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ABSTRACTPeople prefer the central item in an array of items. This could be due to applying a decision heuristic or greater visual attention to the central item. We manipulated task instructions as participants chose one from three consumer items. The instructions were to “think carefully” in one block and to “use gut feeling” in another. A centrality preference appeared only in the “gut” condition, which was also negatively correlated with self-reported reflective thinking disposition (Need-for-Cognition). Eye-movement patterns, however, were equivalent across both instruction conditions with more frequent and longer fixations on the middle items. The findings demonstrate an effect of instructions on the centrality preference for non-identical consumer items, and provide evidence for a heuristic cause of the centrality preference rather than the allocation of visual attention. The results also show that the centrality preference is more likely to be present when people choose quickly and intuitively.

Artificial Intelligence (AI) applications for education are being developed at an increasing pace. It seems reasonable to assume that these applications would enhance student experiences and course satisfaction, and that therefore educational institutions should invest in these technologies to enhance their student offer. However, this should be tested empirically. In the current study a gender-balanced sample of 302 UK students rated course satisfaction, completed the General Attitudes towards AI Scale (GAAIS), comfortableness with AI educational applications, and course satisfaction if AI educational applications were adopted. Although students were, on average, moderately comfortable with AI educational applications, course satisfaction dropped in response to their hypothetical adoption. AI applications that assigned summative grades or that offered wellbeing support gave rise to the highest levels of discomfort. Students were more comfortable with career support, formative course support, and administrative support. Positive and Negative AI attitudes predicted the satisfaction difference, with mediation via comfortableness with applications. We recommend that Higher Education Institutions exercise caution before making major investments in AI educational applications.

This study comprised two experiments to examine the distracting effects of advertisement familiarity, location, and onset on the performance of a selective attention task. In Exp. 1, familiar advertisements presented in peripheral vision disrupted selective attention when the attention task was more demanding, suggesting that the distracting effect of advertisements is a product of task demands and advertisement familiarity and location. In Exp. 2, the onset of the advertisement shortly before, or after, the attention task captured attention and disrupted attentional performance. The onset of the advertisement before the attention task reduced target response time without an increase in errors and therefore facilitated performance. Despite being instructed to ignore the advertisements, the participants were able to recall a substantial proportion of the familiar advertisements. Implications for the presentation of advertisements during human–computer interaction were discussed.

This study examined whether individual differences in the vividness of visual imagery influenced performance on a novel long-term change detection task. Participants were presented with a sequence of pictures, with each picture and its title displayed for 17  s, and then presented with changed or unchanged versions of those pictures and asked to detect whether the picture had been changed. Cuing the retrieval of the picture's image, by presenting the picture's title before the arrival of the changed picture, facilitated change detection accuracy. This suggests that the retrieval of the picture's representation immunizes it against overwriting by the arrival of the changed picture. The high and low vividness participants did not differ in overall levels of change detection accuracy. However, in replication of Gur and Hilgard (1975), high vividness participants were significantly more accurate at detecting salient changes to pictures compared to low vividness participants. The results suggest that vivid images are not characterised by a high level of detail and that vivid imagery enhances memory for the salient aspects of a scene but not all of the details of a scene. Possible causes of this difference, and how they may lead to an understanding of individual differences in change detection, are considered.

Warning signals enhance psychomotor performance by optimising preparation for the arrival of an event. Recent evidence, however, suggests that a warning signal can also disrupt attentional preparation by interfering with the process of preparing. It was hypothesised that a warning signal consisting of a change to the task-relevant items (array onset) may be more effective than a traditional warning signal consisting of the arrival, or removal, of a bar-cue which is independent of the task array. In three experiments array onset was a more effective warning signal than the bar-cue because reaction times were significantly faster without an increase in errors. In addition, an auditory warning signal resulted in faster reaction time than the bar-cue but in performance equivalent to that with an onset warning signal. Thus, the superiority of an auditory warning signal, reported by Davis and Green, was not found, when the interference of the visual warning signal with preparation was reduced. These results suggest that a visual warning signal consisting of a change to the stimulus array is more effective than an event independent from the stimulus array.

The effect of prosodic boundary cues on the off-line disambiguation and on-line parsing of coordination structures was examined. It was found that relative clauses were attached to coordinated object noun phrases in preference to second conjuncts in sentences like: The lawyer greeted the powerful barrister and the wise judge who was/were walking to the courtroom.Naive speakers signalled the syntactic contrast between the two structures by a prosodic break between the conjuncts when the relative clause was attached to the second conjunct. Listeners were able to use this prosodic information in both off-line syntactic disambiguation and on-line syntactic parsing. The findings are compatible with a model in which prosody has a strong immediate effect on parsing. It is argued that the current experimental design has avoided confounds present in earlier studies on the on-line integration of prosodic and syntactic information.

A new General Attitudes towards Artificial Intelligence Scale (GAAIS) was developed. The scale underwent initial statistical validation via Exploratory Factor Analysis, which identified positive and negative subscales. Both subscales captured emotions in line with their valence. In addition, the positive subscale reflected societal and personal utility, whereas the negative subscale reflected concerns. The scale showed good psychometric indices and convergent and discriminant validity against existing measures. To cross-validate general attitudes with attitudes towards specific instances of AI applications, summaries of tasks accomplished by specific applications of Artificial Intelligence were sourced from newspaper articles. These were rated for comfortableness and perceived capability. Comfortableness with specific applications was a strong predictor of general attitudes as measured by the GAAIS, but perceived capability was a weaker predictor. Participants viewed AI applications involving big data (e.g. astronomy, law, pharmacology) positively, but viewed applications for tasks involving human judgement, (e.g. medical treatment, psychological counselling) negatively. Applications with a strong ethical dimension led to stronger discomfort than their rated capabilities would predict. The survey data suggested that people held mixed views of AI. The initially validated two-factor GAAIS to measure General Attitudes towards Artificial Intelligence is included in the Appendix.