Predicting turn-ends in discourse context

Research suggests that during conversation, interlocutors coordinate their utterances by predicting the speaker’s forthcoming utterance and its end. In two experiments, we used a button-pressing task, in which participants pressed a button when they thought a speaker reached the end of their utterance, to investigate what role the wider discourse plays in turn-end prediction. Participants heard two-utterance sequences, in which the content of the second utterance was or was not constrained by the content of the first. In both experiments, participants responded earlier, but not more precisely, when the first utterance was constraining rather than unconstraining. Response times and precision were unaffected by whether they listened to dialogues or monologues (Experiment 1) and by whether they read the first utterance out loud or silently (Experiment 2), providing no indication that activation of production mechanisms facilitates prediction. We suggest that content predictions aid comprehension but not turn-end prediction

Coordinating utterances during turn-taking:The role of prediction, response preparation, and articulation

During conversation, interlocutors rapidly switch between speaker and listener roles and take turns at talk. How do they achieve such fine coordination? Most research has concentrated on the role of prediction, but listeners must also prepare a response in advance (assuming they wish to respond) and articulate this response at the appropriate moment. Such mechanisms may overlap with the processes of comprehending the speaker’s incoming turn and predicting its end. However, little is known about the stages of response preparation and production. We discuss three questions pertaining to such stages: (1) Do listeners prepare their own response in advance?, (2) Can listeners buffer their prepared response?, and (3) Does buffering lead to interference with concurrent comprehension? We argue that fine coordination requires more than just an accurate prediction of the interlocutor’s incoming turn: Listeners must also simultaneously prepare their own response

How do listeners time response articulation when answering questions? The role of speech rate

During conversation, interlocutors often produce their utterances with little overlap or gap between their turns. But what mechanism underlies this striking ability to time articulation appropriately? In 2 verbal yes/no question-answering experiments, we investigated whether listeners use the speech rate of questions to time articulation of their answers. In Experiment 1, we orthogonally manipulated the speech rate of the context (e.g., Do you have a . . .) and final word (e.g., dog?) of questions using time-compression, so that each component was spoken at the natural rate or twice as a fast. Listeners responded earlier when the context was speeded rather than natural, suggesting they used the speaker’s context rate to time answer articulation. Additionally, listeners responded earlier when the speaker’s final syllable was speeded than natural, regardless of context rate, suggesting they adjusted the timing of articulation after listening to a single syllable produced at a different rate. We replicated this final word effect in Experiment 2, which also showed that our speech rate manipulation did not influence the timing of response preparation. Together, these findings suggest listeners use speech rate information to time articulation when answering questions

Evidence against egocentric prediction during language comprehension

Although previous research has demonstrated that language comprehension can be egocentric, there is little evidence for egocentricity during prediction. In particular, comprehenders do not appear to predict egocentrically when the context makes it clear what the speaker is likely to refer to. But do comprehenders predict egocentrically when the context does not make it clear? We tested this hypothesis using a visual-world eye-tracking paradigm, in which participants heard sentences containing the gender-neutral pronoun They (e.g. They would like to wear…) while viewing four objects (e.g. tie, dress, drill, hairdryer). Two of these objects were plausible targets of the verb (tie and dress), and one was stereotypically compatible with the participant's gender (tie if the participant was male; dress if the participant was female). Participants rapidly fixated targets more than distractors, but there was no evidence that participants ever predicted egocentrically, fixating objects stereotypically compatible with their own gender. These findings suggest that participants do not fall back on their own egocentric perspective when predicting, even when they know that context does not make it clear what the speaker is likely to refer to

Prediction involves two stages:Evidence from visual-world eye-tracking

Comprehenders often predict what they are going to hear. But do they make the best predictions possible? We addressed this question in three visual-world eye-tracking experiments by asking when comprehenders consider perspective. Male and female participants listened to male and female speakers producing sentences (e.g., I would like to wear the nice…) about stereotypically masculine (target: tie; distractor: drill) and feminine (target: dress, distractor: hairdryer) objects. In all three experiments, participants rapidly predicted semantic associates of the verb. But participants also predicted consistently – that is, consistent with their beliefs about what the speaker would ultimately say. They predicted consistently from the speaker’s perspective in Experiment 1, their own perspective in Experiment 2, and the character’s perspective in Experiment 3. This consistent effect occurred later than the associative effect. We conclude that comprehenders consider perspective when predicting, but not from the earliest moments of prediction, consistent with a two-stage account

Quantity and quality of interaction between staff and older patients in UK hospital wards: A descriptive study

AbstractBackgroundThe quality of staff-patient interactions underpins the overall quality of patient experience and can affect other important outcomes. However no studies have been identified that comprehensively explore both the quality and quantity of interactions in general hospital settings.Aims & objectivesTo quantify and characterise the quality of staff-patient interactions and to identify factors associated with negative interaction ratings.SettingData were gathered at two acute English NHS hospitals between March and April 2015. Six wards for adult patients participated including medicine for older people (n=4), urology (n=1) and orthopaedics (n=1).MethodsEligible patients on participating wards were randomly selected for observation. Staff-patient interactions were observed using the Quality of Interactions Schedule. 120h of care were observed with each 2h observation session determined from a balanced random schedule (Monday-Friday, 08:00-22:00h). Multilevel logistic regression models were used to determine factors associated with negative interactions.Results1554 interactions involving 133 patients were observed. The median length of interaction was 36s with a mean of 6 interactions per patient per hour. Seventy three percent of interactions were categorized as positive, 17% neutral and 10% negative. Forty percent of patients had at least one negative interaction (95% confidence interval 32% to 49%). Interactions initiated by the patient (adjusted Odds Ratio [OR] 5.30), one way communication (adjusted OR 10.70), involving two or more staff (adjusted OR 5.86 for 2 staff, 6.46 for 3+ staff), having a higher total number of interactions (adjusted OR 1.09 per unit increase), and specific types of interaction content were associated with increased odds of negative interaction (p<0.05). In the full multivariable model there was no significant association with staff characteristics, skill mix or staffing levels. Patient agitation at the outset of interaction was associated with increased odds of negative interaction in a reduced model. There was no significant association with gender, age or cognitive impairment. There was substantially more variation at ward level (variance component 1.76) and observation session level (3.49) than at patient level (0.09).ConclusionThese findings present a unique insight into the quality and quantity of staff-patient interactions in acute care. While a high proportion of interactions were positive, findings indicate that there is scope for improvement. Future research should focus on further exploring factors associated with negative interactions, such as workload and ward culture