Harder than Expected: Increased Conflict in Clearly Disadvantageous Delayed Choices in a Computer Game

<div><p>When choosing between immediate and temporally delayed goods, people sometimes decide disadvantageously. Here, we aim to provide process-level insight into differences between individually determined advantageous and disadvantageous choices. Participants played a computer game, deciding between two different rewards of varying size and distance by moving an agent towards the chosen reward. We calculated individual models of advantageous choices and characterized the decision process by analyzing mouse movements. The larger amount of participants’ choices was classified as advantageous and the disadvantageous choices were biased towards choosing sooner/smaller rewards. The deflection of mouse movements indicated more conflict in disadvantageous choices compared with advantageous choices when the utilities of the options differed clearly. Further process oriented analysis revealed that disadvantageous choices were biased by a tendency for choice-repetition and an undervaluation of the value information in favour of the delay information, making rather simple choices harder than could be expected from the properties of the decision situation.</p></div

The experimental screen.

<p>Participants chose between soon/small and late/large rewards (coins of different size with a red border), moving an agent (red smiling face) across a playing field by clicking with the mouse into horizontally or vertically adjacent fields (white border). They were instructed to maximize their gain within the limited time of 8 minutes per block. The remaining time (“Zeit”) within a block and the cumulated credits (“Gewinn”) were presented next to the playing field.</p

Mouse movement trajectories.

<p>Movements reach from the starting location at the begin of a trial to the first click into a movement field, leading to advantageous (here: left) or disadvantageous (here: right) choices. Direct choice paths mark the shortest way to the movement field. Deflection of trajectories from the direct choice path to the neutral midline between two movement fields indicates conflict in the decision process. Conflict is lowest for clearly advantageous choices and highest for clearly disadvantageous choices. Shaded areas mark standard errors.</p

Beta-weights from time-continuous multiple regression analysis.

<p>Beta-weights represent the different influences on the mouse movement angle on the XY plane (shaded areas around the curves indicate the standard error of beta-weights for each time-slice). Left, beta-weights for advantageous choices. Right, beta-weights for disadvantageous choices. Above each graph, consecutive time-slices with a significant difference from zero (8 consecutive <i>t</i>-tests) are marked for each beta-weight.</p

Temporal discounting.

<p>Indifference points mark the subjective value at each interval between soon/small and late/large option. The advantageous choice model shows the discounting for choosing always the option with the best time/money ratio. Error bars indicate standard errors of the mean over participants.</p

Let’s decide together: Differences between individual and joint delay discounting

<div><p>This study addressed the question whether or not social collaboration has an effect on delay discounting, the tendency to prefer sooner but smaller over later but larger delivered rewards. We applied a novel paradigm in which participants executed choices between two gains in an individual and in a dyadic decision-making condition. We observed how participants reached mutual consent via joystick movement coordination and found lower discounting and a higher decisions’ efficiency. In order to establish the underlying mechanism for dyadic variation, we further tested whether these differences emerge from social facilitation or inner group interchange.</p></div

The epistemic dimension of sustainability transformations: Mapping disruptive agency across system boundaries, levels and time.

The epistemic dimension of sustainability transformations: Mapping disruptive agency across system boundaries, levels and time.</p