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In visual foraging, some studies find that individuals have a bias towards high-value targets even when these targets are scarcer whilst other studies find that this bias disappears when those targets are scarcer. In our proposed study, we will test for a bias for scarce targets using standard feature vs conjunction visual foraging tasks, without an explicit value being given. Based on the idea of commodity theory and implicit value, it is hypothesised that participants will show a scarcity bias. The bias will be investigated using a Bayesian statistical model which has been developed for predicting target-by-target foraging behaviours.

Stage 1 IPA at Cortex

This registration is for a paper reporting two pre-registered eye-tracking experiments that investigated fixation patterns for verbal or numerical quantification in two contexts (nutrition information, e.g., low vs. 20% fat, and weather forecasts, e.g., likely vs. 60% chance of rain), and the impact on judgements and decisions. The data and materials are shared in the project files. Both experiments were pre-registered on AsPredicted.org prior to data collection. The pre-registrations are available in the project data.

Prior research has shown that collaborating with another person can be more beneficial than learning alone, especially in older age. Collaboration often occurs in long-lasting relationships, such as spouses or friendships, and familiar partners can help each other remember. However, previous work on this topic has two potential issues. Firstly, this type of research often uses small samples to study collaborative learning, resulting in underpowered studies. Secondly, many studies compare types of collaborative relationships (e.g., familiar and unfamiliar) but do not include a (relevant) measure of individual performance. Our study will investigate whether collaborative learning is more beneficial than learning alone and whether the benefits of collaboration are dependent on the familiarity of the learning partner, in a larger sample than in previous studies (n = 96). Collaborative learning will be measured using the Barrier Task, while individual learning will be assessed using an adapted version of the Barrier Task. In addition, a series of cognitive measures will be included to assess cognitive functions such as executive functioning and memory. We expect to find that collaborative learning is more beneficial than learning alone, but that this effect is independent of familiarity (familiar partner versus stranger). Practically, this would translate into overall higher performance on the Barrier Task when participants learn together, regardless of the type of partner, and lower performance when learning alone.

Additional file 1.

Supplemental material, sj-docx-1-qjp-10.1177_17470218211048317 for Emotion perception bias associated with the hijab in Austrian and Turkish participants by Sebastian Korb, Tugba Ceren Deniz, Bengi Ünal, Alasdair Clarke and Giorgia Silani in Quarterly Journal of Experimental Psychology

This document outlines the details of the power analysis used to estimate the required sample size for this project.

Overview of measures variables and materials used

Means and standard deviations of COVID-19 preventative behaviours, separated by age group.

This document contains tracked changes between the prior version and the current version of the manuscript. We thought this may be useful to the reviewer(s) when reviewing changes made since the prior submission.

Supplemental material, sj-docx-1-qjp-10.1177_17470218211048317 for Emotion perception bias associated with the hijab in Austrian and Turkish participants by Sebastian Korb, Tugba Ceren Deniz, Bengi Ünal, Alasdair Clarke and Giorgia Silani in Quarterly Journal of Experimental Psychology

The online survey used for this project can be found here.

Supplemental material, sup_mat for Stable individual differences in strategies within, but not between, visual search tasks by Alasdair DF Clarke, Jessica L Irons, Warren James, Andrew B Leber and Amelia R Hunt in Quarterly Journal of Experimental Psychology

Pilot Experiment: In this short experiment, we present participants with homogeneous (easy) search arrays and heterogeneous (difficult) search arrays for just 200ms, which is not enough time to generate useful eye movements to aid in detecting the target. The goal is to establish whether the stimuli are suitable for our main experiment. That is, present/absent responses should be close to ceiling on the easy trials, meaning no eye movements are required to detect the target with high accuracy. On difficult trials, responses should be close to chance, suggesting the target is usually not detectable from the centre. This was the case.

Training an object class detector typically requires a large set of images annotated with bounding-boxes, which is expensive and time consuming to create. We propose novel approach to annotate object locations which can substantially reduce annotation time. We first track the eye movements of annotators instructed to find the object and then propose a technique for deriving object bounding-boxes from these fixations. To validate our idea, we collected eye tracking data for the trainval part of 10 object classes of Pascal VOC 2012 (6,270 images, 5 observers). Our technique correctly produces bounding-boxes in 50%of the images, while reducing the total annotation time by factor 6.8× compared to drawing bounding-boxes. Any standard object class detector can be trained on the bounding-boxes predicted by our model. Our large scale eye tracking dataset is available at groups.inf.ed.ac.uk/calvin/eyetrackdataset/ .

The top-down guidance of visual attention is an important factor allowing humans to effectively process incoming visual information. Our understanding of the processes governing attention is not complete, with growing evidence for attention selection based on cognitive relevance. In this paper, we investigate whether models for salient object detection from computer vision can be used to predict attentional shifts in visual tasks. Our results show that the object-based interpretation of saliency provided by these models is a substantially betterpredictor of fixation locations than traditional pixel-based saliency.

The majority of work on texture analysis in computer vision has concerned texture classification and segmentation, while the problem of measuring and modelling the visual similarity between pairs of textures has been relatively neglected. One likely reason for this is the difficulty in collecting subjective human similarity judgments over a large database of textures. A common approach is to carry out a free-sorting experiment to obtain a similarity matrix which can then be mapped onto a low dimensional space using techniques such as MDS or Isomap. This results in a Euclidean space in which textures are represented as points, and the distance between two points is taken to represent the perceptual visual dissimilarity between the associated pair of textures. However, it is unknown if such a metric can generalise to predict human texture judgements in other tasks, or even if similarity judgements are metric at all. In this study we investigate this question by carrying out an experiment using a pair-of-pairs paradigm and compare these results to the predictions made by a low dimensional model (d = 3) obtained from a free-sorting experiment and find that it agrees with the judgements made by participants. This record was migrated from the OpenDepot repository service in June, 2017 before shutting down., {"references":["[1] M. Amadasun and R. King. Textural features corresponding to textural properties. IEEE Transactions on Systems, Man and Cybernetics, 19:1264–1274, 1989.\n[2] F. Gregory Ashby, Sarah Queller, and Patrica M. Berretty. On the dominance of unidimensional rules in unsupervised categorization. Perception & Psychophysics, 61:1178–1199, 1999.\n[3] Tammo H. A. Bijmolt and Michel Wedel. The effects of alternative methods of collecting similarity data for multidimensional scaling. International Journal of Research in Marketing, 12:363–371, 1995.\n[4] A. D. F. Clarke, F. Halley, A. Newell, L. D. Griffin, and M. J. Chantler. Perceptual similarity: a new texture challenge. In BMVC2011, 2011.\n[5] C. Heaps and S. Handel. Similarity and features of natural textures. Journal of Experimental Psycholog.: Human Perception and Performance, 25:299–320, 1999.\n[6] H. Long and W. K. Leow. Perceptual texture space improves perceptual consistency of computational features. In IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence, 2001.\n[7] A. R. Rao and G. L. Lohse. Identifying high level features of texture perception. CVGIP: Graph. Models Image Process., 55:218–233, May 1993.\n[8] Vithala R. Rao and Ralph Katz. Alternative multidimensional scaling methods for large stimulus sets. Journal of Marketing Research, VIII:488–494, 1971.\n[9] R. N. Shepard. Analysis of proximities: Multidimensional scaling with an unknown distance function. i. Psychometrika, 27:125–140, 1962.\n[10] H. Tamura, S. Mori, and T. Yamawaki. Textural features corresponding to visual perception. IEEE Transactions on Systems, Man and Cybernetics, 8:460–473, 1978.\n[11] J. B. Tenenbaum, V. de Silva, and J. C. Langford. A global geometric framework for nonlinear dimensionality\nreduction. Science, 290:2319–2323, 2000."]}