Dyadic and triadic search: Benefits, costs, and predictors of group performance

Abstract

In daily life, humans often perform visual tasks, such as solving puzzles or searching for a friend in a crowd. Performing these visual searches jointly with a partner can be beneficial: The two task partners can devise effective division of labour strategies and thereby outperform individuals who search alone. To date, it is unknown whether these group benefits scale up to triads or whether the cost of coordinating with others offsets any potential benefit for group sizes above two. To address this question, we compare participants' performance in a visual search task that they perform either alone, in dyads, or in triads. When the search task is performed jointly, co-actors receive information about each other's gaze location. After controlling for speed-accuracy trade-offs, we found that triads searched faster than dyads, suggesting that group benefits do scale up to triads. Moreover, we found that the triads' divided the search space in accordance with the co-actors' individual search performances but searched less efficiently than dyads. We also present a linear model to predict group benefits, which accounts for 70% of the variance. The model includes our experimental factors and a set of non-redundant predictors, quantifying the similarities in the individual performances, the collaboration between co-actors, and the estimated benefits that co-actors would attain without collaborating. Overall, the present study demonstrates that group benefits scale up to larger group sizes, but the additional gains are attenuated by the increased costs associated with devising effective division of labour strategies.

Interpersonal coordination in joint multiple object tracking.

“…As in previous studies (Wahn et al, 2017; Wahn et al, 2020); our reasoning is that if dyads exceed this simulated joint performance, then they attained their higher joint performance by successfully collaborating in the tracking task (i.e., a division a labor division strategy). To differentiate this type of benefit from the collective benefit, and to denote that a collaboration between coactors is required, we henceforth refer to it as a “collaborative benefit” (for a more in-depth discussion of different types of group benefit criteria, see Wahn, Kingstone, & König, 2018).…”

“…Moving further along the information continuum (see Figure 1, second row), the next step is to identify when coactors transition from attaining group benefits without collaborating to attaining group benefits through collaboration. At this point, note that we generally assume that collaboration further enhances joint performance in joint visual tasks as several earlier studies found this to be the case (Brennan et al, 2008; Wahn et al, 2020; Wahn, Czeszumski, & König, 2018). Of course, as previously noted when investigations of joint motor control were reviewed, this is not necessarily true for all joint tasks (Knoblich & Jordan, 2003).…”

“…For joint perceptual decision-making tasks, performance feedback can further enhance an already acquired group benefit, but it is not sufficient on its own to attain one (Bahrami et al, 2010(Bahrami et al, , 2012. For joint visual search tasks, dyads can attain group benefits if they only receive performance feedback (Wahn, Czeszumski, & König, 2018) or if they receive performance feedback and action information (Brennan et al, 2008;Wahn et al, 2020). Yet, only in the latter case are they able to collaborate by coordinating their actions to distribute their labor.…”

“…Since this seminal study on joint visual search, several studies have investigated additional factors that may or may not affect group performance (Brennan & Enns, 2015a; Wahn, Czeszumski, & König, 2018; Wahn et al, 2020); how coactors coordinate their actions (Niehorster et al, 2019); how group performance could be modeled (Brennan & Enns, 2015b; Enright et al, 2020); the neural substrates (Szymanski et al, 2017); and also the role of receiving information about each other’s actions in other joint spatial tasks (McCarley et al, 2020; Neider et al, 2010; Wahn et al, 2017; Yamani et al, 2017). Notably, Brennan and Enns (2015b) devised an important performance baseline to assess whether a group benefit is actually attained via a collaboration (i.e., a labor division is followed) or the mere benefit of performing a task together.…”

Collaboration improves unspeeded search in the absence of precise target information

“…The second was that teams might have interacted in a way that improved their information sampling as they viewed the images. Most obvious was the possibility that searchers adopted a division-of-labor strategy (Malcolmson et al, 2007), either by making individual team members responsible for inspecting particular regions of the display, or alternatively, by making individual team members responsible for detecting particular targets (for evidence of similar strategies in speeded collaborative search, see Brennan, Chen, Dickinson, Neider, & Zelinsky, 2008;Niehorster, Cornelissen, Holmqvist, &Hooge, 2019 andWahn, Czeszumski, Labusch, Kingstone, &König, 2020; though see Yamani, Neider, Kramer, & McCarley, 2017, for evidence of increased overlap in oculomotor scanning in a team search, and McCarley, Leggett, & Enright, 2020, for behavioral evidence of inefficient team performance in a visual monitoring task).…”