Oral messages improve visual search

Input multimodality combining speech and hand gestures has motivated numerous usability studies. Contrastingly, issues relating to the design and ergonomic evaluation of multimodal output messages combining speech with visual modalities have not yet been addressed extensively. The experimental study presented here addresses one of these issues. Its aim is to assess the actual efficiency and usability of oral system messages including brief spatial information for helping users to locate objects on crowded displays rapidly. Target presentation mode, scene spatial structure and task difficulty were chosen as independent variables. Two conditions were defined: the visual target presentation mode (VP condition) and the multimodal target presentation mode (MP condition). Each participant carried out two blocks of visual search tasks (120 tasks per block, and one block per condition). Scene target presentation mode, scene structure and task difficulty were found to be significant factors. Multimodal target presentation proved to be more efficient than visual target presentation. In addition, participants expressed very positive judgments on multimodal target presentations which were preferred to visual presentations by a majority of participants. Besides, the contribution of spatial messages to visual search speed and accuracy was influenced by scene spatial structure and task difficulty: (i) messages improved search efficiency to a lesser extent for 2D array layouts than for some other symmetrical layouts, although the use of 2D arrays for displaying pictures is currently prevailing; (ii) message usefulness increased with task difficulty. Most of these results are statistically significant.Comment: 4 page

Homo economicus in visual search

How do reward outcomes affect early visual performance? Previous studies found a suboptimal influence, but they ignored the non-linearity in how subjects perceived the reward outcomes. In contrast, we find that when the non-linearity is accounted for, humans behave optimally and maximize expected reward. Our subjects were asked to detect the presence of a familiar target object in a cluttered scene. They were rewarded according to their performance. We systematically varied the target frequency and the reward/penalty policy for detecting/missing the targets. We find that 1) decreasing the target frequency will decrease the detection rates, in accordance with the literature. 2) Contrary to previous studies, increasing the target detection rewards will compensate for target rarity and restore detection performance. 3) A quantitative model based on reward maximization accurately predicts human detection behavior in all target frequency and reward conditions; thus, reward schemes can be designed to obtain desired detection rates for rare targets. 4) Subjects quickly learn the optimal decision strategy; we propose a neurally plausible model that exhibits the same properties. Potential applications include designing reward schemes to improve detection of life-critical, rare targets (e.g., cancers in medical images)

When are abrupt onsets found efficiently in complex visual search? : evidence from multi-element asynchronous dynamic search

Previous work has found that search principles derived from simple visual search tasks do not necessarily apply to more complex search tasks. Using a Multielement Asynchronous Dynamic (MAD) visual search task, in which high numbers of stimuli could either be moving, stationary, and/or changing in luminance, Kunar and Watson (M. A Kunar & D. G. Watson, 2011, Visual search in a Multi-element Asynchronous Dynamic (MAD) world, Journal of Experimental Psychology: Human Perception and Performance, Vol 37, pp. 1017-1031) found that, unlike previous work, participants missed a higher number of targets with search for moving items worse than for static items and that there was no benefit for finding targets that showed a luminance onset. In the present research, we investigated why luminance onsets do not capture attention and whether luminance onsets can ever capture attention in MAD search. Experiment 1 investigated whether blinking stimuli, which abruptly offset for 100 ms before reonsetting-conditions known to produce attentional capture in simpler visual search tasks-captured attention in MAD search, and Experiments 2-5 investigated whether giving participants advance knowledge and preexposure to the blinking cues produced efficient search for blinking targets. Experiments 6-9 investigated whether unique luminance onsets, unique motion, or unique stationary items captured attention. The results found that luminance onsets captured attention in MAD search only when they were unique, consistent with a top-down unique feature hypothesis. (PsycINFO Database Record (c) 2013 APA, all rights reserved)

Monitoring Processes in Visual Search Enhanced by Professional Experience: The Case of Orange Quality-Control Workers

Visual search tasks have often been used to investigate how cognitive processes change with expertise. Several studies have shown visual experts' advantages in detecting objects related to their expertise. Here, we tried to extend these findings by investigating whether professional search experience could boost top-down monitoring processes involved in visual search, independently of advantages specific to objects of expertise. To this aim, we recruited a group of quality-control workers employed in citrus farms. Given the specific features of this type of job, we expected that the extensive employment of monitoring mechanisms during orange selection could enhance these mechanisms even in search situations in which orange-related expertise is not suitable. To test this hypothesis, we compared performance of our experimental group and of a well-matched control group on a computerized visual search task. In one block the target was an orange (expertise target) while in the other block the target was a Smurfette doll (neutral target). The a priori hypothesis was to find an advantage for quality-controllers in those situations in which monitoring was especially involved, that is, when deciding the presence/absence of the target required a more extensive inspection of the search array. Results were consistent with our hypothesis. Quality-controllers were faster in those conditions that extensively required monitoring processes, specifically, the Smurfette-present and both target-absent conditions. No differences emerged in the orange-present condition, which resulted to mainly rely on bottom-up processes. These results suggest that top-down processes in visual search can be enhanced through immersive real-life experience beyond visual expertise advantages

Visual Decoding of Targets During Visual Search From Human Eye Fixations

What does human gaze reveal about a users' intents and to which extend can these intents be inferred or even visualized? Gaze was proposed as an implicit source of information to predict the target of visual search and, more recently, to predict the object class and attributes of the search target. In this work, we go one step further and investigate the feasibility of combining recent advances in encoding human gaze information using deep convolutional neural networks with the power of generative image models to visually decode, i.e. create a visual representation of, the search target. Such visual decoding is challenging for two reasons: 1) the search target only resides in the user's mind as a subjective visual pattern, and can most often not even be described verbally by the person, and 2) it is, as of yet, unclear if gaze fixations contain sufficient information for this task at all. We show, for the first time, that visual representations of search targets can indeed be decoded only from human gaze fixations. We propose to first encode fixations into a semantic representation and then decode this representation into an image. We evaluate our method on a recent gaze dataset of 14 participants searching for clothing in image collages and validate the model's predictions using two human studies. Our results show that 62% (Chance level = 10%) of the time users were able to select the categories of the decoded image right. In our second studies we show the importance of a local gaze encoding for decoding visual search targets of use

Priming of Pop-Out Does not Affect the Shooting Line Illusion

We combined a shooting-line illusion with a visual search pop-out task in an effort to determine whether priming of pop-out was due to acceleratcd processing of visual information in the primed dimension. While the priming effect and the line-motion percept were replicated, the visual search task showed no influence on the perceived direction of line motion. These results indicate that the priming effect does not accelerate early visual processing.National Institutes of Health-National Eye Institute (EY05087, 49620-93-1-0407

TRECVid 2006 experiments at Dublin City University

In this paper we describe our retrieval system and experiments performed for the automatic search task in TRECVid 2006. We submitted the following six automatic runs: • F A 1 DCU-Base 6: Baseline run using only ASR/MT text features. • F A 2 DCU-TextVisual 2: Run using text and visual features. • F A 2 DCU-TextVisMotion 5: Run using text, visual, and motion features. • F B 2 DCU-Visual-LSCOM 3: Text and visual features combined with concept detectors. • F B 2 DCU-LSCOM-Filters 4: Text, visual, and motion features with concept detectors. • F B 2 DCU-LSCOM-2 1: Text, visual, motion, and concept detectors with negative concepts. The experiments were designed both to study the addition of motion features and separately constructed models for semantic concepts, to runs using only textual and visual features, as well as to establish a baseline for the manually-assisted search runs performed within the collaborative K-Space project and described in the corresponding TRECVid 2006 notebook paper. The results of the experiments indicate that the performance of automatic search can be improved with suitable concept models. This, however, is very topic-dependent and the questions of when to include such models and which concept models should be included, remain unanswered. Secondly, using motion features did not lead to performance improvement in our experiments. Finally, it was observed that our text features, despite displaying a rather poor performance overall, may still be useful even for generic search topics