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Implementation intentions (strategic “if-then” plans) have been shown to support behaviour change. This may be achieved by mentally forming stimulus-response associations, thereby promoting habit formation. Does this deliberate attempt to instal “strategic automaticity” only offer advantages, or does it also come at the cost of reduced flexibility that characterises learnt habits? To investigate this, we tested healthy, young participants on a computerised instrumental learning task. Critically, we introduced implementation intentions (“if I see stimulus X, then I will respond”) versus goal intentions (“for outcome Z, I will respond)” during instrumental acquisition, and subsequently assessed behavioural flexibility in an outcome-revaluation test. In Experiment 1, we conducted a between-subjects manipulation of strategic planning, and in Experiment 2, a within-subject manipulation. We hypothesised that implementation intentions would lead to strong stimulus-response associations and consequently impair performance when the signalled outcome value changed and therefore required a different response, while benefitting performance when the outcome value (and required response) remained the same. We found that implementation intentions supported instrumental learning, but impaired test performance overall (most robustly in Experiment 2), irrespective of whether the signalled outcome value had changed. We argue that this general detrimental effect of implementation intentions on test performance is likely a consequence of their negative effect on stimulus-outcome learning. Our findings warrant caution when applying if-then plans to situations where the agent does not already possess perfect knowledge of behavioural contingencies.While implementation intentions may support efficient and fast behavioural execution, this may come at the expense of behavioural flexibility.

The translation of the outcome-devaluation paradigm to study habit in humans has yielded interesting insights but proven to be challenging. We present a novel, outcome-revaluation task with a symmetrical design, in the sense that half of the available outcomes are always valuable and the other half not-valuable. In the present studies, during the instrumental learning phase, participants learned to respond (Go) to certain stimuli to collect valuable outcomes (and points) while refraining to respond (NoGo) to stimuli signalling not-valuable outcomes. Half of the stimuli were short-trained, while the other half were long-trained. Subsequently, in the test phase, the signalled outcomes were either value-congruent with training (still-valuable and still-not-valuable), or value-incongruent (devalued and upvalued). The change in outcome value on value-incongruent trials meant that participants had to flexibly adjust their behaviour. At the end of the training phase, participants completed the self-report behavioural automaticity index – providing an automaticity score for each stimulus-response association. We conducted two experiments using this task, that both provided evidence for stimulus-driven habits as reflected in poorer performance on devalued and upvalued trials relative to still-not-valuable trials and still-valuable trials, respectively. While self-reported automaticity increased with longer training, behavioural flexibility was not affected. After extended training (Experiment 2), higher levels of self-reported automaticity when responding to stimuli signalling valuable outcomes were related to more ‘slips of action’ when the associated outcome was subsequently devalued. We conclude that the symmetrical outcome-revaluation task provides a promising paradigm for the experimental investigation of habits in humans.

In the absence of a vaccine, social distancing behaviour is pivotal to mitigate COVID-19 virus spread. In this large-scale behavioural experiment, we gathered data during Smart Distance Lab: The Art Fair (n = 787) between August 28 and 30, 2020 in Amsterdam, the Netherlands. We varied walking directions (bidirectional, unidirectional, and no directions) and supplementary interventions (face mask and buzzer to alert visitors of 1.5 metres distance). We captured visitors' movements using cameras, registered their contacts (defined as within 1.5 metres) using wearable sensors, and assessed their attitudes toward COVID-19 as well as their experience during the event using questionnaires. We also registered environmental measures (e.g., humidity). In this paper, we describe this unprecedented, multi-modal experimental data set on social distancing, including psychological, behavioural, and environmental measures. The data set is available on Figshare and in a MySQL database. It can be used to gain insight into (attitudes toward) behavioural interventions promoting social distancing, to calibrate pedestrian models, and to inform new studies on behavioural interventions.

In the wake of the COVID-19 pandemic, the central importance of human behaviour in mitigating the spread of the virus has become universally recognized. We present a methodology to systematically assess the effectiveness of behavioural interventions to stimulate social distancing. In addition, we demonstrate the feasibility of this framework in a large-scale natural experiment. In an experimental design, we varied behavioural interventions to evaluate the effect of face masks, walking directions, and immediate feedback on visitors’ contacts. We represent visitors as nodes, and their contacts as links in a contact network. Subsequently, we used network modelling to test for differences in these contact networks. We find no evidence that face masks influence social distancing, while unidirectional walking directions and buzzer feedback do positively impact social distancing. The presented methodology represents a practically feasible way to optimize social distancing interventions through scientific research and may directly inform policy.