Robots Learning to Say `No': Prohibition and Rejective Mechanisms in Acquisition of Linguistic Negation

10/28/2018
by   Frank Förster, et al.
University of Hertfordshire
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`No' belongs to the first ten words used by children and embodies the first active form of linguistic negation. Despite its early occurrence the details of its acquisition process remain largely unknown. The circumstance that `no' cannot be construed as a label for perceptible objects or events puts it outside of the scope of most modern accounts of language acquisition. Moreover, most symbol grounding architectures will struggle to ground the word due to its non-referential character. In an experimental study involving the child-like humanoid robot iCub that was designed to illuminate the acquisition process of negation words, the robot is deployed in several rounds of speech-wise unconstrained interaction with naïve participants acting as its language teachers. The results corroborate the hypothesis that affect or volition plays a pivotal role in the socially distributed acquisition process. Negation words are prosodically salient within prohibitive utterances and negative intent interpretations such that they can be easily isolated from the teacher's speech signal. These words subsequently may be grounded in negative affective states. However, observations of the nature of prohibitive acts and the temporal relationships between its linguistic and extra-linguistic components raise serious questions over the suitability of Hebbian-type algorithms for language grounding.

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1. Introduction

In research on early language we often find the claim that children’s early productive vocabularies were dominated by nouns referring to concrete objects such as foods or toys. This assumption appears to have been picked up and reinforced by work in robotics on symbol grounding (cf. (Stramandinoli et al., 2017)). As a consequence there are plenty of studies that focus on the acquisition of precisely these types of words (Hollich et al., 2000).
The importance of mutual or joint reference between mothers and children to perceptible objects and events is emphasized by more recent, so called usage-based theories of language development (Tomasello, 2003). Mother, child, and external referent make up a triadic joint-attentional frame which are very much the focus of these later theories. Cognitively such triadic interactional constellations are more complex than a simple dyadic interaction.

In those areas of developmental robotics concerned with language acquisition in artificial agents the linguistic units in focus are similarly those that can be construed as referents for concrete physical objects, object properties, or perceptible events. Central in this area of research is the notion of symbol grounding, the construction of links between abstract symbols and signals or constructs that are based on the embodiment of the agent (Harnad, 1990). The construction of such links may be regarded as a form of sense making with respect to the linguistic entities under consideration. The linguistic units in question are typically words or simple grammatical constructions. The agent’s embodiment often presents itself with respect to a stream of sensorimotor data.

In stark contrast to this focus on concrete referents, and the words that label them, is the observation that amongst the very first words produced by a toddler are many which do not fall into this category. We find words such as ‘no’, ‘hi’, or ’bye’ (Fenson et al., 1994) which are sometimes referred to as social words. Negation words are thus amongst the very first words in many toddlers’ active vocabularies and are used by them to reject things or to self-prohibit (Gopnik, 1988; Volterra and Antinucci, 1979), the latter being a function that is rarely seen with adult speakers. The idea to link these social words with a robot’s sensorimotor data appears strangely inappropriate. The question then is how these types of words should be handled in an embodied language acquisition framework.

2. Symbol Grounding

Artificial symbol grounding or perceptual symbol systems (Barsalou, 1999) attempt to solve or break out of the symbol grounding problem, the formulation of which is frequently attributed to Harnad (Harnad, 1990): If symbols are recursively defined or explained merely by the concatenation of other symbols, as is the case in a dictionary, how can the agent make sense of such symbols given the often circular relationship between the explanandum and explanans (see (Roy, 2005)

for an example)? The principle method employed in solving this problem is to connect some or all symbols of the system to sensorimotor data that originates in the agent’s own embodiment: its visual sensors, its haptic sensors, its auditory perception, or any kind of derivative constructs that are computed from data originating from such sensory channels. The way existing symbol grounding systems differ from each other is mainly in the method how the link between symbolic and sensor-derived data is established. The methods for constructing and maintaining such links may involve neural networks

(Sugita and Tani, 2005; Cangelosi, 2010)

, symbolic artificial intelligence approaches

(Siskind, 2001; Dominey and Boucher, 2005; Steels and Baillie, 2003), statistical learning methods (Štěpánová et al., 2018), or methods inspired by an enactive approach (Nehaniv et al., 2013; Lyon et al., 2016). The latter are typically data-driven and, arguably, representation-free in the sense that no models of object or event categories are constructed. Technically this can be made possible through the use of lazy learning algorithms (Aha, 1997) which compute retrieval or classification requests directly on the ‘remembered’ data.

Most of the existing work focuses and is limited to the grounding of words that may be seen to either refer to concrete objects (‘concrete nouns’) or to temporally unfolding perceptible events and processes (‘concrete verbs’). More recently Stramandinoli et al. (Stramandinoli et al., 2017) provided an example for grounding more abstract verbs such as ‘use’ or ‘make’ on the back of already grounded concrete verbs such as ‘cut’ or ‘slide’. While this is certainly an improvement in terms of the ability to ground a more general class of words, it is not clear how this approach would help to ground social or socio-pragmatic words such as ‘no’, ‘yes’, or ‘hi’, or emotion words such as ‘sad’, or ‘upset’.

3. Negation and Affect in Language Acquisition

Authors such as Pea emphasize the significance of affect in the context of acquisition of early linguistic negation (Pea, 1980). Less well understood are the concrete ramifications of this primacy of affect for a cognitive architecture in terms of required components, learning mechanisms, or the dynamics of the learning process. Hence roboticists have so far been unable to create machines that could acquire and engage in this aspect of human speech.

Spitz (Spitz, 1957) hypothesized that infants’ major source of negation words is rooted in parental prohibitive utterances. Under this hypothesis, the infant’s frustration, brought about by adult prohibition, leads to negative affect on the child’s part who subsequently associates negative affect with the negative utterance. Via role reversal the child is then thought to use these negative symbols for the purpose of rejection.

3.1. Rejection Experiment

In the rejection experiment (Förster et al., 2017) we tested whether the robot’s display of affect would lead to measurable changes in participants’ speech when addressing the robot. The kind of change we envisioned is best described with the notion of negative intent interpretations: Intent interpretations are linguistic descriptions or ascriptions of the addressee’s affective or volitional state. They have been hypothesized to play a vital role for infants to learn how to express their intent (Ryan, 1974; Pea, 1980). We use the results of the rejection experiment within the present article as point of reference in order to assess the comparative efficacy of the prohibition task in terms of the acquisition of negation words.

Both the prohibition as well as the rejection experiments operationalize affect as central element of their respective hypotheses both of which describe different, yet mutually non-exclusive mechanisms detailing the acquisition of early negation words. The goal of both experiments is two-fold: firstly, we intend to improve our understanding of human language acquisition and, secondly, we intend to use the so acquired knowledge to inform the design of future language grounding systems. We hope that this research will contribute to systems that transcend the restriction of being limited to the acquisition of concrete referential words and phrases. The acquisition system employed in the present study relies on strong interactional regularities that operate on relatively small time windows of several hundred milliseconds to very few seconds. These statistical regularities in human conversational behavior are required for the embodied machine learning to work. One property of these regularities appears to be that they involve a certain asymmetry between the speakers: one party is conversationally leading or stronger, typically the mother or the teacher-participant, while the other party is conversationally weaker or inept such as the infant or the child-humanoid.

3.2. Overview of Prohibition Experiment

The participants employed were naïve with respect to the true purpose of the experiment, that is, they were unaware that linguistic negation was the topic under investigation. They acted as language teachers for the iCub robot (Metta et al., 2008) and were asked to teach it the words for certain objects on a table. The table was situated between them and the robot (see Fig. 1). In order to increase the likelihood of participants employing a speech register akin to child-directed speech we asked them to imagine the robot as a pre-linguistic child. We also stated that the robot would like certain objects and dislike others. We used identical instructions in both the rejection (Förster et al., 2017) as well as the present prohibition experiment both of which, in turn, were very similar to Saunders’ experiment (Saunders et al., 2012). Contrary to the two negation experiments, Saunders’ experiment was genuinely concerned with the acquisition of referentially concrete nouns, verbs, and adjectives. All three experiments utilized the same robot and took place in the same room with the same physical setup in order to render the results comparable. Saunders’ experiment did not model affect. Its focus was sensorimotor association of words with the robot’s embodiment and we will use the results as point of reference later in this article.

The cognitive and behavioral architecture employed in all of these three experiments was largely identical with one important exception: Both the rejection as well as the prohibition experiment utilized a motivation system which modulated the robot’s affective expressions. These expressions were the facial expressions smiling, frowning, and neutral, and matching body behaviors. Apart from modulating the robot’s body behavior its affective state was also fed into the symbol grounding system. As was the case in the rejection experiment, the robot’s affective states were triggered by randomly assigned valences towards objects that were presented to it. The robot would attempt to grasp the object, in the case of a positive valence, or avoid it in the case of a negative valence. In addition, and differing from the setup in the rejection experiment, the robot’s motivational state would turn negative if it experienced external restriction of its arm movement. In absence of the aforementioned motivational triggers, the robot would display a baseline behavior during which it would alternate its gaze between the present objects and the participant’s face.

Figure 1. Experimental setup and interactive robot behaviors as utilized in the experiment. Participants and robot face each other. The teaching objects are located between the two interactants on a table. Upper left: Looking around behavior: no object is being presented to the robot. Upper right: Reaching behavior: triggered by a participant’s presentation of an object with positive valence. Lower left, Avoidance behavior: triggered by a participant’s presentation of an object with negative valence. Lower right: Modified reaching behavior, executed in prohibition experiment, where participants can push back the robot’s arm in order to prevent it from touching a forbidden object.

The duration of the robot’s gaze on a particular target were variable (cf. section 4). If participants present an object with a positive valence the robot smiles and holds out its hand towards them. It presents its palm which signals to participants that they can give it an object if they choose to do so (reaching behavior).

If participants present an object with a negative valence to the robot, it looks at the object briefly, starts to frown, and turns its head away. The dynamics of the interaction can lead to several consecutive ‘turn away’ movements which some participants interpreted as a form of head shake. When presented with an object with neutral valence, the iCub displays a neutral facial expression. Additionally it will look in regular intervals at both the participant’s face and the presented object without approaching it (watching behavior). Prior to participants selecting the first object as well as between object presentations the robot displays a neutral face and switches its focus between all objects and the participant’s face (looking around behavior). The major driver of the robot’s behaviors is thus its motivation system. The motivation system, in turn, is modulated by the external object valences as well as physical restriction of its arm movement. The latter has an exclusively negative impact and there is no positive counterpart.

Every instantiation of the experiment consists of five sessions and each session lasts about five minutes. The experiment was split into multiple sessions because the word learning required some offline processing in between sessions. To this purpose both participants’ speech as the recorded sensorimotor and motivation (smm) data were timestamped in order to allow for temporal alignment. The processing was semi-automatic in nature: the extraction of the prosodically most salient word, one per utterance, was followed by the attaching of the relevant smm data to the word. This is the point where the temporal contiguity mentioned in section 3 becomes crucial: Those parts of the smm data are considered relevant

which were recorded during the production of the respective utterance. From this point on, the salient word is grounded in the robot’s embodied ‘experience’. After all of the participant’s utterances have been processed in this manner, the new set of so grounded words is added to the robot’s embodied lexicon. Note that a separate lexicon is created for each participant such that the robot follows an independent acquisition trajectory for every one of them. The fact that it starts with an empty lexicon for each participant means that no designer knowledge in terms of a set of preselected words is incorporated. Everything that the robot eventually says during the experiment originates from what the respective participant uttered in earlier sessions.

In every session but the first the lexicon is loaded into a memory-based learning system (Daelemans and van den Bosch, 2005). Importantly, the robot is real-time deaf, i.e. no real-time speech detection is in place. Rather than answering to questions, certain trigger behaviors will make the robot query its embodied lexicon. Trigger behaviors are behaviors which are caused by object presentations: grasping for, rejecting, or simply watching a presented object. When triggered the robot matches its smm state against those associated to words in its current embodied lexicon and retrieves the best match. While a trigger behaviour is active this process is continuously performed, which means at about 30 Hertz. As it is both impossible and impractical to speak at such a high rate, and because we expect a certain level of noise within the smm data, a thresholding mechanism is employed. This mechanism both stabilizes its linguistic output with respect to noise as well as adjusts its speech frequency to a more plausible level. The thresholding mechanism maintains a score for each best match word: the score of the respective word is increased whereas all other word scores are decreased. Once the score of a particular word reaches a certain threshold, the word is sent to the speech synthesizer: the robot speaks. After it spoke, all scores are reset to 0. The retrieval process now starts anew but on a reduced lexicon: the just-synthesized word has been removed. The removed word is added back to the lexicon once another word has been synthesized but also if a change in the smm state occurs (see also section 4).

4. Materials and Methods

4.1. Study Design

This prohibition experiment presented in this article was designed in close alignment with the rejection experiment, described in (Förster et al., 2017), in an attempt to assess the linguistic impact of a caretaker’s prohibition (Fig. 2). Every instantiation of the prohibition experiment consisted of five sessions during which both the robot’s behavior as well as the general experimental setup as developed for the rejection experiment provide the baseline behavior and setup. However, during the first sessions, approximately half of the objects were additionally tagged with markers and participants were instructed to prevent the robot from touching these - the prohibition task. The last sessions of the prohibition experiment are identical to the rejection setup such that the robot’s success in acquiring negation words could be compared between the two experiments.

Figure 2. Alignment of Prohibition and Rejection Experiment: Prohibition and Rejection in the table refer to the prohibition and rejection scenarios respectively. Note that the prohibition experiment is composed of both the prohibition and rejection scenarios, whereas the rejection experiment consists of the rejection scenario only. Brackets ({..}) indicate the permutations of the following values: the robot either ‘liked’ (L), ‘disliked’ (D), or ‘felt’ neutral (N) about an object. Only participants knew whether an object was allowed A or prohibited/forbidden (P) for the robot to touch. The mapping of positive/negative/neutral (+/-/0) valences to objects was permuted between sessions, such that each object was twice liked, twice disliked, and once neutral across the 5 sessions (see table 1). The mappings were identical for every participant. The allowed/forbidden markers were permuted as well.
session 1 session 2 session 3 session 4 session 5
triangle 1 -1 1 -1 0
moon 0 1 -1 1 -1
square -1 0 1 -1 1
heart 1 -1 0 1 -1
circle -1 1 -1 0 1
Table 1. Object valences per session for both prohibition and rejection experiment.

4.1.1. Rejection Scenario

In order to increase the likelihood of the production of negative intent interpretations we permuted the object-valence mapping for each session (see table 1). In this way it was impossible for participants to know which object the robot would dislike at the outset of a particular session, which normally meant that they would present it at least once.

4.1.2. Prohibition Scenario

This scenario was designed to test Spitz’ hypothesis that the earliest forms of negation produced by toddlers would originate in parental prohibitive utterances. The rejective scenario is serving as basis but is extended to include the elicitation of prohibitive utterances from the participants. Hence two or three of the five present objects were declared to be forbidden, and were marked with colored dots on the side facing the participants. In addition to the instructions from the rejection scenario participants were told that the marked objects were forbidden objects, and that the humanoid iCub robot Deechee was not allowed to touch them. In order to keep Deechee from touching these forbidden boxes, participants were instructed to physically restrain the robot, in case it tried to touch them. Before the first session, participants were shown how to push the robot’s arm back, firstly, in order to show them the ideal contact point, such that the robot’s hand would not be damaged, and secondly, to take away their potential fear from actually touching the robot. The ideal contact point is the wrist and forearm. In this scenario, force control was used as the control mode for both arms, which makes it possible for participants to manipulate the arms while the robot executes a movement (Pattacini, 2010). The act of pushing the robot’s arm is detected as physical resistance and registered by the perception system as resistance event. The occurrence of such a resistance event leads to the robot’s motivation being set to negative: Deechee subsequently starts to frown. Furthermore the face-related gaze time is increased in order to give this emotional display a slightly higher intensity (see SI table 2).

The assignment of the forbidden and allowed attributes was such that every combination of liked/disliked with allowed/forbidden would occur at least once within each session. This, together with the change of the valence-to-object mapping between subsequent sessions (table 1) then led automatically to a permutation of the allowed and disallowed attribute-to-object mappings across sessions. In general there were either two or three forbidden objects per session, and two or three allowed ones.

In terms of the role reversal mentioned in the introduction none was needed as, in our architecture, there is neither a concept of self nor of other. For the prohibition experiment the behavioral trigger mechanism was modified such that negative motivation caused by participants’ restriction of the robot’s arm movement would not trigger its avoidance behavior. In these cases the robot would nevertheless frown in accordance with its motivational state.

Each participant completed five sessions of approximately five minutes each. The multi-session format facilitated linguistic development over time and was required for the purpose of post-processing of participants’ speech recordings. Participants’ speech was recorded via headsets and each session was video-recorded. Participants were not alone with the robot but either one or two more people were present. An operator was required in order to monitor the robot, and most often a helper was present in order to put boxes back on the table that had been dropped by Deechee. In few sessions the helper was absent such that the operator had to perform both tasks. As depicted in Fig. 1 participant and robot were facing each other with a table separating the two. The teaching objects were 10cm long cardboard cubes which had black-and-white depictions of various shapes glued to each side of each box. The shapes were a square, a triangle, a star, a heart, and a crescent moon and all sides of a box showed identical shapes. Participants first read the instructions and signed the consent form. Afterwards they took their seat opposite the iCub and were subsequently asked to count down “three, two, one, start”. So ‘start’ acted as start marker for the session. After five minutes the operator would give participants a signal. The operator, upon hearing ‘start’, would press a button. The button press led to a time stamp being broadcasted through the architecture which was recorded by the robot’s body memory.

4.2. Recruiting and distribution of participants

We recruited 10 participants all of whom were native English speakers and which were gender-balanced. The majority of participants were students or university employees. They were remunerated with £20 once they completed all five sessions. The protocol was approved by the ethics committee of the University of Hertfordshire under protocol number 0809/88, and approval was extended under protocol number 1112/42.

4.3. Instructions to Participants

The majority of instructions in both negation experiments as well as Saunders’ experiment (Saunders et al., 2012) were identical: participants were told that they ought to teach the robot Deechee about the objects on the table. We tried to prime participants into adopting a style of speech akin to child-directed speech (CDS, (Snow, 1977; Gallaway and Richards, 1994; Newport, 1977)) by telling them to imagine Deechee to be a two-year-old. In addition to what participants were told in Saunders’ experiment we also decided to mention to them that Deechee had likes and dislikes with respect to the objects such that they would not be caught by surprise by Deechee’s emotional displays. It is not clear whether the latter instruction was strictly necessary and whether it had any impact in terms of the content and style of participants’ speech. Only in the prohibition experiment participants were told that the marked objects on the table were forbidden for Deechee to touch. Participants were instructed to push the robot’s arm away if it should approach these objects and they were practically shown how to do this.

4.4. Behavioral Architecture

The behavioral architecture which generates both the humanoid’s bodily and linguistic behaviour consists of the components depicted in Fig. 3.

Figure 3. Functional overview of robotic architecture for language acquisition. Solid lines indicate components that are active during experimental sessions (“online”), dotted lines indicate components that work offline.

We will sketch each component’s purpose only very shortly as more elaborate descriptions have already been provided in (Förster et al., 2017) and (Förster, 2013).

The perception system gathers and processes percepts of all modalities. Visual processing was limited to face and object detection and based on the system developed by Rüsch et al. (Rüsch et al., 2008). We also developed a detector for object-related pick up actions.

The motivation system is responsible for generating the affective-motivational state of the robot which consists of a simple scalar value between and . corresponds to a negative, a positive and a small band around a neutral state (cf. (Varela et al., 1991, Chapter 6)).

The body behavior system generates the humanoid’s robot’s physical behavior which also includes its facial expressions. The behavior is generated contingent upon the inputs from the perception and the motivation system. The behaviors are Rejecting, Watching, Looking around, Reaching for object, and Idle. Other subsystems are informed of changes in the bodily behavior by the broadcasting of unique behavior ids. Relevant time constants for certain parts of the behavior such as the eye gaze are listed in table 2 of the supporting information (SI).

The body memory saves high-level and low-level perceptual data as well as behavior ids and the robot’s motivational state to a file.

The auditory system encompasses speech recognition, word alignment, prosodic labeling, and word extraction, and are based on Saunders’ system (Saunders et al., 2011). Utterance boundaries are set based on statistics of inter-word pause durations and word durations (see (Saunders et al., 2011) for details). Important for the later analysis is the notion of prosodic saliency. Note in this context that the first three aforementioned subsystems produce a sequence of utterances, where each utterance consists of prosodically annotated words. Exactly one word is extracted per utterance and that word is the prosodically most salient one. Prosodic salience is calculated as , where is the maximum fundamental frequency, is the maximum energy, is the word duration, and all of these components are normalized before said formula is applied.

The lexical grounding system performs the association of smm data with the salient words originating from the auditory system (see Fig. 9). The so grounded words are subsequently added to the embodied lexicon.

The languaging system generates the robot’s speech. It does so based on a process that matches the robot’s current smm state (Fig. 4) against the smm states associated to words in the embodied lexicon yielding a best-matching word in combination with a thresholding mechanism. At the core of the matching process is the k-nearest neighbor implementation Tilburg Memory-Based Learner (TiMBL, 2012). A new matching is performed whenever a new smm

-vector is available, which is the case approximately every 30ms.

Figure 4. Sensorimotor-motivational (smm) vector. Solid lines mark those data dimensions that were used for symbol grounding and matching; bid: behavior id, oid: object id, faceDet: face detected, moti: motivation value, resist: resistance detected, encX: encoder #X

The repetitive uttering of the same word is prevented by the use of a so called differential lexicon which prevents the repeated production of the most recently uttered word. For details of both the thresholding mechanism as the differential lexicon see (Förster, 2013; Förster et al., 2017).

5. Data Analysis

The following analysis is based on 5 hours of participants’ speech originating from 50 sessions, 10 participants with 5 sessions each, and was gathered as part of the prohibition experiment (cf. SI table 15). As was the case for the rejection experiment analyses were performed on the word or corpus level, the utterance level, and the pragmatic level for negative words and utterances. In the following the analysis will only be sketched as a more elaborate description has already been given in (Förster et al., 2017).

The following variables were measured on the level of utterances: speech frequency in utterances per minute (u/min), mean length of utterance (MLU), and the number of distinct words (cf. Fig. 7 and SI tables 15 to 20)

Negative utterances are utterances that contain at least one negative word. Whether a word is a negative word was determined manually by examining the global list of distinct words compiled from all participants’ speech transcripts.

On the corpus-level the prohibition corpus (PC) is a list of all words and their frequencies that occur in participants’ speech transcripts taken from the prohibition experiment. The PCS is a subset of the PC containing only those words that were marked prosodically salient. Both corpora are presented together with the corresponding corpora from the rejection and Saunders’ experiment: RC, SC, RCS, and SCS (cf. Fig. 7C).

Only prosodically salient words enter the robot’s embodied lexicon and therefore form the basis of its active vocabulary, hence our particular focus on them.

Human Negation Types pt. 2
(b) Human Negation Types pt. 2
(a) Human Negation Types pt. 1
(a) Human Negation Types pt. 1

[vc,hc][vc,hc] [-b,-l][t,l]

Figure 5. Taxonomy of negation types used by participants. Conv.: conversationally, 1st part-pair, 2nd part-pair: parts of an adjacency pair such as question (1st part-pair) - answer (2nd part-pair)

In order to be able to classify negative utterances of both participants and robot by their communicative function, their

pragmatic type of sorts, we constructed two taxonomies, Figs. 5 and 6. The construction process is described in (Förster et al., 2017) and (Förster, 2013) but we would like to emphasize that the resulting types can be regarded as types of speech acts in a loose sense which were enriched by the notion of conversational adjacency. Conversational adjacency is not part of classical speech act theory ((Austin, 1975; Searle, 1969)). A short sketch of the most important negation types is given below but for a detailed description of all types we refer to the coding scheme (Förster, 2018). Upon completion of the two taxonomies two coders classified the negative utterances by type, where the first coder classified all utterances and the second coder classified a randomly selected subset comprising of all utterances. This enabled us to assess the taxonomies for internal consistency using Cohen’s . Prior to coding the negative utterances for type, the coders coded the robots’ utterances for felicity. This means, they had to make a judgment whether they, by virtue of being fluent English speakers, perceive the negative robot utterance to be adequate or plausible in the respective conversational context. The internal consistency of the human taxonomy in terms of Cohen’s was judged to be good (), but the consistency of the robot taxonomy was judged to be only borderline acceptable, which triggered an automatic attempt to optimize it. Both the optimization attempt as well as our reasons for not adopting the recommended mergers suggested by the optimization are described in (Förster et al., 2017) and (Förster, 2013). Important for our current purposes is the fact that the values for the ratings of both the robot’s felicity () and type () are at the very lower end of what is generally regarded acceptable. This has to be kept in mind when interpreting numbers that are based on these ratings. Importantly, however, there was no indication that any one of the two coders would have judged the the robot’s negative speech systematically more favorably as compared to the other (see SI table 22).

Prior to describing the outcome of these attempts we need to introduce those negation types mentioned within the present article. These include the ones most frequently produced during the experiments. A complete listing of all observed negation types can be found in the coding scheme (Förster, 2018). In the following those types typically found in human participants’ speech are qualified with ‘[H]’. The types typically found in the robot’s speech are marked ‘[R]’. In the examples question marks indicate the intonational contour of a question, full stops the contour of an assertion.
Negative Intent Interpretations (NII [H]) are negative interpretations or ascriptions with regards to the addressee’s motivational, emotional, or volitional state (Pea, 1980, p. 179).
Examples utterances falling into this category are “No, you don’t like fish” or a simple “No” if it is not produced as a genuine question.

Figure 6. Taxonomy of robot negation types. Types of negative utterances produced by the robot and as identified by external coders.

Negative Motivational Questions (NMQ [H]) are very similar to NIIs in that they refer to what is perceived to be the addressee’s negative motivational state. The main difference between NIIs and NMQs is the fact that the latter are considered genuine questions, meaning, the speaker does expect the addressee to respond. Examples would be “Are you not feeling well today?” or “You don’t like apples?” in the context of being offered an apple, rather than the statement of a general preference.
Truth-functional Denials (TFD [H]) are used to deny a truth-functional assertion, with truth-functional assertions being assertions whose truth is independent of either speaker’s preferences or capabilities. Examples are “No, it’s not a hedgehog!” in the presence of an unknown animal and counter the suggestion of some other speaker or, again, a simple “No.” in reply to some positive assertion.
Truth-functional Negations (TFN [H]) in our taxonomy are a catch-all category for all of those kinds of truth-functional negation that are not truth-functional denials such as truth-functional suggestions or speculations, but also negative normative assertions such as “In England you mustn’t drive on the right-hand side.”
Prohibitions (P [H]) are negative utterances whose function is to prevent the addressee from doing something. Considered in isolation, such utterances may not indicate that their function was prohibitive, as for example in the second example below. Taken out of context this utterance may be taken to be a truth-functional negation. However, in context, when looking at a video recording of the actual interaction or when witnessing the latter ‘in vivo’ it becomes clear that this utterance is used as prohibition. In our experiment the prohibitive utterance can or cannot be accompanied by the participant physically restraining the robot’s arm movement.
Examples: “No, you can’t touch that” or “No you’re not holding it, but you can look at them”.
Disallowance (D [H]) Disallowances are similar to prohibitions but, in contrast, capture those utterances that express general negative rules. In this sense disallowance utterances are more detached from the here and now of the interaction than prohibitive utterances. Whereas prohibitive utterances are always triggered by a current action on part of the robot, disallowances can or cannot accompany such an action.
Example: Speaker A takes something from the shelf and shows it to the robot saying “You can’t have this one”.
Negative agreements (A [H+R]) is a negative confirmation in response to a negative statement such as a “no”, uttered by speaker in response or addition to a “So you don’t like peanut butter, hmm?” by speaker 1.
Motivation-dependent Denials [R] are negative answers to motivation-dependent questions or assertions. Their content is dependent on the current emotional or volitional state of the addressee or her current preferences.
Example: “No” in response to “Do you want some ice cream”.
Rejections [R] are very close to motivation-dependent denials. The difference is that the latter is adjacent to an utterance whereas the former is adjacent or in reaction to non-linguistic offers. For example, “no” in response to someone holding out an apple as a offer would fall into this category.
Negative tag question (NTQ [H])

are negative clauses that are attached to the end of the utterance. Semantically and pragmatically they are probably the ‘least negative’ of our negation types but they are easy to spot and appear to be highly frequent in British English. For example “don’t you” as in “You do like it, don’t you?” falls into this category. Another example would be “weren’t you” in “You have been to Cambridge, haven’t you?”

In addition to the linguistic analyses a further analysis on the temporal relationships between participants’ linguistic prohibition and their use of bodily measures to restrain the robot was performed (cf. SI section A.4.1).

6. Results

A

[vc,hc][vc,hc] [-t, -r][t,l] B [-t, -r][t,l] [-t,-r][t,l] [-b,-l][t,l] C [-b, -l][t,r] All words Salient words only Rej. Corpus Pro. Corpus S. Corpus Rej. Corpus Pro. Corpus S. Corpus rank   word %   word %   word %   word %   word %   word % 1   you 7.13   you 6.18   a 8.71   square 4.97   square 4.4   blue 6.91 2   the 5.63   the 5.5   this 4.55   no 4.64   triangle 4.06   red 5.54 3   like 3.31   a 3.74   blue 4.31   triangle 3.95   circle 3.83   circle 5.15 4   a 2.72   this 3.71   is 4   heart 3.8   no 3.66   heart 4.75 5   this 2.7   one 2.8   and 3.9   moon 3.53   one 3.36   green 4.36 6   no 2.39   is 2.45   red 3.75   circle    3.53   heart 3.34   arrow 3.56 7   one 2.27   like 2.05   green 3.55   like 3.2   this 3.09   cross 3.48 8   square 1.93   to 1.83   the 3.29   circles 2.69   moon 2.8   side    3.48 9   do      1.93   no 1.79   that’s 2.54   squares 2.42   ok 2.3   box 2.82 10   to 1.78   it’s 1.66   you 2.41   it 2.36   shape 2.14   shape 2.42 11   it       1.78   heart 1.6   it’s 2   yes 2.28   yes 2.09   and 2.11 12   that 1.73   square 1.51   heart 1.99   one 2.13   crescent 2   moon  2.11 13   moon 1.62   triangle 1.47   circle 1.85   right 1.88   like 1.8   square 2.07 14   heart 1.6   that 1.42   arrow 1.84   this 1.82   circles 1.74   this 2.02 15   is 1.47   it        1.42   side 1.81   ok 1.73   again 1.51   star 1.85 16   triangle 1.45   do 1.4   cross 1.49   again 1.69   good 1.44   ok 1.76 17   circle 1.32   moon 1.38   here 1.39   ok 1.57   it 1.36   is       1.76 18   it’s 1.22   circle 1.29   we 1.38   good 1.55   very 1.25   small 1.54 19   don’t 1.15   that’s 1.28   on 1.37   Deechee 1.46   ok 1.18   right    1.54 +1   see 1.12   shape 1.2   no (50) 0.35   Deechee 1.3   Deechee 1.12   no (32) 0.44 [-b,-l][t,l]

Figure 7. Impact of motivated behavior on linguistic production of participants. (A) The overall production rates between prohibition (upper red), rejection (lower black), and Saunders’ experiment (middle blue) differ only marginally (mean SEM). (B) The production rate of negative utterances only, however, is significantly higher in the prohibition (upper red) and rejection (middle black) as compared to Saunders’ experiment. See Supporting Information for details. *P 0.05, **P 0.01 (C) The large supply of negative utterances has consequences on the corpus level: No is amongst the 10 most frequent words in the rejection and the prohibition corpus, whereas it is located on rank 50 in Saunders’ corpus. In the corpora of prosodically salient words no ranks even higher. This is due to the high saliency of the word and it subsequently enters the robot’s vocabulary frequently. (Arrows () indicate equality of ranks between the stated entry and the next entry above. Negation words are marked through gray background. The ‘+1’ row contains the 20th most-frequent words unless a different rank is specified in brackets.)

In the prohibition experiment, on average, every 7th to 8th utterance of participants contains a negation word which constitutes an increase of compared to the speech recorded in Saunders’ scenario (cf. Fig. 7). This compares to a rise of in the rejection experiment. The frequent occurrence of negative utterances leads to a large increase of prosodically salient negation words which subsequently enter the robot’s active vocabulary. The increase is amplified by the relatively high prosodic saliency rate of ‘no’ in both negation experiments. The prosodic saliency of negation words, chiefly ‘no’, is high both in relation to Saunders’ experiment as well as in relation to the average word saliency within the negation experiments (Fig. 8A). As a consequence ‘no’ rises to the fourth rank in the prohibition corpus of salient words PCS, and even to second rank in the corresponding RCS (Fig. 7).

Analysing these negative utterances with respect to their communicative function reveals that, within the prohibition experiment, linguistic prohibitions, not present within the rejection experiment, occupy the top-rank, making up of all negative utterances. This is remarkable as linguistic prohibitions were only produced when the prohibition task was given, i.e. during the first three sessions, whereas utterances of the other types were produced in all of the five sessions. Prohibitions are followed by negative intent interpretations (), negative motivational questions (), and truth-functional denials () (cf. Fig. 8 and SI table 3), with prosodic saliency rates of , , , and respectively. This means the three motivation-dependent types provide the majority of negation words for the robot’s lexicon due to two factors: Firstly, this type of utterances are dominant in terms of the absolute numbers of productions, and, secondly, the negation words that are part of these utterances have higher rates of prosodic saliency than the truth-functional types.

In comparison, within the rejection experiment, the most frequent negation type, with , are negative intent interpretations (NII), followed by negative motivational questions (NMQ, ). Both of these types have a direct link to the robot’s display of affect (see also SI table 5). Truth-functional denials (TFD) rank third () in the RC but have a lower saliency rate (), relative to the two motivational types (NII: , NMQ: ). From this we can conclude that in the rejection experiment the vast majority of negation words in the robot’s active vocabulary originate from utterances of the two motivation-dependent types, NIIs and NMQs.

When comparing the two negation experiments, it becomes clear that within the prohibition experiment negative intent interpretations and negative motivational questions were produced less frequently than was the case in the rejection experiment. The probable cause for this is the fact that, in both experiments, participants had overall the same amount of time, yet participants in the prohibition experiment spent part of their time with attempts to prohibit the robot leaving them less time to engage in NIIs and NMQs.

When considering the saliency rates of negation words within utterances of the aforementioned types produced within the prohibition experiment it becomes clear that linguistic prohibitions constitute a formidable source of negation words: within this type negation words reach the overall highest saliency rate (), followed by negative motivational questions (), negative intent interpretations (), and truth-functional denials () (see SI table 4 for the complete listing). The combination of high production rate and high saliency rate renders them the top contributors of negation words to the robot’s active vocabulary within this experiment. Every single prohibitive utterance contained at least one negation word whereas intent interpretations and motivational questions were sometimes performed in a non-negative way. Some participants for example used non-negative emotion words in response to the robot’s negative affective display such as ‘sad’ as in “why are you sad?” where others more commonly used the negative “you don’t like it?”. Thus, from a merely lexical perspective, prohibitions appear to be more reliable sources of negation words than intent interpretations and motivational questions.

In Saunders’ experiment in comparison ‘no’ is ranked 50th in the SC and 32nd in the SCS. There it accounts for less than of words in both corpora. Hence both the affective or motivational displays of the robot as well as the prohibition task lead to a considerably higher rate of negative utterances when compared to the setup used by Saunders et al. which used a near-identical setup, but without affective displays and without prohibition task.

To our surprise the robot’s learning success with respect to negation was judged to be considerably lower in the prohibition () as compared to the rejection experiment () (see SI tables 23 and 25). This result triggered an additional analysis where we aligned the signal of the robot’s arm sensor for external pressure with both its affective state as well as the timing of negative utterances of the four most frequent negation types. This analysis showed that participants from the prohibition experiment of the time did not physically restrain the robot’s arm when uttering prohibitions as instructed, and in of all cases uttered prohibitions before applying restraint (see SI table 26). In both cases grounded words enter the robot’s lexicon where the negation word is likely to be associated with positive affect (see SI table 27). This may then lead to the inappropriate usage of the word. In only of cases did our participants utter prohibitions while restraining the robots arm, that is, while it was in a negative affective state. When performing a similar analysis for negative intent interpretations we observed that in approximately two thirds of cases (rejection experiment: , prohibition experiment: ) the robot is in a negative motivational state as opposed to a positive one (rejection experiment: , prohibition experiment: ) such that there is a high likelihood of it associating the negative word with negative affect. For negative motivational questions the results look similar within the prohibition experiment ( performed while in a negative state, and only while in a positive state), while in the rejection experiment the number of performances while in a negative state () is nearly identical to the number of performances while in a neutral state (). Performances of this type while the robot is in a positive state are also not very frequent within this experiment (). Thus, albeit lexically not being equally reliable sources of negation words as prohibitions, negative intent interpretations appear to be better sources for the establishment of an association of the negative word with negative affect if word learning is mainly modeled as a process of establishing associations between sensorimotor-affective ‘concepts’ and linguistic items.

A

[vc,hc][vc,hc] [-t, -r][t,l] [-b,-l][t,l] B [-b, -l][t,r] [-t,-r][t,l]

Figure 8. (A) Frequency of human utterances classified as being of the stated negation types (pragmatic level) and percentage of utterances falling under the respective type with salient negation word (only types with of total number of negative utterances, Pro: Prohibition Experiment, Rej: Rejection Experiment). (B) Prosodic saliency rates of selected words and word groups. ‘No’ has a considerably higher salience rate in the two negation experiments as compared to Saunders’ experiment, PC: Prohibition Corpus, RC: Rejection Corpus, SC: Saunders’ et al. Corpus (see also SI tables S7 to S12).

7. Discussion

No quantitative psycholinguistic data exists that would give us any idea about the felicity rates of infants’ when it comes to the use of negation words. We can therefore make no comparative judgment with respect to the robot’s felicity rates. Young children often do make semantic mistakes during certain developmental stages such as over-generalizing nouns (Gelman et al., 1998) and grammatical constructions (Bowerman, 1988; Brooks et al., 1999). Unfortunately there is no such data for ‘pragmatic accuracy’ in terms of the (non-)successful use of negation words such as ‘no’.

From the rejection experiment we have a good indication that negative intent interpretations on their own could theoretically be sufficient in order to associate negation words and negative affect which may be sufficient to bootstrap linguistic negation.

For prohibitions the picture is somewhat more complicated. Our participants, despite having been instructed on how to physically prevent the robot from touching an object, often chose not to do so. Instead we observed that participants frequently held forbidden objects out of the robot’s reach instead of limiting its arm movement. As the robot does not perform any type of goal-evaluation with respect to having reached or touched the ‘desired’ object it does not become frustrated in these cases. None of the existing studies on children’s acquisition of negation provides us with detail of the interaction akin to the temporal alignment between physical restraint and linguistic prohibition in the present study (cf. SI section A.4.1). As a consequence we do not know if behaviour similar to the one of our participants is typical in the interaction of parent-child dyads, or whether we witnessed a somewhat artificial behaviour as participants may have simply been reluctant to touch the robot.

Even given the lack of quantitative data from psycholinguistics, intuitively a success rate appears to be very low. In our architecture we modeled world learning to be by and large associative or Hebbian. This decision was not made on the basis on principle, but rather by the application of Occam’s razor: given the lack of evidence to the contrary we chose one of the simplest types of learning algorithms. Yu et al. (Yu et al., 2007)

provide a more elaborate discussion on associative learning in word acquisition. If we assume the core word learning mechanism to be roughly associative, and if we further assume the behavior of our participants to be sufficiently similar to that of caretakers’ behavior when prohibiting a child, we can draw tentative conclusions for the acquisition process. Assuming that Spitz’ hypothesis is correct, the child must already be frustrated at the time when the prohibitive

no is being uttered - at least in the majority of cases. In our experiment this was not the case due to the limited ways in which participants could unknowingly frustrate the robot: only the application of physical restraint to its arm would have this effect. But our participants appeared to use physical restraint reluctantly. Thus, if all of the above assumptions hold, there must be sources of frustration other than physical restraint - holding an object out of a child’s reach will probably quickly lead to frustration on part of the child. In this respect our robot’s motivational system is most probably too limited.

However, the low pragmatic success rate also led us to search the video platform YouTube for amateur videos depicting parents that engage in prohibition. Albeit of a somewhat anecdotal character some videos show situations where parents clearly prohibit their children by the mere use of speech and cause the child’s frustration as a consequence of these ‘touch-less’ acts of prohibition (“single whammy prohibition”) (Spivey, 2007). In other videos however, prohibiting utterances are swiftly followed by a combination of corporal restraint and linguistic prohibition akin to the behaviour we expected our participants to engage in (“double whammy prohibition”) (DeWeerd, 2011). Due to the anecdotal character of this evidence it is impossible to tell which of these two variants of prohibition is more typical, whether one is the developmental precursor of the other, or whether it is a matter of the severity of the violation rather than a matter of the developmental stage.

It is not hard to imagine that a child, having been exposed to several instances of “double whammy” prohibition, would learn that ‘resistance is futile’ and that, as a consequence of this learning process, “single-whammy” prohibition suffices to stop the child from engaging in the prohibited behaviour (variant A).

On the other hand it seems equally plausible that prohibitive utterances may carry distinctive prosodic features that let the child infer the caretaker’s negative emotional or volitional stance. We may assume that a child in the relevant age range can perform simple inferences within a lay theory of emotions or volition (Ong et al., 2015, ress). If we then further assume that the child experiences the caretaker’s relative interactional and interventional power on a daily basis, the child should be able to infer that resistance is futile without a need for prior exposure to “double whammy” prohibition (variant B). The lack of more than anecdotal evidence prevents us from excluding one or both of these possibilities.

It may be of interest to observe that both variants A and B

require a more powerful class of learning algorithms than the simple associationistic one employed by us. Both of these variants assume that the agent learns about the efficacy of its actions with respect to some goal. In machine learning this would be typically modeled with some type of reinforcement learning which is arguably a more powerful class of learning algorithms than simple Hebbian-type associative learning. The replacement or supplement of our memory-based learner with some type of reinforcement learning as core learning mechanism and the explicit modeling of goals would therefore most certainly increase the felicity rate when using negation words. In this case the robot’s frustration could be triggered whenever it can’t reach an object within a certain time frame and when this inability is caused by another agent. This would then lead to a rise in the number of grounded negation words with negative motivation value in the data set of the memory-based learner. Yet his extension would also weaken one of our assumptions, namely that the core learning mechanism was one of mere associationism.

Under the assumption of prohibitive utterances being the main source of children’s early negation words, the only potential rescue for a purely associationistic account we can conceive of hinges on the notion of emotional contagion (Hatfield et al., 1993) (variant C). It shares with variant A the idea that acoustic or prosodic qualities of prohibitive utterances, potentially in conjunction with corresponding facial expressions, may carry an emotional charge. As we have seen from our data, prohibitions are typically prosodically salient. Assuming that their acoustic properties may have the potential to negatively impact the affective state of the recipient, corporal restraint might not be necessary to “turn the infant’s mood sour”. Corporal restraint may indeed only be used by the care-giver as the very last resort. If such a mechanism of acoustic affective contagion could be implemented within our learning architecture we would arrive at a point where the non-codified aspects of utterances would contribute to the modulation of an interlocutor’s affective state, which in turn would form part of the basis for grounding the codified units of the same, or adjacent utterances.

The difference between this account of emotional contagion (variant C) and variant A above is that the former does not require any reasoning process operating on actions and goals. It ascribes to the parent the power to impact the child’s emotional state more or less directly by producing utterances with a certain emotional charge. In the account under variant A the parents power to affect the child’s motivational state is more indirect: The utterance’s emotional charge, rather than impacting the child directly, is taken into account by the child’s goal-oriented reasoning process. In order to be efficacious for grounding negative symbols this process must be social: the reasoning must not only take into account the child’s own abilities and goals but also the abilities and goals of the caretaker, and evaluate whether a caretaker’s intervention is likely if a certain action is chosen and given the emotional payload of the previously received utterances and other communicative emotional signals.

As can be seen from these considerations the degrees of freedom for potential modifications of the learning architecture are many. Only studies with a high temporal resolution in their description of parental prohibitive behavior, linguistic as extra-linguistic, appear to have the potential to create the required comparative data set. Such a data set could then not only provide us with better means to evaluate our results but also reduce the degrees of freedom for future modifications of our learning architecture as indicated above.

In the context of the rejection experiment (Förster et al., 2017) we determined that a lack of proper timing, that is uttering a ‘no’ after a pause longer than the important 1 second threshold (Jefferson, 1989), caused confusion in the coders when trying to determine the meaning of the word, and will presumably cause similar problems with the interactor. Similarly the choice between the hypothesized learning mechanisms in the context of prohibitive utterances could be informed by detailed observations of timing. Assuming that emotional appraisal processes are faster than inferential processes, the temporal order between prohibitive utterances and the child’s overt emotional displays could provide clues as to which of the two types of processes is at play.

8. Conclusions

Our architecture is the first to extend symbol grounding beyond the realm of sensorimotor-data to encompass affect and motivation which is in line with recent psychological studies (Kousta et al., 2011). We have demonstrated the capacity to acquire generally felicitous non-referential linguistic behavior such as negation in a developmental scenario with a humanoid robot developing an embodied lexicon based on its sensorimotor motivational experience in interaction with naïve human participants. Based on our results we cannot exclude any of the two hypotheses on the origin of negation, due to a lack of sufficiently detailed data on the precise dynamics how prohibition in mother-child dyads is enacted. We did show however, that at least from a lexical perspective prohibitive utterances are formidable sources of negation words.

If more detailed data on the dynamics of prohibition were to become available the results of the presented research can give strong indications with respect to the underlying acquisition algorithm: If the majority of prohibitive utterances are uttered at a time when the child is already frustrated, Hebbian-style methods may suffice to ground negative words in negative affect. Yet our analysis on the temporal alignment between bodily and linguistic behavior hints towards principal limitations of Hebbian-style learning. If prohibitive utterances typically precede or may even cause a child’s frustration, Hebbian-style methods are unlikely to be a efficacious for affective grounding because they require a certain amount of synchronicity between negative word and negative affect. In this case a more powerful type of learning algorithm would be required. Reinforcement learning, potentially coupled or amplified by some form of social reward signals would be a likely candidate class of learning algorithms. This view appears to be supported by recent work in neuroscience which posits a central role of reinforcement learning in biological decision making (Niv, 2009) if we are willing to assume that language learning may recruit more general learning mechanisms.

Acknowledgements.
The authors would like to thank Kinga Grof for helping with the manual transcription and re-alignment of recorded speech.
The work was supported by the Sponsor EU Integrated Project ITALK Rl((“Integration and Transfer of Action and Language in Robots”) funded by the European Commission under contract number Grant #3.

Appendix A Supplementary Materials

variable value description
face_time 0.8 duration of iCub looking at face when pickup
detected and motivation 0
object_time 3 duration of iCub looking at object when pickup
detected and motivation 0
dwell_time_face 1.2 duration of iCub looking at face when no pickup
detected
dwell_time_object 2 duration of iCub looking at obj when no pickup
detected
maxIdleTime 3 perceptual timeout for high level percepts: if no
objects or faces are perceived for maxIdleTime,
iCub looks back at the table
grumpy_face_time1 1.6 duration of iCub looking at face if physical
restraint is detected (this implies that iCub
was reaching for an object)
grumpy_object_time1 2 duration of iCub looking at object if physical
restraint is detected
  • specific to prohibition scenario

Table 2. Constants for human-robot interaction, all values in seconds

Figure 9. Grounding of (salient) words. The grounding process associates lexical entries, in our case prosodically salient words, with the concurrently occurring sensorimotor-motivational data. In our system the salient word is propagated across the entire duration of the utterance, such that the time stamps, visible in the salient-words-file (top-left) mark the start and end of the respective utterance within which the word was produced. Time stamps for utterance boundaries are symbolized by ‘’. Also notice that we remove duplicates of grounded words that would ensue from the same utterance. In the given example this means that due to the lack of change within the smm data during the production of the utterance the potentially ensuing 23 identical grounded words are collapsed into one (bottom).

a.1. Pragmatic Analysis - Details

Figure 6 in the main text shows the taxonomy of negation types the robot engaged in as identified by the external coders. Figure 5 shows the equivalent taxonomy of negation types engaged in by participants. Tables 3 and 4 show the absolute frequencies of negative utterances categorized by negation type for the Prohibition experiment. Tables 5 and 6 give the corresponding frequencies for the Rejection experiment. All four tables form the basis for figure 8A in the main text. Table 7 breaks down the uses of the occurring negation words into negation types as well as giving absolute and relative frequencies for their salient productions. The combination of these two types of information allows us to deduce which particular negation types are numerically speaking the ones most responsible for the occurrence of negation words in the robot’s lexicon.

Both taxonomies of negation types are derived from the one developed by Roy D. Pea (9) and adapted to the speech encountered in our experiments. Pea’s taxonomy was developed for the various uses of negation of toddlers in the one-word stage and is therefore most similar to the robot’s taxonomy. The construction of the taxonomy for participants’ negation types started with conversationally ‘symmetric’ counterparts: Often times negative second pair-parts such as ‘No’ are preceded by negative first pair-parts such as “Don’t like the square?”.

Notice that, in line with Pea’s taxonomy, the top-most criterion in both taxonomies is conversational adjacency. This notion is used more loosely than is the case in the conversation analytic literature (cf. (Hutchby and Wooffitt, 1999)). It encompasses not only adjacency pairs in the strict sense, where the producer of a second pair-part would be accountable for a non-production, but all utterances that appear to be sequentially linked across speakers, whether the producer of the second pair-part would be accountable for non-production or not (cf. (Förster, 2013) for a more elaborate discussion).

P13 P14 P15 P16 P17 P18 P19 P20 P21 P22 total
prohibition 22 16 39 18 31 13 14 24 16 7 200
neg. intent interpret. 15 0 38 31 13 30 18 22 4 2 173
neg. mot. question 12 0 52 15 7 14 12 38 20 0 170
truth-func. denial 21 3 3 0 6 2 22 4 7 36 104
neg. tag question 1 0 14 30 0 16 1 2 0 3 67
disallowance 0 0 14 4 0 2 3 15 26 1 65
truth-func. negation 0 0 9 12 0 18 4 6 1 0 50
neg. agreement 0 0 15 3 10 0 7 3 5 0 43
mot. dep. assertion 0 0 3 12 1 1 0 5 1 0 23
neg. persp. assertion 1 1 0 4 0 5 1 2 0 1 15
negating self-prohibition 0 0 0 1 0 0 1 1 4 0 7
apostr. negation 0 0 1 1 0 1 0 0 3 0 6
neg. imperative 0 0 0 4 0 0 0 0 1 0 5
rejection 0 0 0 1 0 0 0 0 3 0 4
neg. question 0 0 0 2 2 0 0 0 0 0 4
neg. promise 0 0 2 0 0 0 0 1 1 0 4
neg. persp. question 1 1 0 0 0 0 1 0 0 0 3
? 0 1 1 0 0 0 0 0 0 0 2
mot. dep. exclamation 0 0 0 1 0 0 0 0 0 0 1
total 73 22 191 139 70 102 84 123 92 50 946
Table 3. Frequency of participants’ negation types - Prohibition Experiment. Listed are the counts for all negation types of all participants and all sessions within the prohibition experiment. ‘?’ is not a negation type but indicates that the coder could not decide on a type for a given utterance due to the utterance being incomplete.
P13 P14 P15 P16 P17 P18 P19 P20 P21 P22 total
prohibition 50 87.5 43.6 88.9 74.2 38.5 57.1 54.2 81.3 14.3 60.5
neg. mot. question 33.3 0 48.1 20 85.7 28.6 41.7 39.5 45 0 41.8
neg. intent interpret. 20 0 44.7 32.3 46.2 33.3 44.4 36.4 50 100 38.2
truth-func. denial 19 33.3 0 0 16.7 0 31.8 0 28.6 50 31.7
neg. tag question 0 0 35.7 53.3 0 56.3 100 50 0 33.3 49.3
disallowance 0 0 28.6 75 0 0 100 13.3 57.7 0 41.5
neg. agreement 0 0 46.7 33.3 60 0 71.4 66.7 80 0 58.1
truth-func. negation 0 0 11.1 41.7 0 27.8 25 0 0 0 24
mot. dep. assertion 0 0 33.3 41.7 0 0 0 0 0 0 26.1
neg. persp. assertion 0 100 0 8.3 0 0 100 50 0 0 17.4
rejection 0 0 0 50 0 0 0 0 66.7 0 75
neg. question 0 0 0 100 50 0 0 0 0 0 75
negating self-prohibition 0 0 0 0 0 0 0 0 50 0 28.6
neg. imperative 0 0 0 50 0 0 0 0 0 0 40
apostr. negation 0 0 0 0 0 100 0 0 33.3 0 33.3
neg. persp. question 0 100 0 0 0 0 0 0 0 0 33.3
? 0 100 0 0 0 0 0 0 0 0 50
neg. promise 0 0 0 0 0 0 0 100 0 0 25
mot. dep. exclamation 0 0 0 0 0 0 0 0 0 0 0
total 30.1 81.8 40.3 46.8 61.4 33.3 46.4 35 54.3 44 43.7
Table 4. Percentage of negation types with salient negative word - Prohibition Experiment. Listed are the percentages of utterances, classified by coder 1 as being of the stated negation type, and in which at least one negation word was detected as being salient. All numbers are percentages relative to the total counts given in table 3. ‘?’ is not a negation type but indicates that the coder could not decide on a type for a given utterance due to the utterance being incomplete. The total was calculated by weighing each utterance identically which effectively gives more weight to the salience rates of speakers who produced more utterances of the respective type.
P01 P04 P05 P06 P07 P08 P09 P10 P11 P12 total total
w/o P04
neg. intent interpret. 2 23 36 49 49 18 25 1 19 9 231 208
neg. mot. question 0 24 30 11 72 43 20 9 8 4 221 197
truth-func. denial 18 35 2 1 3 0 9 0 45 39 152 148
neg. agreement 0 4 5 0 16 9 1 0 0 0 35 31
neg. tag question 0 2 5 7 8 0 7 0 2 0 31 29
neg. persp. assertion 0 4 0 1 1 8 6 0 3 3 26 22
mot. dep. assertion 0 3 0 0 5 0 12 0 4 0 24 21
truth-func. negation 0 1 0 0 0 2 4 0 7 0 14 13
neg. imperative 0 1 0 0 6 0 0 0 0 4 11 10
neg. question 0 3 3 0 1 0 0 0 0 0 7 4
apostr. negation 0 1 0 0 0 2 2 0 1 1 7 6
truth-func. question 0 0 0 0 0 0 0 0 0 2 2 2
neg. persp. question 0 1 0 0 0 0 0 0 1 0 2 1
? 0 0 0 0 0 1 0 0 1 0 2 2
rejection 0 0 0 0 0 0 0 0 1 0 1 1
mot. dep. exclamation 0 0 1 0 0 0 0 0 0 0 1 1
neg. promise 0 0 0 0 0 1 0 0 0 0 1 1
total 20 102 82 69 161 84 86 10 92 62 768 666
Table 5. Frequency of participants’ negation types - Rejection Experiment. Listed are the counts for all negation types of all participants and all sessions within the Rejection Experiment. The last column lists the total count for each type across all participants minus the counts of participant P04. This participant had to be factored out for the subsequent consideration of salient words because a different method for detecting salient words was used. ‘?’ is not a negation type but indicates that the coder could not decide on a type for a given utterance due to the utterance being incomplete.
P01 P04 P05 P06 P07 P08 P09 P10 P11 P12 total total
w/o P04
neg. mot. question 0 8.3 56.7 63.6 70.8 48.8 30 22.2 12.5 50 49.3 54.3
neg. intent interpret. 0 4.3 69.4 51 42.9 44.4 48 0 36.8 33.3 44.2 48.6
truth-func. denial 44.4 0 0 0 66.7 0 55.6 0 48.9 15.4 28.3 29.1
neg. agreement 0 0 100 0 87.5 44.4 100 0 0 0 68.6 77.4
neg. tag question 0 0 80 28.6 50 0 42.9 0 100 0 48.4 51.7
neg. persp. assertion 0 0 0 100 100 50 33.3 0 100 33.3 46.2 54.5
neg. question 0 0 100 0 100 0 0 0 0 0 57.1 100
neg. imperative 0 0 0 0 0 0 0 0 0 75 27.3 30
mot. dep. assertion 0 0 0 0 0 0 16.7 0 0 0 8.3 9.5
truth-func. question 0 0 0 0 0 0 0 0 0 100 100 100
rejection 0 0 0 0 0 0 0 0 100 0 100 100
neg. persp. question 0 100 0 0 0 0 0 0 0 0 50 0
? 0 0 0 0 0 100 0 0 0 0 50 50
apostr. negation 0 0 0 0 0 0 0 0 0 100 14.3 16.7
truth-func. negation 0 0 0 0 0 50 0 0 0 0 7.1 7.7
mot. dep. exclamation 0 0 0 0 0 0 0 0 0 0 0 0
neg. promise 0 0 0 0 0 0 0 0 0 0 0 0
total 40 3.9 65.9 50.7 58.4 46.4 36 20 39.1 29 41.8 47.6
Table 6. Percentage of negation types with salient negative word - Rejection Experiment. Listed are the percentages of utterances, classified by coder 1 as the stated negation type, (one of) whose negation words were detected as being salient relative to the total number of utterances of this type. All numbers are percentages relative to the total counts given in table 5. The last column lists the average percentage of salient negation words across participants minus participant P04. For participant P04, one of the first participants, a different algorithm for detecting salient words had been used. ‘?’ is not a negation type but indicates that the coder could not decide on a type for a given utterance due to the utterance being incomplete.
Type neg. intent neg. mot. truth-func. prohi- disallow- truth-func. neg. tag
interpret. question denial bition ance negation question
Word
no 174 (39.2%) 191 (46.2%) 212 (67.9%) 129 (52.9%) 39 (45.3%) 14 (21.2%) 1   (1%)
not 59   (13.3%) 47   (11.4%) 93   (29.8%) 40  (16.4%) 27 (31.4%) 30 (45.5%) 0
don’t 201 (45.3%) 164 (39.7%) 1    (0.3%) 2    (0.8%) 2   (2.3%) 2   (3%) 58 (58.6%)
isn’t 0 9     (2.2%) 4    (1.3%) 0 0 0 18 (18.2%)
can’t 0 0 0 68   (27.9%) 16 (18.6%) 1   (1.5%) 3   (3%)
haven’t 0 0 1    (0.3%) 0 0 7   (10.6%) 1   (1%)
wasn’t 0 0 1    (0.3%) 0 0 0 0
cannot 0 0 0 1    (0.4%) 1   (1.2%) 0 0
neither 0 0 0 0 1   (1.2%) 0 0
didn’t 7    (1.6%) 1    (0.2%) 0 0 0 5   (7.6%) 12 (12.1%)
doesn’t 1    (0.2%) 0 0 0 0 5   (7.6%) 3   (3%)
hasn’t 0 0 0 0 0 1   (1.5%) 2   (2%)
weren’t 0 0 0 0 0 0 1   (1%)
won’t 2    (0.5%) 1    (0.2%) 0 0 0 0 0
mustn’t 0 0 0 4    (1.6%) 0 1   (1.5%) 0

[vc,hc][vc,hc] [-b,-l][t,l] Type neg. intent neg. mot. truth-func. prohibition disallowance interpret. question denial Word no 110 (62.9%) 152 (79.6%) 73 (34.6%) 85 (65.9%) 12 (30.8%) not 17   (28.8%) 8    (17.0%) 7   (7.5%) 7   (17.5%) 4   (14.8%) don’t 39   (19.3%) 24   (14.7%) 0 1   (50.0%) 1   (50.0%) can’t 0 0 0 27 (39.7%) 8   (50.0%) [-b,-l][t,l]

Table 7. Negation words within most frequent negation types. (Top) Listed are the absolute frequencies of negation words grouped by negation types as produced by all participants in all sessions within both experiments. The percentages in brackets give the share of the respective word relative to all negative words produced within the respective type. (Bottom) Listed are the number of salient words for each combination of negation word and type for the most frequently produced types and words. The percentages in brackets give the share of salient productions relative to the total number of productions of the respective word-type combination.

a.2. Corpus / Word Level Analysis - Details

In this section tables of the word corpora underlying Fig. 7C are given. Tables 8 and 10 contain the word frequencies of most frequent and other words of interest. Tables 9 and 11 tabulate the same words but with frequencies for only prosodically salient productions of these words. Both tables were compiled from speech recorded during the rejection and prohibition experiments. Tables 12 and 13 contain the equivalent listings for Saunders’ et al. experiment.

rank word cnt %    rank word cnt %    rank word cnt %
(1) you 1591 6.18    (27) yes 227 0.88    (114) sad 8 0.03
(2) the 1416 5.5    (31) not 198 0.77    (116) smiling 6 0.02
(3) a 962 3.74    (32) crescent 184 0.72    (116) haven’t 6 0.02
(4) this 956 3.71    (33) circles 168 0.65    (117) mustn’t 5 0.02
(5) one 722 2.8    (43) Deechee (2) 132 0.51    (118) won’t 4 0.02
(6) is 632 2.45    (47) box 123 0.48    (119) target 3 0.01
(7) like 527 2.05    (56) Deechee 103 0.4    (119) hasn’t 3 0.01
(8) to 471 1.83    (58) can’t (2) 98 0.38    (120) crescents 2 0.01
(9) no 461 1.79    (63) squares 86 0.33    (120) cannot 2 0.01
(10) it’s 428 1.66    (66) hearts 79 0.31    (120) pyramids 2 0.01
(11) heart 411 1.6    (69) triangles 72 0.28    (121) shouldn’t 1 0
(12) square 389 1.51    (75) nice 59 0.23    (121) moons 1 0
(13) triangle 377 1.47    (81) know 51 0.20    (121) nono 1 0
(15) do 360 1.40    (88) favorite 34 0.13    (121) wouldn’t 1 0
(16) moon 356 1.38    (100) happy 22 0.09    (121) neither 1 0
(17) circle 332 1.29    (101) smile 21 0.08    (121) pyramid 1 0
(19) shape 310 1.2    (102) isn’t 20 0.08    (121) weren’t 1 0
(26) play 229 0.89    (103) didn’t 19 0.07
(26) don’t 229 0.89    (113) doesn’t 9 0.04
Table 8. Word-frequencies in Prohibition Experiment. Listed are the ten most frequent words within said experiment across all participants and sessions. Given are the rank, the word count (cnt) and the percentage relative to the total number of words in the experiment. Apart from the highest-ranking words the same statistics are given for object labels, negation words, and words linked to the motivational state of the robot. See (Förster, 2013) for the complete listing of all words.
rank word cnt %    rank word cnt %    rank word cnt %
(1) square 327 4.4    (25) Deechee 67 0.9    (65) know 9 0.12
(2) triangle 302 4.06    (27) squares 62 0.83    (69) smiling 5 0.07
(3) circle 285 3.83    (27) is 62 0.83    (71) target 3 0.04
(4) no 272 3.66    (32) triangles 50 0.67    (71) sad 3 0.04
(5) one 250 3.36    (35) the 45 0.61    (72) mustn’t 2 0.03
(6) heart 248 3.34    (35) don’t 45 0.61    (72) crescents 2 0.03
(7) this 230 3.09    (37) hearts 43 0.58    (72) cannot 2 0.03
(8) moon 208 2.8    (40) can’t (2) 39 0.53    (72) doesn’t 2 0.03
(9) ok 171 2.3    (45) not 31 0.42    (72) haven’t 2 0.03
(10) shape 159 2.14    (47) do 29 0.39    (73) nono 1 0.01
(11) yes 155 2.09    (49) favorite 27 0.36    (73) won’t 1 0.01
(12) crescent 149 2    (53) a 21 0.28    (73) hasn’t 1 0.01
(13) like 134 1.8    (54) to 20 0.27    (73) pyramids 1 0.01
(14) circles 129 1.74    (54) nice 20 0.27    (73) neither 1 0.01
(20) Deechee (2) 83 1.12    (57) it’s 17 0.23    (73) pyramid 1 0.01
(21) you 79 1.06    (59) smile 15 0.2    (73) weren’t 1 0.01
(23) play 75 1.01    (64) happy 10 0.13
(24) box 72 0.97    (65) isn’t 9 0.12
Table 9. Word-frequencies of prosodically salient words in Prohibition Experiment. Listed are the ten most frequent salient words within said experiment across all participants and sessions. Given are the rank, the word count (cnt) and the percentage relative to the total number of words in the experiment. Apart from the highest-ranking words the same statistics are given for object labels, negation words, and words linked to the motivational state of the robot. See (Förster, 2013) for the complete listing of all words.
rank word cnt %    rank word cnt %    rank word cnt %
(1) you 1245 7.13    (35) Deechee 127 0.73    (93) didn’t 11 0.06
(2) the 983 5.63    (38) not 118 0.68    (94) didn’t (2) 10 0.06
(3) like 579 3.31    (41) box 110 0.63    (95 pyramid 9 0.05
(4) a 475 2.72    (42) Deechee (2) 103 0.59    (93) isn’t 11 0.06
(5) this 471 2.7    (44) triangles 99 0.57    (97) moons 7 0.04
(6) no 417 2.39    (54) don’t (2) 69 0.4    (100) rectangle 4 0.02
(7) one 396 2.27    (58) crescent 58 0.33    (101) won’t 3 0.02
(8) square 337 1.93    (65) sad 43 0.25    (100) smiling 4 0.02
(8) do 337 1.93    (68) shape 39 0.22    (101) can’t 3 0.02
(9) to 311 1.93    (69) happy 38 0.22    (101) pyramids 3 0.02
(11) moon 283 1.62    (69) nice 38 0.22    (103) wouldn’t 1 0.01
(12) heart 279 1.6    (70) favorite 37 0.21    (102) doesn’t 2 0.01
(14) triangle 254 1.45    (71) target 36 0.21    (102) doesn’t (2) 2 0.01
(15) circle 231 1.32    (75) hearts 30 0.17    (103) couldn’t 1 0.01
(17) don’t 200 1.15    (78) arteen 27 0.15    (103) wasn’t 1 0.01
(21) circles 190 1.09    (86) know 18 0.1    (103) weren’t 1 0.01
(23) squares 180 1.03    (91) smile 13 0.07    (103) can’t (2) 1 0.01
(24) yes 179 1.02    (92) rectangles 12 0.07    (100) haven’t 4 0.02
Table 10. Word-frequencies in Rejection Experiment. Listed are the ten most frequent words within said experiment across all participants and sessions. Given are the rank, the word count (cnt) and the percentage relative to the total number of words in the experiment. Apart from the highest-ranking words the same statistics are given for object labels, negation words, and words linked to the motivational state of the robot. See (Förster, 2013) for the complete listing of all words.
rank word cnt %    rank word cnt %    rank word cnt %
(1) square 259 4.97    (24) don’t 52 1    (46) do 12 0.23
(2) no 242 4.64    (27) box 45 0.86    (47) rectangles 11 0.21
(3) triangle 206 3.95    (29) crescent 39 0.75    (47) a 11 0.21
(4) heart 198 3.8    (31) are 36 0.69    (50) pyramid 8 0.15
(5) moon 184 3.53    (32) sad 35 0.67    (53) isn’t 5 0.1
(5) circle 184 3.53    (33) target 31 0.59    (54) rectangle 4 0.08
(6) like 167 3.2    (34) favorite 27 0.52    (55) pyramids 3 0.06
(7) circles 140 2.69    (35) arteen 25 0.48    (55) moons 3 0.06
(8) squares 126 2.42    (36) not 24 0.46    (55) smiling 3 0.06
(9) it 123 2.36    (37) hearts 22 0.42    (56) can’t 2 0.04
(10) yes 119 2.28    (38) to 20 0.38    (56) won’t 2 0.04
(11) one 111 2.13    (38) happy 20 0.38    (57) didn’t (2) 1 0.02
(13) this 95 1.82    (39) shape 19 0.36    (57) couldn’t 1 0.02
(18) Deechee 76 1.46    (39) nice 19 0.36    (57) doesn’t 1 0.02
(19) Deechee (2) 68 1.3    (41) the 17 0.33    (57) didn’t 1 0.02
(21) triangles 62 1.19    (46) smile 12 0.23    (57) haven’t 1 0.02
(23) you 53 1.02    (46) don’t (2) 12 0.23
Table 11. Word-frequencies of prosodically salient words in Rejection Experiment. Listed are the ten most frequent salient words within said experiment across all participants and sessions. Given are the rank, the word count (cnt) and the percentage relative to the total number of words in the experiment. Apart from the highest-ranking words the same statistics are given for object labels, negation words, and words linked to the motivational state of the robot. See (Förster, 2013) for the complete listing of all words.
rank word cnt %    rank word cnt %    rank word cnt %
(1) a 702 8.71    (23) shape 88 1.09    (73) done 22 0.27
(2) this 367 4.55    (24) right 87 1.08    (75) don’t 21 0.26
(3) blue 347 4.31    (25) box 87 1.08    (80) crescent 17 0.21
(4) is 322 4    (28) small 76 0.94    (101) not 14 0.17
(5) and 314 3.9    (30) square 69 0.86    (107) colors 11 0.14
(6) red 302 3.75    (31) like 65 0.81    (109) isn’t 11 0.14
(7) green 286 3.55    (32) star 61 0.76    (137) nice 6 0.07
(8) the 265 3.29    (40) bigger 41 0.51    (150) can’t 4 0.05
(9) that’s 237 2.94    (41) white 41 0.51    (162) didn’t 3 0.04
(10) you 194 2.41    (42) large 40 0.5    (165) favorite 3 0.04
(11) it’s 161 2    (44) colour 37 0.46    (176) aren’t 3 0.04
(12) heart 160 1.99    (53) Deechee 33 0.41    (177) squares 3 0.04
(13) circle 149 1.85    (54) yes 33 0.41    (178) circles 3 0.04
(14) arrow 148 1.84    (64) smile 28 0.35    (229) triangle 1 0.01
(15) side 146 1.81    (65) no 28 0.35    (260) never 1 0.01
(16) cross 120 1.49    (68) shapes 25 0.31    (264) happy 1 0.01
(20) moon 95 1.18    (72) big 22 0.27    (273) excited 1 0.01
Table 12. Word-frequencies in the experiment of Saunders et al. (Saunders et al., 2012). Listed are the ten most frequent words within said experiment across all participants and sessions. Given are the rank, the word count (cnt) and the percentage relative to the total number of words uttered during the entire experiment. Apart from the highest-ranking words the same statistics are given for object labels, object properties, negation words, and words linked to the motivational state of the robot. See (Förster, 2013) for the complete listing of all words.
rank word cnt %    rank word cnt %    rank word cnt %
(1) blue 157 6.91    (19) right 35 1.54    (49) no 10 0.44
(2) red 126 5.54    (23) colour 24 1.06    (50) it’s 10 0.44
(3) circle 117 5.15    (24) good 22 0.97    (52) done 10 0.44
(4) heart 108 4.75    (25) bigger 22 0.97    (54) the 8 0.35
(5) green 99 4.36    (26) a 21 0.92    (67) don’t 6 0.26
(6) arrow 81 3.56    (27) that’s 20 0.88    (71) isn’t 6 0.26
(7) cross 79 3.48    (28) Deechee 19 0.84    (72) big 6 0.26
(8) side 79 3.48    (29) shapes 18 0.79    (78) colors 5 0.22
(9) box 64 2.82    (30) it 18 0.79    (91) didn’t 3 0.13
(10) shape 55 2.42    (32) you 18 0.79    (93) not 3 0.13
(11) and 48 2.11    (33) smile 17 0.75    (97) favorite 3 0.13
(12) moon 48 2.11    (36) white 17 0.75    (100) circles 3 0.13
(13) square 47 2.07    (38) large 16 0.7    (107) nice 3 0.13
(14) this 46 2.02    (41) yes 13 0.57    (119) squares 2 0.09
(15) star 42 1.85    (43) crescent 13 0.57    (146) can’t 1 0.04
(17) is 40 1.76    (46) like 11 0.48    (156) aren’t 1 0.04
(18) small 35 1.54    (47) yea 11 0.48
Table 13. Word-frequencies of prosodically salient words in the experiment of Saunders et al. (Saunders et al., 2012). Listed are the ten most salient words within said experiment across all participants and sessions. Given are the rank, the word count (cnt) and the percentage relative to the total number of words uttered during the entire experiment. Apart from the highest-ranking words the same statistics are given for object labels, negation words, and words linked to the motivational state of the robot. See (Förster, 2013) for the complete listing of all words.
Rejection Experiment Prohibition Experiment
All Words Salient Words All Words Salient Words
Rank Word Rank Word Rank Word Rank Word
1 you 1 triangle 1 you 1 square
2 the 2 no 2 the 2 no
3 a square 3 this 3 circle
4 like 3 heart 4 a 4 moon
no 4 moon 5 is 5 triangle
5 this 5 circle 6 heart 6 heart
6 square 6 yes 7 one one
7 one 7 like 8 to 7 this
8 that 8 it 9 no 8 yes
9 do 9 squares 10 like 9 like
10 moon 10 this 11 triangle ok
11 it 11 one square 10 again
12 triangle 12 ok 12 it 11 shape
13 circle again 13 it’s 12 it
14 it’s 13 circles 14 that 13 crescent
15 heart 14 good and 14 you
16 to 15 oh 15 circle 15 good
17 yes 16 right 16 very 16 very
18 is 17 triangles 17 do 17 ok(2)
19 ok 18 that 18 moon 18 right
20 oh 19 Deechee(2) 19 that’s 19 circles
21 want 20 you 20 shape 20 round
22 don’t 21 about 21 can 21 Deechee(2)
23 well 22 done 22 we 22 done
24 circles 23 ah 23 at 23 today
Table 14. Adjusted accumulated word rankings in both negation experiments. Listed are the 25 top-ranking words of all words and of the subset of salient words only within each experiment. The ranking within each list results from conceiving of the frequency-ordered word lists for each participant as voting ballots that are ordered descendingly with regard to word-frequency. The frequency itself is subsequently ignored. This approach eliminates the greater influence of very talkative participants on the accumulated rankings as opposed to the lesser influence of rather taciturn participants. The voting ballots were processed by the ranked-pair algorithm which determines the ordered list of winners of this “voting process”. A quote (’”’) entry in the rank column indicates a tie: the corresponding word has the same rank as the previous word in the column.

a.3. Utterance Level Analysis - Details

In this section the complete measurements for the utterance level are listed as well as the tables relating to the cross-experimental statistical comparison of the utterances per minute measure. For additional comparisons involving measurements such as the number of distinct words or mean length of utterance (MLU) see (Förster, 2013).

P13 P14 P15 P16 P17 P18 P19 P20 P21 P22
s1 d (s) 301.8 317.8 311.8 332.3 303.8 301.1 318.9 300.4 359.3 319.3
# w 535 279 611 704 200 691 644 508 332 404
# u 134 92 165 194 66 185 179 138 119 124
# dw 110 76 134 195 63 170 178 114 74 99
MLU 4 3 3.7 3.6 3 3.7 3.6 3.7 2.8 3.3
w/min 106.4 52.7 117.6 127.1 39.5 137.7 121.2 101.5 55.4 75.9
u/min 26.6 17.4 31.8 35 13 36.9 33.7 27.6 19.9 23.3
s2 d (s) 324.2 305.7 301.4 307 310.7 296 308.4 308.3 317.6 312.1
# w 653 307 715 830 215 702 535 610 363 332
# u 178 93 187 204 78 188 147 138 133 110
# dw 100 73 126 187 53 188 117 110 75 77
MLU 3.7 3.3 3.8 4.1 2.8 3.7 3.6 4.4 2.7 3
w/min 120.9 60.3 142.3 162.2 41.5 142.3 104.1 118.7 68.6 63.8
u/min 32.9 18.3 37.2 39.9 15.1 38.1 28.6 26.9 25.1 21.1
s3 d (s) 297.4 332.5 294.6 326.8 302.3 309.1 316.2 302.6 306.1 315.8
# w 424 343 717 774 184 702 610 625 329 477
# u 133 95 180 221 71 195 170 137 141 162
# dw 70 70 152 205 52 178 130 87 82 100
MLU 3.2 3.6 4 3.5 2.6 3.6 3.6 4.6 2.3 2.9
w/min 85.5 61.9 146 142.1 36.5 136.3 115.8 123.9 64.5 90.6
u/min 26.8 17.1 36.7 40.6 14.1 37.9 32.3 27.2 27.6 30.8
s4 d (s) 307.6 319.9 308.5 316.1 314.7 314.8 301.7 298.6 301.1 316.2
# w 501 298 698 714 259 750 536 490 332 589
# u 154 89 181 192 83 198 160 131 119 204
# dw 69 55 132 195 40 194 117 93 59 127
MLU 3.3 3.3 3.9 3.7 3.1 3.8 3.4 3.7 2.8 2.9
w/min 97.7 55.9 135.7 135.5 49.4 143 106.6 98.5 66.2 111.7
u/min 30 16.7 35.2 36.4 15.8 37.7 31.8 26.3 23.7 38.7
s5 d (s) 306.7 380.3 312.4 320.1 293.4 302.1 311.2 303.2 306.6 317.4
# w 476 340 656 728 310 716 591 577 333 493
# u 160 100 186 188 102 199 178 157 127 170
# dw 64 52 131 198 42 176 109 105 69 106
MLU 3 3.4 3.5 3.9 3 3.6 3.3 3.7 2.6 2.9
w/min 93.1 53.6 126 136.5 63.4 142.2 113.9 114.2 65.2 93.2
u/min 31.3 15.8 35.7 35.2 20.9 39.5 34.3 31.1 24.9 32.1
Table 15. Utterance-level measures for Prohibition Experiment. All participants and all sessions. Any given number refers to the participant with participant id noted on top the corresponding column and the session number in the corresponding first column. Abbreviations: sX: session nr. X, # w/# u: total number of words/utterances uttered by participant, # dw: number of distinct words, MLU: mean length of utterance, w/min / u/min: words/utterances per minute.
P01 P04 P05 P06 P07 P08 P09 P10 P11 P12
s1 d (s) 272 168.4 308.6 178.3 380.9 290.4 301.9 275.5 298.3 300.5
# w 111 314 370 392 729 505 474 26 458 353
# u 50 67 133 104 225 157 126 16 114 92
# dw 21 101 96 100 140 145 133 6 103 114
MLU 2.2 4.7 2.8 3.8 3.2 3.2 3.8 1.6 4 3.8
w/min 24.5 111.9 71.9 131.9 114.8 104.3 94.2 5.7 92.1 70.5
u/min 11 23.9 25.9 35 35.4 32.4 25 3.5 22.9 18.4
s2 d (s) 285.4 196 305.2 293.2 303.4 259.8 306.6 287.6 298.8 312.7
# w 138 346 323 580 666 414 437 70 338 230
# u 66 83 110 156 183 112 122 36 106 82
# dw 26 77 71 86 107 89 138 27 90 76
MLU 2.1 4.2 2.9 3.7 3.6 3.7 3.6 1.9 3.2 2.8
w/min 29 105.9 63.5 118.7 131.7 95.6 85.5 14.6 67.9 44.1
u/min 13.9 25.4 21.6 31.9 36.2 25.9 23.9 7.5 21.3 15.7
s3 d (s) 307.9 297.1 249.9 318 307.8 296.8 299.2 290.8 306.6 302.5
# w 159 468 302 662 569 431 513 62 242 107
# u 66 111 102 168 180 128 139 31 98 38
# dw 29 107 73 97 100 96 155 27 56 22
MLU 2.4 4.2 3 3.9 3.1 3.4 3.7 2 2.5 2.8
w/min 31 94.5 72.5 124.9 110.3 87.1 102.9 12.8 47.4 21.2
u/min 12.9 22.4 24.5 31.7 35.1 25.9 27.9 6.4 19.2 7.5
s4 d (s) 319.2 265.5 213.2 329.9 307.8 301.5 300.4 289.4 300.2 303.5
# w 204 393 253 685 541 364 495 84 187 152
# u 80 93 89 187 184 105 143 33 70 67
# dw 30 104 67 95 108 88 152 23 58 27
MLU 2.5 4.2 2.8 3.7 2.9 3.5 3.5 2.5 2.7 2.3
w/min 38.3 88.8 71.2 124.6 105.5 72.4 99.1 17.4 37.4 30.1
u/min 15 21 25 34 35.9 20.9 28.6 6.8 14 13.2
s5 d (s) 305.9 220.4 269.1 307.1 314.5 324.5 301.3 290.2 319.5 299
# w 160 370 356 633 582 406 402 66 211 134
# u 61 92 135 157 188 128 132 36 74 54
# dw 23 104 81 89 105 127 116 20 74 36
MLU 2.6 4 2.6 4 3.1 3.2 3 1.8 2.9 2.5
w/min 31.4 100.7 79.4 123.7 111 75.1 80.1 13.6 39.6 26.9
u/min 12 25 30.1 30.7 35.9 23.7 26.3 7.4 13.9 10.8
Table 16. Utterance-level measures for Rejection Experiment. All participants and all sessions. Any given number refers to the participant with participant id noted on top the corresponding column and the session number in the corresponding first column. Abbreviations: sX: session nr. X, # w/# u: total number of words/utterances uttered by participant, # dw: number of distinct words, MLU: mean length of utterance, w/min / u/min: average number of words / utterances per minute
M02 F05 M03 F01 F02 M01 F03 F06 F04
s1 d (s) 172.7 185.5 170.1 107.1 115.2 167.9 n/a 177.3 120.2
# w 156 268 120 130 267 210 n/a 371 290
# u 51 80 41 34 55 62 n/a 99 75
# dw 29 70 34 42 74 58 n/a 103 85
MLU 3.1 3.4 2.9 3.8 4.9 3.4 n/a 3.7 3.9
w/min 54.2 86.7 42.3 72.8 139.1 75.1 n/a 125.5 144.8
u/min 17.7 25.9 14.5 19 28.6 22.2 n/a 33.5 37.5
s2 d (s) 102.4 130.6 118.9 117.9 125.5 136.9 130.7 138.7 119.7
# w 105 205 142 145 249 219 214 264 178
# u 34 48 45 35 55 63 61 77 60
# dw 24 77 37 37 65 44 56 83 41
MLU 3.1 4.3 3.2 4.1 4.5 3.5 3.5 3.4 3
w/min 61.5 94.2 71.7 73.8 119 95.9 98.3 114.2 89.2
u/min 19.9 22.1 22.7 17.8 26.3 27.6 28 33.3 30.1
s3 d (s) 119.3 129.7 115.5 114 122.7 129 123.3 133.7 128.4
# w 99 215 97 123 236 162 200 278 220
# u 31 57 36 34 52 42 64 73 69
# dw 21 57 39 27 57 36 67 79 61
MLU 3.2 3.8 2.7 3.6 4.5 3.9 3.1 3.8 3.2
w/min 49.8 99.4 50.4 64.8 115.4 75.3 97.3 124.7 102.8
u/min 15.6 26.4 18.7 17.9 25.4 19.5 31.1 32.8 32.2
s4 d (s) 125.9 118.3 116.4 115.8 122.5 126.8 116.7 128.5 126.5
# w 90 174 82 107 172 127 200 273 192
# u 35 40 31 28 39 38 61 70 60
# dw 18 44 29 29 43 25 66 86 52
MLU 2.6 4.3 2.6 3.8 4.4 3.3 3.3 3.9 3.2
w/min 42.9 88.2 42.3 55.4 84.3 60.1 102.8 127.5 91
u/min 16.7 20.3 16 14.5 19.1 18 31.4 32.7 28.4
s5 d (s) 205.7 107.4 125.2 113.8 117.7 102.5 130.7 128.6 126.5
# w 160 182 99 122 200 93 233 234 155
# u 53 47 35 28 43 32 76 67 59
# dw 24 48 37 29 45 23 57 83 46
MLU 3 3.9 2.8 4.4 4.7 2.9 3.1 3.5 2.6
w/min 46.7 101.6 47.4 64.3 102 54.5 106.9 109.2 73.5
u/min 15.5 26.2 16.8 14.8 21.9 18.7 34.9 31.3 28
Table 17. Utterance-level measures for participants speech from Saunders et al. (Saunders et al., 2012). Any given number refers to the participant with participant id noted on top the corresponding column and the session number in the corresponding first column. Abbreviations: sX: session nr. X, # w/# u: total number of words/utterances uttered by participant, # dw: number of distinct words, MLU: mean length of utterance, w/min / u/min: average number of words / utterances per minute, n/a: data for corresponding session was not available.
P13 P14 P15 P16 P17 P18 P19 P20 P21 P22
s1 d (s) 301.8 317.8 311.8 332.3 303.8 301.1 318.9 300.4 359.3 319.3
# nw 22 0 61 29 16 36 22 24 33 5
# nu 19 0 52 25 14 36 20 23 20 4
# dnw 7 0 8 5 4 7 5 6 3 4
MLU 4.8 0 4.5 5.6 2.7 4.4 3.8 3.9 3.6 4.8
nw/min 4.4 0 11.7 5.2 3.2 7.2 4.1 4.8 5.5 0.9
nu/min 3.8 0 10 4.5 2.8 7.2 3.8 4.6 3.3 0.8
s2 d (s) 324.2 305.7 301.4 307 310.7 296 308.4 308.3 317.6 312.1
# nw 43 12 45 34 18 19 29 34 36 15
# nu 36 8 37 32 16 18 24 30 24 15
# dnw 5 3 6 6 3 6 4 5 4 2
MLU 4.4 2.4 5.2 4.8 2.1 4.7 4.4 5.7 3.1 3.1
nw/min 8 2.4 9.0 6.6 3.5 3.9 5.6 6.6 6.8 2.9
nu/min 6.7 1.6 7.4 6.3 3.1 3.6 4.7 5.8 4.5 2.9
s3 d (s) 297.4 332.5 294.6 326.8 302.3 309.1 316.2 302.6 306.1 315.8
# nw 7 9 49 34 26 25 29 33 25 19
# nu 7 8 45 28 25 23 25 30 24 18
# dnw 3 3 7 6 5 7 4 5 4 3
MLU 4.4 3 5.0 5.0 1.9 4.9 4.7 5.3 2.4 2.7
nw/min 1.4 1.6 10 6.2 5.2 4.9 5.5 6.5 4.9 3.6
nu/min 1.4 1.4 9.2 5.1 5 4.5 4.7 5.9 4.7 3.4
s4 d (s) 307.6 319.9 308.5 316.1 314.7 314.8 301.7 298.6 301.1 316.2
# nw 5 1 30 28 8 15 6 21 9 7
# nu 5 1 24 25 8 14 6 20 8 7
# dnw 3 1 5 6 1 6 3 6 2 3
MLU 3.8 1 5.0 5.5 1.9 5.1 4.7 4.3 4 3.9
nw/min 1 0.2 5.8 5.3 1.5 2.9 1.2 4.2 1.8 1.3
nu/min 1 0.2 4.7 4.7 1.5 2.7 1.2 4.0 1.6 1.3
s5 d (s) 306.7 380.3 312.4 320.1 293.4 302.1 311.2 303.2 306.6 317.4
# nw 6 3 32 28 7 11 10 20 16 6
# nu 6 3 30 27 7 11 9 20 16 6
# dnw 3 2 3 6 1 4 3 5 2 3
MLU 5.3 1.3 2.8 5.3 1.3 4.6 4.3 4.5 2.5 2.8
nw/min 1.2 0.5 6.1 5.2 1.4 2.2 1.9 4 3.1 1.1
nu/min 1.2 0.5 5.8 5.1 1.4 2.2 1.7 4 3.1 1.1
Table 18. Utterance-level measures for negative utterances in Prohibition Experiment. Numbers refer to the participant with the id noted in the top row and session number in the first column. Abbreviations: sX: session nr. X, # nw/# nu: total number of negation words/negative utterances uttered by participant, # dnw: number of unique negation words, MLU: mean length of utterance, nw/min / nu/min: negation words / negative utterances per minute
P01 P04 P05 P06 P07 P08 P09 P10 P11 P12
s1 d (s) 272 168.4 308.6 178.3 380.9 290.4 301.9 275.5 298.3 300.5
# nw 1 15 25 11 30 22 18 0 25 14
# nu 1 12 24 10 28 21 18 0 21 13
# dnw 1 5 4 3 4 3 5 0 5 3
MLU 2 4.4 1.8 3.7 3.2 3.5 4.5 0 5.6 3.8
nw/min 0.2 5.3 4.9 3.7 4.7 4.5 3.6 0 5 2.8
nu/min 0.2 4.3 4.7 3.4 4.4 4.3 3.6 0 4.2 2.6
s2 d (s) 285.4 196 305.2 293.2 303.4 259.8 306.6 287.6 298.8 312.7
# nw 3 23 21 13 35 18 21 2 41 17
# nu 3 19 20 12 34 17 18 2 29 14
# dnw 2 4 3 2 4 3 4 2 4 3
MLU 2.7 4.9 3 3.5 4.1 4.5 6.2 2.5 4.2 2.8
nw/min 0.6 7 4.1 2.7 6.9 4.2 4.1 0.4 8.2 3.3
nu/min 0.6 5.8 3.9 2.5 6.7 3.9 3.5 0.4 5.8 2.7
s3 d (s) 307.9 297.1 249.9 318 307.8 296.8 299.2 290.8 306.6 302.5
# nw 5 32 10 18 37 15 23 2 18 11
# nu 5 23 10 17 35 15 20 2 15 8
# dnw 2 4 3 4 5 4 6 2 3 1
MLU 3.4 5.3 1.2 4.6 3 2.3 6.2 3 4.4 4
nw/min 1 6.5 2.4 3.4 7.2 3.0 4.6 0.4 3.5 2.2
nu/min 1 4.6 2.4 3.2 6.8 3.0 4 0.4 2.9 1.6
s4 d (s) 319.2 265.5 213.2 329.9 307.8 301.5 300.4 289.4 300.2 303.5
# nw 8 26 12 17 25 21 14 3 21 18
# nu 8 20 12 17 24 20 14 3 20 14
# dnw 2 2 5 4 5 2 6 1 5 2
MLU 1.8 4.6 3.2 3.9 3.2 3.2 5.1 4.7 3.4 2.6
nw/min 1.5 5.9 3.4 3.1 4.9 4.2 2.8 0.6 4.2 3.6
nu/min 1.5 4.5 3.4 3.1 4.7 4 2.8 0.6 4 2.8
s5 d (s) 305.9 220.4 269.1 307.1 314.5 324.5 301.3 290.2 319.5 299
# nw 3 36 19 12 40 11 17 3 9 14
# nu 3 28 16 12 40 11 15 3 6 13
# dnw 2 5 3 3 3 2 3 1 3 2
MLU 3.7 4.7 2.9 4.4 2.7 3.9 3.4 2 3.7 3.3
nw/min 0.6 9.8 4.2 2.3 7.6 2 3.4 0.6 1.7 2.8
nu/min 0.6 7.6 3.6 2.3 7.6 2 3.0 0.6 1.1 2.6
Table 19. Utterance-level measures for negative utterances in Rejection Experiment. All numbers refer to the participant with the id noted in the top row and session number in the first column. Abbreviations: sX: session nr. X, # nw/# nu: total number of negation words/negative utterances uttered by participant, # dnw: number of unique negation words, MLU: mean length of utterance , nw/min / nu/min: negation words / negative utterances per minute
M02 F05 M03 F01 F02 M01 F03 F06 F04
s1 d (s) 172.7 185.5 170.1 107.1 115.2 167.9 n/a 177.3 120.2
# nw 0 0 2 0 0 0 n/a 11 2
# nu 0 0 1 0 0 0 n/a 10 2
# dnw 0 0 2 0 0 0 n/a 5 1
MLU 0 0 19 0 0 0 n/a 6.4 10
nw/min 0 0 0.7 0 0 0 n/a 3.7 1
nu/min 0 0 0.4 0 0 0 n/a 3.4 1
s2 d (s) 102.4 130.6 118.9 117.9 125.5 136.9 130.7 138.7 119.7
# nw 0 0 1 5 0 0 0 14 1
# nu 0 0 1 4 0 0 0 13 1
# dnw 0 0 1 2 0 0 0 6 1
MLU 0 0 3 5.8 0 0 0 5.6 6
nw/min 0 0 0.5 2.5 0 0 0 6.1 0.5
nu/min 0 0 0.5 2 0 0 0 5.6 0.5
s3 d (s) 119.3 129.7 115.5 114 122.7 129 123.3 133.7 128.4
# nw 0 1 3 2 0 0 0 14 3
# nu 0 1 3 2 0 0 0 13 2
# dnw 0 1 2 1 0 0 0 5 1
MLU 0 8 3.7 5 0 0 0 3 2
nw/min 0 0.5 1.6 1.1 0 0 0 6.3 1.4
nu/min 0 0.5 1.6 1.1 0 0 0 5.8 0.9
s4 d (s) 125.9 118.3 116.4 115.8 122.5 126.8 116.7 128.5 126.5
# nw 0 0 2 0 0 0 0 14 0
# nu 0 0 2 0 0 0 0 12 0
# dnw 0 0 1 0 0 0 0 6 0
MLU 0 0 2.5 0 0 0 0 6.6 0
nw/min 0 0 1 0 0 0 0 6.5 0
nu/min 0 0 1 0 0 0 0 5.6 0
s5 d (s) 205.7 107.4 125.2 113.8 117.7 102.5 130.7 128.6 126.5
# nw 0 0 1 1 0 0 1 7 0
# nu 0 0 1 1 0 0 1 6 0
# dnw 0 0 1 1 0 0 1 4 0
MLU 0 0 4 4 0 0 5 6.5 0
nw/min 0 0 0.5 0.5 0 0 0.5 3.3 0
nu/min 0 0 0.5 0.5 0 0 0.5 2.8 0
Table 20. Utterance-level measures for negative utterances of participants speech from Saunders et al. (Saunders et al., 2012). Any given number refers to the participant with participant id noted on top the corresponding column and the session number in the corresponding first column. Abbreviations: sX: session nr. X, # nw/# nu: total number of negative words/utterances uttered by participant, # dnw: number of distinct negative words, MLU: mean length of negative utterances, nw/min / nu/min: average number of neg. words / neg. utterances per minute, n/a: data for corresponding session was not available.
(a) All Utterances
Saunders Rejection Prohibition
mean (sd) mean (sd) mean(sd) F(2,26) p
s1 24.86 (8.01) 23.34 (10.2) 26.52 (8.03) 0.32 0.7290
s2 25.31 (5.04) 22.33 (8.47) 28.32 (8.62) 1.54 0.2340
s3 24.4 (6.68) 21.35 (9.79) 29.11 (8.57) 2.11 0.1410
s4 21.9 (6.99) 21.44 (9.49) 29.23 (8.38) 2.67 0.0882
s5 23.12 (7.31) 21.58 (9.82) 30.08 (7.35) 2.97 0.0689
(a) Negative Utterances Only
Saunders Rejection Prohibition
mean (sd) mean (sd) mean(sd) F(2,26) p
s1 0.6 (1.19) 3.17 (1.73) 4.08 (2.89) 6.31 0.0060
s2 0.96 (1.86) 3.58 (2.13) 4.66 (1.88) 8.83 0.0012
s3 1.1 (1.86) 2.99 (1.86) 4.53 (2.24) 6.98 0.0037
s4 0.73 (1.85) 3.14 (1.3) 2.29 (1.63) 5.45 0.0105
s5 0.48 (0.9) 3.1 (2.57) 2.61 (1.82) 4.93 0.0153
Table 21. Comparison of u/min between negation experiments and Saunders’ et al. experiment.

a.4. Robot: Evaluation of Acquisition

Table 22 compares the relative felicity rates of the robot’s negative utterances as judged by each coder. Initially we also tentatively included so called pragmatic negatives in the coding set which are words which in a certain context may fulfill the same communicative function as a lexical negative. ‘Go’ for example, if expressed with a certain assertive prosodic contour, may do the same communicative work as ‘no’ in a situation where the participant asks whether Deechee wants to play with a particular box or not. We excluded these ‘negatives’ in the end because they constituted a source of disagreement amongst coders. Participant P12 receives this special treatment for the following reason: upon completion of his five sessions, we were made aware of his conscious decision to adopt an unnatural speech register in order to ‘win the teaching game’, based on some hypothesis of his regarding the underlying acquisition algorithm. He thereby explicitly ignored our instruction to speak to the robot as if it was a small child.

Table 23 depicts the outcome of the evaluation of the robot’s negative linguistic productions for felicity, that is whether its utterances would be considered adequate in the respective situations as judged by an external observer. In line with our study design the impact of the prohibition task is measured by comparing the robot’s ‘negative’ linguistic performance of the last two sessions (table 24). Table S25

shows the result of a t-test which was performed in order to ascertain that the results of table

24

were not skewed by those sessions were Deechee was more talkative, i.e. where it uttered more negative utterances. The test is based on the felicity rates of the robot on a per-participant basis where the rates of all sessions are accumulated (row

all in table 24) and where two different exclusion criteria were applied: Under criterion 1 all available data is used but from those set of sessions where the robot did not produce any negative words. Under criterion 2 those participants’ felicity rates are excluded which are based on a total count lower than 10.

coder full - prag. - P12 - P12 && -prag.
c1 53.91 46.94 54.88 48.57
c2 51.94 48.98 54.35 55.71
Table 22. Felicity rate judgements by coder: full: full coding set, no exclusions, -prag.: coding set where utterances containing pragmatic negatives are removed, -P12: coding set where P12’s utterances are excluded, -P12 && -prag.: coding set where both P12’s and pragmatic negatives are excluded.
P01 P04 P05 P06 P07 P08 P09 P10 P11 P12 total
type cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel
TD 0 n/a 5 100 0 n/a 0 n/a 0 n/a 0 n/a 0 n/a 0 n/a 1 0 13 53.85 19 63.16
E 0 n/a 0 n/a 0 n/a 0 n/a 1 100 1 100 0 n/a 0 n/a 0 n/a 0 n/a 2 100
A 0 n/a 4 100 8 100 0 n/a 7 85.71 1 100 4 100 0 n/a 2 100 1 100 27 96.3
R 0 n/a 3 100 17 29.41 0 n/a 4 75 11 36.36 8 100 0 n/a 16 68.75 16 62.5 75 58.67
MD 0 n/a 0 n/a 30 40 0 n/a 15 73.33 13 84.62 7 85.71 0 n/a 10 40 6 33.33 81 56.79
I 0 n/a 0 n/a 0 n/a 0 n/a 0 n/a 0 n/a 20 90 0 n/a 0 n/a 0 n/a 20 90
all 0 n/a 12 100 55 45.45 0 n/a 27 77.78 26 65.38 39 92.31 0 n/a 29 58.62 36 55.56 224 66.07
(a) Rejection Experiment
P13 P14 P15 P16 P17 P18 P19 P20 P21 P22 total
type cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel
TD 5 0 10 0 3 0 1 0 8 12.5 0 n/a 0 n/a 2 0 10 30 3 33.33 42 11.9
E 0 n/a 0 n/a 0 n/a 0 n/a 0 n/a 0 n/a 0 n/a 1 0 0 n/a 2 100 3 66.66
A 0 n/a 0 n/a 12 83.33 4 100 0 n/a 0 n/a 0 n/a 6 83.33 8 75 3 100 33 84.84
R 0 n/a 3 33.33 13 46.15 1 0 15 73.33 2 100 0 n/a 4 75 10 70 1 0 49 61.22
MD 0 n/a 1 0 38 39.47 7 14.29 28 42.86 1 100 0 n/a 12 25 12 58.33 0 n/a 99 39.39
I 0 n/a 0 n/a 0 n/a 4 25 0 n/a 0 n/a 0 n/a 0 n/a 0 n/a 0 n/a 4 25
PD 0 n/a 1 100 0 n/a 0 n/a 1 0 0 n/a 4 100 4 25 0 n/a 0 n/a 10 60
SP 0 n/a 0 n/a 2 0 1 0 0 n/a 0 n/a 16 100 7 42.86 16 68.75 0 n/a 42 71.43
all 5 0 15 13.33 68 45.59 18 33.33 52 46.15 3 100 20 100 36 41.67 56 60.71 9 66.67 282 50
(b) Prohibition Experiment

[vc,hc][vc,hc] [-b,-l][t,l]

Table 23. Accumulated frequencies for robot negation types and their felicity. Displayed are the accumulated frequencies of the various negation types the robot engaged in across sessions and their felicities. The following abbreviations are used for the negation types: TD: truth-func. denial, MD: mot.-dep. denial, A: neg. agreement, R: rejection, I: neg. imperative, E: mot. dep. exclamation, SP: self-prohibition, PD: perspective-dependent denial. See the SI coding scheme for a description of each type including examples.
P01 P04 P05 P06 P07 P08 P09 P10 P11 P12 total
type cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel
E 0 n/a 0 n/a 0 n/a 0 n/a 0 n/a 1 100 0 n/a 0 n/a 0 n/a 0 n/a 1 0
TD 0 n/a 1 100 0 n/a 0 n/a 0 n/a 0 n/a 0 n/a 0 n/a 1 0 4 100 6 83.33
A 0 n/a 4 100 1 100 0 n/a 4 100 1 100 0 n/a 0 n/a 0 n/a 1 100 11 100
R 0 n/a 2 100 6 50 0 n/a 3 100 3 66.67 1 100 0 n/a 10 50 3 100 28 67.86
MD 0 n/a 0 n/a 7 42.86 0 n/a 9 66.67 5 80 1 100 0 n/a 8 25 3 0 33 48.48
I 0 n/a 0 n/a 0 n/a 0 n/a 0 n/a 0 n/a 5 100 0 n/a 0 n/a 0 n/a 5 100
all 0 n/a 7 100 14 50 0 n/a 16 81.25 10 80 7 100 0 n/a 19 36.84 11 72.73 84 67.86
(a) Rejection Experiment
P13 P14 P15 P16 P17 P18 P19 P20 P21 P22 total
type cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel cnt %fel
TD 5 0 10 0 3 0 1 0 6 0 0 n/a 0 n/a 1 0 0 n/a 2 0 28 0
MD 0 0 1 0 17 29.41 6 0 25 36 0 n/a 0 n/a 8 12.5 9 77.78 0 n/a 66 33.33
PD 0 n/a 1 100 0 n/a 0 n/a 1 0 0 n/a 0 n/a 3 33.33 0 n/a 0 n/a 5 40
R 0 n/a 3 33.33 3 33.33 1 0 5 20 0 n/a 0 n/a 1 0 4 100 1 0 18 38.89
A 0 n/a 0 n/a 1 100 3 100 0 n/a 0 n/a 0 n/a 0 n/a 0 n/a 0 n/a 4 100
E 0 n/a 0 n/a 0 n/a 0 n/a 0 n/a 0 n/a 0 n/a 1 0 0 n/a 1 100 2 50
SP 0 n/a 0 n/a 0 n/a 1 0 0 n/a 0 n/a 5 100 2 0 5 0 0 n/a 13 38.46
all 5 0 15 13.33 24 29.17 12 25 37 27.03 0 n/a 5 100 16 12.5 18 61.11 4 25 136 30.15
(b) Prohibition Experiment

[vc,hc][vc,hc] [-b,-l][t,l]

Table 24. Accumulated frequencies for sessions 4+5 for robot negation types and their felicity. Displayed are the accumulated frequencies of the various negation types the robot engaged in during the last two sessions and their felicities. The following abbreviations are used for the negation types: TD: truth-func. denial, MD: mot.-dep. denial, A: neg. agreement, R: rejection, I: neg. imperative, E: mot. dep. exclamation, SP: self-prohibition, PD: perspective-dependent denial. See SI coding scheme for a description of each type including examples.
criterion experiment mean % felicity (std) T
crit. 1 R 74.4 (23.8)
P 32.57 (30.31)
crit. 2 R 64.16 (19.8)
P 28.02 (17.08)
Table 25. Statistical comparison of felicity rates between both experiments

: Given are the mean and standard deviation for the felicities of the robot’s production of negation during the sessions 4 and 5 under two criteria:

Crit. 1: data basis = felicity values of all participants but P01, P06, P10, and P18. Crit. 2: data basis as in crit. 1 plus additional exclusion of P04, P09, P13, P19, and P22. , .

a.4.1. Temporal Relationships between Prohibitive Linguistic and Corporal Action

The following analysis correlates participants’ linguistic prohibition and disallowances, in the following called (see main text and coding scheme (ref_to_coding_scheme) for the distinction of the two types), with participants’ bodily application of restraint, and the robot’s motivational state. Whether participants restrained the humanoid’s arm or not was determined via recorded measurements of its sensor detecting external force and which forms the basis of its compliant behaviour. In order to perform this correlation we fused three different data sources: the pragmatic codes as determined within the pragmatic analysis, the timed transcriptions of participants’ speech, and the timed recording of the said force sensor from the robot’s log files which also contains a record of its motivational state. Figure 10 gives a visual depiction of such an alignment.

Figure 10. Excerpt of reconstructed temporal profile of human-robot interaction: the given excerpt, taken from the reconstructed profile of P14’s 3rd session, displays the temporal relation between prohibitive utterances and utterances of disallowance (top blue line), the robot’s sensing of pressure being applied to its arm (middle red line), and the robot’s internal motivation (bottom green line).

Upon having performed said temporal reconstruction for all participants of the prohibition experiment we determined the prevailing types of temporal relationships between and participants’ application of corporal restraint (in the following just called push). These are depicted in figure 11. Additionally we observed two relations which can be decomposed into the basic relations depicted in said figure:

Based on observations of the experimental video recordings we imposed a time constraint of 4 seconds as maximum gap size between linguistic and corporal action for any two instances of prohibition and push to be considered in any of the stated temporal relations. Table 26 shows the resulting counts for each participant and session in which the prohibitive task was active. As can be seen from there, participants often did not restrain the robot’s arm movement or restrained it only after uttering a prohibition (“before push”, see also main text). This is opposed to how we imagined them to act and leads to a violation of the simultaneity constraint in our learning architecture.

a.4.2. How temporal relations of prohibitive action impact on the acquisition of negation

In the following we give some examples in order for the reader to better understand why our participants’ unexpected behavior, that is participants either not restraining the robot’s arm as advised or them restraining the robot’s arm after uttering prohibitions, is detrimental for the acquisition of negation. Let us assume no as default negative word and let us further assume that no is the salient word of the -type utterances in question.

Similar to other mechanisms, which establish an association between object labels and perceptual features in other symbol-grounding architectures we may regard our memory-based learner as a roughly associative learner. Associations between labels and other sensorimotor-motivational (smm) data come to be by virtue of there being a majority of exemplars in the memory where such an association is established. For our purposes this means that, all other things, i.e. sensorimotor-states, being equal, such an association is established as soon as the majority of no’s are attached to sensorimotor-motivational data with a negative motivational entry. This means that any temporal relation leading to a no with negative motivational value attached being added to the lexicon is beneficial for our learning target. By contrast any temporal relation which leads to a no with positive motivational value being added is detrimental to this purpose.

Our version of symbol grounding is implemented such that the salient word is associated with all variants of the sensorimotor-motivational (smm) vector that co-occurred during the time when the utterance was produced. During short time frames of a few seconds, the typical length of an utterance, in most cases nothing in this vector changes: The robot’s behaviour does not change, the presented object stays the same, and, importantly, the object is recognized by the object detection to be the same. If this is the case while participants produce an utterance, the outcome will be one additional exemplar or lexical entry that is added to the robot’s embodied lexicon. Yet, if one smm change occurs during this production, two lexical entries for the same word will be created, one for each variant of the smm vector. Changes in the smm data are caused through changes of the robot’s behaviour, which for their part are caused by either timeouts in the body behavior system or changes in the object recognition. Also changes in the object id itself are forms of sensorimotor changes and so is the change of the robot’s motivational state. We will in the following make the simplifying assumption that the robot’s object recognition works perfectly.

The humanoids behaviour was implemented such that it would only grasp for objects that it likes, i.e. objects that cause its motivational state to be positive. Under perfect object recognition the robot’s motivational state will be positive before the participant restrains its arm movement (push action). Restraints of the robot’s agency lead immediately to Deechee becoming ‘grumpy’, i.e. a negative motivational state.

Figure 11. Basic temporal relations between corporal constraints and prohibition: The depicted temporal relations between prohibition and corporal constraints (“push”) were observed within the prohibition scenario. Additionally two complex relations were observed which can be decomposed in the depicted ones (see text).

For no push relations the following holds: No is uttered while the robot is and continues to be in a ‘positive mood’, for its agency is not impeded. Instead of restraining the robot’s arm, as they were taught to do, participants often just held the object out of the robot’s reach, which has no impact on its motivational state. Such interaction will lead to at least one exemplar of no in the robot’s lexicon which is associated to a smm vector which has a positive motivational entry. This is detrimental to the learning outcome.

In contrast, Deechee will already be ‘in a negative mood’ in case of participants starting to restrain its arm before uttering a (during push). In this case one embodied word will enter the lexicon: no associated with a smm vector containing a negative motivational value. This is how we imagined the interaction to unfold motivated by assumptions of simultaneity in ostensive theories of meaning. This is beneficial.

In case of a participant starting to produce an utterance followed by him or her constraining the robot’s arm movement during that production (overlap before), two lexical entries will be created: a no, associated with a smm vector with a positive motivational entry, and additionally a no, which is associated with an otherwise identical smm vector but with a negative motivational entry. This is in-between.

If the onset of utterance production happens during a push but extends to after the end of the push (overlap after), the result will be one additional no in Deechee’s lexicon associated to a smm vector with negative motivational entry as long as the utterance is not overly long. The robot’s motivational system is implemented such, that its motivational state has a certain time lag. The only exception to this rule are restrictions of Deechee’s freedom of movement which will make it grumpy immediately. Therefore the presence of said overlap after relation between the mentioned actions is most probably beneficial.

P13 P14 P15 P16 P17 P18 P19 P20 P21 P22
s1 no_push 0 0 15 14 1 4 6 0 1 4
before_push 1 0 0 0 0 1 3 2 5 0
overlap_before 1 0 0 0 1 0 0 0 1 0
overlap_before_and_after 1 0 0 0 2 0 0 0 0 0
after_push 0 0 1 0 1 0 1 2 3 0
overlap_after 1 0 1 0 1 1 0 0 0 0
between_pushes 0 0 0 0 1 0 0 2 1 0
during_push 4 0 1 0 2 0 1 4 3 0
s2 no_push 0 3 5 10 0 0 0 3 30 2
before_push 3 4 1 0 0 2 0 3 0 0
overlap_before 2 0 1 0 1 1 0 1 0 0
overlap_before_and_after 0 0 1 0 0 0 0 1 0 0
after_push 0 1 0 0 0 0 0 1 0 0
overlap_after 1 0 0 0 0 0 0 0 0 0
between_pushes 0 0 0 0 0 0 0 1 0 0
during_several_pushes 0 0 1 0 0 0 0 2 0 0
during_push 5 0 2 0 4 2 0 2 0 0
s3 no_push 0 2 1 3 0 1 0 3 30 5
before_push 0 3 2 0 0 1 3 2 0 0
overlap_before 1 1 6 3 3 0 1 2 0 0
overlap_before_and_after 0 1 0 0 0 1 0 1 0 0
after_push 0 1 0 1 1 1 0 0 0 0
overlap_after 0 0 2 1 1 0 0 1 0 0
between_pushes 0 0 5 1 4 0 0 0 0 0
during_several_pushes 0 0 0 0 0 0 0 1 0 0
during_push 2 0 8 1 8 0 2 5 0 0
total no_push 0 5 21 27 1 5 6 6 61 11
before_push 4 7 3 0 0 4 6 7 5 0
overlap_before 4 1 7 3 5 1 1 3 1 0
overlap_before_and_after 1 1 1 0 2 1 0 2 0 0
after_push 0 2 1 1 2 1 1 3 3 0
overlap_after 2 0 3 1 2 1 0 1 0 0
between_pushes 0 0 5 1 5 0 0 3 1 0
during_several_pushes 0 0 1 0 0 0 0 3 0 0
during_push 11 0 11 1 14 2 3 11 3 0
Table 26. Counts of temporal relationships between physical constraints and prohibitive utterances. Given are the counts of observed temporal relationships. Both prohibitions as well as disallowances were taken into consideration in the given count. Counts are given for all participants and sessions in the prohibition scenario in which participants were told to physically restrain the robot in case of it approaching a forbidden object. Furthermore a total count per participants is given in the last section of the table. A missing relationship type in a session indicates that all counts were 0. Temporal relationships of the listed types set in bold are very likely to be detrimental for an association of the salient word with negative affect in our architecture. Relationships of a type set in italic are less likely to be detrimental for said association depending on the length of the gap between push(es) and utterance and the hypothesized duration of the motivational state triggered by physical restraint.

Table 27 shows the counts of the various temporal relations between corporal restraint and prohibition as well as the motivational states in which the robot was in when the respective form of prohibition was performed. Table 29 shows the motivational states of the robot during the other highly frequent negation types negative intent interpretations and negative motivational questions in the rejection experiment. Table 28 shows the same for our participants from the prohibition experiment.

P13 P14 P15 P16 P17
- O + - O + - O + - O + - O +
no_push 0 0 0 0 0 5 4 4 17 0 1 14 0 0 1
before_push 0 1 4 0 0 7 0 0 3 0 0 0 0 0 0
overlap_before 4 0 4 1 0 1 6 0 6 3 0 3 5 0 3
overlap_before_and_after 11 12 0 1 0 1 1 0 1 0 0 0 2 0 2
after_push 0 0 0 2 0 1 1 0 0 0 0 1 2 0 0
overlap_after 2 0 0 0 0 0 3 1 0 1 0 0 2 0 0
between_pushes 0 0 0 0 0 0 2 0 3 1 1 1 4 0 2
during_several_pushes 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
during_push 11 0 0 0 0 0 11 0 0 1 0 0 14 0 0
P18 P19 P20 P21 P22
- O + - O + - O + - O + - O +
no_push 0 0 5 0 2 4 2 4 1 3 14 16 0 1 7
before_push 0 0 4 0 0 6 0 0 7 0 0 5 0 0 0
overlap_before 1 0 1 1 0 1 3 0 3 1 0 1 0 0 0
overlap_before_and_after 1 0 1 0 0 0 2 0 2 0 0 0 0 0 0
after_push 1 0 0 1 0 1 3 0 0 3 0 0 0 0 0
overlap_after 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0
between_pushes 0 0 0 0 0 0 2 0 1 0 0 1 0 0 0
during_several_pushes 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0
during_push 2 0 0 3 0 0 11 0 0 3 0 0 0 0 0
Table 27. Motivational states during utterances of prohibition and disallowance. Given are the counts of the robot’s motivational states for each temporal relationship between prohibition/disallowance and physical restraint. The counts are listed per participant within the prohibition experiment (see table 26 for the frequencies of these relationships). The counts are accumulated over the first three sessions, i.e. the sessions in which physical restraint could possibly occur. Note that one occurrence of such a temporal relationship can yield more than 1 to the count as the robot’s motivational state can change while the respective utterance is being produced. The entries for P13 for overlap_before_and_after are so large due to a glitch in the motivational and/or behavioral system. Symbols used: -: negative motivation, +: positive motivation, O: neutral motivation
P13 P14 P15 P16 P17
# - O + # - O + # - O + # - O + # - O +
s1 neg. int. int. 6 5 3 1 0 0 0 0 15 9 7 0 7 2 4 1 4 3 1 0
neg. mot. question 4 2 1 1 0 0 0 0 11 5 6 1 3 2 2 0 1 1 0 0
s2 neg. int. int. 6 6 0 0 0 0 0 0 4 4 0 0 4 3 2 0 4 4 1 0
neg. mot. question 1 0 0 1 0 0 0 0 8 2 4 3 3 1 2 0 1 1 0 0
s3 neg. int. int. 0 0 0 0 0 0 0 0 4 3 1 0 2 1 1 0 1 1 0 0
neg. mot. question 1 1 0 0 0 0 0 0 6 4 3 1 1 0 1 0 1 0 1 0
s4 neg. int. int. 1 1 0 0 0 0 0 0 8 5 4 1 5 2 4 1 1 1 0 0
neg. mot. question 4 4 0 0 0 0 0 0 9 4 4 2 4 1 3 1 0 0 0 0
s5 neg. int. int. 2 2 0 0 0 0 0 0 7 5 1 1 13 4 9 0 3 2 1 0
neg. mot. question 2 2 0 0 0 0 0 0 18 9 8 2 3 3 0 0 4 2 2 0
total neg. int. int. 15 14 3 1 0 0 0 0 38 26 13 2 31 12 20 2 13 11 3 0
neg. mot. question 12 9 1 2 0 0 0 0 52 24 25 9 14 7 8 1 7 4 3 0
P18 P19 P20 P21 P22
# - O + # - O + # - O + # - O + # - O +
s1 neg. int. int. 15 9 4 3 4 3 1 0 8 4 5 0 1 1 0 0 2 0 1 1
neg. mot. question 8 7 2 1 4 4 1 0 2 1 1 1 3 1 3 0 0 0 0 0
s2 neg. int. int. 4 4 0 0 4 4 0 0 1 1 0 0 0 0 0 0 0 0 0 0
neg. mot. question 2 1 1 0 3 3 1 0 10 7 4 0 3 3 0 0 0 0 0 0
s3 neg. int. int. 6 4 3 0 5 5 0 0 5 4 2 1 0 0 0 0 0 0 0 0
neg. mot. question 2 0 2 0 0 0 0 0 7 5 3 0 3 2 0 1 0 0 0 0
s4 neg. int. int. 2 2 0 0 1 1 0 0 6 4 2 0 0 0 0 0 0 0 0 0
neg. mot. question 1 1 0 0 3 3 0 0 6 5 2 1 3 2 1 0 0 0 0 0
s5 neg. int. int. 3 2 0 1 4 4 1 0 2 2 1 0 3 3 0 0 0 0 0 0
neg. mot. question 1 1 0 0 2 2 0 0 13 10 3 1 8 7 1 0 0 0 0 0
total neg. int. int. 30 21 7 4 18 17 2 0 22 15 10 1 4 4 0 0 2 0 1 1
neg. mot. question 14 10 5 1 12 12 2 0 38 28 13 3 20 15 5 1 0 0 0 0
Table 28. Motivational states during negative intent interpretations and neg. mot. questions within prohibition experiment. Given are the counts/number of associations of the robot’s motivational states per stated utterance type. These frequencies are listed per session and accumulated across sessions. Symbols used: #: number of occurrences of the stated utterance type, -: frequency of negative motivational state, +: frequency of positive motivational state, O: frequency of neutral motivational state.
P01 P04 P05 P06 P07
# - O + # - O + # - O + # - O + # - O +
s1 neg. int. int. 1 1 0 0 6 6 0 0 17 11 0 7 5 4 1 1 12 10 2 0
neg. mot. question 0 0 0 0 3 3 0 0 6 1 3 2 3 2 0 1 13 5 7 2
s2 neg. int. int. 1 1 0 0 6 6 1 0 7 2 3 2 8 4 3 1 16 8 8 0
neg. mot. question 0 0 0 0 7 7 0 0 11 7 4 1 1 1 1 0 7 1 5 2
s3 neg. int. int. 0 0 0 0 3 3 0 0 6 4 3 0 12 12 2 0 7 5 2 0
neg. mot. question 0 0 0 0 5 3 2 0 2 0 2 1 2 2 0 0 18 2 9 9
s4 neg. int. int. 0 0 0 0 0 0 0 0 3 2 0 1 13 12 3 1 7 6 0 2
neg. mot. question 0 0 0 0 1 0 1 0 5 4 1 1 3 2 1 0 10 0 6 5
s5 neg. int. int. 0 0 0 0 8 7 0 1 3 1 2 0 11 8 5 0 7 6 2 0
neg. mot. question 0 0 0 0 8 8 0 0 6 4 3 1 1 0 1 0 24 5 14 7
total neg. int. int. 2 2 0 0 23 22 1 1 36 20 8 10 49 40 14 3 49 35 14 2
neg. mot. question 0 0 0 0 24 21 3 0 30 16 13 6 10 7 3 1 72 13 41 25
P08 P09 P10 P11 P12
# - O + # - O + # - O + # - O + # - O +
s1 neg. int. int. 6 3 3 0 6 6 1 1 0 0 0 0 9 6 4 1 7 4 2 3
neg. mot. question 10 1 9 1 4 3 2 0 0 0 0 0 3 2 0 1 2 0 2 0
s2 neg. int. int. 3 2 1 0 6 4 4 0 0 0 0 0 4 3 1 0 2 1 1 0
neg. mot. question 7 1 6 0 5 2 1 2 2 2 0 0 0 0 0 0 2 1 1 0
s3 neg. int. int. 1 1 0 0 6 4 1 1 1 1 1 1 6 6 0 0 0 0 0 0
neg. mot. question 10 6 1 3 7 3 6 1 1 1 0 0 2 2 1 0 0 0 0 0
s4 neg. int. int. 4 3 1 0 5 2 5 0 0 0 0 0 0 0 0 0 0 0 0 0
neg. mot. question 10 6 4 0 0 0 0 0 3 3 3 2 2 1 0 1 0 0 0 0
s5 neg. int. int. 4 3 2 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
neg. mot. question 6 3 2 1 4 1 1 3 3 4 2 0 0 0 0 0 0 0 0 0
total neg. int. int. 18 12 7 0 25 18 11 2 1 1 1 1 19 15 5 1 9 5 3 3
neg. mot. question 43 17 22 5 20 9 10 6 9 10 5 2 7 5 1 2 4 1 3 0
Table 29. Motivational states during negative intent interpretations and neg. mot. questions within rejection experiment. Given are the counts/number of associations of the robot’s motivational states per stated utterances type. These frequencies are listed per session and accumulated across sessions. Note that one utterance of these types can co-occur with more than one motivational state such that the sum of motivational states in the table may be larger than the total number of utterances. Symbols used: #: number of occurrences of the stated utterance type, -: frequency of negative motivational state, +: frequency of positive motivational state, O: frequency of neutral motivational state.

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