Participants in conversation who recurrently discuss the same targets require fewer and fewer words to identify them. This has been attributed to the collaborative elaboration of conceptual pacts, that is, semantic coordination. But participants do not only coordinate on the semantics of referring expressions; they also coordinate on how to do the task, that is, on procedural coordination. In a matching task experiment (

Dialogue is a species of joint activity during which two or more people collaborate to make themselves understood to one another. While dialogue can be studied in its own right, it is often produced in the context of other joint activities, which it serves to coordinate (

Lexical entrainment has mainly been examined in experiments involving the

However, successful coordination that leads to a reduction of collaborative effort (fewer words, fewer turns) may not be entirely due to the effects of conceptual pacts. Mills (

Mills (

Beyond specific coordination germane to a particular task, however, any kind of joint action poses similar basic coordination problems; these problems are addressed by dialogue partners through

To summarize, specific and generic procedural coordination likely constitute important demands on participants’ conversation in joint action. Currently, however, little research has been undertaken in order to understand the unique contribution of procedural coordination to reducing collaborative effort in dialogue, separate from that of semantic coordination (

With this study, we make three contributions to the literature on collaborative referring using the matching task. The first contribution is to quantify the amount of communication dedicated to procedural coordination. Given the ubiquity of the matching task in research on collaborative referring, it is important to attribute communication to the correct coordination demands, especially given that previous analyses have tended to implicitly assume that communication is exclusively dedicated to semantic coordination (but see

Because participants in the classic version of the matching task quickly converge on conceptual pacts, the development of procedural coordination is inescapably confounded with increasing semantic coordination. Investigating this third issue thus requires a dataset where semantic coordination can be varied systematically. We used a matching task corpus (transcripts from Experiment 1,

We operationalized four aspects of procedural coordination. First, (

Procedural coordination involves the development of complementary roles (

Participants (

Participants read and signed informed consent forms upon arriving in the lab. They then arranged a set of eight cards depicting humanoid tangram shapes as used in other matching task experiments. These were displayed to participants on a computer screen in two rows of four columns using a program we developed in Flash (Action Script). While the directors’ view included cards already in placement slots, matchers saw their placement slots displayed above the cards. They moved cards to the slots by clicking and dragging them. Directors’ cards could not be moved. When they were done with a trial, they each pressed a blue button on their screen to move on to the next trial. Participants first completed a practice trial where they placed eight cards depicting everyday objects (e.g., sneakers), so that they could familiarize themselves with the task in both conditions. Then, in the main experiment, for 5 trials, they placed eight cards with tangram figures (the same set for both of them but in a different order). The main experiment used a pool of 40 different tangram figures. In the classic condition, participants arranged the same set of cards on each trial (cards were drawn randomly from the pool and the order of the cards was randomized at each trial). In the new cards condition, eight cards were drawn without replacement from the pool on each trial. After having completed the task, participants were debriefed, paid, and dismissed.

There were three main independent variables (IVs) in this study. The first one was condition, which had two levels (between-subjects): classic and new cards. The second was trial number (five levels; within-subjects). Both linear and quadratic trends of trial number were tested (both were centered to simplify the interpretation of the results). We included quadratic trends to be able to detect nonlinear phenomena (e.g., a particular coordination variable decreases over trials but then increases again at the end of the experiment), which are quite common in matching task data (e.g.,

Four dependent variables (DVs) were examined, each measuring a specific aspect of procedural coordination. The first DV was whether or not the participant used card placement (CP) talk (e.g.,

To check interrater agreement, we double-coded data on CP, IGC and EGC from two dyads (one in each condition), and computed the correlations between the number of words coded by each coder for each turn (

Dialogue Example Illustrating Card Placement Coordination, Implicit Generic Coordination, Explicit Generic Coordination and General Procedural Coordination.

Role | Talk | English translation | CP | IGC | EGC | GPC |
---|---|---|---|---|---|---|

M | – | – | – | |||

D | – | – | – | |||

M | – | – | – | |||

D | – | – | – | |||

M | – | – | – | |||

D | – | – | – | – | ||

M | – | – | – | |||

D | – | – | – | |||

M | – | – | – | |||

D | – | – | – | – | ||

M | – | – | – | |||

D | – | – | ||||

M | – | – | – | |||

D | – | – | – |

We performed initial analyses to relate the prevalence of each type of procedural coordination to the overall collaborative effort in terms of word count. We then transformed these word counts into binary data, i.e., whether or not, for each figure, a specific type of procedural coordination (CP, IGC or EGC) was used as highlighted above.

The data were analyzed using logistic mixed models in SAS 9.4 (GLIMMIX procedure). Mixed models allow including random intercepts (accounting for the potential variability across dyads, across participants and across items) and random slopes (accounting for the fact that dyads, participants and items may differ in their sensitivity to any within-units IVs in the design) (

Four sets of analyses were conducted – one per DV. Each set followed a rationale similar to that used by Bangerter et al. (

In line with Barr et al.’s (

Symbols Used in the Equations.

Symbol | Description |
---|---|

_{0} |
Fixed intercept |

_{1} |
Fixed slope (role) |

_{2} |
Fixed slope (condition) |

_{3} |
Fixed slope (trial – linear) |

_{4} |
Fixed slope (trial – quadratic) |

_{5} |
Fixed slope (condition × linear) |

_{6} |
Fixed slope (condition × quadratic) |

_{0} |
By-dyad random intercepts |

_{0} |
By-participant random intercepts |

_{0} |
By-item random intercepts |

_{1} |
By-dyad random slopes (role) |

_{2} |
By-dyad random slopes (condition) |

_{3} |
By-dyad random slopes (trial – linear) |

_{4} |
By-dyad random slopes (trial – quadratic) |

_{1} |
By-participant random slopes (role) |

_{2} |
By-participant random slopes (condition) |

_{3} |
By-participant random slopes (trial – linear) |

_{4} |
By-participant random slopes (trial – quadratic) |

_{1} |
By-item random slopes (role) |

_{2} |
By-item random slopes (condition) |

_{3} |
By-item random slopes (trial – linear) |

_{4} |
By-item random slopes (trial – quadratic) |

Dyad | |

Participant | |

Item | |

Probability of an event occurring |

As an initial descriptive analysis, Table

Mean Number of Words per Dyad (SDs) for Explicit Generic Coordination, Implicit Generic Coordination, Card Placement Coordination, General Procedural Coordination, and Task Completion in Total.

Classic condition | New cards condition | |||
---|---|---|---|---|

Mean | SD | Mean | SD | |

EGC | 36.63 | 19.61 | 97.00 | 73.33 |

IGC | 88.25 | 39.68 | 238.14 | 106.08 |

CP | 83.00 | 39.70 | 250.21 | 152.24 |

GPC | 43.50 | 42.55 | 72.57 | 40.62 |

Total | 846.37 | 338.18 | 2273.28 | 857.89 |

The probability of card placement coordination occurring was .69 (SD = .47) in the talk of Directors and .16 (SD = .37) in the talk of Matchers. The equation of the model used is

As shown in Table

Model Parameters, F statistic and Odds Ratio for Card Placement Coordination.

Random effects | ||||||

By-dyad random intercepts | 0.87 (0.64) | |||||

By-participant random intercepts | 1.56 (0.60) | |||||

Fixed effects | ||||||

Intercept (fixed) | –2.10 (0.36) | <.001 | ||||

Role: Director | 3.40 (0.42) | <.001 | 1, 21 | 66.71 | <.001 | 30.01 (12.62; 71.36) |

The data corresponding to this analysis are shown in Figure

Director data – Probability of card placement coordination occurring as a function of condition and trial number.

The equation of the model used is

As shown in Table

Model Parameters, F statistics and Odds Ratios for Card Placement Coordination – Director Data Only.

Random effects | ||||||

By-dyad random intercepts | 4.07 (1.53) | |||||

By-dyad random slopes corresponding to the linear trend | 0.22 (0.11) | |||||

By-item random slopes corresponding to condition | 0.15 (0.14) | |||||

By-item random slopes corresponding to the linear trend | 0.02 (0.05) | |||||

Fixed effects | ||||||

Intercept (fixed) | 1.81 (0.58) | .005 | ||||

Condition: C | –1.14 (0.94) | .228 | 1, 78 | 1.48 | .228 | 0.32 (0.05; 2.07) |

Linear trend | 0.70 (0.49) | .171 | 1, 18 | 0.62 | .442 | C: 0.93 (0.28; 3.12) |

Quadratic trend | –0.13 (0.08) | .092 | 1, 725 | 4.12 | .043 | C: 0.88 (0.73; 1.07) |

Linear trend × condition: C | –0.77 (0.79) | .326 | 1, 725 | 0.96 | .326 | |

Quadratic trend × condition: C | <0.01 (0.13) | .968 | 1, 725 | <0.01 | .968 |

The data corresponding to this analysis are shown in Figure

Matcher data – Probability of card placement coordination occurring as a function of condition and trial number.

The equation of the model used is

As shown in Table

Model Parameters, F statistics and Odds Ratios for Card Placement Coordination – Matcher Data Only.

Random effects | ||||||

By-dyad random intercepts | 1.12 (0.46) | |||||

Fixed effects | ||||||

Intercept (fixed) | –1.54 (0.31) | <.001 | ||||

Condition: C | –2.64 (0.74) | <.001 | 1, 854 | 12.89 | <.001 | 0.07 (0.02; 0.30) |

Linear trend | –0.74 (0.40) | .064 | 1, 854 | 9.38 | .002 | C: 0.02 (<0.01; 0.33) |

Quadratic trend | 0.06 (0.07) | .372 | 1, 854 | 5.05 | .025 | C: 1.77 (1.04; 3.01) |

Linear trend × condition: C | –3.39 (1.59) | .034 | 1, 854 | 4.53 | .034 | |

Quadratic trend × condition: C | 0.51 (0.28) | .070 | 1, 854 | 3.30 | .070 |

The probability of IGC occurring was 0.61 (SD = .49) for Directors, and .92 (SD = .28) for Matchers. The equation of the model used is

As shown in Table

Model Parameters, F statistic and Odds Ratio for Implicit Generic Coordination.

Random effects | ||||||

By-dyad random intercepts | 0.42 (0.42) | |||||

By-participant random intercepts | 1.16 (0.49) | |||||

By-item random intercepts | 0.07 (0.06) | |||||

Fixed effects | ||||||

Intercept (fixed) | 2.88 (0.32) | <.001 | ||||

Role: Director | –2.30 (0.38) | <.001 | 1, 21 | 37.61 | <.001 | 0.10 (0.05; 0.22) |

The data corresponding to this analysis are shown in Figure

Director data – Probability of implicit generic coordination occurring as a function of condition and trial number.

The equation of the model used is

As shown in Table

Model Parameters, F statistics and Odds Ratios for Implicit Generic Coordination – Director Data Only.

Random effects | ||||||

By-dyad random intercepts | 0.56 (0.25) | |||||

By-dyad random slopes corresponding to the linear trend | 0.01 (0.03) | |||||

By-item random intercepts | 0.06 (0.15) | |||||

By-item random slopes corresponding to the condition | 0.11 (0.17) | |||||

By-item random slopes corresponding to the linear trend | 0.04 (0.04) | |||||

Fixed effects | ||||||

Intercept (fixed) | 1.34 (0.24) | <.001 | ||||

Condition: C | –2.21 (0.39) | <.001 | 1, 58 | 32.35 | <.001 | 0.11 (0.05; 0.24) |

Linear trend | 0.78 (0.39) | .064 | 1, 18 | 0.67 | .424 | C: 0.27 (0.10; 0.73) |

Quadratic trend | –0.14 (0.06) | .031 | 1, 725 | 0.13 | .720 | C: 1.11 (0.94; 1.30) |

Linear trend × condition: C | –2.07 (0.63) | .001 | 1, 725 | 10.68 | .001 | |

Quadratic trend × condition: C | 0.24 (0.11) | .025 | 1, 725 | 5.05 | .025 |

The data corresponding to this analysis are shown in Figure

Matcher data – Probability of implicit generic coordination occurring as a function of condition and trial number.

The equation of the model used is

As shown in Table

Model Parameters, F statistics and Odds Ratios for Implicit Generic Coordination – Matcher Data Only.

Random effects | ||||||||

By-dyad random intercepts | 1.51 (0.78) | |||||||

By-dyad random slopes corresponding to the linear trend | 0.14 (0.12) | |||||||

Fixed effects | ||||||||

Intercept (fixed) | 3.28 (0.41) | <.001 | ||||||

Condition: C | –0.72 (0.66) | .274 | 1, 834 | 1.20 | .274 | 0.49 (0.14; 1.77) | ||

Linear trend | –0.36 (0.73) | .628 | 1, 20 | 1.09 | .309 | C: 0.44 (0.08; 2.41) |
||

Quadratic trend | 0.07 (0.12) | .534 | 1, 834 | 0.34 | .559 | C: 1.03 (0.80; 1.32) |
||

Linear trend × condition: C | –0.47 (1.14) | .681 | 1, 834 | 0.17 | .681 | |||

Quadratic trend × condition: C | –0.05 (0.17) | .786 | 1, 834 | 0.07 | .786 |

The probability of explicit generic coordination occurring was .37 (SD = .48) for directors, and .30 (SD = .46) for matchers. The equation of the model used is

As shown in Table

Model Parameters, F statistic and Odds Ratio for Explicit Generic Coordination.

Random effects | ||||||

By-dyad random intercepts | 0.59 (0.34) | |||||

By-participant random intercepts | 0.66 (0.25) | |||||

By-item random intercepts | 0.09 (0.06) | |||||

By-item random slopes corresponding to role | 0.01 (0.06) | |||||

Fixed effects | ||||||

Intercept (fixed) | –1.01 (0.26) | .001 | ||||

Role: Director | 0.34 (0.27) | .224 | 1, 21 | 1.57 | .224 | 1.41 (0.80; 2.49) |

The data corresponding to this analysis are shown in Figure

Director data – Probability of explicit generic coordination occurring as a function of condition and trial number.

The equation of the model used is

As shown in Table

Model Parameters, F statistics and Odds Ratios for Explicit Generic Coordination – Director Data Only.

Random effects | ||||||

By-dyad random intercepts | 1.05 (0.38) | |||||

By-dyad random slopes corresponding to the linear trend | 0.03 (0.03) | |||||

By-item random intercepts | 0.13 (0.09) | |||||

Fixed effects | ||||||

Intercept (fixed) | –0.11 (0.30) | .703 | ||||

Condition: C | –1.73 (0.50) | .001 | 1, 795 | 12.03 | .001 | 0.18 (0.07; 0.47) |

Linear trend | –0.26 (0.36) | .470 | 1, 18 | 1.44 | .245 | C: 0.58 (0.19; 1.78) |

Quadratic trend | 0.03 (0.06) | .595 | 1, 795 | 0.12 | .727 | C: 1.01 (0.83; 1.23) |

Linear trend × condition: C | –0.28 (0.68) | .675 | 1, 795 | 0.18 | .675 | |

Quadratic trend × condition: C | –0.02 (0.12) | .854 | 1, 795 | 0.03 | .854 |

The data corresponding to this analysis are shown in Figure

Matcher data – Probability of explicit generic coordination occurring as a function of condition and trial number.

The equation of the model used is

As shown in Table

Model Parameters, F statistics and Odds Ratios for Explicit Generic Coordination – Matcher Data Only.

Random effects | ||||||

By-dyad random intercepts | 0.86 (0.32) | |||||

By-dyad random slopes corresponding to the linear trend | 0.02 (0.03) | |||||

By-item random intercepts | 0.04 (0.14) | |||||

By-item random slopes corresponding to the condition | 0.09 (0.16) | |||||

By-item random slopes corresponding to the linear trend | 0.04 (0.04) | |||||

Fixed effects | ||||||

Intercept (fixed) | –0.68 (0.27) | .021 | ||||

Condition: C | –1.03 (0.46) | .029 | 1, 58 | 5.03 | .029 | 0.36 (0.14; 0.90) |

Linear trend | –0.33 (0.36) | .379 | 1, 18 | 8.96 | .008 | C: 0.19 (0.06; 0.56) |

Quadratic trend | 0.02 (0.06) | .717 | 1, 725 | 5.73 | .017 | C: 1.27 (1.06; 1.53) |

Linear trend × condition: C | –1.34 (0.66) | .044 | 1, 725 | 4.07 | .044 | |

Quadratic trend × condition: C | 0.22 (0.11) | .046 | 1, 725 | 4.00 | .046 |

The probability of general procedural coordination occurring is .78 (SD = 0.42) in the talk of Directors and .73 (SD = 0.45) in the talk of Matchers.

The equation of the model used is

As shown in Table

Model Parameters, F statistic and Odds Ratio for General Procedural Coordination.

Random effects | ||||||

By-dyad random intercepts | 1.66 (0.77) | |||||

Fixed effects | ||||||

Intercept (fixed) | 1.20 (0.37) | .004 | ||||

Role: Director | 0.34 (0.35) | .325 | 1, 196 | 0.97 | .325 | 1.41 (0.71; 2.81) |

The data corresponding to this analysis are shown in Figure

Director data – Probability of general procedural coordination occurring as a function of condition and trial number.

The equation of the model used is

As shown in Table

Model Parameters, F statistics and Odds Ratios for General Procedural Coordination – Director Data Only.

Random effects | ||||||

By-dyad random intercepts | 2.58 (1.50) | |||||

By-dyad random slopes corresponding to the linear trend | 0.05 (0.26) | |||||

Fixed effects | ||||||

Intercept (fixed) | 2.60 (0.66) | .001 | ||||

Condition: C | –1.98 (0.99) | .050 | 1, 63 | 4.00 | .050 | 0.14 (0.02; 1.00) |

Linear trend | 1.96 (1.62) | .241 | 1, 20 | 0.97 | .337 | C: 0.01 (<0.01; 0.60) |

Quadratic trend | –0.35 (0.27) | .197 | 1, 63 | 0.42 | .517 | C: 1.84 (1.09; 3.34) |

Linear trend × condition: C | –6.45 (2.57) | .015 | 1, 63 | 6.30 | .015 | |

Quadratic trend × condition: C | 0.95 (0.40) | .020 | 1, 63 | 5.76 | .020 |

The data corresponding to this analysis are shown in Figure

Matcher data – Probability of general procedural coordination occurring as a function of condition and trial number.

The equation of the model used is

As shown in Table

Model Parameters, F statistics and Odds Ratios for General Procedural Coordination – Matcher Data Only.

Random effects | ||||||

By-dyad random intercepts | 1.67 (1.09) | |||||

By-dyad random slopes corresponding to the linear trend | 0.06 (0.23) | |||||

Fixed effects | ||||||

Intercept (fixed) | 1.59 (0.48) | .004 | ||||

Condition: C | –0.17 (0.95) | .860 | 1, 64 | 0.03 | .860 | 0.85 (0.13; 5.63) |

Linear trend | –0.61 (1.18) | .605 | 1, 20 | 8.70 | .008 | C: <0.01 (<0.01; 0.05) |

Quadratic trend | 0.11 (0.19) | .568 | 1, 64 | 8.59 | .005 | C: 3.82 (1.54; 9.48) |

Linear trend × condition: C | –8.91 (3.44) | .012 | 1, 64 | 6.71 | .012 | |

Quadratic trend × condition: C | 1.23 (0.49) | .016 | 1, 64 | 6.17 | .016 |

The purpose of this study was to quantify and qualify the role of procedural coordination in dialogue (e.g.,

Summary of the Results.

Effect of role | Director data only | Matcher data only | |
---|---|---|---|

CP | Director > Matcher | Negative quadratic trend |
Classic < New cards Negative linear trend in the classic condition Positive quadratic trend in both conditions |

IGC | Director < Matcher | Classic < New cards Negative linear trend in the classic condition Negative quadratic trend in the new cards condition |
No significant effects found |

EGC | No significant effect found | Classic < New cards |
Classic < New cards Negative linear trend in the classic condition Positive quadratic trend in the classic condition |

GPC | No significant effect found | Classic < New cards Negative linear trend in the classic condition Positive quadratic trend in the classic condition |
Negative linear trend in both conditions Positive quadratic trend in both conditions |

First, although most researchers would probably agree that part of the participants’ talk in the matching task involves procedural coordination, the exact amount of talk dedicated to procedural coordination (rather than to semantic coordination) was previously unknown. The current study revealed that a substantial proportion of talk in matching task conversations (almost 30%) is dedicated to coordinating the activity itself and not to the establishment of referring conventions.

Second, a closer look at the results from the classic condition suggests that procedural coordination develops over trials. For Matchers, in this condition, where the establishment of conceptual pacts enables rapid completion of the task, explicit generic coordination, card placement coordination, and general procedural coordination decreased over trials. The only kind of procedural coordination that did not decrease for Matchers was implicit generic coordination, i.e., coordinating progress in the task via project markers (

For directors, findings in the classic condition were more complex. Directors’ talk about explicit generic coordination in this condition showed no trends over trials, while their use of card placement and implicit generic coordination decreased over trials. On the other hand, their use of general procedural coordination was similar to Matchers’ in the classic condition (i.e., decrease and an upswing in the last trial). Taken together, these findings suggest that in the classic version of the matching task, there are systematic trends in procedural coordination over trials. However, the trends depend on participants’ roles.

Indeed, and third, there is a division of labor in the accomplishment of procedural coordination according to participants’ roles, as suggested by Mills (

Fourth, procedural coordination is linked with semantic coordination. In the new cards condition, participants dealt with novel referents on each trial. This manipulation precluded the development of conceptual pacts (

Our findings have important implications for the experimental study of dialogue. First, in the matching task, the coordination problems participants must solve together are not only semantic, but also procedural. Reductions in collaborative effort in the matching task are not only due to the elaboration of conceptual pacts, but may reflect a range of coordination processes or even individual-level learning (see e.g.,

Second, the relation between procedural and semantic coordination needs further theoretical elaboration. As a first step, studies might focus on how the development of procedural coordination in the matching task is similar to or different from that of semantic coordination in other experimental dialogue tasks. The relative importance of semantic and procedural coordination may vary depending on the constraints of specific tasks, as suggested by Mills (

Third, investigating differences between semantic and procedural coordination will have important implications for existing models of dialogue (see

In conclusion, this study offers a better understanding of the role of procedural coordination in dialogue, and its interaction with semantic coordination. It sheds further light on the processes at play in the matching task, one of the most widely used tasks in dialogue research. Previous research has suggested that the decrease in collaborative effort usually observed in this kind of task reflects partners establishing and reusing conceptual pacts. The current findings nuance this claim by revealing that part of this decrease is in fact due to dialogue partners coordinating procedurally. We have also shown that all kinds of procedural coordination do not necessarily decrease as the interaction unfolds: this depends on whether coordination is general or specific, and on whether it is implicit or explicit. It also depends on the role in the dyad, and on whether participants can rely on semantic coordination as well.

Materials, transcripts, coded data and analytical scripts are available at

Both word counts and binary data have advantages and disadvantages for our purposes. We decided to use binary data for our main analyses to facilitate two main comparisons. First, some forms of procedural coordination are more wordy than others (e.g., IGC involving single-word back-channels vs. longer expressions for EGC). Second, in the classic condition, coordination required fewer words than in the new cards condition. These comparisons make word counts (divided by total words) less straightforward to interpret than binary data.

In an additional analysis, we tested whether the overall amount of words for procedural coordination was affected by the main independent variables. The main results were that Matchers’ talk was more dedicated to procedural coordination than Directors’, and that Matchers’ talk was more dedicated to procedural coordination in the classic condition than in the new cards condition. The detailed analyses are available in the “additional analyses” folder on

We thank Riccardo Fusaroli and Gregory Mills for their constructive comments on the manuscript.

The authors have no competing interests to declare.

Contributed to conception and design: AB, EM

Contributed to acquisition of data: AB, EM

Contributed to analysis and interpretation of data: AB, DK, EM

Drafted and/or revised the article: AB, DK, EM

Approved the submitted version for publication: AB, DK, EM