Visual search guidance uses coarser template information than target-match decisions

When searching for an object, we use a target template in memory that contains task-relevant information to guide visual attention to potential targets and to determine the identity of attended objects. These processes in visual search have typically been assumed to rely on a common source of template information. However, our recent work (Yu, et al., in press) argued that attentional guidance and target-match decisions rely on different information during search, with guidance using a “fuzzier” version of the template compared to target decisions. However, that work was based on the special case of search for a target amongst linearly separable distractors (e.g., search for an orange target amongst yellower distractors). Real-world search targets, however, are infrequently linearly separable from distractors, and it remains unclear whether the differences between the precision of template information used for guidance compared to target decisions also applies under more typical conditions. In four experiments, we tested this question by varying distractor similarity during visual search and measuring the likelihood of attentional guidance to distractors and target misidentifications. We found that early attentional guidance is indeed less precise than that of subsequent match decisions under varying exposure durations and distractor set sizes. These results suggest that attentional guidance operates on a coarser code than decisions, perhaps because guidance is constrained by lower acuity in peripheral vision or the need to rapidly explore a wide region of space while decisions about selected objects are more precise to optimize decision accuracy.

Analisis Model Literasi Informasi Perguruan Tinggi Kota Malang Di Masa Pandemi COVID-19

The study aims to analyze and evaluate the successful application of the information literacy model implemented during a pandemic. Information literacy models used include the Seven Pillars, The Big6TM and Empowering 8 and The Seven Faces of Information Literacy. The object of research is UPT Library of UMM, UB Library and UPT Library of UM. The research method used is descriptive research with a qualitative approach by conducting interviews. This study analyzed the comparison of the applied information literacy model and the use of the most effective information literacy models and programs in the UPT Library of UMM during the pandemic. The result of the research is that UPT Library of UMM has 3 packages of information literacy classes, and UB Library has a special literacy class which is held online during the pandemic. Meanwhile, the UPT Library of UM carry out literacy activities with individual / group guidance, casual literacy activities or informally at the library cafe. The conclusions show that during the COVID-19 pandemic the information literacy model applied in UMM Library upt and UB Library adopted The Big6TM theory. Information literacy had an impact on the ease of information management to complete scientific tasks and research. Meanwhile, UPT Library of UM is developing an information literacy model and providing information literacy through library cafes, reading wishes and search guidance. This activity has an impact on the ease of information retrieval for the completion of assignments and scientific papers. Inputs for the three libraries are adding literacy activities by providing space for creativity, holding information retrieval classes, and literacy competitions and being able to identify outputs/ products from information literacy.

Egocentric and Allocentric Reference Frames Can Flexibly Support Contextual Cueing

We investigated if contextual cueing can be guided by egocentric and allocentric reference frames. Combinations of search configurations and external frame orientations were learned during a training phase. In Experiment 1, either the frame orientation or the configuration was rotated, thereby disrupting either the allocentric or egocentric and allocentric predictions of the target location. Contextual cueing survived both of these manipulations, suggesting that it can overcome interference from both reference frames. In contrast, when changed orientations of the external frame became valid predictors of the target location in Experiment 2, we observed contextual cueing as long as one reference frame was predictive of the target location, but contextual cueing was eliminated when both reference frames were invalid. Thus, search guidance in repeated contexts can be supported by both egocentric and allocentric reference frames as long as they contain valid information about the search goal.

Get Your Guidance Going: Investigating the Activation of Spatial Priors for Efficient Search in Virtual Reality

Repeated search studies are a hallmark in the investigation of the interplay between memory and attention. Due to a usually employed averaging, a substantial decrease in response times occurring between the first and second search through the same search environment is rarely discussed. This search initiation effect is often the most dramatic decrease in search times in a series of sequential searches. The nature of this initial lack of search efficiency has thus far remained unexplored. We tested the hypothesis that the activation of spatial priors leads to this search efficiency profile. Before searching repeatedly through scenes in VR, participants either (1) previewed the scene, (2) saw an interrupted preview, or (3) started searching immediately. The search initiation effect was present in the latter condition but in neither of the preview conditions. Eye movement metrics revealed that the locus of this effect lies in search guidance instead of search initiation or decision time, and was beyond effects of object learning or incidental memory. Our study suggests that upon visual processing of an environment, a process of activating spatial priors to enable orientation is initiated, which takes a toll on search time at first, but once activated it can be used to guide subsequent searches.

From Support Propagation to Belief Propagation in Constraint Programming (Extended Abstract)

The distinctive driving force of constraint programming (CP) to solve combinatorial problems has been a privileged access to problem structure through the high-level models it uses. We investigate a richer propagation medium for CP made possible by recent work on counting solutions inside constraints. Beliefs about individual variable-value assignments are exchanged between contraints and iteratively adjusted. Its advantage over standard belief propagation is that the higher-level models do not tend to create as many cycles, which are known to be problematic for convergence. We find that it significantly improves search guidance.

From Support Propagation to Belief Propagation in Constraint Programming

The distinctive driving force of constraint programming to solve combinatorial problems has been a privileged access to problem structure through the high-level models it uses. From that exposed structure in the form of so-called global constraints, powerful inference algorithms have shared information between constraints by propagating it through shared variables’ domains, traditionally by removing unsupported values. This paper investigates a richer propagation medium made possible by recent work on counting solutions inside constraints. Beliefs about individual variable-value assignments are exchanged between contraints and iteratively adjusted. It generalizes standard support propagation and aims to converge to the true marginal distributions of the solutions over individual variables. Its advantage over standard belief propagation is that the higher-level models featuring large-arity (global) constraints do not tend to create as many cycles, which are known to be problematic for convergence. The necessary architectural changes to a constraint programming solver are described and an empirical study of the proposal is conducted on its implementation. We find that it provides close approximations to the true marginals and that it significantly improves search guidance.