Groups of individuals need to coordinate in many real world domains. However, coordination failure is common and not well understood. There are few coordination measurements, analyses focus on averaged data, and models lack coordination strategies and clear correspondence to cognitive mechanisms. Here, we present a thorough analysis of human data from a difficult coordination scenario and a cognitive model implemented within the ACT-R cognitive architecture to fit and explain the data. Data were explored to better understand coordination strategies and group dynamics. The cognitive model included pre-game preferences, coordination strategies like signaling, and other player choice predictions. This work highlights the need for deeper data explorations and presents challenges for modeling related to coordination dynamics, strategies, and how players form beliefs about others.

When it comes to decision making, the dominant viewsuggests that engaging in a detailed analytical thought processis more beneficial than deciding based on one’s feelings.However, there seems to be a tradeoff, as the complexity andamount of elements on which to base the decision increases,decisions based on affect seem to be more accurate thandecisions based on a thorough analytical process in specificcontexts. In those last cases, an affective modulation ofmemory may help to make better decisions in complex tasksthat exceed human’s limited cognitive capacities. Some dualprocess accounts, ‘‘deliberation-without-attention’’hypothesis (Dijksterhuis et al., 2006), oppose a cognitive (i.e.,conscious) route to an affective (i.e., unconscious) route.Since most dual process accounts suggest one type of processis better than the other, the interaction and integration ofaffective and more conscious analytical processes in decisionmaking have been understudied. To address this issue, wepropose an explanation of the dynamics and interaction ofcognitive (i.e., explicit) and affective (i.e., implicit) encodingand retrieval of elements in memory, using a unified theorybased on core affect (Russell, 2003), in the shape of acognitive model in the ACT-R cognitive architecture.

Peer-assisted learning (PAL) can have both benefits and costs when compared to individual learning. In an empirical study aimed at testing whether PAL yields better learning outcomes than individual learning in a set of insight problems from the Remote Associates Test (RAT), participants in the PAL condition learned from interacting with scripted peers and outperformed participants in the individual learning condition. Although interaction was limited, there is evidence for knowledge spillover from peers as a cognitive mechanism in PAL. The peer-assisted learning transferred to a final test that was completed individually. The benefit of peer-assisted learning persisted in spite of significantly greater error exposure in the PAL condition. In addition, the superiority of peer-assisted learning over individual learning was more pronounced for the more difficult RAT problems. These findings suggest that PAL may be an effective approach to developing insight problem solving skills.

When given the chance to choose between two tasks, one willmore likely choose the easier, less demanding task. Thiseffect has been shown in various domains and referred to asthe law of minimum effort or demand avoidance. Themeasure of demand avoidance that is currently used is theproportion of low-demand choices. We show that the currentmeasure is not appropriate for accurately assessing individualdifferences in demand avoidance, because the process ofdemand selection is contingent upon the process of demanddetection. Subsequently, we suggest a new measure ofdemand avoidance that combines demand detection anddemand selection. We show that the new measure of demandavoidance correlates in the expected direction (i.e.,negatively) with established measures of willingness andability to carry out cognitively demanding tasks. We proposea novel, performance-based measure of cognitive effortavoidance that can be used to enhance the validity of researchin cognition, perception, and neurosciences.

When given the chance to choose between two tasks, one will more likely choose the easier, less demanding task.This effect has been shown in various domains and referred to as the law of minimum effort or demand avoidance. Kooland colleagues (2010, 2013) designed the demand selection task (DST) and showed that most of their participants exhibitedclear demand avoidance. We attempted to replicate and extend their results in a series of three studies. Here we argue thatDST confounds demand detection and demand selection, which weakens its ability to reliably measure demand avoidance indifferent populations. In our first study, most participants did not show reliable demand avoidance and those who showed ithad higher working memory capacity. The following two studies aimed to de-confound the two processes. We define a newmeasure of demand avoidance that affords a more robust estimation of demand avoidance in different populations.