Beyond discrete-choice options.

While decision theories have evolved over the past five decades, their focus has largely been on choices among a limited number of discrete options, even though many real-world situations have a continuous-option space. Recently, theories have attempted to address decisions with continuous-option spaces, and several computational models have been proposed within the sequential sampling framework to explain how we make a decision in continuous-option space. This article aims to review the main attempts to understand decisions on continuous-option spaces, give an overview of applications of these types of decisions, and present puzzles to be addressed by future developments.

Follow my example, for better and for worse: The influence of behavioral traces on recycling decisions.

Recycling behavior can recover valuable materials and mitigate green house gas emissions from landfills and incinerators. The potential positive impact of individuals' recycling behavior depends on others also making an effort, for instance, avoiding contamination. Knowing what other people have done may therefore influence recycling behavior. Behavioral traces are evidence of other people's behavior in a shared environment. Here, they relate to waste items already placed in one of two bins, a mixed recycling bin and a nonrecyclable waste bin. In two online experiments and one real-life intervention study, we investigate the role of behavioral traces on the willingness to recycle as well as the correctness of recycling. We find that seeing behavioral traces of previous recycling behavior 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. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Human optional stopping in a heteroscedastic world.

When making decisions, animals must trade off the benefits of information harvesting against the opportunity cost of prolonged deliberation. Deciding when to stop accumulating information and commit to a choice is challenging in natural environments, where the reliability of decision-relevant information may itself vary unpredictably over time (variable variance or "heteroscedasticity"). We asked humans to perform a categorization task in which discrete, continuously valued samples (oriented gratings) arrived in series until the observer made a choice. Human behavior was best described by a model that adaptively weighted sensory signals by their inverse prediction error and integrated the resulting quantities with a linear urgency signal to a decision threshold. This model approximated the output of a Bayesian model that computed the full posterior probability of a correct response, and successfully predicted adaptive weighting of decision information in neural signals. Adaptive weighting of decision information may have evolved to promote optional stopping in heteroscedastic natural environments. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Time pressure changes how people explore and respond to uncertainty.

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.

It's new, but is it good? How generalization and uncertainty guide the exploration of novel options.

How do people decide whether to try out novel options as opposed to tried-and-tested ones? We argue that they infer a novel option's reward from contextual information learned from functional relations and take uncertainty into account when making a decision. We propose a Bayesian optimization model to describe their learning and decision making. This model relies on similarity-based learning of functional relationships between features and rewards, and a choice rule that balances exploration and exploitation by combining predicted rewards and the uncertainty of these predictions. Our model makes 2 main predictions. First, decision makers who learn functional relationships will generalize based on the learned reward function, choosing novel options only if their predicted reward is high. Second, they will take uncertainty about the function into account, and prefer novel options that can reduce this uncertainty. We test these predictions in 3 preregistered experiments in which we examine participants' preferences for novel options using a feature-based multiarmed bandit task in which rewards are a noisy function of observable features. Our results reveal strong evidence for functional exploration and moderate evidence for uncertainty-guided exploration. However, whether or not participants chose a novel option also depended on their attention, as well as reflecting on the value of the options. These results advance our understanding of people's reactions in the face of novelty. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Uncertainty in learning, choice, and visual fixation.

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.

Social exclusion affects working memory performance in young adolescent girls.

Adolescence has been proposed to be a sensitive period of social development, during which the social environment has a heightened effect on brain and behaviour. As such, negative social experiences, such as social exclusion, may have particularly detrimental effects on psychological well-being. However, little is known about how social exclusion affects cognitive performance during this time of life. Here, we compared the effects of exclusion between adolescence and adulthood. We recruited 98 females in three age groups: young adolescents (N = 36, aged 10.1-14.0), mid-adolescents (N = 35, aged 14.3-17.9) and adults (N = 27, aged 18.3-38.1). All age groups showed reductions in mood after exclusion, compared to inclusion, in a virtual ball-tossing game. Young adolescents also showed reduced verbal working memory accuracy following exclusion. There was no effect of exclusion on visuo-spatial working memory in any age group. These results suggest young adolescent girls' verbal working memory accuracy was affected by a short, virtual social exclusion experience. This highlights the importance of the social environment in adolescence and underlines the need to consider age differences in response to exclusion in the design and timing of social exclusion interventions in schools.

Protocol for an app-based affective control training for adolescents: proof-of-principle double-blind randomized controlled trial.

Background: 75% of all mental health problems have their onset before the end of adolescence. Therefore, adolescence may be a particularly sensitive time period for preventing mental health problems. Affective control, the capacity to engage with goal relevant and inhibit distracting information in affective contexts, has been proposed as a potential target for prevention. In this study, we will explore the impact of improving adolescents' affective control capacity on their mental health. Methods: The proof-of-principle double-blind randomized controlled trial will compare the effectiveness of an app-based affective control training (AffeCT) to a placebo training (P-Training) app. In total, 200 (~50% females) adolescents (11-19 years) will train for 14 days on their training app. The AffeCT will include three different n-back tasks: visuospatial, auditory and dual (i.e., including both modalities). These tasks require participants to flexibly engage and disengage with affective and neutral stimuli (i.e., faces and words). The P-Training will present participants with a perceptual matching task. The three versions of the P-Training tasks vary in the stimuli included (i.e., shapes, words and faces). The two training groups will be compared on gains in affective control, mental health, emotion regulation and self-regulation, immediately after training, one month and one year after training. Discussion: If, as predicted, the proposed study finds that AffeCT successfully improves affective control in adolescents, there would be significant potential benefits to adolescent mental health. As a free app, the training would also be scalable and easy to disseminate across a wide range of settings. Trial registration: The trial was registered on December 10th 2018 with the International Standard Randomised Controlled Trial Number (Registration number: ISRCTN17213032).

Failures to replicate a key result of the selective accessibility theory of anchoring.

Numerical anchoring effects describe the assimilative effect of a previously presented number on subsequent numerical estimates. Such effects are robust and consequential. A number of different accounts have been proposed to explain these effects. What is currently unclear is under which situations different mechanisms play more or less critical roles. An extant test from the literature is proposed as a "signature test" for the operation of selective accessibility mechanisms. Four experiments were conducted to ascertain the evidence for selective accessibility with this test, evidence which was not obtained. A fifth experiment employed a different methodology, and again failed to show evidence for selective accessibility. Subsequent discussion suggests that the robustness of anchoring effects is remarkable, but the theoretical basis for some previous tests of the selective accessibility account of anchoring is shaky, and we advise against their use in this capacity. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Fitness Landscape of the Fission Yeast Genome.

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.

Generalization and Search in Risky Environments.

How do people pursue rewards in risky environments, where some outcomes should be avoided at all costs? We investigate how participant search for spatially correlated rewards in scenarios where one must avoid sampling rewards below a given threshold. This requires not only the balancing of exploration and exploitation, but also reasoning about how to avoid potentially risky areas of the search space. Within risky versions of the spatially correlated multi-armed bandit task, we show that participants' behavior is aligned well with a Gaussian process function learning algorithm, which chooses points based on a safe optimization routine. Moreover, using leave-one-block-out cross-validation, we find that participants adapt their sampling behavior to the riskiness of the task, although the underlying function learning mechanism remains relatively unchanged. These results show that participants can adapt their search behavior to the adversity of the environment and enrich our understanding of adaptive behavior in the face of risk and uncertainty.

Cross-dimensional magnitude interactions arise from memory interference.

Magnitudes from different dimensions (e.g., space and time) interact with each other in perception, but how these interactions occur remains unclear. In four experiments, we investigated whether cross-dimensional magnitude interactions arise from memory interference. In Experiment 1, participants perceived a constant-length line consisting of two line segments of complementary lengths and presented for a variable stimulus duration; then they received a cue about which of the two segment lengths to later reproduce. Participants were to first reproduce the stimulus duration and then the cued length. Reproduced durations increased as a function of the cued length if the cue was given before duration was retrieved from memory for reproduction (i.e. before duration reproduction; Experiment 1) but not if it was given after the duration memory had been retrieved from memory (i.e. after the start of duration reproduction; Experiment 2). These findings demonstrate that space-time interaction arises as a result of memory interference when length and duration information co-exist in working memory. Experiment 3 further demonstrated spatial interference on duration memories from memories of filled lengths (i.e. solid line segments) but not from noisier memories of unfilled lengths (demarcated empty spatial intervals), thus highlighting the role of memory noise in space-time interaction. Finally, Experiment 4 showed that time also exerted memory interference on space when space was presented as (relatively noisy) unfilled lengths. Taken together, these findings suggest that cross-dimensional magnitude interactions arise as a result of memory interference and the extent and direction of the interaction depend on the relative memory noises of the target and interfering dimensions. We propose a Bayesian model whereby the estimation of a magnitude is based on the integration of the noisily encoded percept of the target magnitude and the prior knowledge that magnitudes co-vary across dimensions (e.g., space and time). We discuss implications for cross-dimensional magnitude interactions in general.

Generalization guides human exploration in vast decision spaces.

From foraging for food to learning complex games, many aspects of human behaviour can be framed as a search problem with a vast space of possible actions. Under finite search horizons, optimal solutions are generally unobtainable. Yet, how do humans navigate vast problem spaces, which require intelligent exploration of unobserved actions? Using various bandit tasks with up to 121 arms, we study how humans search for rewards under limited search horizons, in which the spatial correlation of rewards (in both generated and natural environments) provides traction for generalization. Across various different probabilistic and heuristic models, we find evidence that Gaussian process function learning-combined with an optimistic upper confidence bound sampling strategy-provides a robust account of how people use generalization to guide search. Our modelling results and parameter estimates are recoverable and can be used to simulate human-like performance, providing insights about human behaviour in complex environments.

Putting bandits into context: How function learning supports decision making.

The authors introduce the contextual multi-armed bandit task as a framework to investigate learning and decision making in uncertain environments. In this novel paradigm, participants repeatedly choose between multiple options in order to maximize their rewards. The options are described by a number of contextual features which are predictive of the rewards through initially unknown functions. From their experience with choosing options and observing the consequences of their decisions, participants can learn about the functional relation between contexts and rewards and improve their decision strategy over time. In three experiments, the authors explore participants' behavior in such learning environments. They predict participants' behavior by context-blind (mean-tracking, Kalman filter) and contextual (Gaussian process and linear regression) learning approaches combined with different choice strategies. Participants are mostly able to learn about the context-reward functions and their behavior is best described by a Gaussian process learning strategy which generalizes previous experience to similar instances. In a relatively simple task with binary features, they seem to combine this learning with a probability of improvement decision strategy which focuses on alternatives that are expected to lead to an improvement upon a current favorite option. In a task with continuous features that are linearly related to the rewards, participants seem to more explicitly balance exploration and exploitation. Finally, in a difficult learning environment where the relation between features and rewards is nonlinear, some participants are again well-described by a Gaussian process learning strategy, whereas others revert to context-blind strategies. (PsycINFO Database Record

Task complexity moderates the influence of descriptions in decisions from experience.

Decisions-makers often have access to a combination of descriptive and experiential information, but limited research so far has explored decisions made using both. Three experiments explore the relationship between task complexity and the influence of descriptions. We show that in simple experience-based decision-making tasks, providing congruent descriptions has little influence on task performance in comparison to experience alone without descriptions, since learning via experience is relatively easy. In more complex tasks, which are slower and more demanding to learn experientially, descriptions have stronger influence and help participants identify their preferred choices. However, when the task gets too complex to be concisely described, the influence of descriptions is reduced hence showing a non-monotonic pattern of influence of descriptions according to task complexity. We also propose a cognitive model that incorporates descriptive information into the traditional reinforcement learning framework, with the impact of descriptions moderated by task complexity. This model fits the observed behavior better than previous models and replicates the observed non-monotonic relationship between impact of descriptions and task complexity. This research has implications for the development of effective warning labels that rely on simple descriptive information to trigger safer behavior in complex environments.

Compositional inductive biases in function learning.

How do people recognize and learn about complex functional structure? Taking inspiration from other areas of cognitive science, we propose that this is achieved by harnessing compositionality: complex structure is decomposed into simpler building blocks. We formalize this idea within the framework of Bayesian regression using a grammar over Gaussian process kernels, and compare this approach with other structure learning approaches. Participants consistently chose compositional (over non-compositional) extrapolations and interpolations of functions. Experiments designed to elicit priors over functional patterns revealed an inductive bias for compositional structure. Compositional functions were perceived as subjectively more predictable than non-compositional functions, and exhibited other signatures of predictability, such as enhanced memorability and reduced numerosity. Taken together, these results support the view that the human intuitive theory of functions is inherently compositional.

A Window of Opportunity for Cognitive Training in Adolescence.

In the current study, we investigated windows for enhanced learning of cognitive skills during adolescence. Six hundred thirty-three participants (11-33 years old) were divided into four age groups, and each participant was randomly allocated to one of three training groups. Each training group completed up to 20 days of online training in numerosity discrimination (i.e., discriminating small from large numbers of objects), relational reasoning (i.e., detecting abstract relationships between groups of items), or face perception (i.e., identifying differences in faces). Training yielded some improvement in performance on the numerosity-discrimination task, but only in older adolescents or adults. In contrast, training in relational reasoning improved performance on that task in all age groups, but training benefits were greater for people in late adolescence and adulthood than for people earlier in adolescence. Training did not increase performance on the face-perception task for any age group. Our findings suggest that for certain cognitive skills, training during late adolescence and adulthood yields greater improvement than training earlier in adolescence, which highlights the relevance of this late developmental stage for education.

Continuous Theta Burst Stimulation Over the Dorsolateral Prefrontal Cortex and the Pre-SMA Alter Drift Rate and Response Thresholds Respectively During Perceptual Decision-Making.

BACKGROUND: The speed-accuracy trade-off (SAT) refers to the balancing of speed versus accuracy during decision-making. SAT is very commonly investigated with perceptual decision-making tasks such as the moving dots task (MDT). The dorsolateral prefrontal cortex (DLPFC) and the pre-supplementary motor area (pre-SMA) are two brain regions considered to be involved in the control of SAT. OBJECTIVES/HYPOTHESES: The study tested whether the DLPFC and the pre-SMA play an essential role in the control of SAT. We hypothesized that continuous theta burst stimulation (cTBS) over the right DLPFC would primarily alter the rate of accumulation of evidence, whereas stimulation of the pre-SMA would influence the threshold for reaching a decision. METHODS: Fifteen (5 females; mean age = 30, SD =5.40) healthy volunteers participated in the study. We used two versions of the MDT and cTBS over the right DLPFC, pre-SMA and sham stimulation. The drift diffusion model was fit to the behavioural data (reaction time and error rate) in order to calculate the drift rate, boundary separation (threshold) and non-decision time. RESULTS: cTBS over the right DLPFC decreased the rate of accumulation of evidence (i.e. the drift rate from the diffusion model) in high (0.35 and 0.5) but not in low coherence trials. cTBS over the pre-SMA changed the boundary separation/threshold required to reach a decision on accuracy, but not on speed trials. CONCLUSIONS: The results suggest for the first time that both the DLPFC and the pre-SMA make essential but distinct contributions to the modulation of SAT.

Subthalamic nucleus deep brain stimulation induces impulsive action when patients with Parkinson's disease act under speed pressure.

The subthalamic nucleus (STN) is proposed to modulate response thresholds and speed-accuracy trade-offs. In situations of conflict, the STN is considered to raise response thresholds, allowing time for the accumulation of information to occur before a response is selected. Conversely, speed pressure is thought to reduce the activity of the STN and lower response thresholds, resulting in fast, errorful responses. In Parkinson's disease (PD), subthalamic nucleus deep brain stimulation (STN-DBS) reduces the activity of the nucleus and improves motor symptoms. We predicted that the combined effects of STN stimulation and speed pressure would lower STN activity and lead to fast, errorful responses, hence resulting in impulsive action. We used the motion discrimination 'moving-dots' task to assess speed-accuracy trade-offs, under both speed and accuracy instructions. We assessed 12 patients with PD and bilateral STN-DBS and 12 age-matched healthy controls. Participants completed the task twice, and the patients completed it once with STN-DBS on and once with STN-DBS off, with order counterbalanced. We found that STN stimulation was associated with significantly faster reaction times but more errors under speed instructions. Application of the drift diffusion model showed that stimulation resulted in lower response thresholds when acting under speed pressure. These findings support the involvement of the STN in the modulation of speed-accuracy trade-offs and establish for the first time that speed pressure alone, even in the absence of conflict, can result in STN stimulation inducing impulsive action in PD.