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Referring expression generation (REG) presents the converse problem to visual search: given a scene and a specified target, how does one generate a description which would allow somebody else to quickly and accurately locate the target? Previous work in psycholinguistics and natural language processing has failed to find an important and integrated role for vision in this task. That previous work, which relies largely on simple scenes, tends to treat vision as a pre-process for extracting feature categories that are relevant to disambiguation. However, the visual search literature suggests that some descriptions are better than others at enabling listeners to search efficiently within complex stimuli. This paper presents a study testing whether participants are sensitive to visual features that allow them to compose such "good" descriptions. Our results show that visual properties (salience, clutter, area, and distance) influence REG for targets embedded in images from the Where's Wally? books. Referring expressions for large targets are shorter than those for smaller targets, and expressions about targets in highly cluttered scenes use more words. We also find that participants are more likely to mention non-target landmarks that are large, salient, and in close proximity to the target. These findings identify a key role for visual salience in language production decisions and highlight the importance of scene complexity for REG. [ABSTRACT FROM AUTHOR]

In complex stimuli, there are many different possible ways to refer to a specified target. Previous studies have shown that when people are faced with such a task, the content of their referring expression reflects visual properties such as size, salience, and clutter. Here, we extend these findings and present evidence that (i) the influence of visual perception on sentence construction goes beyond content selection and in part determines the order in which different objects are mentioned and (ii) order of mention influences comprehension. Study 1 (a corpus study of reference productions) shows that when a speaker uses a relational description to mention a salient object, that object is treated as being in the common ground and is more likely to be mentioned first. Study 2 (a visual search study) asks participants to listen to referring expressions and find the specified target; in keeping with the above result, we find that search for easy-to-find targets is faster when the target is mentioned first, while search for harder-to-find targets is facilitated by mentioning the target later, after a landmark in a relational description. Our findings show that seemingly low-level and disparate mental "modules" like perception and sentence planning interact at a high level and in task-dependent ways. [ABSTRACT FROM AUTHOR]

Difficult tasks should be attempted one at a time, while easytasks can be undertaken in parallel. Reinforcing our previ-ous conclusion that people are surprisingly poor at applyingthis logic, we find people fail to select standing positions thatmaximize their probability of success in throwing a beanbaginto one of two possible hoops. We asked participants to ex-plicitly report their odds of successfully throwing a beanbaginto each hoop from the location they had chosen to stand,and estimates were highly accurate. Nonetheless, participantsfailed to use estimates of success appropriately to maximizesuccess, suggesting a failure of insight, rather than limited orinaccurate information, can account for suboptimal decisionsabout standing position.

The recent rise in artificial intelligence systems, such as ChatGPT, poses a fundamental problem for the educational sector. In universities and schools, many forms of assessment, such as coursework, are completed without invigilation. Therefore, students could hand in work as their own which is in fact completed by AI. Since the COVID pandemic, the sector has additionally accelerated its reliance on unsupervised 'take home exams'. If students cheat using AI and this is undetected, the integrity of the way in which students are assessed is threatened. We report a rigorous, blind study in which we injected 100% AI written submissions into the examinations system in five undergraduate modules, across all years of study, for a BSc degree in Psychology at a reputable UK university. We found that 94% of our AI submissions were undetected. The grades awarded to our AI submissions were on average half a grade boundary higher than that achieved by real students. Across modules there was an 83.4% chance that the AI submissions on a module would outperform a random selection of the same number of real student submissions. [ABSTRACT FROM AUTHOR]