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.
9 July 2020 in Quarterly Journal of Experimental Psychology
A striking range of individual differences has recently been reported in three different visual search tasks. These differences in performance can be attributed to strategy, that is, the efficiency with which participants control their search to complete the task quickly and accurately. Here, we ask whether an individual’s strategy and performance in one search task is correlated with how they perform in the other two. We tested 64 observers and found that even though the test–retest reliability of the tasks was high, an observer’s performance and strategy in one task was not predictive of their behaviour in the other two. These results suggest search strategies are stable over time, but context-specific. To understand visual search, we therefore need to account not only for differences between individuals but also how individuals interact with the search task and context.
This study aimed to investigate age differences in risk-taking concerning the coronavirus pandemic, while disentangling the contribution of risk attitude, objective risk and numeracy. We tested (i) whether older and younger adults differed in taking coronavirus-related health risks, (ii) whether there are age differences in coronavirus risk, risk attitude and numerical ability and (iii) whether these age differences in coronavirus risk, attitude and numerical ability are related to coronavirus risk-taking. The study was observational, with measures presented to all participants in random order. A sample of 469 participants reported their coronavirus-related risk-taking behaviour, objective risk, risk attitude towards health and safety risks, numerical ability and risk perception. Our findings show that age was significantly related to coronavirus risk-taking, with younger adults taking more risk, and that this was partially mediated by higher numeracy, but not objective risk or risk attitude. Exploratory analyses suggest that risk perception for self and others partially mediated age differences in coronavirus risk-taking. The findings of this study may better our understanding of why age groups differ in their adoption of protective behaviours during a pandemic and contribute to the debate whether age differences in risk-taking occur due to decline in abilities or changes in risk attitude.
Much effort has been made to explain eye guidance during natural scene viewing. However, a substantial component of fixation placement appears to be a set of consistent biases in eye movement behavior. We introduce the concept of saccadic flow, a generalization of the central bias that describes the image-independent conditional probability of making a saccade to (xi+1,yi+1), given a fixation at (xi, yi). We suggest that saccadic flow can be a useful prior when carrying out analyses of fixation locations, and can be used as a submodule in models of eye movements during scene viewing. We demonstrate the utility of this idea by presenting bias-weighted gaze landscapes, and show that there is a link between the likelihood of a saccade under the flow model, and the salience of the following fixation. We also present a minor improvement to our central bias model (based on using a multivariate truncated Gaussian), and investigate the leftwards and coarse-to-fine biases in scene viewing.
Previous work has demonstrated that search for a target in noise is consistent with the predictions of the optimal search strategy, both in the spatial distribution of fixation locations and in the number of fixations observers require to find the target. In this study we describe a challenging visual-search task and compare the number of fixations required by human observers to find the target to predictions made by a stochastic search model. This model relies on a target-visibility map based on human performance in a separate detection task. If the model does not detect the target, then it selects the next saccade by randomly sampling from the distribution of saccades that human observers made. We find that a memoryless stochastic model matches human performance in this task. Furthermore, we find that the similarity in the distribution of fixation locations between human observers and the ideal observer does not replicate: Rather than making the signature doughnut-shaped distribution predicted by the ideal search strategy, the fixations made by observers are best described by a central bias. We conclude that, when searching for a target in noise, humans use an essentially random strategy, which achieves near optimal behavior due to biases in the distributions of saccades we have a tendency to make. The findings reconcile the existence of highly efficient human search performance with recent studies demonstrating clear failures of optimality in single and multiple saccade tasks.