Reference to objects is one of the most basic and prevalent uses of language. In order to refer, speakers must choose from among a wealth of referring expressions they have at their disposal. How does a speaker choose whether to refer to an object as the animal, the dog, the dalmatian, or the big mostly white dalmatian? The context within which the object occurs (other non-dogs, other dogs, other dalmatians) plays a large part in determining which features the speaker chooses to include in their utterance – speakers aim to be sufficiently informative to establish unique reference to the intended object. However, speakers' utterances often exhibit what has been claimed to be overinformativeness: referring expressions are often more specific than necessary for establishing unique reference, and they are more specific in systematic ways. For instance, speakers are likely to produce referring expressions like the small blue pin instead of the small pin in contexts like Figure 1 , even though the color modifier provides no additional information BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . Similar use of redundant size modifiers, in contrast, is rare. Providing a unified theory for speakers' systematic patterns of overinformativeness has so far proven elusive.This paper is concerned with accounting for these systematic patterns in overinformative referring expressions. We restrict ourselves to definite descriptions of the form the (ADJ?)+ NOUN, that is, noun phrases that minimally contain the definite determiner the followed by a head noun, with any number of adjectives occurring between the determiner and the noun. A model of such referring expressions will allow us to unify two domains in language production that have been typically treated as separate. The choice of adjectives in (purportedly) overmodified referring expressions has been a primary focus of the language production literature BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF2 , BIBREF3 , BIBREF10 , while the choice of noun in simple nominal expressions has so far mostly received attention in the concepts and categorization literature BIBREF11 , BIBREF12 and in the developmental literature on generalizing basic level terms BIBREF13 . In the following, we review some of the key phenomena and puzzles in each of these literatures. We then present a model of referring expression production within the Rational Speech Act framework BIBREF14 , BIBREF15 , BIBREF16 , which treats speakers as boundedly rational agents who optimize the tradeoff between utterance cost and informativeness. Our key innovation is to relax the assumption that semantic truth functions are deterministic. Under this relaxed semantics, where certain terms may apply better than others without strictly being true or false, it can be useful and informative to add seemingly overinformative modifiers or use nouns that are seemingly too specific; not doing so might allow the listener to go astray, or to invest too much processing effort in inferring the speaker's intention. This model provides a unified explanation for a number of seemingly disparate phenomena from the modified and nominal referring expression literature.We spend the remainder of the paper demonstrating how this account applies to various phenomena. In Section "Overinformativeness in referring expressions" we spell out the problem and introduce the key overinformativeness phenomena. In Section "Modeling speakers' choice of referring expression" we introduce the basic Rational Speech Act framework with deterministic semantics and show how it can be extended to a relaxed semantics. In Sections 3 - 5 we evaluate the relaxed semantics RSA model on data from interactive online reference game experiments that exhibit the phenomena introduced in Section "Overinformativeness in referring expressions" : size and color modifier choice under varying conditions of scene complexity; typicality effects in the choice of color modifier; and choice of nominal level of reference. We wrap up in Section "General Discussion" by summarizing our findings and discussing the far-reaching implications of and further challenges for this line of work.How should a cooperative speaker choose between competing referring expressions? Grice, in his seminal work, provided some guidance by formulating his famous conversational maxims, intended as a guide to listeners' expectations about good speaker behavior BIBREF17 . His maxim of Quantity, consisting of two parts, requires of speakers to:Quantity-1: Make your contribution as informative as is required (for the purposes of the exchange).Quantity-2: Do not make your contribution more informative than is required.That is, speakers should aim to produce neither under- nor overinformative utterances. While much support has been found for the avoidance of underinformativeness BIBREF18 , BIBREF19 , BIBREF20 , BIBREF21 , BIBREF9 , BIBREF22 , speakers seem remarkably willing to systematically violate Quantity-2. In modified referring expressions, they routinely produce modifiers that are not necessary for uniquely establishing reference (e.g., the small blue pin instead of the small pin in contexts like Figure 1 ). In simple nominal expressions, speakers routinely choose to refer to an object with a basic level term even when a superordinate level term would have been sufficient for establishing reference BIBREF23 e.g., the dog instead of the animal in contexts like Figure 18 ;>Rosch1976, hoffmann1983objektidentifikation, TanakaTaylor91BasicLevelAndExpertise, Johnson1997, brown1958words.These observations have posed a challenge for theories of language production, especially those positing rational language use (including the Gricean one): why this extra expenditure of useless effort? Why this seeming blindness to the level of informativeness requirement? Many have argued from these observations that speakers are in fact not economical BIBREF9 , BIBREF5 . Some have derived a built-in preference for referring at the basic level from considerations of perceptual factors such as shape BIBREF12 , BIBREF11 , BIBREF24 . Others have argued for salience-driven effects on willingness to overmodify BIBREF1 , BIBREF25 . In all cases, it is argued that informativeness cannot be the key factor in determining the content of speakers' referring expressions.Here we revisit this claim and show that systematically relaxing the requirement of a deterministic semantics for referring expressions also systematically changes the informativeness of utterances. This results in a reconceptualization of what have been termed overinformative referring expressions as rationally redundant referring expressions. We begin by reviewing the phenomena of interest that a revised theory of definite referring expressions should be able to account for.Most of the literature on overinformative referring expressions has been devoted to the use of overinformative modifiers in modified referring expressions. The prevalent observation is that speakers frequently do not include only the minimal modifiers required for establishing reference, but often also include redundant modifiers BIBREF5 , BIBREF6 , BIBREF8 , BIBREF9 , BIBREF2 , BIBREF3 . However, not all modifiers are created equal: there are systematic differences in the overmodification patterns observed for size adjectives (e.g., big, small), color adjectives (e.g., blue, red), material adjectives (e.g., plastic, wooden), and others BIBREF7 . Here we review some key patterns of overmodification that have been observed, before spelling out our account of these phenomena in Section "Modeling speakers' choice of referring expression" .In Figure 1 , singling out the object highlighted by the green border requires only mentioning its size (the small pin). But it is now well-documented that speakers routinely include redundant color adjectives (the small blue pin) which are not necessary for uniquely singling out the intended referent in these kinds of contexts BIBREF5 , BIBREF26 , BIBREF0 . However, the same is not true for size: in contexts like Figure 1 , where color is sufficient for unique reference (the blue pin), speakers overmodify much more rarely. Though there is quite a bit of variation in proportions of overmodification, this asymmetry in the propensity for overmodifying with color but not size has been documented repeatedly BIBREF5 , BIBREF7 , BIBREF0 , BIBREF10 , BIBREF25 , BIBREF27 .Explanations for this asymmetry have varied. Pechmann1989 was the first to take the asymmetry as evidence for speakers following an incremental strategy of object naming: speakers initially start to articulate an adjective denoting a feature that listeners can quickly and easily recognize (i.e., color) before they have fully inspected the display and extracted the sufficient dimension. However, this would predict that speakers routinely should produce expressions like the blue small pin, which violate the preference for size adjectives to occur before color adjectives in English BIBREF28 , BIBREF29 . While Pechmann did observe such violations in his dataset, most cases of overmodification did not constitute such violations, and he himself concluded that incrementality cannot (on its own) account for the asymmetry in speakers' propensity for overmodifying with color vs. size.Another explanation for the asymmetry is that speakers try to produce modifiers that denote features that are reasonably easy for the listener to perceive, so that, even when a feature is not fully distinguishing in context, it at least serves to restrict the number of objects that could plausibly be considered the target. Indeed, there has been some support for the idea that overmodification can be beneficial to listeners by facilitating target identification BIBREF2 , BIBREF10 , BIBREF30 . We return to this idea in Section "Modeling speakers' choice of referring expression" and the General Discussion.There have been various attempts to capture the color-size asymmetry in computational natural language generation models. The earliest contenders for models of definite referring expressions like the Full Brevity algorithm BIBREF31 or the Greedy algorithm BIBREF31 focused only on discriminatory value – that is, an utterance's informativeness – in generating referring expressions. This is equivalent to the very simple interpretation of Grice laid out above, and consequently these models demonstrated the same inability to capture the color-size asymmetry: they only produced the minimally specified expressions. Subsequently, the Incremental algorithm BIBREF32 incorporated a preference order on features, with color ranked higher than size. The order is traversed and each encountered feature included in the expression if it serves to exclude at least one further distractor. This results in the production of overinformative color but not size adjectives. However, the resulting asymmetry is much greater than that evident in human speakers, and is deterministic rather than exhibiting the probabilistic production patterns that human speakers exhibit. More recently, the PRO model BIBREF33 has sought to integrate the observation that speakers seem to have a preference for including color terms with the observation that a preference does not imply the deterministic inclusion of said color term. The model is specifically designed to capture the color-size asymmetry: in a first step, the uniquely distinguishing property (if there is one) is first selected deterministically. In a second step, an additional property is added probabilistically, depending on both a salience parameter associated with the additional property and a parameter capturing speakers' eagerness to overmodify. If both properties are uniquely distinguishing, a property is selected probabilistically depending on its associated salience parameter. The second step proceeds as before.However, while the PRO model – the most state-of-the-art computational model of human production of modified referring expressions – can capture the color-size asymmetry, it is neither flexible enough to be extended straightforwardly to other modifiers beyond color and size, nor can it straightforwardly be extended to capture the more subtle systematicity with which the preference to overmodify with color changes based on various features of context.Speakers' propensity to overmodify with color is highly dependent on features of the distractor objects in the context. In particular, as the variation present in the scene increases, so does the probability of overmodifying BIBREF22 , BIBREF27 . How exactly scene variation is quantified differs across experiments. One very clear demonstration of the scene variation effect was given by Koolen2013, who quantified scene variation as the number of feature dimensions along which objects in a scene vary. Over the course of three experiments, they compared a low-variation condition in which objects never differed in color with a high-variation condition in which objects differed in type, color, orientation, and size. They consistently found higher rates of overmodification with color in the high-variation (28-27%) than in the low-variation (4-10%) conditions. Similarly, Davies2013 found that listeners judge overmodified referring expressions in low-variation scenes of four objects as less natural than in high-variation scenes of 4 potentially compositional `objects-on-objects' (e.g., a button on a sock). And finally, gatt2017, while not reporting differences in overmodification behavior, did find that when size and color are jointly disambiguating, speech onset times for non-redundant color-and-size utterances increased as the number of distractors in the display increased.The effect of scene variation on propensity to overmodify has typically been explained as the result of the demands imposed on visual search: in low-variation scenes, it is easier to discern the discriminating dimensions than in high-variation scenes, where it may be easier to simply start naming features of the target that are salient BIBREF27 .Above, we have considered three different ways of quantifying scene variation: the number of dimensions along which objects differ, whether objects are `simple' or `compositional', and the number of distractors present in a scene. A model of referring expression generation should ideally capture all of these types of variation in a unified way.Modifier type and amount of scene variation are not the only factors determining overmodification. Overmodification with color has been shown to be systematically related to the typicality of the color for the object. Building on work by sedivy2003a, Westerbeek2015 (and more recently, rubiofernandez2016) have shown that the more typical a color is for an object, the less likely it is to be mentioned when not necessary for unique reference. For example, speakers never refer to a yellow banana in the absence of other bananas as the yellow banana (see Figure 2 ), but they sometimes refer to a brown banana as the brown banana, and they almost always refer to a blue banana as the blue banana (see Figure 2 ). Similar typicality effects have been shown for other (non-color) properties. For example, Mitchell2013 showed that speakers are more likely to include an atypical than a typical property (either shape or material) when referring to everyday objects like boxes when mentioning at least one property was necessary for unique reference.Whether speakers are more likely to mention atypical properties over typical properties because they are more salient to them or because they are trying to make reference resolution easier for the listener, for whom presumably these properties are also salient, is an open question BIBREF25 . Some support for the audience design account comes from a study by Huettig2011, who found that listeners, after hearing a noun with a diagnostic color (e.g., frog), are more likely to fixate objects of that diagnostic color (green), indicating that typical object features are rapidly activated and aid visual search. Similarly, Arts2011 showed that overspecified expressions result in faster referent identification. Nevertheless, the benefit for listeners and the salience for speakers might simply be a happy coincidence and speakers might not, in fact, be designing their utterances for their addressees. We return to this issue in the General Discussion.Even in the absence of adjectives, a referring expression can be more or less informative: the dalmatian communicates more information about the object in question than the dog (being a dalmatian entails being a dog), which in turn is globally more informative than the animal. Thus, this choice can be considered analogous to the choice of adding more modifiers – in both cases, the speaker has a choice of being more or less specific about the intended referent. However, the choice of reference level in simple nominal referring expressions is also interestingly different from that of adding modifiers in that there is no additional word-level cost associated with being more specific – the choice is between different one-word utterances, not between utterances differing in word count.Nevertheless, cognitive cost affects the choice of reference level: in particular, speakers prefer more frequent words over less frequent ones BIBREF34 , and they prefer shorter ones over longer ones BIBREF35 , BIBREF36 . This may go part of the way towards explaining the well-documented effect from the concepts and categorization literature that speakers prefer to refer at the basic level BIBREF12 , BIBREF37 . That is, in the absence of other constraints, even when a superordinate level term would be sufficient for establishing reference (as in Figure 3 ), speakers prefer to say the dog rather than the animal.Contextual informativeness is another factor that has been shown to affect speakers' nominal production choices BIBREF23 e.g.,>brennan1996. For instance, in a context like Figure 3 , speakers should use the subordinate level term dalmatian to refer to the target marked with a green border, because a higher-level term (dog, animal) would be contextually underinformative. However, there are nevertheless cases of contexts where either the superordinate animal or the basic level dog term would be sufficient for unique reference, as in Figure 3 , in which speakers nevertheless prefer to use the subordinate level term the dalmatian. This is the case when the object is a particularly good instance of the subordinate level term or a particularly bad instance of the basic level term, compared to the other objects in the context. For example, penguins, which are rated as particularly atypical birds, are often referred to at the subordinate level penguin rather than at the basic level bird, despite the general preference for the basic level BIBREF38 .In sum, the production of modified and simple nominal referring expressions is governed by many factors, including an utterance's informativeness, its cost relative to alternative utterances, and the typicality of an object or its features. Critically, these factors are all in play at once, potentially interacting in rich and complex ways. In the next section, we provide an explicit computational account of these different factors and how they interact, with a focus on cases where speakers appear to be overinformative – either by adding more modifiers or by referring at a more specific level than necessary for establishing unique reference. A summary of the effects we will focus on in the remainder of the paper is provided in Table 1 .To date, there is no theory to account for all of these different phenomena; and no model has attempted to unify overinformativeness in the domain of modified and nominal referring expressions. We touched on some of the explanations that have been proposed for these phenomena. We also indicated where computational models have been proposed for individual phenomena. In the next section, we present the Rational Speech Act modeling framework, which we then use to capture these disparate phenomena in one model.Here we propose a computational model of referring expression production that accounts for the phenomena introduced above. The model is formulated within the Rational Speech Act (RSA) framework BIBREF14 , BIBREF15 . It provides a principled explanation for the phenomena reviewed in the previous section and holds promise for being generalizable to many further production phenomena related to overinformativeness, which we discuss in Section "General Discussion" . We proceed by first presenting the general framework in Section "Basic RSA" , and show why the most basic model, as formulated by frank2012, does not produce the phenomena outlined above due to its strong focus on speakers maximizing the informativeness of expressions under a deterministic semantics. In Section "RSA with continuous semantics – emergent color-size asymmetry" we introduce the crucial innovation: relaxing the assumption of a deterministic semantics. We show that the model can qualitatively account both for speakers' asymmetric propensity to overmodify with color rather than with size and (in Section "RSA with continuous semantics – scene variation" ) for speakers' propensity to overmodify more with increasing scene variation.The production component of RSA aims to soft-maximize the utility of utterances, where utility is defined in terms of the contextual informativeness of an utterance, given each utterance's literal semantics. Formally, this is treated as a pragmatic speaker $S_1$ reasoning about a literal listener $L_0$ , who can be described by the following formula: $$P_{L_0}(o | u) \propto \mathcal {L}(u,o).$$   (Eq. 23) The literal listener $L_0$ observes an utterance $u$ from the set of utterances $U$ , consisting of single adjectives denoting features available in the context of a set of objects $O$ , and returns a distribution over objects $o \in O$ . Here, $\mathcal {L}(u,o)$ is the lexicon that encodes deterministic lexical meanings such that: $$\mathcal {L}(u,o) = \left\lbrace 
\begin{array}{rl}
1 & \text{if } u \text{ is true of } o\\
0 & \text{otherwise}.
\end{array} \right.$$   (Eq. 24) Thus, $P_{L_0}(o | u)$ returns a uniform distribution over all contextually available $o$ in the extension of $u$ . For example, in the size-sufficient context shown in Figure 1 , $U = \lbrace \textrm {\emph {big}}, \textrm {\emph {small}}, \textrm {\emph {blue}}, \textrm {\emph {red}}\rbrace $ and $O = \lbrace o_{\textrm {big\_blue}}, o_{\textrm {big\_red}}, o_{\textrm {small\_blue}}\rbrace $ . Upon observing blue, the literal listener therefore assigns equal probability to $o_{\textrm {big\_blue}}$ and $o_{\textrm {small\_blue}}$ . Values of $P_{L_0}(o | u)$ for each $u$ are shown on the left in Table 2 .The pragmatic speaker in turn produces an utterance with probability proportional to the utility of that utterance: $$P_{S_1}(u | o) \propto e^{U(u,o)}$$   (Eq. 25) The speaker's utility $U(u,o)$ is a function of both the utterance's informativeness with respect to the literal listener $P_{L_0}(o | u)$ and the utterance's cost $c(u)$ : $$U(u,o) = \beta _{i} \ln P_{L_0}(o | u) - \beta _c c(u)$$   (Eq. 26) Two free parameters, $\beta _i$ and $\beta _c$ enter the computation, weighting the respective contributions of informativeness and utterance cost, respectively. In order to understand the effect of $\beta _i$ , it is useful to explore its effect when utterances are cost-free. In this case, as $\beta _i$ approaches infinity, the speaker increasingly only chooses utterances that maximize informativeness; if $\beta _i$ is 0, informativeness is disregarded and the speaker chooses randomly from the set of all available utterances; if $\beta _i$ is 1, the speaker probability-matches, i.e., chooses utterances proportional to their informativeness BIBREF23 equivalent to Luce's choice rule,>luce1959. Applied to the example in Table 2 , if the speaker wants to refer to $o_{\textrm {small\_blue}}$ they have two semantically possible utterances, small and blue, where small is twice as informative as blue. They produce small with probability 1 when $\beta _i \rightarrow \infty $ , probability 2/3 when $\beta _c$0 and probability 1/4 when $\beta _c$1 .Conversely, disregarding informativeness and focusing only on cost, any asymmetry in costs will be exaggerated with increasing $\beta _c$ , such that the speaker will choose the least costly utterance with higher and higher probability as $\beta _c$ increases.As has been pointed out by GattEtAl2013, the basic Rational Speech Act model described so far BIBREF14 does not generate overinformative referring expressions for two reasons. One of these is trivial: $U$ only contains one-word utterances. We can ameliorate this easily by allowing complex two-word utterances. We assume an intersective semantics for complex utterances $u_{\textrm {complex}}$ that consist of a two adjective sequence $u_{\textrm {size}} \in \lbrace \textrm {\emph {big}}, \textrm {\emph {small}}\rbrace $ and $u_{\textrm {color}} \in \lbrace \textrm {\emph {blue}}, \textrm {\emph {red}}\rbrace $ , such that the meaning of a complex two-word utterance is defined as $$\mathcal {L}(u_{\text{complex}},o) = \mathcal {L}(u_{\text{size}},o) \times \mathcal {L}(u_{\text{color}},o).$$   (Eq. 29) The resulting renormalized literal listener distributions for our example size-sufficient context in Figure 1 are shown in the middle columns in Table 2 .Unfortunately, simply including complex utterances in the set of alternatives does not solve the problem. Let's turn again to the case where the speaker wants to communicate the small blue object. There are now two utterances, small and small blue, which are both more informative than blue and equally informative as each other, for referring to the small blue object. Because they are equally contextually informative, the only way for the complex utterance to be chosen with greater probability than the simple utterance is if it was the cheaper one. While this would achieve the desired mathematical effect, the cognitive plausibility of complex utterances being cheaper than simple utterances is highly dubious. Even if it wasn't dubious, as mentioned previously proportions of overinformative referring expressions are variable across experiments. The only way to achieve that variability under the basic model is to assume that the costs of utterances vary from task to task. This also seems to us an implausible assumption. Thus we must look elsewhere to account for overinformativeness. We propose that the place to look is the computation of informativeness itself.Here we introduce the crucial innovation: rather than assuming a deterministic truth-conditional semantics that returns true (1) or false (0) for any combination of expression and object, we relax to a continuous semantics that returns real values in the interval $[0,1]$ . Formally, the only change is in the values that the lexicon can return: $$\mathcal {L}(u,o) \in [0, 1] \subset \mathbb {R}$$   (Eq. 32) That is, rather than assuming that an object is unambiguously big (or not) or unambiguously blue (or not), this continuous semantics captures that objects count as big or blue to varying degrees BIBREF23 similar to approaches in fuzzy logic and prototype theory,>zadeh1965fuzzy, Rosch1973.To see the basic effect of switching to a continuous semantics, and to see how far we can get in capturing overinformativeness patterns with this change, let us explore a simple semantic theory in which all colors are treated the same, all sizes are as well, and the two compose via a product rule. That is, when a size adjective would be `true' of an object under a deterministic semantics, we take $\mathcal {L}(u,o) = x_{\text{size}}$ , a constant; when it is `false' of the object, $\mathcal {L}(u,o) = 1 - x_{\text{size}}$ . Similarly for color adjectives. This results in two free model parameters, $x_{\text{size}}$ and $x_{\text{color}}$ , that can take on different values, capturing that size and color adjectives may apply more or less well/reliably to objects. Together with the product composition rule, Eq. 29 , this fully specifies a relaxed semantic function for our reference domain.Now consider the RSA literal listener, Eq. 23 , who uses these relaxed semantic values. Given an utterance, the listener simply normalizes over potential referents. As an example, the resulting renormalized literal listener distributions for the size-sufficient example context in Figure 1 are shown for values $x_{\text{size}} = .8$ and $x_{\text{color}} = .99$ on the right in Table 2 . Recall that in this context, the speaker intends for the listener to select the small blue pin. To see which would be the best utterance to produce for this purpose, we compare the literal listener probabilities in the $o_{\text{small\_blue}}$ column. The two best utterances under both the deterministic and the continuous semantics are bolded in the table: under the deterministic semantics, the two best utterances are small and small blue, with no difference in listener probability. In contrast, under the continuous semantics small has a smaller literal listener probability (.67) of retrieving the intended referent than the redundant small blue (.80). Consequently, the pragmatic speaker will be more likely to produce small blue than small, though the precise probabilities depend on the cost and informativeness parameters $\beta _c$ and $\beta _i$ .Crucially, the reverse is not the case when color is the distinguishing dimension. Imagine the speaker in the same context wanted to communicate the big red pin. The two best utterances for this purpose are red (.99) and big red (.99). In contrast to the results for the small blue pin, these utterances do not differ in their capacity to direct the literal listener to the intended referent. The reason for this is that we defined color to be almost noiseless, with the result that the literal listener distributions in response to utterances containing color terms are more similar to those obtained via a deterministic semantics than the distributions obtained in response to utterances containing size terms. The reader is encouraged to verify this by comparing the row-wise distributions under the deterministic and continuous semantics in Table 2 .To gain a wider understanding of the effects of assuming continuous meanings in contexts like that depicted in Figure 1 , we visualize the results of varying $x_{\text{size}}$ and $x_{\text{color}}$ in Figure 4 . To orient the reader to the graph: the deterministic semantics of utterances is approximated where the semantic values of both size and color utterances are close to 1 (.999, top right-most point in graph). In this case, the simple sufficient (small pin) and complex redundant utterance (small blue pin) are equally likely, around .5, because they are both equally informative and utterances are assumed to have 0 cost. All other utterances are highly unlikely. The interesting question is under which circumstances, if any, the standard color-size asymmetry emerges. This is the yellow/orange/red space in the `small blue' facet, characterized by values of $x_{\text{size}}$ that are lower than $x_{\text{color}}$ , with high values for $x_{\text{color}}$ . That is, redundant utterances are more likely than sufficient utterances when the redundant dimension (in this case color) is less noisy than the sufficient dimension (in this case size) and overall is close to noiseless.Thus, when size adjectives are noisier than color adjectives, the model produces overinformative referring expressions with color, but not with size – precisely the pattern observed in the literature BIBREF5 , BIBREF0 . Note also that no difference in adjective cost is necessary for obtaining the overinformativeness asymmetry, though assuming a greater cost for size than for color does further increase the observed asymmetry. We defer a discussion of costs to Section "Experiment 1: scene variation in modified referring expressions" , where we infer the best parameter values for both the costs and the semantic values of size and color, given data from a reference game experiment.We defer a complete discussion of the important potential psychological and linguistic interpretation of these continuous semantic values to the General Discussion in Section "General Discussion" . However, it is worth reflecting on why size adjectives may be inherently noisier than color adjectives. Color adjectives are typically treated as absolute adjectives while size adjectives are inherently relative BIBREF42 . That is, while both size and color adjectives are vague, size adjectives are arguably context-dependent in a way that color adjectives are not – whether an object is big depends inherently on its comparison class; whether an object is red does not. In addition, color as a property has been claimed to be inherently salient in a way that size is not BIBREF2 , BIBREF33 . Finally, we have shown in recent work that color adjectives are rated as less subjective than size adjectives BIBREF43 . All of these suggest that the use of size adjectives may be more likely to vary across people and contexts than color.To summarize, we have thus far shown that RSA with continuous adjective semantics can give rise to the well-documented color-size asymmetry in the production of overinformative referring expressions when color adjectives are closer to deterministic truth-functions than size adjectives. The crucial mechanism is that when modifiers are relaxed, adding additional, `stricter' modifiers adds information. From this perspective, these redundant modifiers are not overinformative; they are rationally redundant, or sufficiently informative given the needs of the listener.As discussed in Section "Overinformativeness in referring expressions" , increased scene variation has been shown to increase the probability of referring expressions that are overmodified with color. Here we simulate the experimental conditions reported by Koolen2013 and explore the predictions that continuous semantics RSA – henceforth cs-RSA – makes for these situations. Koolen2013 quantified scene variation as the number of feature dimensions along which pieces of furniture in a scene varied: type (e.g., chair, fan), size (big, small), and color (e.g., red, blue). Here, we simulate the high and low variation conditions from their Experiments 1 and 2, reproduced in Figure 5 .In both conditions in both experiments, color was not necessary for establishing reference; that is, color mentions were always redundant. The two experiments differed in the dimension necessary for unique reference. In Exp. 1, only type was necessary (fan and couch in the low and high variation conditions in Figure 5 , respectively). In Exp. 2, size and type were necessary (big chair and small chair in Figure 5 , respectively). Koolen2013 found lower rates of redundant color use in the low variation conditions (4% and 9%) than in the high variation conditions (24% and 18%).We generated model predictions for precisely these four conditions. Note that by adding the type dimension as a distinguishing dimension, we must allow for an additional semantic value $x_{\text{type}}$ , which encodes how noisy nouns are.Koolen2013 counted any mention of color as a redundant mention. In Exp. 1, this includes the simple redundant utterances like blue couch as well as complex redundant utterances like small blue couch. In Exp. 2, where size was necessary for unique reference, only the complex redundant utterance small brown chair was truly redundant (brown chair was insifficient, but still included in coounts of color mention). The results of simulating these conditions with parameters $\beta _i = 30$ , $ \beta _c = c(u_{\textrm {size}}) = c(u_{\textrm {color}}) = 1$ , $x_{\text{size}} = .8$ , $x_{\text{color}} = .999$ , and $x_{\text{type}} = .9$ are shown in Figure 5 , under the assumption that the cost of a two-word utterance $c(u)$ is the sum of the costs of the one-word sub-utterances. For both experiments, the model exhibits the empirically-observed qualitative effect of variation on the probability of redundant color mention: when variation is greater, redundant color mention is more likely. Indeed, this effect of scene variation is predicted by the model anytime the semantic values for size, type, and color are ordered as: $x_{\text{size}} \le x_{\text{type}} < x_{\text{color}}$ . If, on the other hand, $x_{\text{type}}$ is greater than $x_{\text{color}}$ , the probability of redundantly mentioning color is close to zero and does not differ between variation conditions (in those cases, color mention reduces, rather than adds, information about the target).To further explore the scene variation effect predicted by RSA, turn again to Figure 1 . Here, the target item is the small blue pin and there are two distractor items: a big blue pin and a big red pin. Thus, for the purpose of establishing unique reference, size is the sufficient dimension and color the insufficient dimension. We can measure scene variation as the proportion of distractor items that do not share the value of the insufficient feature with the target, that is, as the number of distractors $n_{\textrm {diff}}$ that differ in the value of the insufficient feature divided by the total number of distractors $n_{\textrm {total}}$ : $
\textrm {scene variation} = \frac{n_{\textrm {diff}}}{n_{\textrm {total}}}
$ In Figure 1 , there is one distractor that differs from the target in color (the big red pin) and there are two distractors in total. Thus, $\textrm {scene variation} = \frac{1}{2} = .5$ . In general, this measure of scene variation is minimal when all distractors are of the same color as the target, in which case it is 0. Scene variation is maximal when all distractors except for one (in order for the dimension to remain insufficient for establishing reference) are of a different color than the target. That is, scene variation may take on values between 0 and $\frac{n_{\textrm {total}} - 1}{n_{\textrm {total}}}$ .Using the same parameter values as above, we generate model predictions for size-sufficient and color-sufficient contexts, manipulating scene variation by varying number of distractors (2, 3, or 4) and number of distractors that don't share the insufficient feature value. The resulting model predictions are shown in Figure 6 . The predicted probability of redundant adjective use is largely (though not completely) correlated with scene variation. Redundant adjective use increases with increasing scene variation when size is sufficient (and color redundant), but not when color is sufficient (and size redundant). The latter prediction depends, however, on the actual semantic value of color—with slightly lower semantic values for color, the model predicts small increases in redundant size use. In general: increased scene variation is predicted to lead to a greater increase in redundant adjective use for less noisy adjectives.RSA with a continuous semantics thus captures the qualitative effects of color-size asymmetry and scene variation in production of redundant expressions, and it makes quantitative predictions for both. Testing these quantitative predictions, however, will require more data. In Sections 3, 4, and 5 we quantitatively evaluate cs-RSA on datasets capturing the phenomena described in the Introduction (Table 1 ): modifier type and scene variation effects on modified referring expressions, typicality effects on color mention, and the choice of taxonomic level of reference in nominal choice.Adequately assessing the explanatory value of RSA with continuous semantics requires evaluating how well it does at predicting the probability of various types of utterances occurring in large datasets of naturally produced referring expressions. We first report the results of a web-based interactive reference game in which we systematically manipulate scene variation (in a somewhat different way than Koolen2013 did). We then perform a Bayesian data analysis to both assess how likely the model is to generate the observed data – i.e., to obtain a measure of model quality – and to explore the posterior distribution of parameter values – i.e., to understand whether the assumed asymmetries in the adjectives' semantic values and/or cost discussed in the previous section are validated by the data.We saw in Section "RSA with continuous semantics – scene variation" that cs-RSA correctly predicts qualitative effects of scene variation on redundant adjective use. In particular, we saw that color is more likely to be used redundantly when objects vary along more dimensions. To test the model predictions, we conducted an interactive web-based production study within a reference game setting. Speakers and listeners were shown arrays of objects that varied in color and size. Speakers were asked to produce a referring expression to allow the listener to identify a target object. We manipulated the number of distractor objects in the grid, as well as the variation in color and size among distractor objects.We recruited 58 pairs of participants (116 participants total) over Amazon's Mechanical Turk who were each paid $1.75 for their participation. Data from another 7 pairs who prematurely dropped out of the experiment and who could therefore not be compensated for their work, were also included. Here and in all other experiments reported in this paper, participants' IP address was limited to US addresses and only participants with a past work approval rate of at least 95% were accepted.Participants were paired up through a real-time multi-player interface BIBREF44 . For each pair, one participant was assigned the speaker role and one the listener role. They initially received written instructions that informed participants that one of them would be the Speaker and the other the Listener. They were further told that they would see some number of objects on each round and that the speaker's task is to communicate one of those objects, marked by a green border, to the listener. They were explicitly told that using locative modifiers (like left or right) would be useless because the order of objects on their partner's screen would be different than on their own screen. Before continuing to the experiment, participants were required to correctly answer a series of questions about the experimental procedure. These questions are listed in Appendix "Pre-experiment quiz" .On each trial participants saw an array of objects. The array contained the same objects for both speaker and listener, but the order of objects was randomized and was typically different for speaker and listener. In the speaker's display, one of the objects – henceforth the target – was highlighted with a green border. See Figure 7 for an example of the listener's and speaker's view on a particular trial.The speaker produced a referring expression to communicate the target to the listener by typing into an unrestricted chat window. After pressing Enter or clicking the `Send' button, the speaker's message was shown to the listener. The listener then clicked on the object they thought was the target, given the speaker's message. Once the listener clicked on an object, a red border appeared around that object in both the listener and the speaker's display for 1 second before advancing to the next trial. That is, both participants received feedback about the speaker's intended referent and the listener's inference.Both speakers and listeners could write in the chat window, allowing listeners to request clarification if necessary. Listeners were able to click on an object, advancing to the next trial, only once the speaker sent an initial message.Participants proceeded through 72 trials. Of these, half were critical trials of interest and half were filler trials. On critical trials, we varied the feature that was sufficient to mention for uniquely establishing reference, the total number of objects in the array, and the number of objects that shared the insufficient feature with the target.Objects varied in color and size. On 18 trials, color was sufficient for establishing reference. On the other 18 trials, size was sufficient. Figure 7 shows an example of a size-sufficient trial. We further varied the amount of variation in the scene by varying the number of distractor objects in each array (2, 3, or 4) and the number of distractors that did share the redundant feature value with the target. That is, when size was sufficient, we varied the number of distractors that shared the same color as the target. This number had to be at least one, since otherwise the redundant property would have been sufficient for uniquely establishing reference, i.e. mentioning it would not have been redundant. Each total number of distractors was crossed with each possible number of distractors that shared the redundant property, leading to the following nine conditions: 2-1, 2-2, 3-1, 3-2, 3-3, 4-1, 4-2, 4-3, and 4-4, where the first number indicates the total number and the second number the shared number of distractors. Each condition occurred twice with each sufficient dimension. Objects never differed in type within one array (e.g., all objects are pins in Figure 7 ) but always differed in type across trials. Each object type could occur in two different sizes and two different colors. We deliberately chose photo-realistic objects of intuitively fairly typical colors. The 36 different object types and the colors they could occur with are listed in Appendix "Exp. 1 items" .Fillers were target trials from Exp. 2, a replication of GrafEtAl2016. Each filler item contained a three-object grid. None of the filler objects occurred on target trials. Objects stood in various taxonomic relations to each other and required neither size nor color mention for unique reference. See Section "Unmodified referring expressions: nominal taxonomic level" for a description of these materials.We collected data from 2177 critical trials. Because we did not restrict participants' utterances in any way, they produced many different kinds of referring expressions. Testing the model's predictions required, for each trial, classifying the produced utterance as an instance of a color-only mention, a size-only mention, or a color-and-size mention (or excluding the trial if no classification was possible). To this end we conducted the following semi-automatic data pre-processing.An R script first automatically checked whether the speaker's utterance contained a precoded color (i.e. black, blue, brown, gold, green, orange, pink, purple, red, silver, violet, white, yellow) or size (i.e. big, bigger, biggest, huge, large, larger, largest, little, small, smaller, smallest, tiny) term. In this way, 95.7 % of cases were classified as mentioning size and/or color. However, this did not capture that sometimes, participants produced meaning-equivalent modifications of color/size terms for instance by adding suffixes (e.g., bluish), using abbreviations (e.g., lg for large or purp for purple), or using non-precoded color labels (e.g., lime or lavender). Expressions containing a typo (e.g., pruple instead of purple) could also not be classified automatically. In the next step, one of the authors (CG) therefore manually checked the automatic coding to include these kinds of modifications in the analysis. This covered another 1.9% of trials. Most of the time, participants converged on a convention of producing only the target's size and/or color, e.g., purple or big blue, but not an article (e.g., the) or the noun corresponding to the object's type (e.g., comb). Articles were omitted in 88.6 % of cases and nouns were omitted in 71.6 % of cases. We did not analyze this any further.There were 50 cases (2.3%) in which the speaker made reference to the distinguishing dimension in an abstract way, e.g. different color, unique one, ripest, very girly, or guitar closest to viewer. While interesting as utterance choices, these cases were excluded from the analysis. There were 3 cases that were nonsensical, e.g. bigger off a shade, which were also excluded. In 6 cases only the insufficient dimension was mentioned – these were excluded from the analysis reported in the next section, where we are only interested in minimal or redundant utterances, not underinformative ones, but were included in the Bayesian data analysis reported in Section "Model evaluation" . Finally, we excluded six trials where the speaker did not produce any utterances, and 33 trials on which the listener selected the wrong referent, leading to the elimination of 1.5% of trials. After the exclusion, 2076 cases classified as one of color, size, or color-and-size entered the analysis.Proportions of redundant color-and-size utterances are shown in Figure 8 alongside model predictions (to be explained further in Section "Model evaluation" ). There are three main questions of interest: first, do we replicate the color/size asymmetry in probability of redundant adjective use? Second, do we replicate the previously established effect of increased redundant color use with increasing scene variation? Third, is there an effect of scene variation on redundant size use and if so, is it smaller compared to that on color use, as is predicted under asymmetric semantic values for color and size adjectives?We addressed all of these questions by conducting a single mixed effects logistic regression analysis predicting redundant over minimal adjective use from fixed effects of sufficient property (color vs. size), scene variation (proportion of distractors that does not share the insufficient property value with the target), and the interaction between the two. The model included the maximal random effects structure that allowed the model to converge: by-speaker and by-item random intercepts.We observed a main effect of sufficient property, such that speakers were more likely to redundantly use color than size adjectives ( $\beta = 3.54$ , $SE = .22$ , $p < .0001$ ), replicating the much-documented color-size asymmetry. We further observed a main effect of scene variation, such that redundant adjective use increased with increasing scene variation ( $\beta = 4.62$ , $SE = .38$ , $p < .0001$ ). Finally, we also observed a significant interaction between sufficient property and scene variation ( $\beta = 2.26$ , $SE = .74$ , $p < .003$ ). Simple effects analysis revealed that the interaction was driven by the scene variation effect being smaller in the color-sufficient condition ( $\beta = 3.49$ , $SE = .22$0 , $SE = .22$1 ) than in the size-sufficient condition ( $SE = .22$2 , $SE = .22$3 , $SE = .22$4 ), as predicted if size modifiers are noisier than color modifiers. That is, while the color-sufficient condition indeed showed a scene variation effect—and as far as we know, this is the first demonstration of an effect of scene variation on redundant size use—this effect was tiny compared to that of the size-sufficient condition.In order to evaluate RSA with continuous semantics we conducted a Bayesian data analysis. This allowed us to simultaneously generate model predictions and infer likely parameter values, by conditioning on the observed production data (coded into size, color, and size-and-color utterances as described above) and integrating over the five free parameters. To allow for differential costs for size and color, we introduce separate cost weights ( $\beta _{c(\textrm {size})}, \beta _{c(\textrm {color})}$ ) applying to size and color mentions, respectively, in addition to semantic values for color and size ( $x_{\textrm {color}}$ , $x_{\textrm {size}}$ ) and an informativeness parameter $\beta _i$ . We assumed uniform priors for each parameter: $x_{\textrm {color}}, x_{\textrm {size}} \sim \mathcal {U}(0,1)$ , $\beta _{c(\textrm {size})}, \beta _{c(\textrm {color})} \sim \mathcal {U}(0,40)$ , $\beta _i \sim \mathcal {U}(0,40)$ . Inference for the cognitive model was exact. We used Markov Chain Monte Carlo (MCMC) with a burn-in of 10000 and lag of 10 to draw 2000 samples from the joint posteriors on the five free parameters.Point-wise maximum a posteriori (MAP) estimates of the model's posterior predictives for just redundant utterance probabilities are shown alongside the empirical data in Figure 8 . In addition, MAP estimates of the model's posterior predictives for each combination of utterance, sufficient dimension, number of distractors, and number of different distractors (collapsing across different items) are plotted against all empirical utterance proportions in Figure 9 . At this level, the model achieves a correlation of $r = .99$ . Looking at results additionally on the by-item level yields a correlation of $r = .85$ (this correlation is expected to be lower both because each item contains less data, and because we did not provide the model any means to refer differently to, e.g., combs and pins). The model thus does a very good job of capturing the quantitative patterns in the data.Posteriors over parameters are shown in Figure 10 . Crucially, the semantic value of color is inferred to be higher than that of size – there is no overlap between the 95% highest density intervals (HDIs) for the two parameters. That is, size modifiers are inferred to be noisier than color modifiers. The high inferred $\beta _i$ (MAP $\beta _i$ = 31.4, HDI = [30.7,34.5]) suggests that this difference in semantic value contributes substantially to the observed color-size asymmetries in redundant adjective use and that speakers are maximizing quite strongly. As for cost, there is a lot of overlap in the inferred weights of size and color modifiers, which are both skewed very close to zero, suggesting that a cost difference (or indeed any cost at all) is neither necessary to obtain the color-size asymmetry and the scene variation effects, nor justified by the data. Recall further that we already showed in Section "RSA with continuous semantics – emergent color-size asymmetry" that the color-size asymmetry in redundant adjective use requires an asymmetry in semantic value and cannot be reduced to cost differences. An asymmetry in cost only serves to further enhance the asymmetry brought about by the asymmetry in semantic value, but cannot carry the redundant use asymmetry on its own.We evaluated the cs-RSA model on the obtained production data from Exp. 2. In particular, we were interested in using model comparison to address the following issues: First, can RSA using elicited typicality as the semantic values account for quantitative details of the production data? Second, are typicality values sufficient, or is there additional utility in including a noise offset determined by the type of modifier, as was used in the previous section? Third, does utterance cost explain any of the observed production behavior.While the architecture of the model remained the same as that of the model presented in Section "RSA with continuous semantics – emergent color-size asymmetry" , we briefly review the minor necessary changes, some of which we already mentioned at the beginning of this section. These changes concerned the semantic values and the cost function.Whereas for the purpose of evaluating the model in Section "Modified referring expressions: size and color modifiers under different scene variation conditions" we only considered the utterance alternatives color, size, and color-size, collapsing over the precise attributes, here we included in the lexicon each possible color adjective, type noun, and combination of the two. This substantially increased the size of the lexicon to 37 unique utterances. For each combination of utterance $u$ and object $o$ that occurred in the experiment, we included a separate semantic value $x_{u,o}$ , elicited in the norming experiments described in Section UID83 (rather than inferred as done for Exp. 1, to avoid overfitting). For any given context, we assumed the utterance alternatives that correspond to the individually present features and their combinations. For example, for the context in Figure 13 , the set of utterance alternatives was yellow, green, pear, banana, avocado, yellow pear, yellow banana, and green avocado.We compared two choices of semantics for the model. In the empirical semantics version, the empirically elicited typicality values were directly used as semantic values. In the more complex fixed plus empirical semantics version, we introduce an additional parameter interpolating between the empirical typicality values and inferred values for each utterance type as employed in Section "Modified referring expressions: size and color modifiers under different scene variation conditions" (e.g. one value for color terms and another for type terms, which are multiplied when the terms are composed in an utterance). Note that this allows us to perform a nested model comparison, since the first model is a special case of the second.For the purpose of evaluating the model in Section "Modified referring expressions: size and color modifiers under different scene variation conditions" we inferred two constant costs (one for color and one for size), and found in the Bayesian Data Analysis that the role of cost in explaining the data was minimal at best. Here, we compared two different versions of utterance cost. In the fixed cost model we treated cost the same way as in the previous section and included only a color and type level cost, inferred from the data. We then compared this model to an empirical cost model, in which we included a more complex cost function. Specifically, we defined utterance cost $c(u)$ as follows: $$ 
c(u) = \beta _F\cdot p(u) + \beta _L\cdot l(u)$$   (Eq. 98) Here, $p(u)$ is negative log utterance frequency, as estimated from the Google Books corpus (years 1950 to 2008); $l(u)$ is the mean empirical length of the utterance in characters in the production data (e.g., sometimes yellow was abbreviated as yel, leading to an $l(u)$ smaller than 6); $\beta _F$ is a weight on frequency; and $\beta _L$ is a weight on length. Both $p(u)$ and $l(u)$ were normalized to fall into the interval $[0,1]$ . The empirical cost function thus prefers short and frequent utterances (e.g., blue) over long and infrequent ones (turquoise-ish bananaesque thing). We compared both of these models to a simpler baseline in which utterances were assumed to have no cost.To evaluate the effect of these choices of semantics and cost, we conducted a full Bayesian model comparison. Specifically, we computed the Bayes Factor for each comparison, a measure quantifying the support for one model over another in terms of the relative likelihood they each assign to the observed data. As opposed to classical likelihood ratios, which only use the maximum likelihood estimate, the likelihoods in the Bayes Factor integrate over all parameters, thus automatically correcting for the flexibility due to extra parameters (the “Bayesian Occam's Razor”). Because it was intractable to analytically compute these integrals for our recursive model, we used Annealed Importance Sampling (AIS), a Monte Carlo algorithm commonly used to approximate these quantities. To ensure high-quality estimates, we took the mean over 100 independent samples for each model, with each chain running for 30,000 steps. The marginal log likelihoods for each model are shown in Table 6 . The best performing model used fixed plus empirical semantics and did not include a cost term. Despite the greater number of parameters associated with adding the fixed semantics to the empirical semantics, the fixed plus empirical semantics models were preferred across the board compared to their empirical-only counterparts ( $BF = 3.7 \times 10^{48}$ for fixed costs, $BF = 2.1 \times 10^{60}$ for empirical costs, and $BF = 1.4 \times 10^{71}$ for no cost). In comparison, additional cost-related parameters were not justified, with $BF = 5.7 \times 10^{21}$ for no cost compared to fixed cost and $BF = 2.1 \times 10^{27}$ for compared to empirical cost.The correlation between empirical utterance proportions and the best model's MAP predictions at the by-item level was $r=.94$ . Predictions for the best-performing model are visualized alongside empirical proportions in Figure 16 . The model successfully reproduces the empirically observed typicality effects in all four experimental conditions, with a reasonably good quantitative agreement. The interpolation weight between the fixed and empirical semantic values $\beta _{\textrm {fixed}}$ (Figure 17 ) is in the intermediate range: this provides evidence that a noisy truth-conditional semantics as employed in Exp. 1 is justified, but that taking into account graded category membership or typicality in an utterance's final semantic value is also necessary.There is one major, and interesting, divergence from the empirical data in conditions without color competitors. Here, color-and-type utterances are systematically somewhat underpredicted in the informative condition, and systematically somewhat overpredicted in the overinformative condition. The reverse is true for color-only utterances. It is worth looking at the posterior over parameters, shown in Figure 17 , to understand the pattern. In particular, the utterance type level semantic value of type is inferred to be systematically higher than that of color, capturing that type utterances are less noisy than color utterances. An increase in color-only mentions in the overinformative condition could be achieved by reducing the semantic value for type. However, that would lead to a further and undesirable increase in color-only mentions in the informative condition as well. That is, the two conditions are in a tug-of-war with each other.We evaluated cs-RSA on the production data from Exp. 3. The architecture of the model is identical to that of the model presented in Section "Model evaluation" . The only difference is that the set of alternatives contained only the three potential target utterances (i.e., the target's sub, basic, and super label). Whereas the modifier models from the previous sections treat all individual features and feature combinations represented in the display as utterance alternatives, for computational efficiency we restrict alternatives in the nominal choice model, considering only the three different levels of reference to the target as alternatives, e.g., dalmatian, dog, animal. (So, when a German Shepherd is a distractor, German Shepherd is not considered an alternative. This has minimal effects on model predictions as long as German Sheperd has low semantic fit to the dalmatian target.)For the previous dataset, we tested which of three different semantics was most justified – a fixed compositional semantics with type-level semantic values, the empirically elicited typicality semantics, or a combination of the two. For the current dataset, this question did not arise, because we investigated only one word utterances (all nouns). We hence only considered the empirical semantics. However, like in the previous dataset, we evaluated which cost function was best supported by the data: the one defined in ( 98 ) (a linear weighted combination of an utterance's length and its frequency) or a simpler baseline in which utterances were assumed to have no cost.We employed the same procedure as in the previous section to compute the Bayes Factor for the comparison between the two cost models, and to compute the posteriors over parameters. Priors were again $\beta _i \sim \mathcal {U}(0,20)$ , $\beta _{F} \sim \mathcal {U}(0,5)$ , $\beta _{L} \sim \mathcal {U}(0,5)$ , $\beta _t \sim \mathcal {U}(0,5)$ .Despite the greater number of parameters associated with adding the cost function, the model that includes non-zero costs was preferred compared to its no-cost counterpart ( $BF = 2.8 \times 10^{77}$ ). Posteriors over parameters are shown in Figure 20 . It is worth noting that the weight on frequency is close to zero. That is, in line with the results from the mixed effects regression, it is an utterance's length, but not its frequency, that affects the probabilitiy with which it is produced in this paradigm.Empirical utterance proportions are shown against MAP model predictions in Figure 21 . The correlation between empirical utterance proportions and the model's MAP predictions at the level of targets, utterances, and conditions was $r = .86$ . Further collapsing across targets yields a correlation of $r = .95$ . While the model overpredicts subordinate level and underpredicts basic level choices in the sub necessary condition, it otherwise captures the patterns in the data very well.In this section, we reported the results of a dataset of freely collected referring expressions that replicated the well-documented color-size asymmetry in redundant adjective use, the effect of scene variation on redundant color use, and showed a novel effect of scene variation on redundant size use. We also showed that cs-RSA provides an excellent fit to these data. In particular, the crucial element in obtaining the color-size asymmetry in overmodification is that size adjectives be noisier than color adjectives, captured in RSA via a lower semantic value for size compared to color. The effect is that color adjectives are more informative than size adjectives when controlling for the number of distractors each would rule out under a deterministic semantics. Asymmetries in the cost of the adjectives were not attested, and would only serve to further enhance the modification asymmetry resulting from the asymmetry in semantic value. In addition, we showed that asymmetric effects of scene variation on overmodification straightforwardly fall out of cs-RSA: scene variation leads to a greater increase in overmodification with less noisy than with more noisy modifiers because the less noisy modifiers (colors) on average provide more information about the target.These results raise interesting questions regarding the status of the inferred semantic values: do color modifiers have inherently higher semantic values than size modifiers? Is the difference constant? What if the color modifier is a less well known one like mauve? The way we have formulated the model thus far, there would indeed be no difference in semantic value between red and mauve. Moreover, the model is not equipped to handle potential object-level idiosyncracies such as the typicality effects discussed in Section "Experiment 3 items" . We defer a fuller discussion of the status of the semantic value term to the General Discussion (Section "Continuous semantics" ) and turn first to cs-RSA's potential for capturing these typicality effects.In this section we demonstrated that cs-RSA predicts color typicality effects in the production of referring expressions. The model employed here did not differ in its architecture from that employed in Section "Modified referring expressions: size and color modifiers under different scene variation conditions" , but only in that a) semantic values were assumed to operate at the individual utterance/object level in addition to at the utterance type/object level; b) semantic values for individual utterances were empirically elicited via typicality norming studies; and c) an utterance's cost was allowed to be a function of its mean empirical length and its corpus frequency instead of having a constant utterance type level value, though utterance cost ultimately was found not to play a role in predicting utterance choice.This suggests that the dynamics at work in the choice of color vs. size and in the choice of color as a function of the object's color typicality are very similar: speakers choose utterances by considering the fine-grained differences in information about the intended referent communicated by the ultimately chosen utterance compared to its competitor utterances. For noisier utterances (e.g., banana as applied to a blue banana), including the `overinformative' color modifier is useful because it provides information. For less noisy utterances (e.g., banana as applied to a yellow banana), including the color modifier is useless because the unmodified utterance is already highly informative with respect to the speaker's intention. These dynamics can sometimes even result in the color modifier being left out altogether, even when there is another—very atypical—object of the same type present, simply because the literal listener is expected to prefer the typical referent strongly enough.Model comparison demonstrated the need for assuming a semantics that interpolates between a noisy truth-conditional semantics as employed in Exp. 1 and empirically elicited typicality values. This may reflect semantic knowledge that goes beyond graded category membership, additional effects of compositionality, or perhaps simply differences between our empirical typicality measure and the “semantic fit” expected by RSA models. Perhaps surprisingly, we replicated the result from Exp. 1 that utterance cost does not add any predictive power, even when quantified via a more sophisticated cost function that takes into account an utterance's length and frequency.In the next section, we move beyond the choice of modifier and ask whether cs-RSA provides a good account of content selection in referring expressions more generally. To answer this question we turn to simple nominal referring expressions.In Section "Modified referring expressions: size and color modifiers under different scene variation conditions" we showed that cs-RSA successfully captures both the basic asymmetry in overmodification with color vs. size as well as effects of scene variation on overmodification. In Section "Experiment 3 items" we discussed a further characteristic of speakers' overmodification behavior: speakers are more likely to redundantly produce modifiers that denote atypical rather than typical object features, i.e., they are more likely to refer to a blue banana as a blue banana rather than as a banana, and they are more likely to refer to a yellow banana as a banana than as a yellow banana BIBREF7 , BIBREF25 . So far we have not included any typicality effects in the semantics of our RSA model, hence the model so far would not capture this asymmetry.A natural first step is to introduce a more nuanced semantics for nouns in our model. In particular, we could imagine a continuous semantics in which banana fits better (i.e. has a semantic value closer to 1 for) the yellow banana than the brown, and fits the brown better than the blue; specific such hypothetical values are shown in the first row of Table 3 . Let us further assume that modifying the noun with a color adjective leads to uniformly high semantic values close to 1 for those objects that a simple truth-conditional semantics would return `true' for (see diagonal in Table 3 ) and a very low semantic value close to 0 for any utterance applied to any object that a simple truth-conditional semantics would return `false' for.The effect of running the speaker model forward with the standard literal listener treatment of the values in Table 3 for the three contexts in Figure 11 , where banana is the strictly sufficient utterance for unique reference (i.e., color is redundant under the standard view) is as follows: with $\beta _i$ = 12 and $\beta _c$ = 5, the resulting speaker probabilities for the minimal utterance banana are .95, .29, and .04, to refer to the yellow banana, the brown banana, and the blue banana, respectively. In contrast, the resulting speaker probabilities for the redundant yellow banana, brown banana, and blue banana are .05, .71, and .96, respectively. That is, redundant color mention increases with decreasing semantic value of the simple banana utterance.This shows that cs-RSA can predict typicality effects if the semantic fit of the noun (and hence also of color-noun compounds) to an object is modulated by typicality. The reason the typicality effect arises is that, with the hypothetical values we assumed, the gain in informativeness between using the unmodified banana and the modified COLOR banana is greater in the blue than in the yellow banana case.This example is somewhat oversimplified. In practice, speakers sometimes mention an object's color without mentioning the noun. In the contexts presented in Figure 11 this does not make much sense because there is always a competitor of the same color present. In contrast, in the contexts in Figure 13 and Figure 13 , color alone disambiguates the target. This suggests that we should consider among the set of utterance alternatives not just the simple type mentions (e.g., banana) and color-and-type mentions (e.g., yellow banana), but also simple color mentions (e.g., yellow). The dynamics of the model proceed as before.An additional, more theoretically fraught, simplification concerns where typicality can enter into the semantics and how compositions proceeds. In the above, we have assumed that the semantic value of the modified expression is uniformly high, which is qualitatively what is necessary (and, as we will see below, empirically correct) in order for the typicality effects to emerge. However, there is no straightforward way to compositionally derive such uniformly high values from the semantic values of the nouns and the semantic values of the color modifiers, which we have not yet discussed. Indeed, compositional semantics of graded meanings is a well known problem for theories of modification BIBREF48 . Rather than try to solve it here, we note that RSA works at the level of whole utterances. Hence, if we can reasonably measure the semantic fit of each utterance to each possible referent, then cs-RSA will make predictions for production without the need to derive the semantic values compositionally. That is, if we can measure the typicality of the phrase blue banana for a banana, we don't need to derive it from blue, banana, and a theory of composition. This separates pragmatic aspects of reference, which are the topic of this paper, from issues in compositional semantics, which are not; hence we will take this approach for experimentally testing the predictions of relaxed semantics RSA for typicality effects.The stimuli for Exp. 1 were specifically designed to be realistic objects with low color-diagnosticity, so they did not include objects with low typicality values or large degrees of variation in typicality. This makes the dataset from Exp. 1 not well-suited for investigating typicality effects. We therefore conducted a separate production experiment in the same paradigm but with two broad changes: first, objects' color varied in typicality; and second, we did not manipulate object size, focusing only on color mention. This allows us to ask three questions: first, do we replicate the typicality effects reported in the literature – that is, are less color-typical objects more likely to lead to redundant color use than more color-typical objects? Second, does cs-RSA with empirically elicited typicality values as proxy for a continuous semantics capture speakers' behavior? Third, does the semantic value depend only on typicality, or is there still a role for modifier type noise of the kind we investigated in the previous section? In addition, we can investigate the extent to which utterance cost, which we found not to play a role in the previous section, affects the choice of referring expression.We recruited 61 pairs of participants (122 participants total) over Amazon's Mechanical Turk who were each paid $1.80 for their participation.The procedure was identical to that of Exp. 1. See Figure 12 for an example speaker and listener perspective.Each participant completed 42 trials. In this experiment, there were no filler trials, since pilot studies with and without fillers delivered very similar results. Each array presented to the participants consisted of three objects that could differ in type and color. One of the three objects functioned as a target and the other two as its distractors.The stimuli were selected from seven color-diagnostic food items (apple, avocado, banana, carrot, pear, pepper, tomato), which all occurred in a typical, mid-typical and atypical color for that object. For example, the banana appeared in the colors yellow (typical), brown (midtypical), and blue (atypical). All items were presented as targets and as distractors. Pepper additionally occurred in a fourth color, which only functioned as a distractor due to the need for a green color competitor (as explained in the following paragraph).We refer to the different context conditions as “informative”, “informative-cc”, “overinformative”, and “overinformative-cc” (see Figure 13 ). A context was “overinformative” (Figure 13 ) when mentioning the type of the item, e.g., banana, was sufficient for unambiguously identifying the target. In this condition, the target never had a color competitor. This means that mentioning color alone (without a noun) was also unambiguously identifying. In contrast, in the overinformative condition with a color competitor (“overinformative-cc”, Figure 13 ), color alone was not sufficient. In the informative conditions, color and type mention were necessary for unambiguous reference. Again, one context type did (Figure 13 ) and one did not (Figure 13 ) include a color competitor among its distractors.Each participant saw 42 different contexts. Each of the 21 items (color-type combinations) was the target exactly twice, but the context in which they occurred was drawn randomly from the four possible conditions mentioned above. In total, there were 84 different possible configurations (seven target food items, each of them in three colors, where each could occur in four contexts). Trial order was randomized.Two participant-pairs were excluded because they did not finish the experiment and therefore could not receive payment. Trials on which the speaker did not produce any utterances were also excluded, resulting in the exclusion of two additional participant-pairs. Finally, there were 10 speakers who consistently used roundabout descriptions instead of direct referring expressions (e.g., monkeys love... to refer to banana). These pairs were also excluded, since such indirect expressions do not inform our questions about modifier production.We analyzed data from 1974 trials. Just as in Exp. 1, participants communicated freely, which led to a vast amount of different referring expressions. To test the model's predictions, the utterance produced for each trial was to be classified as belonging to one of the following categories: type-only (“banana”), color-and-type (“yellow banana”), and color-only (“yellow”) utterances. Referring expressions that included categories (“yellow fruit”), descriptions (“has green stem”), color-circumscriptions (“funky carrot”), and negations (“yellow but not banana”) were regarded as other and excluded. To this end we conducted the following semi-automatic data pre-processing.The referring expressions were analyzed similarly to Exp. 1. First, 32 trials (1.6%) were excluded because the listener selected the wrong referent. 109 trials (5.6%) were excluded because the referring expressions included one of the exceptional cases described above (e.g., using negations). An R script then automatically checked the remaining 1833 utterances for whether they contained a precoded color term (i.e. green, purple, white, black, brown, yellow, orange, blue, pink, red, grey) or type (i.e. apple, banana, carrot, tomato, pear, pepper, avocado). This way, 96.5% of the remaining cases were classified as mentioning type and/or color.However, this did not capture that sometimes, participants produced meaning-equivalent modifications of color/type terms for instance by adding suffixes (e.g., pinkish), using abbreviations (e.g., yel for yellow), or using non-precoded color and type labels (e.g., lavender or jalapeno). In addition, expressions that contained a typo (e.g., blakc instead of black) could also not be classified automatically. One of the authors (EK) therefore manually hand-coded these cases. There were 6 cases (0.3%) that could not be categorized and were excluded. Overall, 1827 utterances were classified as one of color, type, or color-and-type entered the analysis.In order to test for typicality effects on the production data and to evaluate cs-RSA's performance, we collected empirical typicality values for each utterance/object pair in three separate studies. The first study collected typicalities for color-and-type/object pairs (e.g., yellow banana as applied to a yellow banana, a blue banana, an orange pear, etc., see Figure 14 ). The second study collected typicalities for type-only/object pairs (e.g., banana as applied to a yellow banana, a blue banana, an orange pear, etc., Figure 14 ). The third study collected typicalities for color/color pairs (e.g., yellow as applied to a color patch of the average yellow from the yellow banana stimulus or to a color patch of the average orange from the orange pear stimulus, and so on, for all other colors, Figure 14 ).On each trial of the type or color-and-type studies, participants saw one of the stimuli used in the production experiment in isolation and were asked: “How typical is this object for a utterance”, where utterance was replaced by an utterance of interest. In the color typicality study, they were asked “How typical is this color for the color color?”, where color was replaced by one of the relevant color terms. They then adjusted a continuous sliding scale with endpoints labeled “very atypical” and “very typical” to indicate their response. A summary of the the three typicality norming studies is shown in Table 4 .Slider values were coded as falling between 0 (`very atypical') and 1 (`very typical'). For each utterance-object combination, we computed mean typicality ratings. As an example, the means for the banana items and associated color patches are shown in Table 5 . The values exhibit the same gradient as those hypothesized for the purpose of the example in Table 3 . The means for all items are visualized in Figure 15 . Mean typicality values for utterance-object pairs obtained in the norming studies are used in the analyses and visualizations in the following.Proportions of type-only (banana), color-and-type (yellow banana), color-only (yellow), and other (funky carrot) utterances are shown in Figure 16 as a function of the described item's mean type-only (banana) typicality. Visually inspecting just the explicitly marked yellow banana, brown banana, and blue banana cases suggests a large typicality effect in the overinformative conditions as well as a smaller typicality effect in the informative conditions, such that color is less likely to be produced with increasing typicality of the object.The following questions are of interest. First, do we replicate the previously documented typicality effect on redundant color mention (as suggested by the visual inspection of the banana item)? Second, does typicality affect color mention even when color is informative (i.e., technically necessary for establishing unique reference)? Third, are speakers sensitive to the presence of color competitors in their use of color or are typicality effects immune to the nature of the distractor items?To address these questions we conducted a mixed effects logistic regression predicting color use from fixed effects of typicality, informativeness, and color competitor presence. We used the typicality norms obtained in the type/object typicality elicitation study reported above (see Figure 14 ) as the continuous typicality predictor. The informativeness condition was coded as a binary variable (color informative vs. color overinformative trial) as was color competitor presence (absent vs. present). All predictors were centered before entering the analysis. The model included by-speaker and by-item random intercepts, which was the maximal random effects structure that allowed the model to converge.There was a main effect of typicality, such that the more typical an object was for the type-only utterance, the lower the log odds of color mention ( $\beta $ = -4.17, $SE$ = 0.45, $p <$ .0001), replicating previously documented typicality effects. Stepwise model comparison revealed that including interaction terms was not justified by the data, suggesting that speakers produce more typical colors less often even when the color is in principle necessary for establishing reference (i.e., in the informative conditions). This is notable: speakers sometimes call a yellow banana simply a banana even when other bananas are present, presumably because they can rely on listeners drawing the inference that they must have meant the most typical banana. In contrast, blue bananas' color is always mentioned in the informative conditions.There was also a main effect of informativeness, such that color mention was less likely when it was overinformative than when it was informative ( $\beta $ = -5.56, $SE$ = 0.33, $p <$ .0001). Finally, there was a main effect of color competitor presence, such that color mention was more likely when a color competitor was absent ( $\beta $ = 0.71, $SE$ = 0.16, $p <$ .0001). This suggests that speakers are indeed sensitive to the contextual utility of color – color typicality alone does not capture the full set of facts about color mention, as we already saw in Section "Modified referring expressions: size and color modifiers under different scene variation conditions" .In this section we investigate whether cs-RSA accounts for referring expression production beyond the choice of modifier. In particular, we focus on speakers' choice of taxonomic level of reference in nominal referring expressions. A particular object can be referred to at its subordinate (dalmatian), basic (dog), or superordinate (animal) level, among other choices. As discussed in Section "Nominal referring expressions" , multiple factors play a role in the choice of nominal referring expression, including an expression's contextual informativeness, its cognitive cost BIBREF23 short and frequent terms are preferred over long and infrequent ones,>griffin1998,jescheniak1994, and its typicality BIBREF23 an utterance is more likely to be used if the object is a good instance of it,> Jolicoeur1984. Thus, we explore the same factors as potential contributors to nominal choice that we explored in previous sections for modification.In order to evaluate cs-RSA for nominal choice, we proceeded as in Section "Modified referring expressions: color typicality" : we collected production data within the same reference game setting, but varied the contextual informativeness of utterances by varying whether distractors shared the same basic or superordinate category with the target (see Figure 18 ). We also elicited typicality ratings for object-utterance combinations, which entered the model as the semantic values via the lexicon. We then conducted Bayesian data analysis, as in previous sections, for model comparison.We recruited 58 pairs of participants (116 participants total, the same participants as in Exp. 1) over Amazon's Mechanical Turk who were each paid $1.75 for their participation.The procedure was identical to that of Exp. 1. Participants proceeded through 72 trials. Of these, half were critical trials of interest and half were filler trials (the critical trials from Exp. 1). On critical trials, we varied the level of reference that was sufficient to mention for uniquely establishing reference.Stimuli were selected from nine distinct domains, each corresponding to distinct basic level categories such as dog. For each domain, we selected four subcategories to form our target set (e.g. dalmatian, pug, German Shepherd and husky). See Table 9 in Appendix "Experiment 3 items" for a full list of domains and their associated target items. Each domain also contained an additional item which belonged to the same basic level category as the target (e.g., greyhound) and items which belonged to the same supercategory but not the same basic level (e.g., elephant or squirrel). The latter items were used as distractors.Each trial consisted of a display of three images, one of which was designated as the target object. Each pair of participants saw each target exactly once, for a total of 36 trials. These target items were randomly assigned distractor items which were selected from three different context conditions, corresponding to different communicative pressures (see Figure 18 ). The subordinate necessary contexts contained one distractor of the same basic category and one distractor of the same superordinate category (e.g., target: dalmatian, distractors: greyhound (also a dog) and squirrel (also an animal)). The basic sufficient contexts contained either two distractors of the same superordinate category but different basic category as the target (e.g., target: husky, distractors: hamster and elephant) or one distractor of the same superordinate category and one unrelated item (e.g., target: pug, distractors: cow and table). The superordinate sufficient contexts contained two unrelated items (e.g., target: German Shepherd, distractors: shirt and cookie).This context manipulation served as a manipulation of utterance informativeness: any target could be referred to at the subordinate (dalmatian), basic (dog) or superordinate (animal) level. However, the level of reference necessary for uniquely referring differed across contexts.In order to test for typicality effects on the production data and to evaluate cs-RSA's performance, we collected empirical typicality values for each utterance/object pair (see Appendix "Typicality norms for Experiment 3" for details).We collected 2193 referring expressions. To determine the level of reference for each trial, we followed the following procedure. First, speakers' and listeners' messages were parsed automatically; the referring expression used by the speaker was extracted for each trial and checked for whether it contained the current target's correct sub(ordinate), basic, or super(ordinate) level term using a simple grep search. In this way, 71.4% of trials were labelled as mentioning a pre-coded level of reference. In the next step, remaining utterances were checked manually by one of the authors (CG) to determine whether they contained a correct level of reference term which was not detected by the grep search due to typos or grammatical modification of the expression. In this way, meaning-equivalent alternatives such as doggie for dog, or reduced forms such as gummi, gummies and bears for gummy bears were counted as containing the corresponding level of reference term. This covered another 15.0% of trials. 41 trials on which the listener selected the wrong referent were excluded, leading to the elimination of 2.1% of trials. Six trials were excluded because the speaker did not produce any utterances. Additionally, a total of 12.5% of correct trials were excluded because the utterance consisted only of an attribute of the superclass (the living thing for animal), of the basic level (can fly for bird), of the subcategory (barks for dog) or of the particular instance (the thing facing left) rather than a category noun. These kinds of attributes were also mentioned in addition to the noun on trials which were included in the analysis for 8.9% of sub level terms, 18.9% of basic level terms, and 60.9% of super level terms. On 1.2% of trials two different levels of reference were mentioned; in this case the more specific level of reference was counted as being mentioned in this trial. After all exclusion and pre-processing, 1872 cases classified as one of sub, basic, or super entered into the analysis.Proportions of sub, basic, and super level utterances are shown in Figure 19 . Overall, super level mentions are highly dispreferred ( $< 2\%$ ), so we focus in this section only on predictors of sub over basic level mentions. The clearest pattern of note is that sub level mentions are only preferred in the most constrained context that necessitates the sub level mention for unique reference (e.g., target: dalmatian, distractor: greyhound; see Figure 18 ). Nevertheless, even in these contexts there is a non-negligible proportion of basic level mentions (28%). In the remaining contexts, where the sub and basic level are equally informative, there is a clear preference for the basic level. In addition, mitigating this context effect, sub level mentions increased with increasing typicality of the object as an instance of the sub level utterance.What explains these preferences? In order to test for effects of informativeness, length, frequency, and typicality on nominal choice we conducted a mixed effects logistic regression predicting sub over basic level mention from centered predictors for the factors of interest and the maximal random effects structure that allowed the model to converge (random by-speaker and by-target intercepts).Frequency was coded as the difference between the sub and the basic level's log frequency, as extracted from the Google Books Ngram English corpus ranging from 1960 to 2008.Length was coded as the ratio of the sub to the basic level's length. We used the mean empirical lengths in characters of the utterances participants produced. For example, the minivan, when referred to at the subcategory level, was sometimes called “minivan” and sometimes “van” leading to a mean empirical length of 5.71. This is the value that was used, rather than 7, the length of “minivan”. That is, a higher frequency difference indicates a lower cost for the sub level term compared to the basic level, while a higher length ratio reflects a higher cost for the sub level term compared to the basic level.Typicality was coded as the ratio of the target's sub to basic level label typicality. That is, the higher the ratio, the more typical the object was for the sub level label compared to the basic level; or in other words, a higher ratio indicates that the object was relatively atypical for the basic label compared to the sub label. For instance, the panda was relatively atypical for its basic level “bear” (mean rating 0.75) compared to the sub level term “panda bear” (mean rating 0.98), which resulted in a relatively high typicality ratio.Informativeness condition was coded as a three-level factor: sub necessary, basic sufficient, and super sufficient, where basic sufficient (two superordinate distractors) and basic sufficient (one superordinate distractor) were collapsed into basic sufficient. Condition was Helmert-coded: two contrasts over the three condition levels were included in the model, comparing each level against the mean of the remaining levels (in order: sub necessary, basic sufficient, super sufficient). This allowed us to determine whether the probabilities of type mention for neighboring conditions were significantly different from each other, as suggested by Figure 19 .The log odds of mentioning the sub level term were greater in the sub necessary condition than in either of the other two conditions ( $\beta = 2.11$ , $SE = .17$ , $p < .0001$ ), and greater in the basic sufficient condition than in the super sufficient condition ( $\beta = .60$ , $SE = .15$ , $p < .0001$ ), suggesting that the contextual informativeness of the sub level mention has a gradient effect on utterance choice. There was also a main effect of typicality, such that the sub level term was preferred for objects that were more typical for the sub level compared to the basic level description ( $\beta = 4.82$ , $SE = 1.35$ , $SE = .17$0 ). In addition, there was a main effect of length, such that as the length of the sub level term increased compared to the basic level term (“chihuahua”/“dog” vs. “pug”/“dog”), the sub level term was dispreferred (“chihuahua” is dispreferred compared to “pug”, $SE = .17$1 , $SE = .17$2 , $SE = .17$3 ). The main effect of frequency did not reach significance ( $SE = .17$4 , $SE = .17$5 , $SE = .17$6 ).Unsurprisingly, there was also significant by-participant and by-domain variation in sub level term mention. For instance, mentioning the sub over the basic level term was preferred more in some domains (e.g. in the “candy” domain) than in others. Likewise, some domains had a greater preference for basic level terms (e.g. the “shirt” domain). Using the super term also ranged from hardly being observable (e.g., plant in the “flower” domain) to being used more frequently (e.g., furniture in the “table” domain and vehicle in the “car” domain).We thus replicated the well-documented preference to refer to objects at the basic level, which is partly modulated by contextual informativeness and partly a result of the basic level term's cognitive cost and typicality compared to its sub level competitor, mirroring the results from Exp. 2.Perhaps surprisingly, we did not observe an effect of frequency on sub level term mention. This is likely due to the modality of the experiment: the current study was a written production study, while most studies that have identified frequency as a factor governing production choices are spoken production studies. It may be that the cognitive cost of typing longer words may be disproportionately higher than that of producing longer words in speech, thus obscuring a potential effect of frequency. Support for this hypothesis comes from studies comparing written and spoken language, which has found that spoken descriptions are likely to be longer than written descriptions and, in English, seem to have a lower propositional information density than written descriptions BIBREF50 .In this paper we have provided a unified account of referring expression choice that solves a long-recognized puzzle for rational theories of language use: why do speakers' referring expressions often and systematically exhibit seeming overinformativeness? We have shown here that by allowing contextual utterance informativeness to be computed with respect to a continuous (or noisy) rather than a Boolean semantics, utterances that seem overinformative can in fact be sufficiently informative. This happens when what seems like the prima facie sufficiently informative utterance is in fact noisy and may lead a literal listener astray; adding redundancy ensures successful communication. This simple modification to the Rational Speech Act approach allowed us to capture: the basic well-documented asymmetry for speakers to be more likely to redundantly use color adjectives than size adjectives; the interaction between sufficient dimension and scene variation in the probability of redundancy; and typicality effects in both color modifier choice and noun choice.We have thus shown that with one key innovation – a continuous semantics – one can retain the assumption that speakers rationally trade off informativeness and cost of utterances in language production. Rather than being wastefully overinformative, adding redundant modifiers or referring at a lower taxonomic level than strictly necessary is in fact appropriately informative. This innovation thus not only provides a unified explanation for a number of key patterns within the overinformative referring expression literature that have thus far eluded a unified explanation; it also extends to the domain of nominal choice. And in contrast to previously proposed computational models, it is straightforwardly extendable to any instance of definite referring expressions of the sort we have examined here.While the core architecture with relaxe

Q: Does the paper describe experiments with real humans?