Age-related differences in diffusion model boundary optimality with both trial-limited and time-limited tasks

Abstract

In two-choice decision tasks, Starns and Ratcliff (Psychology and Aging 25: 377-390, 2010) showed that older adults are farther from the optimal speed-accuracy trade-off than young adults. They suggested that the age effect resulted from differences in task goals, with young participants focused on balancing speed and accuracy and older participants focused on minimizing errors. We compared speed-accuracy criteria with a standard procedure (blocks that had a fixed numbers of trials) to a condition in which blocks lasted a fixed amount of time and participants were instructed to get as many correct responses as possible within the time limit-a goal that explicitly required balancing speed and accuracy. Fits of the diffusion model showed that criteria differences persisted in the fixed-time condition, suggesting that age differences are not solely based on differences in task goals. Also, both groups produced more conservative criteria in difficult conditions when it would have been optimal to be more liberal.

People adopt optimal policies in simple decision-making, after practice and guidance

“…However, due to interpretation issues associated with multiple difficulties that are explained below, they were unable to conclusive identify whether participants were adopting a single sub-optimal threshold, or multiple thresholds where some were optimal, and some were sub-optimal. Starns and Ratcliff (2012) made similar observations for younger participants and older participants. A difficulty with Starns and Ratcliff's findings is that participants were not explicitly instructed to optimize reward rate, which leaves open the question of whether people cannot perform optimally, or simply do not do so by default.…”

“…Starns and Ratcliff (2012) also investigated this same manipulation, and we hoped to replicate their finding that fixed time blocks lead to better reward rate optimization.…”

“…Simen et al (2009) found that people appropriately identified situations in which the optimal decision threshold changed towards more cautious or more urgent policies, and people followed the direction of these changes in response to manipulations of experiment timing, stimulus contingencies, and rewards. However, changes in task difficulty have shown the opposite trend, with both Starns and Ratcliff (2012) and Balci et al (2011) observing that participants adopted cautious strategies with increasing difficulty, where the optimal adjustment would dictate being less cautious.…”

“…The participant usually leaves the experiment whenever they have made a fixed number of decisions, rather than when a fixed amount of time elapses. Starns and Ratcliff (2012) investigated this methodological explanation by directly comparing performance in blocks where the duration was controlled by the experimenter versus by the participant. Consistent with animal research, Starns et al found that experimentercontrolled blocks led participants to adopt speed-accuracy tradeoff settings that were closer to optimal, although these settings were still decidedly sub-optimal in most conditions.…”

“…In those cases, reward rate can be increased beyond the maximum rate attainable with a standard decision-making model via the adoption of time-varying decision boundaries (Thura et al, 2012;Drugowitsch et al, 2012). Starns and Ratcliff (2012) separated the different difficulties into different blocks, which potentially allow different, optimal tradeoff policies to be adopted for each separate difficulty. While possible, this presumably increases the cognitive load, and may have contributed to the difficulty their participants had in setting reward-rate optimal decision policies.…”