Human heuristics for AI-generated language are flawed.

Human communication is increasingly intermixed with language generated by AI. Across chat, email, and social media, AI systems suggest words, complete sentences, or produce entire conversations. AI-generated language is often not identified as such but presented as language written by humans, raising concerns about novel forms of deception and manipulation. Here, we study how humans discern whether verbal self-presentations, one of the most personal and consequential forms of language, were generated by AI. In six experiments, participants (N = 4,600) were unable to detect self-presentations generated by state-of-the-art AI language models in professional, hospitality, and dating contexts. A computational analysis of language features shows that human judgments of AI-generated language are hindered by intuitive but flawed heuristics such as associating first-person pronouns, use of contractions, or family topics with human-written language. We experimentally demonstrate that these heuristics make human judgment of AI-generated language predictable and manipulable, allowing AI systems to produce text perceived as "more human than human." We discuss solutions, such as AI accents, to reduce the deceptive potential of language generated by AI, limiting the subversion of human intuition.

Leveraging artificial intelligence to improve people's planning strategies.

SignificanceMany bad decisions and their devastating consequences could be avoided if people used optimal decision strategies. Here, we introduce a principled computational approach to improving human decision making. The basic idea is to give people feedback on how they reach their decisions. We develop a method that leverages artificial intelligence to generate this feedback in such a way that people quickly discover the best possible decision strategies. Our empirical findings suggest that a principled computational approach leads to improvements in decision-making competence that transfer to more difficult decisions in more complex environments. In the long run, this line of work might lead to apps that teach people clever strategies for decision making, reasoning, goal setting, planning, and goal achievement.

Adaptive RAxML-NG: Accelerating Phylogenetic Inference under Maximum Likelihood using Dataset Difficulty.

Phylogenetic inferences under the maximum likelihood criterion deploy heuristic tree search strategies to explore the vast search space. Depending on the input dataset, searches from different starting trees might all converge to a single tree topology. Often, though, distinct searches infer multiple topologies with large log-likelihood score differences or yield topologically highly distinct, yet almost equally likely, trees. Recently, Haag et al. introduced an approach to quantify, and implemented machine learning methods to predict, the dataset difficulty with respect to phylogenetic inference. Easy multiple sequence alignments (MSAs) exhibit a single likelihood peak on their likelihood surface, associated with a single tree topology to which most, if not all, independent searches rapidly converge. As difficulty increases, multiple locally optimal likelihood peaks emerge, yet from highly distinct topologies. To make use of this information, we introduce and implement an adaptive tree search heuristic in RAxML-NG, which modifies the thoroughness of the tree search strategy as a function of the predicted difficulty. Our adaptive strategy is based upon three observations. First, on easy datasets, searches converge rapidly and can hence be terminated at an earlier stage. Second, overanalyzing difficult datasets is hopeless, and thus it suffices to quickly infer only one of the numerous almost equally likely topologies to reduce overall execution time. Third, more extensive searches are justified and required on datasets with intermediate difficulty. While the likelihood surface exhibits multiple locally optimal peaks in this case, a small proportion of them is significantly better. Our experimental results for the adaptive heuristic on 9,515 empirical and 5,000 simulated datasets with varying difficulty exhibit substantial speedups, especially on easy and difficult datasets (53% of total MSAs), where we observe average speedups of more than 10×. Further, approximately 94% of the inferred trees using the adaptive strategy are statistically indistinguishable from the trees inferred under the standard strategy (RAxML-NG).

Learning From Aggregated Opinion.

The capacity to leverage information from others' opinions is a hallmark of human cognition. Consequently, past research has investigated how we learn from others' testimony. Yet a distinct form of social information-aggregated opinion-increasingly guides our judgments and decisions. We investigated how people learn from such information by conducting three experiments with participants recruited online within the United States (N = 886) comparing the predictions of three computational models: a Bayesian solution to this problem that can be implemented by a simple strategy for combining proportions with prior beliefs, and two alternatives from epistemology and economics. Across all studies, we found the strongest concordance between participants' judgments and the predictions of the Bayesian model, though some participants' judgments were better captured by alternative strategies. These findings lay the groundwork for future research and show that people draw systematic inferences from aggregated opinion, often in line with a Bayesian solution.

The impact of cognitive resource constraints on goal prioritization.

Many decisions we face daily entail deliberation about how to coordinate resources shared between multiple, competing goals. When time permits, people appear to approach these goal prioritization problems by analytically considering all goal-relevant information to arrive at a prioritization decision. However, it is not yet clear if this normative strategy extends to situations characterized by resource constraints such as when deliberation time is scarce or cognitive load is high. We evaluated the questions of how limited deliberation time and cognitive load affect goal prioritization decisions across a series of experiments using a gamified experimental task, which required participants to make a series of interdependent goal prioritization decisions. We fit several candidate models to experimental data to identify decision strategy adaptations at the individual subject-level. Results indicated that participants tended to opt for a simple heuristic strategy when cognitive resources were constrained rather than making a general tradeoff between speed and accuracy (e.g., the type of tradeoff that would be predicted by evidence accumulation models). The most common heuristic strategy involved disproportionately weighing information about goal deadlines compared to other goal-relevant information such as the goal's difficulty and the goal's subjective value.

Inside the Black Box of Deliberate Practice: How do Coaches Coach to Improve Trauma Triage.

INTRODUCTION: Deliberate practice, goal-oriented training with feedback from a coach, is a common tool for improving physicians' performance. However, little is known about how coaches foster performance improvement. METHODS: A content analysis of video-recorded training sessions was performed to analyze the coaches' behaviors during a pilot randomized trial of deliberate practice in trauma triage. The intervention consisted of three video-conference sessions during which trial physicians, under the supervision of a coach, played a customized video game designed to review trauma triage principles. A multidisciplinary team specified tasks (e.g., create collaborative learning environment) that coaches should complete, and suggested 19 coaching strategies (e.g., encourage culture of error) to allow execution of these tasks. Two independent raters translated those strategies into a coding framework and applied it deductively to the recorded sessions. The frequencies of the coaching strategies were summarized, and tested for variation across coaches and time. RESULTS: Thirty physicians received the intervention across two 1-mo blocks. Most (28 [93%]) completed three sessions, each covering two (interquartile range 1-2) triage principles. Coaches used coaching strategies 18 (interquartile range 14.5-22) times per triage principle, using some often (2-3 times/principle) and others infrequently (<1 time/principle). The three coaches used similar numbers (20 versus 16 versus 18.5, P = 0.07) and types of strategies. However, use increased over time (16.8 [Block 1] versus 20 [Block 2] P = 0.018). CONCLUSIONS: Coaches used 19 coaching strategies to deliver this deliberate practice intervention, with behavior that evolved over time. Future trials should isolate the most potent strategies and should assess the best method of standardizing coaching.

Surgeon perceptions of personality as an influencing factor on anastomotic decision-making: A qualitative analysis.

AIM: Surgeon personality is a factor influencing rectal anastomotic decision-making. However, it is unclear how or why this may be the case, or what aspects of personality are involved. The aim of this study was to investigate the views of colorectal surgeons on how their individual personality may influence variation in anastomotic decision-making. METHOD: Purposive sampling was used to invite certified UK-based colorectal surgeons to participate, with individual interviews used for data collection. Participants were recruited until ongoing data review indicated no new codes were generated (i.e. data sufficiency). Data were analysed thematically following Braun and Clarke's six-step framework. RESULTS: Seventeen colorectal surgeons (eight female, nine male) participated. Two key themes relating to personality and decision-making were identified: (1) surgeon development and training and (2) patient-surgeon interactions, each with relevant subthemes. Surgeons described how their personality may influence patients' postoperative outcomes (e.g. decision-making, team working and communication) and potential mechanisms for how their personality may influence operative risk-taking. Following anastomotic leakage, surgeons described a disproportionate sense of guilt and responsibility. There appears to be a significant transition in responsibility from trainee to newly appointed consultant, which may be part of the 'hidden curriculum' of surgical training. CONCLUSION: Colorectal surgeons have described their perceptions of how personality traits may impact variation in decision-making and patient outcomes for the first time. Early career surgeons felt ill-prepared for the level of guilt experienced when managing complications. Surgeons appear open to personality assessment if this was through an educational lens, with the aim of improving decision-making following complications and overall performance.

DHFS-ECM: Design of a Dual Heuristic Feature Selection-based Ensemble Classification Model for the Identification of Bamboo Species from Genomic Sequences.

Background: Analyzing genomic sequences plays a crucial role in understanding biological diversity and classifying Bamboo species. Existing methods for genomic sequence analysis suffer from limitations such as complexity, low accuracy, and the need for constant reconfiguration in response to evolving genomic datasets. Aim: This study addresses these limitations by introducing a novel Dual Heuristic Feature Selection-based Ensemble Classification Model (DHFS-ECM) for the precise identification of Bamboo species from genomic sequences. Methods: The proposed DHFS-ECM method employs a Genetic Algorithm to perform dual heuristic feature selection. This process maximizes inter-class variance, leading to the selection of informative N-gram feature sets. Subsequently, intra-class variance levels are used to create optimal training and validation sets, ensuring comprehensive coverage of class-specific features. The selected features are then processed through an ensemble classification layer, combining multiple stratification models for species-specific categorization. Results: Comparative analysis with state-of-the-art methods demonstrate that DHFS-ECM achieves remarkable improvements in accuracy (9.5%), precision (5.9%), recall (8.5%), and AUC performance (4.5%). Importantly, the model maintains its performance even with an increased number of species classes due to the continuous learning facilitated by the Dual Heuristic Genetic Algorithm Model. Conclusion: DHFS-ECM offers several key advantages, including efficient feature extraction, reduced model complexity, enhanced interpretability, and increased robustness and accuracy through the ensemble classification layer. These attributes make DHFS-ECM a promising tool for real-time clinical applications and a valuable contribution to the field of genomic sequence analysis.

Inconsistencies in rater-based assessments mainly affect borderline candidates: but using simple heuristics might improve pass-fail decisions.

INTRODUCTION: Research in various areas indicates that expert judgment can be highly inconsistent. However, expert judgment is indispensable in many contexts. In medical education, experts often function as examiners in rater-based assessments. Here, disagreement between examiners can have far-reaching consequences. The literature suggests that inconsistencies in ratings depend on the level of performance a to-be-evaluated candidate shows. This possibility has not been addressed deliberately and with appropriate statistical methods. By adopting the theoretical lens of ecological rationality, we evaluate if easily implementable strategies can enhance decision making in real-world assessment contexts. METHODS: We address two objectives. First, we investigate the dependence of rater-consistency on performance levels. We recorded videos of mock-exams and had examiners (N=10) evaluate four students' performances and compare inconsistencies in performance ratings between examiner-pairs using a bootstrapping procedure. Our second objective is to provide an approach that aids decision making by implementing simple heuristics. RESULTS: We found that discrepancies were largely a function of the level of performance the candidates showed. Lower performances were rated more inconsistently than excellent performances. Furthermore, our analyses indicated that the use of simple heuristics might improve decisions in examiner pairs. DISCUSSION: Inconsistencies in performance judgments continue to be a matter of concern, and we provide empirical evidence for them to be related to candidate performance. We discuss implications for research and the advantages of adopting the perspective of ecological rationality. We point to directions both for further research and for development of assessment practices.

Reflexive control in emergency medicine.

Emergency physicians (EPs) navigate high-pressure environments, making rapid decisions amidst ambiguity. Their choices are informed by a complex interplay of experience, information, and external forces. While cognitive shortcuts (heuristics) expedite assessments, there are multiple ways they can be subtly manipulated, potentially leading to reflexive control: external actors steering EPs' decisions for their own benefit. Pharmaceutical companies, device manufacturers, and media narratives are among the numerous factors that influence the EPs' information landscape. Using tactics such as selective data dissemination, framing, and financial incentives, these actors can exploit pre-existing cognitive biases like anchoring, confirmation, and availability. This creates fertile ground for reflexive control, where EPs may believe they are acting independently while unknowingly serving the goals of external influencers. The consequences of manipulated decision making can be severe: misdiagnoses, inappropriate treatments, and increased healthcare costs. Ethical dilemmas arise when external pressures conflict with patient well-being. Recognizing these dangers empowers EPs to resist reflexive control through (1) critical thinking: examining information for potential biases and prioritizing evidence-based practices, (2) continuous education: learning about cognitive biases and mitigation strategies, and (3) institutional policies: implementing regulations to reduce external influence and to promote transparency. This vulnerability of emergency medicine decision making highlights the need for awareness, education, and robust ethical frameworks. Understanding reflexive control techniques is crucial for safeguarding patient care and promoting independent, ethical decision making in emergency medicine.

Easy-fix attentional focus manipulation boosts the intuitive and deliberate use of base-rate information.

In addressing human reasoning biases, "easy-fix" attentional focus interventions have shown that we can prompt reasoners to align responses with logico-mathematical principles. The current study aimed to test the impact of such interventions on both intuitive and deliberate responses on base-rate items. Using a two-response paradigm, participants provided initial intuitive responses under time constraints and cognitive load, followed by deliberate responses. During the intervention, we used attentional focus manipulations with base-rate items that aimed to redirect participants' attention toward the "logical" base-rate cue (i.e., the logical intervention) or toward the "heuristic" descriptive cue (i.e., the heuristic intervention). The results indicate that the logical intervention led to improved alignment with logico-mathematical principles in both intuitive and deliberate responses, albeit with a modest effect size. Conversely, the heuristic intervention had no discernible impact on accuracy. This indicates that our attentional focus manipulation is more effective at getting reasoners to respect rather than to override base-rates.

Clinical reasoning in real-world practice: a primer for medical trainees and practitioners.

Clinical reasoning is a crucial skill and defining characteristic of the medical profession, which relates to intricate cognitive and decision-making processes that are needed to solve real-world clinical problems. However, much of our current competency-based medical education systems have focused on imparting swathes of content knowledge and skills to our medical trainees, without an adequate emphasis on strengthening the cognitive schema and psychological processes that govern actual decision-making in clinical environments. Nonetheless, flawed clinical reasoning has serious repercussions on patient care, as it is associated with diagnostic errors, inappropriate investigations, and incongruent or suboptimal management plans that can result in significant morbidity and even mortality. In this article, we discuss the psychological constructs of clinical reasoning in the form of cognitive 'thought processing' models and real-world contextual or emotional influences on clinical decision-making. In addition, we propose practical strategies, including pedagogical development of a personal cognitive schema, mitigating strategies to combat cognitive bias and flawed reasoning, and emotional regulation and self-care techniques, which can be adopted in medical training to optimize physicians' clinical reasoning in real-world practice that effectively translates learnt knowledge and skill sets into good decisions and outcomes.

Comparison of the Working Alliance in Blended Cognitive Behavioral Therapy and Treatment as Usual for Depression in Europe: Secondary Data Analysis of the E-COMPARED Randomized Controlled Trial.

BACKGROUND: Increasing interest has centered on the psychotherapeutic working alliance as a means of understanding clinical change in digital mental health interventions in recent years. However, little is understood about how and to what extent a digital mental health program can have an impact on the working alliance and clinical outcomes in a blended (therapist plus digital program) cognitive behavioral therapy (bCBT) intervention for depression. OBJECTIVE: This study aimed to test the difference in working alliance scores between bCBT and treatment as usual (TAU), examine the association between working alliance and depression severity scores in both arms, and test for an interaction between system usability and working alliance with regard to the association between working alliance and depression scores in bCBT at 3-month assessments. METHODS: We conducted a secondary data analysis of the E-COMPARED (European Comparative Effectiveness Research on Blended Depression Treatment versus Treatment-as-usual) trial, which compared bCBT with TAU across 9 European countries. Data were collected in primary care and specialized services between April 2015 and December 2017. Eligible participants aged 18 years or older and diagnosed with major depressive disorder were randomized to either bCBT (n=476) or TAU (n=467). bCBT consisted of 6-20 sessions of bCBT (involving face-to-face sessions with a therapist and an internet-based program). TAU consisted of usual care for depression. The main outcomes were scores of the working alliance (Working Alliance Inventory-Short Revised-Client [WAI-SR-C]) and depressive symptoms (Patient Health Questionnaire-9 [PHQ-9]) at 3 months after randomization. Other variables included system usability scores (System Usability Scale-Client [SUS-C]) at 3 months and baseline demographic information. Data from baseline and 3-month assessments were analyzed using linear regression models that adjusted for a set of baseline variables. RESULTS: Of the 945 included participants, 644 (68.2%) were female, and the mean age was 38.96 years (IQR 38). bCBT was associated with higher composite WAI-SR-C scores compared to TAU (B=5.67, 95% CI 4.48-6.86). There was an inverse association between WAI-SR-C and PHQ-9 in bCBT (B=-0.12, 95% CI -0.17 to -0.06) and TAU (B=-0.06, 95% CI -0.11 to -0.02), in which as WAI-SR-C scores increased, PHQ-9 scores decreased. Finally, there was a significant interaction between SUS-C and WAI-SR-C with regard to an inverse association between higher WAI-SR-C scores and lower PHQ-9 scores in bCBT (b=-0.030, 95% CI -0.05 to -0.01; P=.005). CONCLUSIONS: To our knowledge, this is the first study to show that bCBT may enhance the client working alliance when compared to evidence-based routine care for depression that services reported offering. The working alliance in bCBT was also associated with clinical improvements that appear to be enhanced by good program usability. Our findings add further weight to the view that the addition of internet-delivered CBT to face-to-face CBT may positively augment experiences of the working alliance. TRIAL REGISTRATION: ClinicalTrials.gov NCT02542891, https://clinicaltrials.gov/study/NCT02542891; German Clinical Trials Register DRKS00006866, https://drks.de/search/en/trial/DRKS00006866; Netherlands Trials Register NTR4962, https://www.onderzoekmetmensen.nl/en/trial/25452; ClinicalTrials.Gov NCT02389660, https://clinicaltrials.gov/study/NCT02389660; ClinicalTrials.gov NCT02361684, https://clinicaltrials.gov/study/NCT02361684; ClinicalTrials.gov NCT02449447, https://clinicaltrials.gov/study/NCT02449447; ClinicalTrials.gov NCT02410616, https://clinicaltrials.gov/study/NCT02410616; ISRCTN Registry ISRCTN12388725, https://www.isrctn.com/ISRCTN12388725?q=ISRCTN12388725&filters=&sort=&offset=1&totalResults=1&page=1&pageSize=10; ClinicalTrials.gov NCT02796573, https://classic.clinicaltrials.gov/ct2/show/NCT02796573. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-016-1511-1.

Usability Evaluation Methods Used in Electronic Discharge Summaries: Literature Review.

BACKGROUND: With the widespread adoption of digital health records, including electronic discharge summaries (eDS), it is important to assess their usability in order to understand whether they meet the needs of the end users. While there are established approaches for evaluating the usability of electronic health records, there is a lack of knowledge regarding suitable evaluation methods specifically for eDS. OBJECTIVE: This literature review aims to identify the usability evaluation approaches used in eDS. METHODS: We conducted a comprehensive search of PubMed, CINAHL, Web of Science, ACM Digital Library, MEDLINE, and ProQuest databases from their inception until July 2023. The study information was extracted and reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). We included studies that assessed the usability of eDS, and the systems used to display eDS. RESULTS: A total of 12 records, including 11 studies and 1 thesis, met the inclusion criteria. The included studies used qualitative, quantitative, or mixed methods approaches and reported the use of various usability evaluation methods. Heuristic evaluation was the most used method to assess the usability of eDS systems (n=7), followed by the think-aloud approach (n=5) and laboratory testing (n=3). These methods were used either individually or in combination with usability questionnaires (n=3) and qualitative semistructured interviews (n=4) for evaluating eDS usability issues. The evaluation processes incorporated usability metrics such as user performance, satisfaction, efficiency, and impact rating. CONCLUSIONS: There are a limited number of studies focusing on usability evaluations of eDS. The identified studies used expert-based and user-centered approaches, which can be used either individually or in combination to identify usability issues. However, further research is needed to determine the most appropriate evaluation method which can assess the fitness for purpose of discharge summaries.

An Evaluation Model for Node Influence Based on Heuristic Spatiotemporal Features.

The accurate assessment of node influence is of vital significance for enhancing system stability. Given the structural redundancy problem triggered by the network topology deviation when an empirical network is copied, as well as the dynamic characteristics of the empirical network itself, it is difficult for traditional static assessment methods to effectively capture the dynamic evolution of node influence. Therefore, we propose a heuristic-based spatiotemporal feature node influence assessment model (HEIST). First, the zero-model method is applied to optimize the network-copying process and reduce the noise interference caused by network structure redundancy. Second, the copied network is divided into subnets, and feature modeling is performed to enhance the node influence differentiation. Third, node influence is quantified based on the spatiotemporal depth-perception module, which has a built-in local and global two-layer structure. At the local level, a graph convolutional neural network (GCN) is used to improve the spatial perception of node influence; it fuses the feature changes of the nodes in the subnetwork variation, combining this method with a long- and short-term memory network (LSTM) to enhance its ability to capture the depth evolution of node influence and improve the robustness of the assessment. Finally, a heuristic assessment algorithm is used to jointly optimize the influence strength of the nodes at different stages and quantify the node influence via a nonlinear optimization function. The experiments show that the Kendall coefficients exceed 90% in multiple datasets, proving that the model has good generalization performance in empirical networks.

Constraint-based reasoning in cell biology: on the explanatory role of context.

Cell biologists, including those seeking molecular mechanistic explanations of cellular phenomena, frequently rely on experimental strategies focused on identifying the cellular context relevant to their investigations. We suggest that such practices can be understood as a guided decomposition strategy, where molecular explanations of phenomena are defined in relation to natural contextual (cell) boundaries. This "top-down" strategy contrasts with "bottom-up" reductionist approaches where well-defined molecular structures and activities are orphaned by their displacement from actual biological functions. We focus on the central role of microscopic imaging in cell biology to uncover possible constraints on the system. We show how identified constraints are used heuristically to limit possible mechanistic explanations to those that are biologically meaningful. Historical examples of this process described here include discovery of the mechanism of oxidative phosphorylation in mitochondria, molecular explanation of the first steps in protein secretion, and identification of molecular motors. We suggest that these instances are examples of a form of downward causation or, more specifically, constraining relations, where higher-level structures and variables delimit and enable lower-level system states. The guided decomposition strategy in our historical cases illustrates the irreducibility of experimentally identified constraints in explaining biological activities of cells. Rather than viewing decomposition and recomposition as separate epistemic activities, we contend that they need to be iteratively integrated to account for the ontological complexity of multi-level systems.

Heuristics and semantic spaces for the analysis of students' work in mathematical problem solving.

In this research, our objective is to characterize the problem-solving procedures of primary and lower secondary students when they solve problems in real class conditions. To do so, we rely first on the concept of heuristics. As this term is very polysemic, we exploit the definition proposed by Rott (2014) to develop a coding manual and thus analyze students' procedures. Then, we interpret the results of these analyses in a qualitative way by mobilizing the concept of semantic space (Poitrenaud, 1998). This detailed analysis of students' procedures is made possible by collecting audiovisual data as close as possible to the students' work using an action camera mounted on the students' heads. We thus succeed in highlighting three different investigation profiles that we have named explorer, butterfly, and prospector. Our first results tend to show a correlation with these profiles and the success in problem-solving, yet this would need more investigation.

Hierarchical inference as a source of human biases.

The finding that human decision-making is systematically biased continues to have an immense impact on both research and policymaking. Prevailing views ascribe biases to limited computational resources, which require humans to resort to less costly resource-rational heuristics. Here, we propose that many biases in fact arise due to a computationally costly way of coping with uncertainty-namely, hierarchical inference-which by nature incorporates information that can seem irrelevant. We show how, in uncertain situations, Bayesian inference may avail of the environment's hierarchical structure to reduce uncertainty at the cost of introducing bias. We illustrate how this account can explain a range of familiar biases, focusing in detail on the halo effect and on the neglect of base rates. In each case, we show how a hierarchical-inference account takes the characterization of a bias beyond phenomenological description by revealing the computations and assumptions it might reflect. Furthermore, we highlight new predictions entailed by our account concerning factors that could mitigate or exacerbate bias, some of which have already garnered empirical support. We conclude that a hierarchical inference account may inform scientists and policy makers with a richer understanding of the adaptive and maladaptive aspects of human decision-making.

Quantum Cognition.

Uncertainty is an intrinsic part of life; most events, affairs, and questions are uncertain. A key problem in behavioral sciences is how the mind copes with uncertain information. Quantum probability theory offers a set of principles for inference, which align well with intuition about psychological processes in certain cases: cases when it appears that inference is contextual, the mental state changes as a result of previous judgments, or there is interference between different possibilities. We motivate the use of quantum theory in cognition and its key characteristics. For each of these characteristics, we review relevant quantum cognitive models and empirical support. The scope of quantum cognitive models encompasses fallacies in decision-making (such as the conjunction fallacy or the disjunction effect), question order effects, conceptual combination, evidence accumulation, perception, over-/underdistribution effects in memory, and more. Quantum models often formalize psychological ideas previously expressed in heuristic terms, allow unified explanations of previously disparate findings, and have led to several surprising, novel predictions. We also cast a critical eye on quantum models and consider some of their shortcomings and issues regarding their further development.

How can we apply decision-making theories to wild animal behavior? Predictions arising from dual process theory and Bayesian decision theory.

Our understanding of decision-making processes and cognitive biases is ever increasing, thanks to an accumulation of testable models and a large body of research over the last several decades. The vast majority of this work has been done in humans and laboratory animals because these study subjects and situations allow for tightly controlled experiments. However, it raises questions about how this knowledge can be applied to wild animals in their complex environments. Here, we review two prominent decision-making theories, dual process theory and Bayesian decision theory, to assess the similarities in these approaches and consider how they may apply to wild animals living in heterogenous environments within complicated social groupings. In particular, we wanted to assess when wild animals are likely to respond to a situation with a quick heuristic decision and when they are likely to spend more time and energy on the decision-making process. Based on the literature and evidence from our multi-destination routing experiments on primates, we find that individuals are likely to make quick, heuristic decisions when they encounter routine situations, or signals/cues that accurately predict a certain outcome, or easy problems that experience or evolutionary history has prepared them for. Conversely, effortful decision-making is likely in novel or surprising situations, when signals and cues have unpredictable or uncertain relationships to an outcome, and when problems are computationally complex. Though if problems are overly complex, satisficing via heuristics is likely, to avoid costly mental effort. We present hypotheses for how animals with different socio-ecologies may have to distribute their cognitive effort. Finally, we examine the conservation implications and potential cognitive overload for animals experiencing increasingly novel situations caused by current human-induced rapid environmental change.