Original: E MERGENT C OMMUNICATION FOR U NDERSTANDING H UMAN L ANGUAGE E VOLUTION : W HAT ’ S M ISSING ?
Revised: E MERGENT C OMMUNICATION FOR U NDERSTANDING H UMAN L ANGUAGE E VOLUTION : W HAT ’ S M ISSING ?

Original: Anonymous authors

Original: Paper under double-blind review

Original: A BSTRACT
Revised: A BSTRACT

Original: Emergent communication protocols among humans and artiﬁcial neural network agents do not yet share the same properties and show some critical mismatches in results.
Revised: Emergent communication protocols among humans and artiﬁcial neural network agents do not yet share the same properties and show some critical mismatches in results.

Original: We describe three important phenomena with respect to the emergence and beneﬁts of compositionality: ease-of-learning, generalization, and group size effects (i e., larger groups create more systematic languages).
Revised: We describe three important phenomena with respect to the emergence and beneﬁts of compositionality: ease-of-learning, generalization, and group size effects (i.e., larger groups create more systematic languages).

Original: The latter two are not fully replicated with neural agents, which hinders the use of neural emergent communication for language evolution research.
Revised: The latter two are not fully replicated with neural agents, which hinders the use of neural emergent communication for language evolution research.

Original: We argue that one possible reason for these mismatches is that key cognitive and communicative constraints of humans are not yet integrated.
Revised: We argue that one possible reason for these mismatches is that key cognitive and communicative constraints of humans are not yet integrated.

Original: Speciﬁcally, in humans, memory constraints and alternating between the roles of speaker and listener underlie the emergence of linguistic structure, yet these constraints are typically absent in neural simulations.
Revised: Speciﬁcally, in humans, memory constraints and alternating between the roles of speaker and listener underlie the emergence of linguistic structure, yet these constraints are typically absent in neural simulations.

Original: We suggest that introducing such communicative and cognitive constraints would promote more linguistically plausible behaviors with neural agents.
Revised: We suggest that introducing such communicative and cognitive constraints would promote more linguistically plausible behaviors with neural agents.

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: Emergent communication has been widely studied in deep learning (Lazaridou & Baroni, 2020), and in language evolution (Selten & Warglien, 2007; Winters et al., 2015; Raviv et al., 2019).
Revised: Emergent communication has been widely studied in deep learning (Lazaridou & Baroni, 2020), and in language evolution (Selten & Warglien, 2007; Winters et al., 2015; Raviv et al., 2019).

Original: Both ﬁelds share communication games as a common experimental framework: a speaker describes an input, e. g., an object or a scene, and transmits a message to a listener, which has to guess or reconstruct the speaker’s input.
Revised: Both ﬁelds share communication games as a common experimental framework: a speaker describes an input, e. g., an object or a scene, and transmits a message to a listener, which has to guess or reconstruct the speaker’s input.

Original: However, the languages of artiﬁcial neural network agents (neural agents) do not always exhibit the same linguistic properties as human languages.
Revised: However, the languages of artiﬁcial neural network agents (neural agents) do not always exhibit the same linguistic properties as human languages.

Original: This presents a problem for using emergent communication as a model for language evolution of humans (or animals (Prat, 2019)).
Revised: This presents a problem for using emergent communication as a model for language evolution of humans (or animals (Prat, 2019)).

Original: Here, we emphasize three important phenomena in human language evolution (described in detail in
Revised: Here, we emphasize three important phenomena in human language evolution (described in detail in

Original: Section 2) that relate to the emergence of compositional structure—all of which have been discussed theoretically and conﬁrmed experimentally with humans.
Revised: Section 2) that relate to the emergence of compositional structure—all of which have been discussed theoretically and conﬁrmed experimentally with humans.

Original: First, compositional languages are easier to learn (Kirby et al., 2014; Carr et al., 2017; Raviv et al., 2021).
Revised: First, compositional languages are easier to learn (Kirby et al., 2014; Carr et al., 2017; Raviv et al., 2021).

Original: Second, more compositionality allows for better generalization and facilitates convergence between strangers (Wray & Grace, 2007;
Revised: Second, more compositionality allows for better generalization and facilitates convergence between strangers (Wray & Grace, 2007;

Original: Raviv et al., 2021).
Revised: Raviv et al., 2021).

Original: Third, larger populations typically develop more structured languages (Lupyan & Dale, 2010; Raviv et al., 2019).
Revised: Third, larger populations typically develop more structured languages (Lupyan & Dale, 2010; Raviv et al., 2019).

Original: In emergent communication among neural agents, however, two of the three phenomena are not yet replicated (see Section 3).
Revised: In emergent communication among neural agents, however, two of the three phenomena are not yet replicated (see Section 3).

Original: While the ease-of-learning effect for compositional structure has been conﬁrmed in multiple experiments (Li & Bowling, 2019; Guo et al., 2019; Chaabouni et al., 2020), recent work has shown that compositional structure is not necessary for generalization (Lazaridou et al., 2018; Chaabouni et al., 2020).
Revised: While the ease-of-learning effect for compositional structure has been conﬁrmed in multiple experiments (Li & Bowling, 2019; Guo et al., 2019; Chaabouni et al., 2020), recent work has shown that compositional structure is not necessary for generalization (Lazaridou et al., 2018; Chaabouni et al., 2020).

Original: As for the group size effect, this could so far only be conﬁrmed with continuous communication channels (Tieleman et al., 2019).
Revised: As for the group size effect, this could so far only be conﬁrmed with continuous communication channels (Tieleman et al., 2019).

Original: With discrete communication, an increase in group size does not lead to the emergence of more compositional languages (Chaabouni et al., 2022).
Revised: With discrete communication, an increase in group size does not lead to the emergence of more compositional languages (Chaabouni et al., 2022).

Original: Only recently, Rita et al.
Revised: Only recently, Rita et al.

Original: (2022) have shown that the group size effect can be partially recovered by explicitly modeling population heterogeneity.
Revised: (2022) have shown that the group size effect can be partially recovered by explicitly modeling population heterogeneity.

Original: We propose two potential explanations for the striking mismatches between humans and neural agents.
Revised: We propose two potential explanations for the striking mismatches between humans and neural agents.

Original: We argue that emergent communication simulations with neural networks typically lack two key communicative and cognitive factors that drive the emergence of compositionality in humans, and whose omission essentially eliminates the beneﬁts of compositionality: memory constraints, and alternating roles.
Revised: We argue that emergent communication simulations with neural networks typically lack two key communicative and cognitive factors that drive the emergence of compositionality in humans, and whose omission essentially eliminates the beneﬁts of compositionality: memory constraints, and alternating roles.

Original: These are described in detail in Section 4, along with suggestions on how to tackle them, namely, limiting model capacity and sharing parameters within single agents.
Revised: These are described in detail in Section 4, along with suggestions on how to tackle them, namely, limiting model capacity and sharing parameters within single agents.

Original:  I MPORTANT P ROPERTIES OF H UMAN L ANGUAGES
Revised:  I MPORTANT P ROPERTIES OF H UMAN L ANGUAGES

Original: Two of the most fundamental properties of human languages are 1.
Revised: Two of the most fundamental properties of human languages are 1.

Original: a consistent form-to-meaning mapping and 2.
Revised: a consistent form-to-meaning mapping and 2.

Original: a compositional structure (Hockett, 1960).
Revised: a compositional structure (Hockett, 1960).

Original: Previous work suggests that compositional structure evolves as the trade-off between a compressability pressure (i.e., languages should be as simple and learnable as possible) and an expressivity pressure (i.e., languages should be able to successfully disambiguate between a variety of meanings)
Revised: Previous work suggests that compositional structure evolves as the trade-off between a compressability pressure (i.e., languages should be as simple and learnable as possible) and an expressivity pressure (i.e., languages should be able to successfully disambiguate between a variety of meanings)

Original: (Kirby et al., 2015).
Revised: (Kirby et al., 2015).

Original: Without a compressibility pressure, languages would become completely holistic (that is, with a unique symbol for each meaning) - which is highly expressive but poorly compressed.
Revised: Without a compressibility pressure, languages would become completely holistic (that is, with a unique symbol for each meaning) - which is highly expressive but poorly compressed.

Original: Without an expressivity pressure, languages would become degenerate (that is, comprised of a single symbol) - which is highly compressed but not expressive.
Revised: Without an expressivity pressure, languages would become degenerate (that is, comprised of a single symbol) - which is highly compressed but not expressive.

Original: Yet with both pressures present, as in the case of natural languages, structured languages with compositionality would emerge -as these strike a balance between compressibility and expressivity.
Revised: Yet, with both pressures present, as in the case of natural languages, structured languages with compositionality would emerge — as these strike a balance between compressibility and expressivity.

Original: Notably, three phenomena related to compositionality have been demostrated and discussed with humans:
Revised: Notably, three phenomena related to compositionality have been demonstrated and discussed with humans:

Original: Compositional Languages are easier to learn.
Revised: Compositional Languages are easier to learn.

Original: The driving force behind a compressibility pressure is that languages have to be transmitted to, learned by, and used by multiple individuals, often from limited input and under a limited exposure time (Smith et al., 2003).
Revised: The driving force behind a compressibility pressure is that languages must be transmitted to, learned by, and used by multiple individuals, often from limited input and with a limited exposure time (Smith et al., 2003).

Original: Numerous iterated learning studies have shown that artiﬁcial languages become easier to learn over time (Kirby et al., 2008;
Revised: Numerous iterated learning studies have shown that artiﬁcial languages become easier to learn over time (Kirby et al., 2008;

Original: Winters et al., 2015; Beckner et al., 2017) - a ﬁnding that is attributed to a simultaneous increase in compositionality.
Revised: Winters et al., 2015; Beckner et al., 2017) — a ﬁnding that is attributed to a simultaneous increase in compositionality.

Original: Indeed, artiﬁcial language learning experiments directly conﬁrm that more compositional languages are learned better and faster by adults (Raviv et al., 2021).
Revised: Indeed, artiﬁcial language learning experiments directly conﬁrm that more compositional languages are learned better and faster by adults (Raviv et al., 2021).

Original: Compositionality aids Generalization.
Revised: Compositionality aids Generalization.

Original: When a language exhibits a compositional structure, it is easier to generalize (describe new meanings) in a way that is transparent and understandable to other speakers (Kirby, 2002; Zuidema, 2002).
Revised: When a language exhibits a compositional structure, it is easier to generalize (describe new meanings) in a way that is transparent and understandable to other speakers (Kirby, 2002; Zuidema, 2002).

Original: For instance, the need to communicate over a growing number of different items in an open-ended meaning space promotes the emergence of more compositional systems Carr et al.
Revised: For example, the need to communicate over a growing number of different items in an open-ended meaning space promotes the emergence of more compositional systems Carr et al.

Original: Recently, Raviv et al.
Revised: Recently, Raviv et al.

Original: (2021) showed that compositional structure predicts generalization performance, with compositional languages allowing for better generalizations that are also shared across different individuals .The
Revised: (2021) showed that compositional structure predicts generalization performance, with compositional languages allowing for better generalizations that are also shared across different individuals .The

Original: rationale is that humans cannot remember a holistic language (compressability pressure), and when they need to invent descriptions for new meanings, compositionality enables systematic and transparent composition of new label-meaning pairs from existing part-labels.
Revised: rationale is that humans cannot remember a holistic language (compressability pressure), and when they need to invent descriptions for new meanings, compositionality enables systematic and transparent composition of new label-meaning pairs from existing part-labels.

Original: Larger groups create more compositional languages.
Revised: Larger groups create more compositional languages.

Original: It has long been assumed that sociodemographic factors such as population size are important determinants of language evolution (Wray & Grace, 2007; Nettle, 2012; Lupyan & Dale, 2010).
Revised: It has long been assumed that sociodemographic factors such as population size are important determinants of language evolution (Wray & Grace, 2007; Nettle, 2012; Lupyan & Dale, 2010).

Original: Speciﬁcally, global cross-linguistic studies found that bigger communities tend to have languages with more systematic and transparent structures (Lupyan & Dale, 2010).
Revised: Speciﬁcally, global cross-linguistic studies found that bigger communities tend to have languages with more systematic and transparent structures (Lupyan & Dale, 2010).

Original: This result has also been conﬁrmed experimentally, where bigger groups of interacting participants created more compositional languages (Raviv et al., 2019).
Revised: This result has also been experimentally conﬁrmed, where larger groups of interacting participants created more compositional languages (Raviv et al., 2019).

Original: These ﬁndings are attributed to compressibility pressures arising during communication: remembering partner-speciﬁc variants becomes increasingly more challenging as group size increases, and so memory constraints force larger groups to converge on more transparent languages.
Revised: These ﬁndings are attributed to compressibility pressures arising during communication: remembering partner-speciﬁc variants becomes increasingly more challenging as group size increases, and so memory constraints force larger groups to converge on more transparent languages.

Original:  R EPLICATION A TTEMPTS WITH N EURAL A GENTS
Revised:  R EPLICATION A TTEMPTS WITH N EURAL A GENTS

Original: Computational modeling has long been used to study language evolution (Kirby, 2002;
Revised: Computational modeling has long been used to study language evolution (Kirby, 2002;

Original: Smith et al., 2003; Kirby et al., 2004; Smith & Kirby, 2008;
Revised: Smith et al., 2003; Kirby et al., 2004; Smith & Kirby, 2008;

Original: Gong et al., 2008; Dale & Lupyan, 2012; Perfors & Navarro, 2014; Kirby et al., 2015; Steels, 2016).
Revised: Gong et al., 2008; Dale & Lupyan, 2012; Perfors & Navarro, 2014; Kirby et al., 2015; Steels, 2016).

Original: More recently, emergent communication with neural networks and reinforcement learning techniques was introduced (Lazaridou et al., 2017;
Revised: More recently, emergent communication with neural networks and reinforcement learning techniques was introduced (Lazaridou et al., 2017;

Original: Havrylov & Titov, 2017; Kottur et al., 2017).
Revised: Havrylov & Titov, 2017; Kottur et al., 2017).

Original: Table 1 offers a (non-exhaustive) summary of recent emergent communication experiments with neural agents.
Revised: Table 1 offers a (non-exhaustive) summary of recent emergent communication experiments with neural agents.

Original: Both symbolic and visual inputs have been used (Lazaridou et al., 2018), and the channel bandwidth (alphabet size to the power of message length) has been increasing with time.
Revised: Both symbolic and visual inputs have been used (Lazaridou et al., 2018), and the channel bandwidth (alphabet size to the power of message length) has been increasing with time.

Original: Most experiments use long short-term memory (LSTM) (Hochreiter & Schmidhuber, 1997) as the agents’ architecture, and training is most often carried out by the REINFORCE algorithm (Williams, 1992).
Revised: Most experiments use long short-term memory (LSTM) (Hochreiter & Schmidhuber, 1997) as the agents’ architecture, and training is most often carried out by the REINFORCE algorithm (Williams, 1992).

Original: Crucially, most experiments have been limited to a pair of agents, although Li & Bowling (2019) experimented with a single speaker and multiple listeners.
Revised: Crucially, most experiments have been limited to a pair of agents, although Li & Bowling (2019) experimented with a single speaker and multiple listeners.

Original: Only recently, Chaabouni et al.
Revised: Only recently, Chaabouni et al.

Original: (2022) and Rita et al.
Revised: (2022) and Rita et al.

Original: (2022) scaled up the group size.
Revised: (2022) scaled up the group size.

Original: In all experiments except Graesser et al.
Revised: In all experiments except Graesser et al.

Original: (2019), agents do not alternate between the roles of speaker and listener, and instead employ distinct models forthe speaker and listener.
Revised: (2019), agents do not alternate between the roles of speaker and listener and instead employ distinct models for speaker and listener.

Original: For comparison, experiments with humans by Raviv et al.
Revised: For comparison, experiments with humans by Raviv et al.

Original: (2019) have had 4 × 16 distinct visual meanings, a discrete communication channel with bandwidth 16 16 , and group sizes of 4-8 participants, who alternated between being speaker and listener roles
Revised: (2019) have had 4 × 16 distinct visual meanings, a discrete communication channel with bandwidth 16 16 , and group sizes of 4-8 participants, who alternated between being speakers and listeners.

Original: [Table]
Revised: , or comma-separated lists mean that settings were varied

Original: [Table]
Revised: [Table]

Original: With respect to the three linguistic phenomena described above for human participants, neural agents show a mixed pattern of results: First, ease-of-learning of compositional languages has been conﬁrmed in emergent communication with neural agents (Guo et al., 2019;
Revised: With respect to the three linguistic phenomena described above for human participants, neural agents show a mixed pattern of results: First, ease-of-learning of compositional languages has been conﬁrmed in emergent communication with neural agents (Guo et al., 2019;

Original: Chaabouni et al., 2020; 2022).
Revised: Chaabouni et al., 2020; 2022).

Original: For example, more compositionality emerges when agents are being constantly reset and need to learn the language over and over again (similar to the iterated learning paradigm)
Revised: For example, more compositionality emerges when agents are being constantly reset and need to learn the language over and over again (similar to the iterated learning paradigm)

Original: (Li &
Revised: (Li &

Original: Bowling, 2019).
Revised: Bowling, 2019).

Original: Similarly, Guo et al.
Revised: Similarly, Guo et al.

Original: (2019) found that compositional languages emerges in iterated learning experiments with neural agents because they are easier to learn.
Revised: (2019) found that compositional languages emerge in iterated learning experiments with neural agents because they are easier to learn.

Original: Second, in contrast to humans, neural agents can generalize well even without compositional communication protocols.
Revised: Second, in contrast to humans, neural agents can generalize well even without compositional communication protocols.

Original: Chaabouni et al.
Revised: Chaabouni et al.

Original: (2020) have found that compositionality is not necessary for generalization in neural agents (in-line with earlier ﬁndings from Lazaridou et al.
Revised: (2020) have found that compositionality is not necessary for generalization in neural agents (in-line with earlier ﬁndings from Lazaridou et al.

Original: Although they argue that structure (however it emerges) prevails throughout evolution because of its implied learnability advantage (in line with Kirby et al. (2015)), the ﬁnding that compositionality aids generalization has nevertheless not been replicated with neural agents yet.
Revised: Although they argue that structure (however it emerges) prevails throughout evolution because of its implied learnability advantage (in line with Kirby et al. (2015)), the ﬁnding that compositionality aids generalization has nevertheless not been replicated with neural agents yet.

Original: Third, in populations of autoencoders, where the encoder and decoders were decoupled and exchanged throughout training, larger communities produced representations with less idiosyncrasies (Tieleman et al., 2019).
Revised: Third, in populations of autoencoders, where the encoder and decoders were decoupled and exchanged throughout training, larger communities produced representations with less idiosyncrasies (Tieleman et al., 2019).

Original: However, these experiments used a continuous, rather than discrete, channel, which has only recently been analyzed with an increasing population size (Chaabouni et al., 2022; Rita et al., 2022).
Revised: However, these experiments used a continuous, rather than discrete, channel, which has only recently been analyzed with an increasing population size (Chaabouni et al., 2022; Rita et al., 2022).

Original: Although Chaabouni et al.
Revised: Although Chaabouni et al.

Original: (2022) argue that it is necessary to scale up emergent communication experiments to better align neural emergent communication with human language evolution, they have not found a systematic advantage of population size in generalization and ease-of-learning (in contrast with (Tieleman et al., 2019)).
Revised: (2022) argue that it is necessary to scale up emergent communication experiments to better align neural emergent communication with human language evolution, they have not found a systematic advantage of population size in generalization and ease-of-learning (in contrast with (Tieleman et al., 2019)).

Original: Similarly, Rita et al.
Revised: Similarly, Rita et al.

Original: (2022) found that language properties are not enhanced by population size alone.
Revised: (2022) found that language properties are not enhanced by population size alone.

Original: However, when adding heterogeneity via different learning rates, increased population size led to an increase in structure.
Revised: However, when adding heterogeneity via different learning rates, increased population size led to an increase in structure.

Original:  P OTENTIAL R EASONS FOR THE M ISMATCH IN R ESULTS
Revised:  P OTENTIAL R EASONS FOR THE M ISMATCH IN R ESULTS

Original: Why does the population size not affect emergent neural communication?
Revised: Why does the population size not affect emergent neural communication?

Original: And why do neural agents do not need compositionality to generalize?
Revised: And why do neural agents do not need compositionality to generalize?

Original: Our key argument is that crucial communicative and cognitive factors in humans have not yet been appropriately introduced to neural agents in emergent communication experiments.
Revised: Our key argument is that crucial communicative and cognitive factors in humans have not yet been appropriately introduced to neural agents in emergent communication experiments.

Original: We argue that omitting these factors effectively removes the compress- ibility pressure that underlies the emergence and beneﬁts of compositionality.
Revised: We argue that omitting these factors effectively removes the compressibility pressure that underlies the emergence and beneﬁts of compositionality.

Original: In the following, we highlight two crucial factors: memory constraints and speaker/listener role alternation.
Revised: In the following, we highlight two crucial factors: memory constraints and speaker/listener role alternation.

Original: Memory Constraints Human memory limitation is one of the most important constraints of language learning, and underlies the compressibility pressure in language use and transmission.
Revised: Memory Constraints Human memory limitation is one of the most important constraints of language learning, and underlies the compressibility pressure in language use and transmission.

Original: Indeed, sequence memory constraints induce structure emergence in iterated learning Cornish et al.
Revised: Indeed, sequence memory constraints induce structure emergence in iterated learning Cornish et al.

Original: (2017), and underlie group size effects in real-world settings (Meir et al., 2012; Wray & Grace, 2007) and in communication games with humans (Raviv et al., 2019), where more compositionality is promoted because individuals cannot memorize partner-speciﬁc variations as group size increases.
Revised: (2017), and underlie group size effects in real-world settings (Meir et al., 2012; Wray & Grace, 2007) and in communication games with humans (Raviv et al., 2019), where more compositionality is promoted because individuals cannot memorize partner-speciﬁc variations as group size increases.

Original: In contrast, a key ingredient for the success of neural networks is their overparameterization (Nakkiran et al., 2021), which means that their capacity is in fact sufﬁcient to completely ”memorize” senderspeciﬁc idiosyncratic languages.
Revised: In contrast, a key ingredient for the success of neural networks is their overparameterization (Nakkiran et al., 2021), which means that their capacity is in fact sufﬁcient to completely ”memorize” senderspeciﬁc idiosyncratic languages.

Original: We propose that this overparametrization signiﬁcantly reduces the compressibility pressure, effectively eliminating the potential beneﬁts of compositional structure for learning and generalization.
Revised: We propose that this overparametrization signiﬁcantly reduces the compressibility pressure, effectively eliminating the potential beneﬁts of compositional structure for learning and generalization.

Original: We therefore suggest that in communication games, the model capacity, i. e., how much information the model can store, (well quantiﬁable, e. g., see MacKay et al.
Revised: Therefore, we suggest that in communication games, the model capacity, i. e., how much information the model can store (well quantiﬁable, e. g., see MacKay et al.

Original: (2003)) should be considered in relation to the number of different meanings and the space of all possible messages, i. e. alphabet size to the power of (maximum) message length |A| L .
Revised: (2003)), should be considered in relation to the number of different meanings and the space of all possible messages, i. e. alphabet size to the power of (maximum) message length |A| L .

Original: We hypothesize that for compositionality to emerge, the model capacity needs to be less than required to memorize a separate message for each meaning from every agent but also less than the theoretical channel bandwidth, such that it becomes necessary to reuse substructures within the messages.
Revised: We hypothesize that for compositionality to emerge, the model capacity needs to be less than required to memorize a separate message for each meaning from every agent, but also less than the theoretical channel bandwidth, such that it becomes necessary to reuse substructures within the messages.

Original: In agreement with our position, Resnick et al.
Revised: Consistent with our position, Resnick et al.

Original: (2020) and Gupta et al.
Revised: (2020) and Gupta et al.

Original: (2020) veriﬁed that learning an underlying compositional structure requires less capacity than memorizing a dataset.
Revised: (2020) veriﬁed that learning an underlying compositional structure requires less capacity than memorizing a dataset.

Original: Similar to us, the authors of both works assume a range in model capacity that facilitates compositionality, but so far only determine a lower bound, while we argue here about the upper bound(s).
Revised: Similarly to us, the authors of both works assume a range in model capacity that facilitates compositionality, but so far only determine a lower bound, while we argue here about the upper bound(s).

Original: Role Alternation In current neural emergent communication experiments, one agent generates the message, and the other only processes it.
Revised: Role Alternation In current neural emergent communication experiments, one agent generates the message, and the other only processes it.

Original: This in sharp contrast to human communication, where speakers can reproduce any linguistic message that they can understand (Hockett, 1960).
Revised: This in sharp contrast to human communication, where speakers can reproduce any linguistic message that they can understand (Hockett, 1960).

Original: Indeed, dyadic and group communication experiments with humans typically have people alternating between the roles of speaker and listener throughout the experiment (Kirby et al., 2015; Raviv et al., 2019; Motamedi et al., 2021).
Revised: Indeed, dyadic and group communication experiments with humans typically have people alternating between the roles of speaker and listener throughout the experiment (Kirby et al., 2015; Raviv et al., 2019; Motamedi et al., 2021).

Original: This is only rarely reﬂected in work with neural agents (Table 1).
Revised: This is only rarely reﬂected in work with neural agents (Table 1).

Original: A straight-forward implementation of role alternation is to have shared parameters within the (generative) speaker role and the (discriminative) listener role of the same neural agent.
Revised: A straight-forward implementation of role alternation is to have shared parameters within the (generative) speaker role and the (discriminative) listener role of the same neural agent.

Original: This would make neural agents’ architecture  more similar to the human brain, where shared neural mechanisms support both  production and  comprehension of natural speech (Silbert et al., 2014).
Revised: This would make the architecture of neural agents more similar to the human brain, where shared neural mechanisms support both the production and the comprehension of natural speech (Silbert et al., 2014).

Original: One way to do this would be to tie the output layer’s weights to input embedding, a well known concept in language modeling (Mikolov et al., 2013; Raffel et al., 2020).
Revised: One way to do this would be to tie the output layer’s weights to input embedding, a well known concept in language modeling (Mikolov et al., 2013; Raffel et al., 2020).

Original: Some experiments already implement role alternation, e. g., in multi-agent communication with given language descriptions (Graesser et al., 2019), or in language acquisition from image captions where agents simultaneously learn by cognition and production (Nikolaus & Fourtassi, 2021).
Revised: Some experiments already implement role alternation, e. g., in multi-agent communication with given language descriptions (Graesser et al., 2019), or in language acquisition from image captions where agents simultaneously learn by cognition and production (Nikolaus & Fourtassi, 2021).

Original: We suggest role alternation should also implemented in emergent communication experiments to ensure more linguistically plausible dynamics.
Revised: We suggest role alternation should also implemented in emergent communication experiments to ensure more linguistically plausible dynamics.

Original:  C ONCLUSION
Revised:  C ONCLUSION

Original: We have outlined important discrepancies in results between emergent communication with human vs. neural agents and suggested these can be explained by the absence of key cognitive and communicative factors that drive human language evolution: memory constraints and speaker-listener role alternation.
Revised: We have outlined important discrepancies in results between emergent communication with human vs. neural agents and suggested these can be explained by the absence of key cognitive and communicative factors that drive human language evolution: memory constraints and speaker-listener role alternation.

Original: We suggest that including these factors in future work would mimic the compressability pressure and compositionality beneﬁts observed with human agents, and consequentially make emergent neural communication protocols more linguistically plausible.
Revised: We suggest that including these factors in future work would mimic the compressability pressure and compositionality beneﬁts observed with human agents, and consequentially make emergent neural communication protocols more linguistically plausible.

Original: Notably, additional psychoand socio-linguistic factors may affect language evolution, and might also play a role in explaining the discrepancy in results.
Revised: Notably, additional psychoand socio-linguistic factors may affect language evolution and might also play a role in explaining the discrepancy in results.