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This paper explores the use of linguistic strategies, specifically discourse markers like 'but', to express contrasts between expectations and reality when faced with unexpected events. The study concentrates on Denial of Expectation (DofE), themost powerful form of contrast, which arises when the expected value based on background assumptions is not met. The main focus of this paper is to model DofE as a weighted homogeneous relationship between object properties. The aim is to predict DofE for numerical properties in specific contexts. I aim to address a gap in previous models by considering the role of context. This is achieved by analyzing contrastive sentences from German car and motorcycle reviews. The research presents the concept of expectation intervals for scalar properties. These intervals align with expectations and exceeding them triggers a potential contrast. The study incorporates causality, expected behavior, and a shift function in selecting contrastive pairs, transforming the conditions into an algorithm.Keywords: contrast; computational and cognitive modeling; discourse analysis

Citizens in a democracy must navigate an increasingly dense information landscape. Regulation can aid this navigation by mandating disclosures of the source and nature of political campaign material. In many countries, legislators are increasing transparency requirements for online advertising in particular. The current paper looks at how and if citizens use such disclaimers to infer the intent of political advertisers during the process of a trust evaluation. This paper describes a survey experiment that specifically investigates evaluations of unknown campaigners, theorising such conditions will maximise any effect disclaimers have on trust. Testing both sponsorship and micro-targeting disclaimers, no support is found for the theoretical claim that viewing a disclaimer can increase how trustworthy a political advertiser is perceived to be. There is preliminary support that, for some individuals, viewing a disclaimer increases scepticism.

Since the landmark paper of Spivey et al. (2005), there has been an explosion in the use of continuous trajectories for studying decision-making. In these studies, the participant makes their decision by reaching to a target. By extracting trajectory-based measures, it is possible to observe how the decision unfolds over time. However, studies have shown that these trajectories may not continuously track the decision-making process, but rather result from several discrete submovements executed intermittently. Due to this, the conclusions of papers using purely kinematic measures may be affected by these motor control properties. This study presents simulations of trajectories using human motor control models and demonstrates the relationship between non-decision-related parameters and outcome measures typically used. We also comment on the implications of these findings for using kinematic measures in decision-making and make recommendations for how the decision variable can be more appropriately sampled.

People tend to avoid wasting resources, but little is known about when this emerges in development. Though young children are often wasteful with food and other items, previous work suggests that children consider waste in other judgments. Here, we examined if children anticipate that others should minimize waste. In two experiments (total N = 195), children chose which of two foods someone should eat (Experiment 1; 3-7-year-olds) or two papers someone should make a snowflake with (Experiment 2; 5-year-olds). One of the options would result in minimal waste (i.e., a small food/paper) while the other would result in greater waste (i.e., a large food/paper). Children did not anticipate that others would choose smaller foods, however, at around five years they predicted that others would choose smaller paper. These findings contribute to our knowledge of the development of waste aversion and may extend our understanding of waste aversion as a form of efficiency.

Efforts have always been deployed to surpass limitations in human cognitive abilities to enhance aspects such as task accuracy, work effectiveness and error management. Cognitive enhancement is a field aiming at improving human cognition in order to overcome those limitations. It bears important interest from the human factors community given its potential for reducing errors in complex operational environments. Yet, cognitive enhancement strategies are rarely used outside the lab and practical applications are scarce. The current paper presents a brief summary of the literature on human cognitive enhancement and discusses key operational applications of the main methods reported in this field. Using a human factors perspective, the paper also outlines how such techniques could be integrated into decision-support tools to support operators facing cognitive challenges in complex operational domains, including those experiencing functional limitations preventing them to contribute to the workforce.

In this paper, we explored the attribution of causal responsibility in a causal chain of events, where an agent A instructs an intermediate agent B to execute some harmful action which leads to a bad outcome. In Study 1, participants judged B to be more causally responsible, more blameworthy, and more deserving of punishment than A. In Study 2, we explored the effect of proximity on judgments of the two agents by adding a third, subsequent contributing cause, such that B's action no longer directly caused the final outcome. Participants judged both agents A and B to be less causally responsible and deserving of punishment (but not less blameworthy) when they were less proximal to the outcome, and there were no differences in judgments between the two agents. In Study 3, we varied whether each of the two agents (A and B) intended for the final outcome to occur. We find an interaction between role and intent, where participants only mitigated judgments for A when A did not intend for the outcome to occur – regardless of B's intent. We discuss possible explanations for our findings and its implications for moral and legal decision-making.

Helping is a universal human behavior, and is a core aspect of a functioning society. However, the decision to provide help, and what type of help to provide, is a complex cognitive calculation that weights many costs and benefits simultaneously. In this paper, we explore how various costs influence the moment-to-moment decision to help in a simple video game. Participants were paired with another human participant and were asked to make repeated decisions that could benefit either themselves or their partner. Several preregistered manipulations altered the cost each person paid for actions in the environment, the intrinsic resource capacity of individuals to perform the task, the visibility of the other player's score, and the affordances within the environment for helping. The results give novel insight into the cost-benefit analyses that people apply when providing help, and highlight the role of reciprocity in influencing helping decisions.

How do humans decide what to look at and when to stop looking? The Rational Action, Noisy Choice for Habituation (RANCH) model formulates looking behaviors as a rational information acquisition process. RANCH instantiates a hypothesis about the perceptual encoding process using a neural network-derived embedding space, which allows it to operate on raw images. In this paper, we show that the model not only captures key looking time patterns such as habituation and dishabituation, but also makes fine-grained, out-of-sample predictions about magnitudes of dishabituation to previously unseen stimuli. We validated those predictions experimentally with a self-paced looking time task in adults (N = 468). We also show that model fits are robust across parameters, but that assumptions about the perceptual encoding process, the learning process and the decision process are all critical for predicting human performance.

To date, no account of lie-truth judgement formation has been capable of explaining how core cognitive mechanisms such as memory encoding and retrieval are employed to reach a judgement of either truth or lie. One account, the Adaptive Lie Detector theory (ALIED: Street, Bischof, Vadillo, & Kingstone, 2016) is sufficiently well defined that its assumptions may be implemented in a computational model. In this paper we describe our attempt to ground ALIED in the representations and mechanisms of the ACT-R cognitive architecture and then test the model by comparing it to human data from an experiment conducted by Street et al. (2016). The model provides a close fit to the human data and a plausible mechanistic account of how specific and general information are integrated in the formation of truth-lie judgements.

Command and control is a key activity in a war that determines whether the war is won or lost. A person's cognition can influence the decisions he makes in command and control. In this paper, we propose a method to assess the impact of cognitive manipulation techniques in command and control. We divide command and control activities into eight segments and use a hierarchical latent Dirichlet allocation model in natural language processing to discover the most important cognitive manipulation techniques at present. We completed the assessment process using the Quality Function Deployment Model. The results of the assessment show that judgement, decision-making, and planning in the command and control perspective are susceptible to cognitive manipulation techniques; the tactical level is more susceptible to cognitive manipulation techniques than the strategic campaign. Life sciences, digital technology, and other fields play a key role in command and control research under cognitive manipulation.

In this paper, we developed three ACT-R cognitive models to simulate the learning process of abacus gestures. Abacus gestures are mid-air gestures, each representing a number between 0 and 99. Our models learn to predict the response time of making an abacus gesture. We found the accuracy of a model's predictions depends on the structure of its declarative memory. A model with 100 chunks cannot simulate human response, whereas models using fewer chunks can, as segmenting chunks increase both the frequency and recency of information retrieval. Furthermore, our findings suggest that the mind is more likely to represent abacus gestures by dividing attention between two hands rather than memorizing and outputting all gestures directly. These insights have important implications for future research in cognitive science and human-computer interaction, particularly in developing vision and motor modules for mental states in existing cognitive architectures and designing intuitive and efficient mid-air gesture interfaces.

In this paper, we explore the objective-Bayesian principle of minimum information and Maximum Entropy as a solution to the problem of awareness growth: how should rational agents adjust their beliefs upon becoming aware of new possibilities? We introduce the Maximum Entropy principle as a theoretical solution to the problem of awareness growth and present the results of two experiments conducted to compare human reasoners' responses with the theoretical prescriptions of the Maximum Entropy approach. We discover that, although the MaxEnt method may appear computationally demanding, participants' responses are largely consistent with the theoretical prescription.

The paper presents a model and a discussion of the computational representation of virtue ethics in autonomous devices. One of the key problems in formal modeling of virtue ethics is the computational representation of the concept of virtue. In our model, the virtue is represented by a set of minimal extents to which a set of values, relevant to the virtue, should be satisfied. A device will be moral if any decision made satisfies all relevant values above the declared thresholds.

In this paper, we first introduce the `complex hidden profile', a previously overlooked category of hidden profiles that arises from complex inferential relations among arguments.Second, in order to investigate the conditions under which interrelated arguments can generate hidden profiles, we introduce a novel Bayesian agent-based framework for collective reasoning with complex argument structures.Finally, we show that that many possible argument structures can generate hidden profiles, even when agents do not have any information in common.

Emotions can be insensitive to certain attributes of a situation. A large body of evidence shows that information on probabilities, large numerical counts, or intentions is frequently disregarded in the elicitation and regulation of emotions. To date, no existing theory comprehensively accounts for the features that tend to be overlooked by emotion. In this paper I call attention to the common denominator of such features: they cannot be perceived nor contribute to the iconic representation of events. For instance, the exceedingly low probability of a plane crash does not affect its imagistic representation (i.e., the iconic representation of the event is silent about the event's probability). I introduce the Iconic Prioritization Hypothesis, positing that the prioritization of the iconic format in emotion can explain the neglect of information that is representationally silent in this format. Emotion may favour iconicity as it is the format of immediate, first-hand evidence about our surroundings.

In the field of medical decision-making, understanding the treatment intentions of clinicians is crucial for effective treatment strategies. However, these intentions are often implicit and challenging to quantify. In this paper, we propose a novel two-module model to infer and implement clinicians' treatment intentions through treatment records. We construct the InfCTI module, which infers intentions and quantifies them numerically, and the ImpCTI module, which generates treatment strategies based on inferred intentions. Our experiments demonstrate that the treatment strategies obtained by ImpCTI reflect clinicians' intentions and the intention values obtained by InfCTI are reasonable. This model has the potential to improve the quality of care provided to patients.

The Traveling Salesperson Problem (TSP) is a combinatorial optimization problem originally of interest to mathematicians, but more recently used also in the context of cognitive and comparative psychology. Humans perform extremely well on spatial versions of this task, despite its mathematical complexity, making it an appealing tool for the study of spatial and mathematical cognition. We presented participants with three versions of a TSP in navigational space; one that could be solved visually, one with visual distractors, and one that also required the use of memory. The task was preceded by instructions that promoted either a ‘growth mindset' or ‘fixed mindset' approach. Results indicated that performance on this navigational version of the TSP is generally good, though not quite as efficient as solutions reported in the traditional pencil-and-paper version of the task. The effects of visual distractors and of memory requirements were greater in problems with a larger number of targets. Instructions had no significant effect on performance.

We are constantly faced with the question of how to aggregate preferences, views, perspectives and values. This is a problem for groups attempting to accommodate individuals with differing needs and interests, as will be our focus. The problem of ``value aggregation'' therefore crops up in myriads of places across the social sciences---in rational decision theory, social choice models, and proposals for systems of democratic voting, for instance. These sub-disciplines have formalized proposals for how to deal with value aggregation, though, remarkably, no research has yet directly compared people's intuitions of two of the most obvious candidates for aggregation--taking the sum of all the values (the classic ``Utilitarian'' approach) and the product (a less well-known ``contractualist'' approach).In this paper, we systematically explore the proposals suggested by each algorithm, focusing on aggregating preferences across groups. We find that both humans and performant LLMs prefer a contractualist approach.

When faced with uncertainty, human observers maximize performance by integrating sensory information with learned task-relevant regularities. Does this behavior similarly occur in social settings? In this paper, we explore how reward-seeking behavior in an intergroup context is affected by readily available but task-irrelevant social information (in the form of group membership) when task-relevant reward information can be learned over time. Across two experiments, we show that participants learned and utilized task-relevant regularities to inform their choices. We also show that human observers are not universally biased towards utilizing social information in all settings––participants learned to disregard social information when not relevant to the task at hand. However, learning about the utility of social information (Experiment 2) had a long-term influence on observers’ ability to subsequently learn and utilize available sources of information. Real-world intergroup contexts typically encompass situations and stimuli that have been previously experienced by the observer. Our findings highlight the powerful influence of learning in such contexts.

It is rather uncontroversial that gender should have no influence on treating others as equal epistemic agents. However, is this view reflected in practice? This paper aims to test whether the gender of the testifier and the accused of assault is related to the perception of a testimony’s reliability and the guilt of the potential perpetrator. Two experiments were conducted, in which the subjects (n = 361, 47% females, 53% males) assessed the reliability of the testifier and the guilt of the potential perpetrator in four scenarios of assault accusation, in which the only difference was the gender of the people presented. During the study, we have observed dependencies of gender and ascription of reliability, and, to a lesser extent, of guilt. The results of our research may constitute an argument for the existence of different epistemic status endowed on people depending on their gender and existing gender stereotypes.

Despite the importance of risk attitude on decision making, its role on belief updating has been overlooked. In this paper, we aim to answer this fundamental question. We show, using economic theory, that stronger risk aversion drives more conservative actions, decreases the instrumental value of information relative to the importance of belief-based utility. Thus, with utility for clarity, stronger risk attitude yields to more belief change; whereas with updating cost, stronger risk attitude leads to less belief change. We confirm the result experimentally in two settings where subjects update their belief about their IQ and a randomly drawn number, respectively.

The present paper reports an experiment (N = 348) with a two-year-delayed (M = 695 days) follow-up that tests an approach to raising willingness to take climate actions. Here we focus on the longitudinal results. Our experimental materials were designed to harness the power of two cognitive constraints — coherence and causal invariance, which map onto two narrative proclivities that anthropologists have identified as universal — to promote climate action across the political spectrum. Towards that goal, the essential role of these constraints in belief formation predicts that climate-change information would be more persuasive when it is embedded in a personal climate-action narrative, the evocation of which can benefit from exposure to parsimonious scientific explanations of indisputable everyday observations, juxtaposed with reasoners’ own, typically less coherent explanations, occurring in a context that engages their moral stance. Our brief one-time intervention, conducted in U.S. states with the highest level of climate skepticism, showed that across the political spectrum, our materials raised willingness to take climate actions in the immediate assessment. It also raised the likelihood of reports two years later of having taken or would have taken those actions had the opportunity existed, suggesting long-lasting effects. Our approach adopts the framework that conceptions of reality are representations, and adaptive solutions in that infinite space of representations require cognitive constraints to narrow the search.

Helping is an inherently cooperative behavior, but the cognitive mechanisms underlying this behavior remain relatively underexplored. In this paper, we introduce a novel gamified paradigm for understanding a variety of cognitive behaviors associated with helping. Principals are assigned secret goals in a block-based grid (e.g., move all blue blocks to room C), and helpers can either pass their turn or make a move that could help the principal. We show that principals make useful and pragmatic first moves and helpers accumulate evidence over time before initiating a helpful move. We also introduce a preliminary set of computational models based on recursive pragmatic inference and utility maximization that attempt to account for these behavioral findings.

We introduce the Cognitive Science Community to the challenges faced in human-in-the-loop (HiTL) decision-making for complex and dynamic industrial chemical processes. A microworld is presented which includes advanced dynamic simulations, an optimal control-based decision-support algorithm and a preliminary human machine interface. Three example applications are discussed, including decision-making in plant startup and shutdown, shared control using human and HiTL supervisory Model Predictive Control (MPC), and a pilot study of operator eye-tracking during a complex startup task. The paper also discusses ongoing and future experimental efforts highlighting the utility of the microworld in understanding human cognition and designing advanced operator training software for various process operation related complex tasks.

On-line decision problems – in which a decision is made basedon a sequence of past events without knowledge of the future –have been extensively studied in theoretical computer science.A famous example is the Prediction from Expert Advice problem,in which an agent has to make a decision informed by thepredictions of a set of experts. An optimal solution to this problemis the Multiplicative Weights Update Method (MWUM).In this paper, we investigate how humans behave in a Predictionfrom Expert Advice task. We compare MWUM and severalother algorithms proposed in the computer science literatureagainst human behavior. We find that MWUM providesthe best fit to people’s choices.

Face stereotypes are prevalent and consequential. In this paper, we investigate an experience sampling account of face stereotyping. In two experiments, we show that, in an experience-sampling-based Trust Game, participants in the role of an investor were much more likely to play the game with trustees with trustworthy faces than those with untrustworthy faces (Experiment 1). Crucially, this endogenous experience sampling bias has amplified the behavioral consequences of the facial trustworthiness stereotype. In contrast, when the information was directly described with no sampling constraint for the participants, the face stereotype had a very limited effect on investment decisions (Experiment 2). We conclude by suggesting that the description-experience gap paves a promising avenue for studying sampling-based accounts in social cognition and behavior.

Contractualist moral theories hold that morality is primarily about acting according to what would be agreed by rational agents. The contractualist tradition has received little attention in empirical work compared to competing normative ethical theories that are primarily concerned with rules (deontology) and consequences (consequentialism). Recent theoretical developments and empirical results suggest that agreement-based considerations and forms of reasoning could play a central role in moral judgment and decision making. However, the computational foundations of moral contractualism have scarcely been explored. In this paper, we present a flexible experimental paradigm to study contractualist moral behavior in an incentivized setting. We then develop a simple computational model of agreement-based moral decision making that captures participants’ behavior in the task. Results suggest that agreement-based cognitive processes are required to explain the nuances of observed behavioral patterns. Contractualism could offer a fruitful framework to understand how humans coordinate their behavior in morally ambiguous contexts.

The Interface Transparency Thesis (ITT) proposes that people tend to use a canonical interpretation of linguistic expressions, even when this interpretation is sub-optimal for the task at hand. The current paper sought to investigate this claim further by adding a time-pressure manipulation to a quantified sentence verification task and analyzing the results through a computational model of decision-making. The results indicate that time pressure -while effectively changing behavioral responses- does not alter cognitive processes associated with quantifier verification, thus supporting the ITT.

It is intuitive to believe that humans take considerations of mental effort into account when making decisions. However, it has proved difficult to differentiate theories of mental effort in the absence of direct measurements of this psychological construct. Existing measurements of mental effort using response times and revealed preferences have low reliability. In this paper, we present a new experimental task - selecting between visuomotor lotteries using eye-tracking for sampling lotteries - that enables direct measurement of mental effort. Unlike response time-based measures, effort measurements in this task are not confounded by actual effort allocation. Unlike revealed preference-based measures, effort measurements in this task are acquired on a natural scale unitized by automatic visual selection processes. We also report results from a simple experiment conducted using this task, which reproduce existing findings of costly effort-aversion, and also demonstrate adaptive adjustment of mental effort.

There has been a small but now growing interest in studying decision making in real-world contexts where part of the problem faced by decision makers is to generate candidate options they will actually decide between. While some of this work has employed large decision spaces where options are discrete and valuation is computationally tractable (e.g., chess), very little work has focused on genuinely open-ended decision contexts that more closely mirror mundane real-world decisions. This paper leverages large language models to investigate how people generate options when facing genuinely open-ended problems. Across three experiments, we apply semantic similarity and sentiment analyses to the options that participants sequentially generate for real-world decision problems. We find that the first options generated tend to be sampled from a relatively local region of semantic space and are typically of high value. As additional options are generated, they become increasingly dissimilar and are of lower value. These patterns held both at the level of individual option generation trajectories within a given participant and at the level of individual differences across participants.

The purpose of this paper is to present a novel conceptualisation of an intuitive, primitive form of trust termed proto-trust. This concept is proposed in order to account for the many different senses, types and domains in which trust has traditionally been defined and theorised. A brief review of the literature on affective and intuitive trust is presented, informing the definition and formalisation of proto-trust. Following this, a preliminary empirical investigation of proto-trust is described, where intuitive trust assessments are compared to analytical trust decisions, under various attribution prompts. Results showed effects of attribution prompts on changes to trust assessments from intuitive to deliberative decisions. In addition, qualitative data are presented for the various reasons participants gave for their trust decisions. One of these reasons (emotional reaction) was found to affect the degree of difference between intuitive and deliberative trust assessments.

How do humans generalise to make better decisions? Previous work has investigated this question using reward-guided decision-making tasks with low-dimensional and artificial stimuli. In this paper, we extend this work by presenting participants with a naturalistic decision-making task, in which options were images of real-world objects and the underlying reward function was based on one of their latent dimensions. Even though participants received no explicit instruction about object features, they quickly learned to do the task and generalised to unseen objects. To understand how they accomplished this, we tested a range of computational models and found that human behaviour is overall best explained by a linear model but that participants' strategies changed during the experiment. Lastly, we showed that combining pixel-based representations extracted from convolutional neural networks with the original latent dimensions further improved our models. Taken together, our study offers new insights into human decision making under naturalistic settings.

Recent work in judgment and decision making has focused on which actions people consider when solving open-ended problems and found that the actions that come to mind tend to have particular features, such as having a high historical value. Here, we pursue the idea that the process of generating actions for decision-making tasks may actually reflect more general mechanisms for generating kinds of things. We provide evidence that what comes to mind may simply be a reflection of participants sampling from the most relevant part of the representational space they use to encode the type of thing they are generating. In this paper, we (1) introduce an approach for empirically describing a category in terms of the features that people use to represent category members, and for locating category members within that feature space, (2) show that certain locations in a category's feature space predict an item's likelihood of coming to mind, (3) introduce an approach for understanding the relevance of various features to people's representations of category members, and (4) show that features which are most involved in people's representations of category members are also predictors of what comes to mind within a category. We close by proposing that features that are most relevant to our representations of category members and predict coming to mind are those for which it has been historically useful to have information about during past experiences with the category in question.

In many real-life situations, we make decisions between a defined set of options, which can be either discrete (as when deciding between going on driving and stopping the car) or continuous (as when stirring the wheel, the possible range of angles goes from $-30$ to $30$ degrees). However, most computational models for decision-making focus on decisions between a discrete set of options. While there are a few sequential sampling models that can explain behavioral patterns (i.e., choices and response times) of decisions in a continuous option space (i.e., the CDM and the SCDM), these models have a few limitations. For example, these models assume no leakage in the evidence accumulation process and no spatial inhibition (i.e., inhibition among different areas of the option space depending on their distance to each other). In this paper, we propose a novel sequential sampling model based on an existing computational model (i.e., the leaky competing accumulator model) for decisions in a continuous option space. Our proposed model includes leakage and spatial inhibition and is thus more biologically plausible.

Many models in Cognitive Science require data to calibrate parameters. Some modelers calibrate their models’ parameters for each individual in a data set, and others work at the aggregate level. Generally, the accuracy of a model is judged by the degree to which human data are replicated, and the model parameters are interpreted accordingly. It is not too surprising that models that are developed for a particular task and fit to each individual’s data in such a task replicate the human data well. The question is, however, whether those models can make predictions in the absence of human data. In this paper, we present a theory-driven model of a well-known sequential decision task (the Balloon Analog Risk Task, BART) which is able to make predictions in the absence of human data. The cognitive model is grounded on the processes and mechanisms of Instance-Based Learning (IBL) Theory of experiential choice. We demonstrate the simulation predictions from an IBL model and those of a well-known model of the BART, which depends on the fits to human data. We further show that when making predictions without data, the IBL model provides predictions that are both theoretically founded and accurate, while the Two-Parameter model performs much worse than when fit to data. We conclude with a discussion of the benefits of making theory-based predictions in the absence of human data for our community.

Subway systems in large cities witness high volumes of commuter traffic, with crowded coaches and limited seats. In such scenarios, commuters often carefully position themselves in strategic locations with the aim of maximizing their chances of getting a place to sit. While user behavior in subways around the world have been the focus of multiple studies in the past, these everyday acts of ‘optimal decision making’ is of particular interest to the cognitive scientist. This paper inquires into commuter positioning choices in simulated subway coaches, within the framework of Bayesian probabilistic modelling. Data on preferred standing positions were collected across 20 subjects for 30 co-passenger configurations, through an interactive computer game. A generative model based on a Bayesian network involving three key spatial parameters was constructed, and used for inferring preferred positions conditioned on the specific configurations. The model was able to accurately simulate the quick and intuitive decisions made by the players under constraints of time, and also effectively capture noise in responses across subjects

Burns & Krygier (2015) demonstrated that people could exhibit a strong bias towards the smaller first digits, in a way similar to that described by Benford’s law. This paper sought to expand the scope of this phenomenon and to test a possible explanation, the Recognition Hypothesis that a Benford bias is due to life-long environmental exposure to this statistical relationship. Participants completed three numerical tasks: A Generation Task requiring answering trivia questions; a Selection Task requiring selecting between two numerical responses; and an Estimation task requiring estimating the number of jelly beans in a jar. The results found no evidence of any first digit effect in the Recognition Task, some evidence of Benford bias in the Generation Task and strong evidence in the Estimation Task. Future research should focus on alternatives to the Recognition Hypothesis and investigate the parameters of Benford bias in generation tasks

The paper aims to examine participants’ open-text ‘think aloud’ explanations of their reasoning while making a judgement about an ambiguous scenario. It aims to consider this data in light of frameworks such as causal modelling, intuitive theories, coherence and the story model. Consistent with these frameworks, we find that participants bring in a large amount of world knowledge to connect ambiguous evidence to unobserved, inferred variables and, via these, to the target judgement. We attempt to represent these chains of inferences using causal diagrams and find that participants interpretations of the scenario can be lumped into one of two distinct causal models, each presenting an internally coherent ‘image’ of the ambiguous scenario. Furthermore, participants’ judgement predicts which of those two models they adhere to. We discuss the limitations and merits of this methodological approach for investigating these types of frameworks.

Probability matching—where subjects given probabilistic in-put respond in a way that is proportional to those inputprobabilities—has long been thought to be characteristic ofprimate performance in probability learning tasks in a vari-ety of contexts, from decision making to the learning of lin-guistic variation in humans. However, such behaviour is puz-zling because it is not optimal in a decision theoretic sense;the optimal strategy is to always select the alternative with thehighest positive-outcome probability, known as maximising(in decision making) or regularising (in linguistic tasks). Whilethe tendency to probability match seems to depend somewhaton the participants and the task (i.e., infants are less likelyto probability match than adults, monkeys probability matchless than humans, and probability matching is less likely inlinguistic tasks), existing studies suffer from a range of defi-ciencies which make it difficult to robustly assess these dif-ferences. In this paper we present three experiments whichsystematically test the development of probability matchingbehaviour over time in simple decision making tasks, acrossspecies (humans and Guinea baboons), task complexity, andtask domain (linguistic vs non-linguistic). In Experiments 1and 2 we show that adult humans and Guinea baboons exhibitsimilar behaviour in a non-linguistic decision-making task and,contrary to the prevailing view, a tendency to maximise (ba-boons) or significantly over-match (humans) rather than prob-ability match, which strengthens over time and more so withgreater task complexity; our non-human sample size (N = 20baboons) is unprecedented in the probability-matching litera-ture. Experiment 3 provides evidence against domain-specificprobability learning mechanisms, showing that human subjectsover-match high positive-outcome probabilities to a similar de-gree across linguistic and non-linguistic tasks. Our results sug-gest that previous studies may simply have insufficient trials toshow maximising, or be too short to show maximising strate-gies which unfold over time. We thus provide evidence ofshared probability learning mechanisms not only across lin-guistic and non-linguistic tasks but also across primate species.

Smith (2015) describes an explosion of interest in Benford’slaw, that for data from many domains the first digits have a logdistribution. Few studies have similarly asked whether thenumbers people generate fit to Benford’s law, but recent datashow a reasonable fit. This paper argues that testing for fit toBenford’s law is the wrong question for behavioural data,instead we should think in terms of a “Benford bias” in whichthe first-digit distribution is distorted towards Benford’s law.We propose calculating the effect size of this bias by testing alinear contrast weighted by Benford’s law. Analyses of existingdata sets yielded effect sizes of 0.43-0.52. Applying thisapproach to a new task extended the scope of Benford bias topredicting outputs of a linear system and found an effect sizeof .40. Benford bias may be a ubiquitous influence onjudgments and decisions based on numbers.

People’s judgments and decisions often deviate from classicalnotions of rationality, incurring costs to both themselves and tosociety. Previous research has proposed that the cost of thesebiases can be reduced by redesigning decision problems basedon theories of human decision making. These modifications—or nudges—can have dramatic results and have been success-fully applied to variety of domains. However, the formal un-derpinning of nudge theory is limited, and it is not always clearwhat the effect of a nudge will be before it is implemented. Asa result, designing nudges can be time consuming and error-prone. In this paper, we propose an automatic method for de-riving optimal nudges. The method is based on a resource-rational model, which assumes that people make decisions ina way that achieves a near-optimal tradeoff between the costand benefits of deliberation. We then frame nudges as modi-fications to the costs of different cognitive operations, encour-aging the cognitively frugal decision maker to consider someproblem features over others. As a proof of concept, we applythe method to the Mouselab process-tracing paradigm, findingthat optimal nudges lead participants to make better decisionswith less cognitive effort.

Testimony is a fundamental feature of human life: typically, wereceive testimonial evidence from others multiple times each day.Often, we have more than one source attesting to a particularclaim. This paper examines the way people integrate testimonialevidence from multiple sources. We find evidence that participantsdeviate substantially from the normative expectation. Instead,results seem indicative of the operation of simple, non-compensatory heuristics, at least some of the time.

In this paper, we introduce CALM, a process model that is de-signed to abstract solutions in simple and complex categorylearning tasks. The model includes strong assumptions aboutthe interaction of processes driving learning behavior, typicallyaddressed in terms of feature attention, stimulus generaliza-tion, rule abstraction and knowledge partitioning. We presentsimulations of CALM, showing that the model can accountboth for systematic variations in Type II category difficulty,and for individual differences in extrapolation of an XOR cat-egory structure.

Traditionally, models of the decision-making process have fo-cused on the case where a decision-maker must choose be-tween two alternatives. The most successful of these, sequen-tial sampling models, have been extended from the binary caseto account for choices and response times between multiplealternatives. In this paper, I present a geometric representa-tion of diffusion and accumulator models of multiple-choicedecisions, and show how these can be analyzed as Markovprocesses on lattices. I then introduce psychological relation-ships between choice alternatives and show how this impactsthe sequential sampling process. I conclude with two examplesshowing how one can predict distributions of responses on acontinuum as well as response times by incorporating psycho-logical representations into a multi-dimensional random walkdiffusion process.

In this paper, we develop a memory model that predictsretrieval characteristics of real-world facts. First, we showhow ACT-R’s memory model can be used to predict people’sknowledge about real-world objects. The model assumes theprobability of retrieving a chunk of information about anobject and the time to retrieve this information depend on thepattern of prior environmental exposure to the object. Second,we use frequencies of information appearing on the Internetas a proxy for what information people would encounter intheir natural environment, outside the laboratory. In twoExperiments, we use this model to account for subjects’associative knowledge about real-world objects as well as theassociated retrieval latencies. Third, in a computer simulation,we explore how such model predictions can be used tounderstand the workings and performance of decisionstrategies that operate on the contents of declarative memory.

This paper examines the development of therepresentativeness heuristic in early childhood. Using a novelparadigm, we investigated 3- to 6-year-old children’s abilityto use base-rate and individuating information in theirpredictive inferences. In Experiment 1, we presented childrenwith base-rate and individuating information separately to testtheir ability to use each independently. In Experiment 2, wepresented children with base-rate and individuatinginformation together. Two critical trial types were used, onein which the base-rate information and individuatinginformation pointed to the same response and one in whichthe base-rate and individuating information pointed toconflicting responses. Results suggest that children progressto adult-like heuristic-based responding at 6 years of age.

With the ever increasing amount of information available, theability to prioritize the most relevant items for full processingis increasingly necessary to maintain expertise in a domain.As a result, accurate triage decisions--initial decisions aboutthe relevance of a given article, book, or talk in order todetermine whether to pursue that information further--arevery important. In the present paper, we present a model oftriage decision making that includes both an informationsearch component to determine reading strategy and adecision making component to make the final decision. Weapply the model to human relevance ratings as well as binarydecisions of relevance for a set of emails.

A popular conception about language development is that comprehensionprecedes production. Although this is certainlytrue during the earliest stages of phonological development,once a child possesses the basic articulatory skills required forimitation, it need not necessarily be the case. A child couldproduce a word without possessing the fully formed lexicalrepresentation through imitation. In some cases, such as inresponse to questions containing fixed choices, this behaviorcould be mistaken for a deeper understanding of the words’semantic content. In this paper, we present evidence that 2-to 3-year-old children exhibit a robust recency bias when verballyresponding to two-alternative choice questions (i.e., theyselect the second, most recently mentioned option), possiblydue to the availability of the second word in phonologicalmemory. We find further evidence of this effect outside ofa laboratory setting in naturalistic conversational contexts inCHILDES (MacWhinney, 2000), a large corpus of transcribedchild-adult interactions.