Roos Arwen Doekemeijer’s research while affiliated with Ghent University and other places

Response inhibition is key to flexible behavior, and while its mechanistic process dynamics are increasingly better understood, there are indications for important neuromodulatory influences that are thus far mostly considered in animal and pharmaceutical work. Specifically, such work has indicated a tight link between response inhibition and norepinephrine (NE) levels in the brain. In the present work, we used pre-trial pupil measures as established proxies of tonic NE levels to investigate the link with response inhibition using the stop-signal task. We did so in two healthy student samples, one performing in a standard stop-signal task, and one in a variant in which half of the experiment, stop-signals were to be ignored. Our results showed that (1) (faster) GoRT was predicted by (larger) pretrial pupil measures, which was stronger in the stop context induced by the standard stop-signal task; (2) (lower) stopping success was predicted by (larger) pretrial pupil measures, which may be explained by a faster go response working against successful inhibition; (3) (shorter) stop-signal reaction times (SSRT) were also associated with (larger) pretrial pupil measures, but somewhat less consistently. Taken together, our findings show a clear pattern that pre-trial pupil measures predict response speed on go trials, in particular in a stopping context, and a slightly less clear relationship with measures of response inhibition. Our results therefore support a link between fluctuating tonic NE levels and the process dynamics in response inhibition, but in a fashion that is less exclusive to core inhibition processes than might have been expected.

People are able to stop actions before they are executed, and proactively slow down the speed of going in line with their expectations of needing to stop. Such slowing generally increases the probability that stopping will be successful. Surprisingly though, no study has clearly demonstrated that the speed of stopping (measured as the stop-signal reaction time, SSRT) is reduced by such proactive adjustments. In addition to a number of studies showing non-significant effects, the only study that initially had observed a clear effect in this direction found that it was artifactually driven by a confounding variable (specifically, by context-independence violations, which jeopardize the validity of the SSRT estimation). Here, we tested in two well-powered and well-controlled experiments whether the SSRT is shorter when stopping is anticipated. In each experiment, we used a Stop-Signal Task, in which the stop-trial frequency was either high (50%) or low (20%). Our results robustly show that the SSRT was shorter when stop signals were more anticipated (i.e., in the high-frequent condition) while carefully controlling for context-independence violations. Hence, our study is first to demonstrate a clear proactive benefit on the speed of stopping, in line with an ability to emphasize going or stopping, by trading off the speed of both.

The tendency to continue or intensify gambling after losing (loss-chasing) is widely regarded as a defining feature of gambling disorder. However, loss-chasing in real gambling contexts is multifaceted, and some aspects are better understood than others. Gamblers may chase losses between multiple sessions or within a single session. Furthermore, within a session, loss-chasing can be expressed in the decision of (1) when to stop, (2) how much stake to bet, and (3) the speed of play after winning and losing. Using a large player-tracking data set (>2500 players, >10 million rounds) collected from the online commercial game Mystery Arena, we examined these three behavioral expressions of within-session loss-chasing. While the first two aspects (when to stop and how much stake to bet) have been examined previously, the current research is the first large-scale study to examine the effects of wins and losses on the speed of play in real gambling. The players were additionally assigned different involvement levels by the operator based on their gambling behavior on the operator’s own platform, which further allowed us to examine group differences in loss-chasing. We found that after winning, both the high- and low-involvement groups were less likely to stop, and increased the stake amount, thus showing win-chasing instead of loss-chasing in these two facets. After losing, both groups played more quickly though, which may reflect an urge to continue gambling (as an expression of loss-chasing). Wins and losses had a smaller influence on the speed of play for the high-involvement players, suggesting that they might have reduced sensitivity to wins and/or losses. Future work can further examine chasing in different gambling products and in people with gambling problems to assess the generalizability of these findings.

The tendency to continue or intensify gambling after losing (loss-chasing) is widely regarded as a defining feature of gambling disorder. However, loss-chasing in real gambling contexts is multifaceted, and some aspects are better understood than others. Gamblers may chase losses between multiple sessions or within a single session. Furthermore, within a session, loss-chasing can be expressed in the decision of (1) when to stop, (2) how much stake to bet, and (3) the speed of play after winning and losing. Using a large player-tracking data set (>2500 players, >10 million rounds) collected from the online commercial game Mystery Arena, we examined these three behavioral expressions of within-session loss-chasing. While the first two aspects (when to stop and how much stake to bet) have been examined previously, the current research is the first large-scale study to examine the effects of wins and losses on the speed of play in real gambling. The players were additionally assigned different involvement levels by the operator based on their gambling behavior on the operator’s own platform, which further allowed us to examine group differences in loss-chasing. We found that after winning, both the high- and low-involvement groups were less likely to stop, and increased the stake amount, thus showing win-chasing instead of loss-chasing in these two facets. After losing, both groups played more quickly though, which may reflect an urge to continue gambling (as an expression of loss-chasing). Wins and losses had a smaller influence on the speed of play for the high-involvement players, suggesting that they might have reduced sensitivity to wins and/or losses. Future work can further examine chasing in different gambling products and in people with gambling problems to assess the generalizability of these findings.

Response inhibition is typically understood as the ability to stop inappropriate actions and is often investigated using the stop-signal task, in which a go response, triggered by a go signal, has to be inhibited upon the onset of a stop signal. In this task, response inhibition has been formalized as a race between a go and a stop process, which allows the latency of the stop process (stop-signal reaction time; SSRT) to be estimated. Yet, non-parametric SSRT estimations assume that the stop process is initiated without fail, which appears problematic as it is known that participants fail to do so on a subset of trials (”trigger failures”). Importantly, non-parametric methods systematically overestimate SSRT when trigger failures are present, and a growing literature is demonstrating that reported SSRT differences between groups and individuals are also (or rather) driven by differential trigger-failure rates. In the present study, we extend this line of research to a within-individual manipulation, namely the influence of reward on stop performance. We first reanalyzed four data sets of studies that had reported a facilitating effect of stimulus-based reward on SSRTs. Reanalyzing this data, we found that reward decreased the rates of trigger failures. When accounting for these differential trigger-failure rates, the effect of reward on SSRTs (i.e. stop latency) appeared to be virtually abolished. We then conducted a preregistered online follow-up study, implementing a typical block-based reward manipulation. The results of this study indicated simultaneous reward effects on trigger-failure rates and on SSRT. In sum, the present results indicate that trigger failures are an important source of variance in response inhibition, dovetailing with an evolving multicomponential view of response inhibition.