and Christensen2017). Iconicity was grand mean-centered, and categorical fixed effects were coded using weighted mean-centered (Helmert) contrast coding, with the level mentioned first for each factor coded as negative and the level mentioned second coded as positive. The model included fixed effects of learning conditions (observation, production), delay (five minutes, one week), iconicity, and random effects of participant and sign. A data-driven approach to model selection was used such that the maximal random effect structure permitting convergence was used to reduce Type 1 error rates (Barr et al., Reference Barr, Levy, Scheepers and Tily2013). Models with random slopes and intercepts were tested to determine whether they converged. If multiple models converged, an ANOVA was conducted to determine whether the model with random slopes accounted for significantly more variance than the model with random intercepts only, and if so, the model with random slopes was reported. De-identified data and analysis scripts are publicly available via the following link:


Table 1 displays parameter estimates for the model for sign recall, and Figure 3 displays sign recall by learning condition, delay, and iconicity. Critically, the main effect of the learning condition was significant, indicating that signs learned via production (M = 1.55, SD = 0.69) were recalled more accurately than signs learned via observation (M = 1.21, SD = 0.81). In addition, there was a significant main effect of delay, indicating a more accurate recall of signs after five minutes (M = 1.47, SD = 0.71) than one week (M = 1.19, SD = 0.83), and a significant main effect of iconicity, indicating that more iconic signs were recalled more accurately than less iconic signs. The interaction between learning condition and iconicity was significant, indicating that as iconicity decreased, the facilitatory effect of production increased. Finally, the interaction between delay and iconicity was significant, indicating that iconicity had a larger effect on sign recall after one week than after five minutes. Consistent with our hypothesis, these results indicate that motor encoding from production facilitates M2L2 sign recall and that this facilitatory effect may be particularly powerful for low iconicity signs.

Table 1. Fixed effect (top) and variance estimates (bottom) for the multi-level model for sign recall

Figure 3. Sign recall by learning condition, delay, and iconicity.


This study sought to reveal the impact of motor encoding (somatosensory feedback) and the extent to which it interacts with iconicity on M2L2 sign learning. We compared recall accuracy for signs that varied in iconicity when they were learned via motor encoding in an eyes-closed production condition with recall accuracy for signs learned via visual encoding in a sign observation condition. The results show that signs learned in the production condition were recalled more accurately than those learned in the observation condition, providing evidence that motor encoding from production enhances M2L2 sign learning more effectively than visual encoding from observation. Furthermore, the results indicate that signs low in iconicity were recalled more accurately when they were learned in the production condition than in the observation condition, suggesting that motor encoding from production helps compensate for low iconicity. These results are the first to show that motor encoding facilitates M2L2 sign learning in the absence of visual feedback and that motor encoding interacts with iconicity, highlighting the importance of motor encoding as a key mechanism underlying the effect of production on M2L2 sign learning.

Notably, the findings indicate that feeling oneself producing M2L2 signs enhances learning and subsequent recall to a greater extent than seeing someone else produce signs. This result is consistent with previous research showing that producing signs enhances recall of nouns and action phrases similarly to producing actions associated with them (Zimmer & Engelkamp, Reference Zimmer and Engelkamp2003) and that producing hand gestures conveying the meanings of words from an unfamiliar spoken L2 enhances learning more than observing someone else produce such hand gestures (Garcia-Gamez & Macizo, Reference Garcia-Gamez and Macizo2019; Morett, Reference Morett2018). In both this previous work and the current work, production is executed by oneself, whereas observation involves watching another person’s production. Although we did not include a condition in which participants imagined themselves producing signs or probe whether participants imagined signs prior to production, future work examining the impact of imagining oneself signing may shed light on its impact on M2L2 sign learning. In addition, because of concerns about reliability and demand characteristics, participants were not explicitly instructed to refrain from producing signs between the first and second sessions and were not asked whether they had done so. Therefore, representations of signs may have been strengthened via production between the first and second sessions, further reinforcing the match between the encoding and retrieval contexts, both of which entailed production. Because participants closed their eyes when producing signs, they did not observe themselves signing, suggesting that the beneficial effect of production on M2L2 sign learning may be due to somatosensory feedback rather than visual feedback. This is consistent with previous work showing that deaf signers use somatosensory feedback rather than visual feedback from their own signing to self-monitor and detect errors (Emmorey, Bosworth, et al., Reference Emmorey, Bosworth and Kraljic2009). Compared with visual encoding from sign observation, motor encoding may strengthen motoric memory traces for signs, resulting in more accurate sign production during recall. Alternatively, compared with visual encoding, motor encoding during learning may increase the efficiency of sign recall tested via production due to a practice effect, resulting in greater automaticity of sign production. Future research can differentiate between these alternatives by testing memory for signs via recognition in addition to production and by measuring latency in addition to accuracy, providing additional insight into why motor feedback enhances M2L2 sign learning.

As in previous research on M2L2 sign learning (e.g., Campbell et al., Reference Campbell, Martin and White1992), more iconic signs were recalled with greater accuracy than less iconic signs, and the facilitatory effect of iconicity increased over time. This finding complements evidence that iconic gestures facilitate word learning in an unfamiliar spoken L2, particularly over extended delays (Morett, Reference Morett2014). Thus, the facilitatory effect of iconicity applies to signed and spoken L2 learning. Indeed, more iconic signs may have been more easily guessed than less iconic signs because of the greater resemblance between their physical form and semantic features of their referents, such as iconic gestures. Although sign-gesture resemblance was not examined in the current study, many signs with high iconicity ratings resembled gestures used to convey their referents. Therefore, experience with these gestures may have facilitated sign-referent associations (Ortega et al., Reference Ortega, Schiefner and Özyürek2019) and supported representations of M2L2 signs rather than undermining them, as observed in previous research (Ortega & Morgan, Reference Ortega and Morgan2015a, Reference Ortega and Morgan2015b). Whereas previous research demonstrating gesture-sign interference examined immediate sign imitation, the current study examined sign recall in response to English translations following a delay. Furthermore, we coded sign recall accuracy categorically rather than granularly with respect to phonological parameters. In the context of previous research, the positive relationship between iconicity and delayed recall suggests that iconic gestures serve as a firm but imperfect foundation for M2L2 sign representations, which may increase in accuracy with consolidation over time.

The interaction between motor encoding and iconicity suggests that somatosensory feedback from sign production during learning may facilitate M2L2 acquisition of signs low in iconicity by strengthening their motoric memory. In doing so, somatosensory feedback may help compensate for the difficulty in associating low iconicity signs with their English translations, thereby improving recall. Due to the weakness of this interaction relative to other effects observed in the current study and its absence in previous research (Morett, Reference Morett2015), it will be important to gauge its robustness by determining the extent to which it can be replicated in future research. One way to do so would be to examine memory for M2L2 signs varying in iconicity learned with and without motor encoding via a forced-choice recognition task in which target signs differ minimally from distractor signs in form. This would provide insight into whether the effects of motor encoding, iconicity, and their interaction on M2L2 sign learning observed in the current study generalize to sign recognition based on fine-grained phonological features or are constrained to sign recall based on semantic representations assessed by production.

In conclusion, the results of the current study provide the first evidence that motor encoding from production promotes M2L2 sign recall. Furthermore, it provides the first evidence of an interaction between motor encoding and iconicity, suggesting that motor encoding may compensate for low iconicity in the context of M2L2 sign learning. These findings may be explained by motor encoding, which strengthens motoric memory traces for signs—particularly signs low in iconicity, enhancing their association with referents. These findings have practical implications for M2L2 sign learning, highlighting the benefits of sign production even when signs cannot be observed, particularly for signs low in iconicity. Overall, the results reveal the importance of motor encoding as a key mechanism underlying the effect of production on M2L2 sign learning, demonstrating that feeling signs promote learning and recall.

Open practices statement

Data and analysis code are available at the Open Science Framework via the following link: The sign videos are available in the ASL-LEX database via the following link: The experiments reported here were not preregistered.


This research was funded by a Women in Cognitive Science Networking Award to L.M.M. The authors thank Shelby Ballard, Carlee Brandon, Nate Bischoff, Ashton Haimann, Ty Martin, and AJ Oliff for their assistance with data collection and the attendees of Psychonomics 2022 and 2023 for helpful feedback.

Competing interest

The authors declare none.

Appendix A

ASL Signs Presented in Learning Phase with Non-Signer Iconicity Ratings

Appendix B

English Catch Words Presented in Learning Phase






















1 To address concerns about whether minimal exposure to and knowledge of ASL was a confounding factor, we conducted an alternate analysis excluding all such participants. Despite the sample size falling below the minimum needed for sensitivity to detect interactions, all the main effects and interactions remained significant, except for the interaction between learning condition and iconicity.


Atkinson, D. (2010). Extended, embodied cognition and second language acquisition. Applied Linguistics, 31(5), 599622. Scholar
Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68(3), 255278. ScholarPubMed
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67, 148. Scholar
Brozdowski, C., & Emmorey, K. (2020). Shadowing in the manual modality. Acta Psychologica, 208, 103092. ScholarPubMed
Campbell, R., Martin, P., & White, T. (1992). Forced choice recognition of sign in novice learners of British Sign Language. Applied Linguistics, 13, 185201. Scholar
Caselli, N. K., & Pyers, J. E. (2017). The road to language learning is not entirely iconic: Iconicity, neighborhood density, and frequency facilitate acquisition of sign language. Psychological Science, 28(7), 979987.10.1177/0956797617700498CrossRefGoogle Scholar
Caselli, N. K., Sehyr, Z. S., Cohen-Goldberg, A. M., & Emmorey, K. (2017). ASL-LEX: A lexical database of American Sign Language. Behavior Research Methods, 49(2), 784801. ScholarPubMed
Chen Pichler, D. (2011). Sources of handshape error in first-time signers of ASL. In Mathur, G. & Napoli, D. J. (Eds.), Deaf around the world: The impact of language (pp. 96121). Oxford University Press.Google Scholar
Chen Pichler, D., & Koulidobrova, H. (2016). Acquisition of sign language as a second language. In Marschark, M. & Spencer, P. E. (Eds.), The Oxford handbook of deaf studies in language (pp. 218230). Oxford University Press.Google Scholar
Corina, D. P., & Gutierrez, E. (2016). Embodiment and American Sign Language: Exploring sensory-motor influences in the recognition of American Sign Language. Gesture, 15(3), 291305. Scholar
D’Ausilio, A., Pulvermüller, F., Salmas, P., Bufalari, I., Begliomini, C., & Fadiga, L. (2009). The motor somatotopy of speech perception. Current Biology, 19(5), 381385. ScholarPubMed
Dijkstra, K., & Post, L. (2015). Mechanisms of embodiment. Frontiers in Psychology, 6, 1525. ScholarPubMed
Dingemanse, M., Blasi, D. E., Lupyan, G., Christiansen, M. H., & Monaghan, P. (2015). Arbitrariness, iconicity, and systematicity in language. Trends in Cognitive Sciences, 19(10), 603615. ScholarPubMed
Emmorey, K., Bosworth, R., & Kraljic, T. (2009). Visual feedback and self-monitoring of sign language. Journal of Memory and Language, 61(3), 398411. ScholarPubMed
Garcia-Gamez, A. B., & Macizo, P. (2019). Learning nouns and verbs in a foreign language: The role of gestures. Applied Psycholinguistics, 40(2), 473507. Scholar
Goppelt-Kunkel, M., Stroh, A.-L., & Hänel-Faulhaber, B. (2023). Sign learning of hearing children in inclusive day care centers—Does iconicity matter? Frontiers in Psychology, 14. 10.3389/fpsyg.2023.1196114CrossRefGoogle ScholarPubMed
Hickok, G., Costanzo, M., Capasso, R., & Miceli, G. (2011). The role of Broca’s area in speech perception: Evidence from aphasia revisited. Brain and Language, 119(3), 214220. ScholarPubMed
Hofweber, J., Aumônier, L., Janke, V., Gullberg, M., & Marshall, C. (in press). Which aspects of visual motivation aid the implicit learning of signs at first exposure? Language Learning. Scholar
Iacoboni, M. (2008). The role of premotor cortex in speech perception: Evidence from fMRI and rTMS. Journal of Physiology-Paris, 102(1–3), 3134. ScholarPubMed
Kapnoula, E. C., & Samuel, A. G. (2022). Reconciling the contradictory effects of production on word learning: Production may help at first, but it hurts later. Journal of Experimental Psychology: Learning, Memory, and Cognition, 48(3), 394. ScholarPubMed
Karadöller, D. Z., Peeters, D., Manhardt, F., Özyürek, A., & Ortega, G. (in press). Iconicity and gesture jointly facilitate learning of L2 signs at first exposure in hearing non-signers. Language Learning.Google Scholar
Kelly, S. D., McDevitt, T., & Esch, M. (2009). Brief training with co-speech gesture lends a hand to word learning in a foreign language. Language and Cognitive Processes, 24(2), 313334. Scholar
Klima, E. S., & Bellugi, U. (1979). The signs of language. Harvard University Press.Google Scholar
Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmerTest package: Tests in linear mixed effects models. Journal of Statistical Software, 82, 126. Scholar
Longcamp, M., Boucard, C., Gilhodes, J.-C., Anton, J.-L., Roth, M., Nazarian, B., & Velay, J.-L. (2008). Learning through hand-or typewriting influences visual recognition of new graphic shapes: Behavioral and functional imaging evidence. Journal of Cognitive Neuroscience, 20(5), 802815. ScholarPubMed
Morett, L. M. (2014). When hands speak louder than words: The role of gesture in the communication, encoding, and recall of words in a novel second language. The Modern Language Journal, 98(3), 834853. Scholar
Morett, L. M. (2015). Lending a hand to signed language acquisition: Enactment and iconicity enhance sign recall in hearing adult American Sign Language learners. Journal of Cognitive Psychology, 27(3), 251276. Scholar
Morett, L. M. (2018). In hand and in mind: Effects of gesture production and viewing on second language word learning. Applied Psycholinguistics, 39(2), 355381. Scholar
Morett, L. M., & MacWhinney, B. (2013). Syntactic transfer in English-speaking Spanish learners. Bilingualism: Language and Cognition, 16(1), 132151. Scholar
Newmeyer, F. J. (1992). Iconicity and generative grammar. Language, 68(4), 756796. Scholar
Novogrodsky, R., & Meir, N. (2020). Age, frequency, and iconicity in early sign language acquisition: Evidence from the Israeli Sign Language MacArthur–Bates Communicative Developmental Inventory. Applied Psycholinguistics, 41(4), 817845. Scholar
Ortega, G. (2017). Iconicity and sign lexical acquisition: A review. Frontiers in Psychology, 8, 1280. ScholarPubMed
Ortega, G., & Morgan, G. (2015a). Input processing at first exposure to a sign language. Second Language Research, 31(4), 443463. Scholar
Ortega, G., & Morgan, G. (2015b). Phonological development in hearing learners of a sign language: The influence of phonological parameters, sign complexity, and iconicity. Language Learning, 65(3), 660688. Scholar
Ortega, G., Schiefner, A., & Özyürek, A. (2019). Hearing non-signers use their gestures to predict iconic form-meaning mappings at first exposure to signs. Cognition, 191, 103996. ScholarPubMed
Peirce, J. (2022). PsychoPy (1.4) [Computer software]. Open Science Tools Ltd.Google Scholar
Peirce, J., Gray, J. R., Simpson, S., MacAskill, M., Höchenberger, R., Sogo, H., Kastman, E., & Lindeløv, J. K. (2019). PsychoPy2: Experiments in behavior made easy. Behavior Research Methods, 51, 195203. ScholarPubMed
Perniss, P., Thompson, R. L., & Vigliocco, G. (2010). Iconicity as a general property of language: Evidence from spoken and signed languages. Frontiers in Psychology, 1, 227. ScholarPubMed
Perniss, P., & Vigliocco, G. (2014). The bridge of iconicity: From a world of experience to the experience of language. Phil. Trans. R. Soc. B, 369(1651), 20130300. Scholar
Schönström, K. (2021). Sign languages and second language acquisition research: An introduction (1). The Journal of Euro SLA, 5(1), 30–43. Article 1. Scholar
Sehyr, Z. S., Caselli, N., Cohen-Goldberg, A. M., & Emmorey, K. (2021). The ASL-LEX 2.0 Project: A database of lexical and phonological properties for 2,723 signs in American Sign Language. The Journal of Deaf Studies and Deaf Education, 26(2), 263277. Scholar
Sehyr, Z. S., & Emmorey, K. (2019). The perceived mapping between form and meaning in American Sign Language depends on linguistic knowledge and task: Evidence from iconicity and transparency judgments. Language and Cognition, 11(2), 208234. ScholarPubMed
Sümer, B., Grabitz, C., & Küntay, A. (2017). Early produced signs are iconic: Evidence from Turkish Sign Language. The 39th Annual Conference of the Cognitive Science Society (CogSci 2017), 32733278. Scholar
Thompson, R. L., Vinson, D. P., Woll, B., & Vigliocco, G. (2012). The road to language learning is iconic: Evidence from British Sign Language. Psychological Science, 23, 14431448. ScholarPubMed
Tulving, E., & Thompson, D. M. (1973). Encoding specificity and retrieval processes in episodic memory. Psychological Review, 80(5), 352373. Scholar
Watkins, F., & Thompson, R. L. (2017). The relationship between sign production and sign comprehension: What handedness reveals. Cognition, 164, 144149. ScholarPubMed
Yang, Y., Chen, X., & Xiao, Q. (2022). Cross-linguistic similarity in L2 speech learning: Evidence from the acquisition of Russian stop contrasts by Mandarin speakers. Second Language Research, 38(1), 329. Scholar
Zimmer, H. D., & Engelkamp, J. (2003). Signing enhances memory like performing actions. Psychonomic Bulletin & Review, 10(2), 450454. ScholarPubMed
Figure 0

Figure 1. Sample learning trials from (A) production and (B) observation conditions.

Figure 1

Figure 2. Sample sign recall trial.

Figure 2

Table 1. Fixed effect (top) and variance estimates (bottom) for the multi-level model for sign recall

Figure 3

Figure 3. Sign recall by learning condition, delay, and iconicity.