The Effects of Working Memory, Perceptual Speed, and Inhibition in Aggregated Search

Abstract

Prior work has studied how different characteristics of individual users (e.g., personality traits and cognitive abilities) can impact search behaviors and outcomes. We report on a laboratory study (N = 32) that investigated the effects of three different cognitive abilities (perceptual speed, working memory, and inhibition) in the context of aggregated search. Aggregated search systems combine results from multiple heterogeneous sources (or verticals) in a unified presentation. Participants in our study interacted with two different aggregated search interfaces (a within-subjects design) that differed based on the extent to which the layout distinguished between results originating from different verticals. The interleaved interface merged results from different verticals in a fairly unconstrained fashion. Conversely, the blocked interface displayed results from the same vertical as a group, displayed each group of vertical results in the same region on the SERP for every query, and used a border around each group of vertical results to help distinguish among results from different sources. We investigated three research questions (RQ1-RQ3). Specifically, we investigated the effects of the interface condition and each cognitive ability on three types of outcomes: (RQ1) participants' levels of workload, (RQ2) participants' levels of user engagement, and (RQ3) participants' search behaviors. Our results found different main and interaction effects. Perceptual speed and inhibition did not significantly affect participants' workload and user engagement but significantly affected their search behaviors. Specifically, with the interleaved interface, participants with lower perceptual speed had more difficulty finding relevant results on the SERP, and participants with lower inhibitory attention control searched at a slower pace. Working memory did not have a strong effect on participants' behaviors but had several significant effects on the levels of workload and user engagement reported by participants. Specifically, participants with lower working memory reported higher levels of workload and lower levels of user engagement. We discuss implications of our results for designing aggregated search interfaces that are well suited for users with different cognitive abilities.

Using Principal Component Analysis to Better Understand Behavioral Measures and their Effects

“…Next, we describe the three studies that generated the data used in our analyses. We refer the reader to the original papers for more details [1,2,6]. Studies 1-3 had similarities and differences.…”

“…Study Overview: Study 1 [2] was a lab-based study with 32 participants. Each participant completed four tasks of the same type and interacted with two different aggregated search interfaces that combined search results from various back-end services (or verticals).…”

“…An alternative to studying behavioral measures individually is to uncover and study the latent phenomena captured by different measures. In this paper, we report on a secondary analysis of behavioral data gathered from three previously published studies [1,2,6]. In each secondary analysis, we used principal component analysis (PCA), a common dimensionality reduction technique, to uncover the latent phenomena captured by a wide range of individual behavioral measures.…”

Two-Stage Search on Web Pages: Effects of Information Organisation and Task Complexity

“…В гораздо меньшем числе работ рассматриваются факторы пространственной организации и компоновки сайта [9; 13]. Однако для изучения работы пользователей с агрегаторами пространственное расположение информации имеет существенное значение [2].…”

“…В когнитивных исследованиях было показано, что сложность поисковой задачи зависит от числа параметров, задающих цель поиска, их связанности друг с другом, а также количества дистракторов и их сходства с целевым стимулом [32]. Сложность также определяется необходимостью выполнять ментальные операции [29] и уровнем когнитивной нагрузки 2 [16], связанными с обращением к ресурсам рабочей памяти 3 . При этом, чем выше сложность, тем менее быстрым и точным становится поиск [33].…”