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Uncertainty plays a critical role in reinforcement learning and decision making. However, exactly how it influences behavior remains unclear. Multiarmed-bandit tasks offer an ideal test bed, since computational tools such as approximate Kalman filters can closely characterize the interplay between trial-by-trial values, uncertainty, learning, and choice. To gain additional insight into learning and choice processes, we obtained data from subjects’ overt allocation of gaze. The estimated value and estimation uncertainty of options influenced what subjects looked at before choosing; these same quantities also influenced choice, as additionally did fixation itself. A momentary measure of uncertainty in the form of absolute prediction errors determined how long participants looked at the obtained outcomes. These findings affirm the importance of uncertainty in multiple facets of behavior and help delineate its effects on decision making.

How does time pressure influence exploration and decision-making? We investigated this question with several four-armed bandit tasks manipulating (within subjects) expected reward, uncertainty, and time pressure (limited vs. unlimited). With limited time, people have less opportunity to perform costly computations, thus shifting the cost-benefit balance of different exploration strategies. Through behavioral, reinforcement learning (RL), reaction time (RT), and evidence accumulation analyses, we show that time pressure changes how people explore and respond to uncertainty. Specifically, participants reduced their uncertainty-directed exploration under time pressure, were less value-directed, and repeated choices more often. Since our analyses relate uncertainty to slower responses and dampened evidence accumulation (i.e., drift rates), this demonstrates a resource-rational shift towards simpler, lower-cost strategies under time pressure. These results shed light on how people adapt their exploration and decision-making strategies to externally imposed cognitive constraints.

Information from traces of other people’s behaviour can give clues about the quality of a behaviour or option. In three experiments we test whether the number of food items allegedly already consumed by others (scarcity-due-to-popularity effect) as well as the space initially allocated to the options (default effect) influence people’s selection of edible items. Participants made choices from a set of two plant and two meat-based finger foods (Experiment 1) and between two types of unlabeled finger foods (Experiments 2 and 3). In the first two experiments, we find a preference for fuller trays (reversed scarcity effect) but no effect for the size of trays. For the choice within plant and within meat options as well as among the unlabeled options, the preference for fullness is moderated by scarcity-due-to-popularity beliefs, such that a stronger belief that scarcity relates to popularity lessens or even reverses the preference for fuller trays. When it is made explicit that the options are of equal freshness (Experiment 3), participants showed a preference for emptier trays. This preference was more pronounced the stronger their scarcity-due-to-popularity beliefs. Contrary to previous research, we show that scarcity affects the choice of food items, but only when scarcity is attributed to popularity and when there are no strong prior preferences guided by labels (e.g., plant- vs meat-based options).

The current climate crisis requires pro-environmental behaviours to be developed, engaged in, and spread to other people. Behavioural traces, i.e. evidence of other people's pro-environmental behaviour left in the shared environment, have shown to influence people towards being more pro-environmental.In a set of three surveys, we investigate which behavioural traces correspond to a number of pro- and anti-environmental behaviours identified from previous literature, how frequently these behavioural traces are encountered, and their relation with engagement in behaviours.All studies are survey based with a mix of open-ended questions (Surveys 1 & 3) and rating scales (Survey 2). We use network analysis to identify partial correlations within behaviours and traces, respectively, and between behaviours and traces.A total of 91 traces uniquely attributed to 51 pro- and anti-environmental behaviours were identified. On average, behavioural traces are encountered monthly, with a range from never to daily. Noticing traces correlated with engaging in related behaviours for the areas 'Activism' and 'Food', but not 'Building', 'Transport', 'Waste' and 'Stuff'. Participants report that they expect seeing traces more frequently increases the likelihood of adopting the behaviour that produced them. Finally, participants were generally able to infer the causing behaviours when only presented the traces.We show that unique behavioural traces exist for the majority of pro- and anti-environmental behaviours. Traces are noticed and relate to the causing behaviours based on correlational and self-report evidence. Because of the wide variation between behaviours and their traces, further research into specific behaviours is warranted. Use of these findings for interventions are discussed.

Recycling behaviour can recover valuable materials and mitigate green house gas emission from landfills and incinerators. The potential positive impact of individuals' recycling behaviour depends on others also making an effort, for instance avoiding contamination. Knowing what other people have done may therefore influence recycling behaviour. Behavioural traces are evidence of other people's behaviour in a shared environment. Here they relate to waste items already placed in one of two bins, a mixed-recycling bin and a non-recyclable waste bin. In two online experiments and one real-life intervention study we investigate the role of behavioural traces on the willingness to recycle as well as the correctness of recycling. We find that seeing behavioural traces of previous recycling behaviour makes recycling generally more likely and people tend to copy item placement. This in turn increases correctness in groups where the average individual has good knowledge of recycling. Introducing correct items at the start of the day in the intervention study did not increase correctness, possibly because the correct items were soon buried by other items. Implications and future directions are discussed.

Stigmergy refers to the coordination of agents via artifacts of behaviours (behavioural traces) in the shared environment. Whilst primarily studied in biology and computer science/robotics, stigmergy underlies many human indirect interactions, both offline (e.g., trail building) and online (e.g., development of open-source software). In this review, we provide an introduction to stigmergy and emphasise how and where human stigmergy is distinct from animal or robot stigmergy, such as intentional communication via traces and causal inferences from the traces to the causing behaviour. Cognitive processes discussed on the agent level include attention, motivation, meaning and meta-cognition, as well as emergence/immergence, iterative learning and exploration/exploitation at the interface of individual agent and multi-agent systems. Characteristics of one-agent, two-agent and multi-agent systems are discussed and areas for future research highlighted.

Understanding the scientific consensus on anthropogenic climate change has been dubbed a ‘gateway belief’ to engaging people in sustainable behaviour. We consider the question of how the impact of a consensus communication can be maximised. Firstly, the credibility of the communicator should be maximised. One way of achieving this is to present the opinions of a sample of scientists directly to individuals. The decision-making literature suggests that such a technique will confer an additional advantage over standard descriptions of consensus (e.g. ‘97% of scientists agree’). In decision-making research, low probabilities tend to be overweighted when probabilities are described, but underweighted when probability information is experienced. Consequently, statements of high consensus may lead to an overweighting of the dissensus, a phenomenon that may be reversed were the consensus to be ‘experienced.’ We obtain some positive support for our proposal that consensus is best ‘experienced’ in one of two experiments. We suggest that the lack of stronger positive support could relate to ceiling effects for the topics studied and propose that investigation of effective methods for ‘experiencing’ the consensus is a fruitful area for future research.

The relationship between DNA sequence, biochemical function, and molecular evolution is relatively well-described for protein-coding regions of genomes, but far less clear in noncoding regions, particularly, in eukaryote genomes. In part, this is because we lack a complete description of the essential noncoding elements in a eukaryote genome. To contribute to this challenge, we used saturating transposon mutagenesis to interrogate the Schizosaccharomyces pombe genome. We generated 31 million transposon insertions, a theoretical coverage of 2.4 insertions per genomic site. We applied a five-state hidden Markov model (HMM) to distinguish insertion-depleted regions from insertion biases. Both raw insertion-density and HMM-defined fitness estimates showed significant quantitative relationships to gene knockout fitness, genetic diversity, divergence, and expected functional regions based on transcription and gene annotations. Through several analyses, we conclude that transposon insertions produced fitness effects in 66–90% of the genome, including substantial portions of the noncoding regions. Based on the HMM, we estimate that 10% of the insertion depleted sites in the genome showed no signal of conservation between species and were weakly transcribed, demonstrating limitations of comparative genomics and transcriptomics to detect functional units. In this species, 3′- and 5′-untranslated regions were the most prominent insertion-depleted regions that were not represented in measures of constraint from comparative genomics. We conclude that the combination of transposon mutagenesis, evolutionary, and biochemical data can provide new insights into the relationship between genome function and molecular evolution., L.G. was supported by a UCL Grand Challenges Award to J.B. C.J.R.I. was supported by a Sir Henry Dale Fellowship, jointly funded by the Wellcome Trust and the Royal Society (Grant No. 101239/Z/13/Z). This work was supported by a Wellcome Trust Senior Investigator Award to J.B. (Grant No. 095598/Z/11/Z).

The relationship between DNA sequence, biochemical function, and molecular evolution is relatively well-described for protein-coding regions of genomes, but far less clear in noncoding regions, particularly, in eukaryote genomes. In part, this is because we lack a complete description of the essential noncoding elements in a eukaryote genome. To contribute to this challenge, we used saturating transposon mutagenesis to interrogate the Schizosaccharomyces pombe genome. We generated 31 million transposon insertions, a theoretical coverage of 2.4 insertions per genomic site. We applied a five-state hidden Markov model (HMM) to distinguish insertion-depleted regions from insertion biases. Both raw insertion-density and HMM-defined fitness estimates showed significant quantitative relationships to gene knockout fitness, genetic diversity, divergence, and expected functional regions based on transcription and gene annotations. Through several analyses, we conclude that transposon insertions produced fitness effects in 66–90% of the genome, including substantial portions of the noncoding regions. Based on the HMM, we estimate that 10% of the insertion depleted sites in the genome showed no signal of conservation between species and were weakly transcribed, demonstrating limitations of comparative genomics and transcriptomics to detect functional units. In this species, 3′- and 5′-untranslated regions were the most prominent insertion-depleted regions that were not represented in measures of constraint from comparative genomics. We conclude that the combination of transposon mutagenesis, evolutionary, and biochemical data can provide new insights into the relationship between genome function and molecular evolution.