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Background: To assess the associations of replacing sedentary behavior with different types of physical activity with mortality among the US adults of varying diabetes statuses. Methods: This prospective cohort study included 21,637 participants (mean age, 48.5 y) from the National Health and Nutrition Examination Survey 2007–2018. Physical activity including leisure-time moderate-vigorous-intensity activity (MVPA), walking/bicycling, worktime MVPA, and sedentary behavior. We conducted an isotemporal substitution analysis using Cox regression to estimate the associations between replacements and mortality risks. Results: We found significant protective associations between replacing 30 minutes per day sedentary behavior with 3 types of physical activity and all-cause, cardiovascular disease (CVD) mortality risk (except worktime MVPA for CVD mortality) among total participants, with hazard ratio (HR; 95% confidence interval [CI]) ranging from 0.86 (0.77–0.95) to 0.96 (0.94–0.98). Among participants with diagnosed diabetes, replacing sedentary behavior with leisure-time MVPA was associated with a lower all-cause mortality risk (HR 0.81, 95% CI, 0.70–0.94), which was also observed in other subgroups, with HRs (95% CI) ranging from 0.87 (0.80–0.94) to 0.89 (0.81–0.99). Among those with prediabetes/undiagnosed diabetes, replacing sedentary behavior with walking/bicycling was associated with lower CVD mortality risk, and replacement to work-time MVPA was associated with lower all-cause and CVD mortality risk, with HRs (95% CI) ranging from 0.72 (0.63–0.83) to 0.96 (0.92–0.99). Conclusions: Replacing sedentary behaviors with 30 minutes per day leisure-time MVPA was associated with lower all-cause mortality, regardless of diabetes statuses. Among people with prediabetes/undiagnosed diabetes, walking/bicycling was additionally associated with lower CVD mortality, and worktime MVPA was associated with lower all-cause and CVD mortality. [ABSTRACT FROM AUTHOR]

To conceptualise biological and mental processes, often a dynamical systems perspective is suggested. In addition to dynamics, the structure of the contextual makeup or world configuration (of an organism or brain) plays a crucial role too, as well as adaptivity of the processes. This paper provides a conceptual perspective where the structure, dynamics, and adaptivity of these processes are distinguished and related to each other via adaptive dynamical systems. Moreover, it is shown how networks can be used to represent this conceptual perspective. Here an adaptive dynamical system of any order of adaptivity can be covered where any level can exert control over the level below. The approach is illustrated by case studies for higher-order adaptive evolutionary processes. One of these case studies shows a fifth-order adaptive dynamical system that models how due to bad environmental influences at a young age, epigenetic effects can lead to a lifelong mental disorder.

Cognitive science research has far-reaching implications, but many graduate students are trained solely for tenure-track faculty positions. Academic training develops a wide range of skills in behavioral research, literature reviewing, data analysis, scientific publishing, grant writing, teaching, and student mentorship. These skills have direct application in many other careers, but training within academia typically neglects to address how these skills translate to other work environments and career paths. As growth in the number of doctoral trainees continues to outpace permanent academic positions (Kolata, 2016; Larson et al., 2013; Lederman, 2016), more doctoral recipients have been seeking employment beyond faculty positions and academia (National Science Board, 2018). Those who are interested in exploring alternative career paths may not know where to turn for guidance. Our goal in this professional development workshop is to offer such guidance and an opportunity to network with scholars in similar situations.

To communicate is to move. There is no way around that. Ifwe pick up comprehensive handbooks or introductory textsin movement science (Hong and Bartlett (2008)) we seethat there is very rich knowledge and tractable mathematicalmodels about different aspects of movements. Yet, we findno chapter on communicative movements. While the fieldof speech motor control is a developed area on its own(Parrell and Lammert (2019)), there is no movement scienceof communication proper, which would include whole-body-,hand-gestural-, signed-, and inter-bodily actions.

Goals are a central pillar of everyday mental activity. From finding your way home to solving a puzzle and ordering food delivery, much of human action and cognition is goal-directed. Perhaps unsurprisingly, theories of goals are a central focus in the psychology of motivation (Elliott & Dweck, 1988), in social and personality psychology (Fishbach & Ferguson, 2007), as well as research aimed at understanding factors contributing to task achievement in educational and industrial settings (Ames & Ames, 1984; Locke & Latham, 2002). In this symposium, we highlight recent work emphasizing a goal-centric outlook on learning, exploration, and communication.

Human learning does not stop at solving a single problem. Instead, we seek new challenges, define new goals, and come up with new ideas. What drives people to disrupt the existing conceptual landscape and create new things? Here, we examine the decision to create new things under different levels of expected returns. We formalize innovation as stochastically recombining existing ideas, where successful and more complex combinations generate higher returns. This formalization allows us to cast innovation-seeking as a Markov decision process, and derive optimal policies under different settings. Data collected through an online behavioral experiment confirm our prediction that people should invest more time and effort in seeking innovations when they know the chances of success are high and the potential new ideas would be rewarding. However, people also deviate from being optimal, both innovating more and less than they should in different settings.

Music-making relies on precise temporal control and mutual coordination among performers, particularly to maintain tempo. We evaluate the impact of human-machine interaction and rhythmic subdivisions on tempo change in musical trios. A synchronization-continuation task was performed by trios of human participants interacting with confederates or with algorithmic (i.e. machine) models. Sounded tone onsets were produced by a linear error-correction model, delay-coupled model, and Kuramoto model that replaced a human participant. Inter-onset intervals were examined from participants who performed rhythms in both in-phase and anti-phase conditions while a third group member was either a human or algorithmic model. Trios drifted toward faster tempi more when they contained a human than an algorithmic model. Tempo drift also increased for the aligned rhythms (in-phase) compared to rhythms with rhythmic subdivisions (anti-phase). Finally, the tested algorithmic models replicated the confederate's tempo drift without the use of any period correction mechanisms. This research advances our understanding of unintentional tempo drift, offering insights into ensemble dynamics and models of temporal coordination in groups. Implications for musical coordination and avenues for future research are discussed.

Video recording is a widely used tool for studying animal behavior, especially in fields such as primatology. Primatologists rely on video data to analyze and research topics such as social grooming to uncover subtle mechanisms behind complex social behavior and structures. Insights into these social behaviors may provide us with a better understanding of our closest living relatives, but also new theories and insights into our own behavior. However, analyzing this type of data using manual annotation is currently a time-consuming task. Here we present an end-to-end pipeline to track chimpanzee (Pan troglodytes) poses using DeepLabCut (DLC) which then serves as input to a support vector machine. This classifier was trained to detect role transitions within grooming interactions. We replicate a recent study showing that DLC has usability value for chimpanzee data collected in natural environments. Our combined method of tracking and classification is remarkably successful in detecting the presence of grooming, indicating the directionality and a change in turn with an accuracy above 86% on unseen videos. We can identify particular pose features used in the classification of grooming, which will contribute to the exploration of turn-taking dynamics on a scale that would otherwise be difficult to attain with traditional methods. Finally, our pipeline can in principle be applied to recognize a variety of other socially interactive behaviors that are largely recognizable by (joint) postural states.

Social media is often used as a platform where individuals engage in debate regarding topics that are important to them. Not all arguments are equally convincing, and whilst a given argument may be persuasive to some people, it is often seen as inadequate by others. We are interested in both the individual and argument level differences that make ‘everyday' arguments such as those on social media persuasive. In a replication of our Everyday Belief Bias Task (Deans-Browne & Singmann, 2024), we investigate this question using a paradigm that consists of two parts. In the first part, we measure participant's individual beliefs about eight claims each referring to a political topic (e.g., Abortion should be legal). In the second part, participants rated an argument for each of these claims that was deemed as either good, inconsistent (containing internal inconsistencies), or authority-based (being centered around appeals to authority). We replicated the belief consistency effect – participants preferred arguments that were also in line with their beliefs. We also found that authority-based arguments were rated as worse than inconsistent arguments, and that both types of arguments were rated as worse than good arguments. The implications are first that people do not evaluate arguments independently of the background beliefs held about them. Secondly, people are willing to ignore inconsistencies in arguments more than they are willing to accept the endorsement of authority figures as adequate evidence for arguments.

Toddlers can only say one or two words at a time. What do they choose to talk about? We report preliminary results (N=167; mean; 19.5 months) from a pre-registered online experiment on productive language. Toddlers saw six movies. A curtain opened on an introductory scene, the parent closed their eyes, and a new event happened. The curtain closed and the child was asked what happened. On two trials the unseen event was new to the parent (Novel event); on two trials, one of two animals ate the only food in the scene (Agent ambiguous); on two trials, the only animal ate one of the two foods (Patient ambiguous). We predicted that toddlers would selectively generate informative utterances (i.e., referring to the novel event, the agent, and the patient, respectively). Toddlers' productive language was indeed sensitive to what listeners' know; however, unlike adults, they selectively referred to information in common ground.

Content-curating algorithms provide a crucial service for social media users by surfacing relevant content, but they can also bring about harms when their objectives are misaligned with user values and welfare. Yet, potential behavioral consequences of this alignment problem remain understudied in controlled experiments. In a preregistered, two-wave, collaborative filtering experiment, we demonstrate that small changes to the metrics used for sampling and ranking posts affect the beliefs people form. Our results show observable differences in two types of outcomes within statisticized groups: belief accuracy and consensus. We find partial support for hypotheses that the recently proposed approaches of "bridging-based ranking" and "intelligence-based ranking" promote consensus and belief accuracy, respectively. We also find that while personalized, engagement-based ranking promotes posts that participants perceive favorably, it simultaneously leads those participants to form more polarized and less accurate beliefs than any of the other algorithms considered.

Autoregressive Large Language Models (LLMs) trained for next-word prediction have demonstrated remarkable proficiency at producing coherent text. But are they equally adept at forming coherent probability judgments? We use probabilistic identities and repeated judgments to assess the coherence of probability judgments made by LLMs. Our results show that the judgments produced by these models are often incoherent, displaying human-like systematic deviations from the rules of probability theory. Moreover, when prompted to judge the same event, the mean-variance relationship of probability judgments produced by LLMs shows an inverted-U-shaped like that seen in humans. We propose that these deviations from rationality can be explained by linking autoregressive LLMs to implicit Bayesian inference and drawing parallels with the Bayesian Sampler model of human probability judgments.

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.

Metaphors are often used to intuitively communicate about movement. Here, expert pianists played two melodies while keeping eight different metaphors in mind, contrasting arousal level, valence direction, and metaphor type (action-related and emotion-related metaphors). Measures of keystroke timing and velocity were analyzed to assess the relative contribution of metaphor content and melodic note sequence to motor performance, alongside ratings of semantic similarity between metaphors. Using Bayesian multilevel models, results indicate that the arousal level of the metaphor has the most influence on keystroke force, average tempo, and tempo variability. Additionally, interactions with valence are seen for the timing measures, and for both valence and type in force. No effects of the melody sequence were found. Similarity ratings of metaphor pairs indicate that mental similarities largely mirror performance similarities. These findings show the potential effects of mental imagery on motor performance and have implications for teaching complex movements in practical settings.

Simulating sampling algorithms with people has proven a useful method for efficiently probing and understanding their mental representations. We propose that the same methods can be used to study the representations of Large Language Models (LLMs). While one can always directly prompt either humans or LLMs to disclose their mental representations introspectively, we show that increased efficiency can be achieved by using LLMs as elements of a sampling algorithm. We explore the extent to which we recover human-like representations when LLMs are interrogated with Direct Sampling and Markov chain Monte Carlo (MCMC). We found a significant increase in efficiency and performance using adaptive sampling algorithms based on MCMC. We also highlight the potential of our method to yield a more general method of conducting Bayesian inference with LLMs.

How do people perform general-purpose physical reasoning across a variety of scenarios in everyday life? Across two studies with seven different physical scenarios, we asked participants to predict whether or where two objects will make contact. People achieved high accuracy and were highly consistent with each other in their predictions. We hypothesize that this robust generalization is a consequence of mental simulations of noisy physics. We designed an "intuitive physics engine'' model to capture this generalizable simulation. We find that this model generalized in human-like ways to unseen stimuli and to a different query of predictions. We evaluated several state-of-the-art deep learning and scene feature models on the same task and found that they could not explain human predictions as well. This study provides evidence that human's robust generalization in physics predictions are supported by a probabilistic simulation model, and suggests the need for structure in learned dynamics models.

Autonomous agents often need to decide between choosing actions that are familiar and have previously yielded positive results (exploitation) and seeking new information that could help uncover more effective actions (exploration). We present an “observe or bet” task that separates “pure exploration” from “pure exploitation”: 75 five-to-seven-year-old children, 60 adults and computational agents have to decide either to observe an outcome without reward, or to bet on an action without immediate feedback at varying probability levels. Their performances were measured against solutions from the partially observable Markov decision process and meta-RL models. Children and adults tended to choose observation more than both algorithm classes would suggest. Children also modulated their betting policy based on the probability structure and amount of evidence, exhibiting “hedging behavior” a strategy not evident in standard bandit tasks. The results provide a benchmark for reasoning about reward and information in humans and neural network models.

Recent research has identified substantial individual differences in how people solve value-based tasks. Here, we examine such differences in the motivational Go/NoGo task, which orthogonalizes action and valence, using open-source data from 817 participants. Using computational modeling and behavioral analysis, we identified four distinct clusters of people. Three clusters corresponded to previous models of the task, including people with different learning rates for cues that signal rewarding and punishing states and with different sensitives for rewards and punishments. The fourth cluster of people acted like naïve reinforcement learners, with their responses shaped by outcomes in a manner that was independent of the state information provided by the cues. In addition to providing evidence that state-independent learning is a common disposition, we show that not considering such learning can dramatically affect the results of computational modeling. We discuss the implications for the modeling of data from heterogeneous populations.

Investors rely on judgmental heuristics and comparative analysis for future stock price prediction based on specific components of information in hand. Information components are used as anchors for price estimation. Through an eye-tracking experiment, we aim to understand the perceived significance of various formats of information, particularly focusing on graphical and numerical components, and to explore the influence of complex time-varying patterns in stock price line plots. Results show that graphical components capture higher visual attention. Participants are not always loss-averse and prominently exhibit disposition effects for investment decisions in profitable scenarios. The 52-week high is allotted the highest fixation duration, signifying its perception as a strong reference point. Investment choices were found to be varying based on levels of prior knowledge and experience. The visual gaze analysis provides behavioural insights into complex decision-making processes.

The dominant mental timelines of native Chinese speakers (Exp1) and Mandarin learners of near-native proficiency (Exp2) was examined with the spontaneous gesture task. The results demonstrated that (1) both groups produced horizontal, vertical, sagittal, fused horizontal and vertical, and fused horizontal and sagittal gestures for all kinds of Chinese temporal words, indicating a strong preference for horizontal over vertical gestures. (2) Negligible correlations between immediate spatio-temporal metaphors and the mental timelines were observed, with an almost non-existent difference in gesture distribution across metaphorical types between the two groups. The findings indicate that (1) the horizontal mental timeline is the dominant timeline for two groups; (2) visuospatial experience exerts a greater influence on temporal cognition; (3) mental timelines formed by the long-term effects of language may operate beyond the immediate metaphors, similar to the horizontal gestures. A unified model proposing embodied experience as the mechanism for activating mental timelines is presented.

Prior literature shows that some mechanisms, e.g., commitment, could give rise to cooperation. However, participants' diverse propensities to cooperate may limit such mechanisms' effectiveness. Thus, we bring individual differences in their propensities to cooperate into the reasoning of long-term social dynamics of cooperation through an agent-based modeling approach. Our results suggest that commitment may still guarantee cooperation when individuals have different propensities to cooperate but has weaker effects, and the setups of commitment are also important. Our study highlights the importance of integrating individual preferences in analyzing collective dynamics of a population consisting of individuals of heterogeneous characteristics, thus offering implications to facilitate cooperation in rich real-world scenarios.

Humans can coordinate their behavior with others through interactions; however, not all pairs can coordinate. From the perspective of predictive processing, different social interaction patterns can be explained by the diversity of individuals' belief strength. To investigate the relationship between coordination and belief strength, we conducted an interaction experiment using a Simon electronic light-sequence game in which participants memorize the order of color sequences. The results of our experiment, involving 23 pairs of participants, revealed diversity in the degree of coordination within pairs and the strength of belief between individuals. Our analysis supports the hypothesis that belief strength explains the success or failure of coordination: Coordination fails when both individuals in a pair have weak beliefs, whereas it succeeds when one person becomes the leader and the other becomes the follower because of the different strengths of their beliefs. Our findings suggest that predictive processing theory can be applied to situations involving social interactions.

We propose two novel benchmarks for assessing models of probability judgments: the impact of Cognitive Reflection Test (CRT) on probability judgment expressions and 16 ``normality identities" expected to sum to 1 under classical probability theory. We compared three models on these benchmarks – the Probability Plus Noise Model (PPN), the Bayesian Sampler (BS), and the Quantum Sequential Sampler (QSS) – using the largest dataset to date for probability judgments. Our results reveal that higher CRT scores are associated with fewer probabilistic fallacies and identity violations, a trend most accurately captured by the QSS, although we also identified QSS limitations. Regarding the normality identities, the QSS outperformed the PPN and the BS, which had difficulty with both the average values of the normality identities and their dependence on CRT scores. Additionally, we uncovered a unique ``1 crossing" effect for normality identities N8 and N11, an effect PPN and BS cannot capture.

The steady increase in time spent on smartphone applications and particularly on social networks, raises questions about the environmental and societal sustainability of such a phenomenon. Utility and enjoyment have a key role in such practices, but other factors such as passing time may also contribute. From May to November 2023, 5,028 people took part in a web survey aiming at producing durations prospectively using mobile applications like Facebook, Instagram, TikTok, Reading. The protocol introduces variables known to have an effect on time perception. On average, the produced durations were underestimated. This result is in line with the notion that tracking information and tracking time compete for the brain's limited attentional resources and, hence, that attention plays a critical role in time estimation. Significant differences emerged between the applications tested. TikTok and Reading tasks appear the most underestimate but with opposite dynamics as the level of satisfaction and familiarity are lower for the first compared to the former. Among the variables studied to explain the difficulties in evaluating time spent, the importance of familiarity with the activity is undoubtedly something worth exploring in the context of the race between new algorithms and cognitive adaptability.

Peak shifting is a key method to enhance the resilience of water supply infrastructure. Due to limitations related to the costs of smart meters, experimentally investigating the impact of a time-of-use tariff on inducing peak shifting in water usage has been challenging. Having introduced smart meters across 1,890 households, this study investigated behavioral responses to reduced water prices during off-peak hours (i.e., by increasing the relative cost of peak times). This was achieved by implementing a pricing scheme offering a 60% discount from 23:00 to 05:59 hours and a 20% discount from 10:00 to 16:59 hours. The results revealed significant changes in water usage behavior near the boundary between off-peak and peak times, suggesting a shift in water usage behavior towards the off-peak period. Moreover, a time-series analysis demonstrated the peak shifting induced by off-peak price reductions. Furthermore, this study examines cognitive processing and its impact on altering water usage behaviors on an hourly basis. These findings suggest that reducing prices during off-peak periods can effectively induce peak shifting across a broad range of times.

Practitioners of mentalism can perform apparently impossible feats, but when performing for an audience these feats are attributed to pseudoscientific explanations such as advanced psychological skills. Research that has investigated the psychological foundations of mentalism has found a strong tendency for people to believe these explanations. In three experiments, we investigated the strength of this belief by comparing apparently impossible effects relating to mental phenomena with physical phenomena. We observed that mental magic tricks are readily explained in terms of advanced psychological skills, whereas physical tricks are not. This was true: i) even when alternative feasible explanations are explicitly presented; ii) when they are presented as mentalism effects but the effects themselves are classical card tricks; iii) regardless of the context in which the effects are observed (a research laboratory vs. a theater). We interpreted the tendency to appeal to this pseudo-explanation (and the changes in narratives employed by mentalists across the decades) in terms of the community of knowledge framework.

People apply more frequently when “apply” is the default choice (Apply Default architecture) than when “do not apply” is the default choice (Not-Apply Default architecture). However, Apply Default architecture might let them make choices inconsistent with their preferences as this architecture is counterintuitive. Those trying to apply might mistakenly choose to not apply under Apply Default architecture. In this study, we hypothesized that people's choices under No-Default architecture (i.e., a choice architecture without a default option) are less consistent with those under Apply Default architecture than those under Not-Apply Default architecture (Hypothesis 1). We also hypothesized that people who spent more time on making decisions would make choices consistent with their preferences because when people spend sufficient time to understand the construction of Apply Default architecture, they can make choices consistent with their preferences (Hypothesis 2). We recruited 997 participants and asked them to make decisions under No-Default and Default architectures (Apply Default or Not-Apply Default architecture). The results supported both Hypothesis 1 and Hypothesis 2. A method to help applicants make choices consistent with their preferences is finally discussed.

Are people who agree on something more likely to be right and competent? Evidence suggests that people tend to make this inference. However, standard wisdom of crowds approaches only provide limited normative grounds. Using simulations, we argue that when individuals make independent and unbiased estimates, under a wide range of parameters, individuals whose answers converge with each other tend to have more accurate answers and to be more competent. In 2 experiments (UK participants, total N = 399), we show that participants infer that informants who agree have more accurate answers and are more competent, even when they have no priors, and that these inferences are weakened when the informants are systematically biased. In conclusion, we speculate that inferences from convergence to accuracy and competence might help explain why people deem scientists competent, even if they have little understanding of science.

Question-asking is a crucial aspect of human interaction. Questions fuel engagement, stimulate thought processes, foster learning, and facilitate information seeking behavior. Yet, scarce empirical research on question-asking, or its relation to related cognitive capacities such as creativity and intelligence, exists. We empirically investigate how people ask questions and the connections between question-asking and creativity through the domain of interactive gaming. To do so, we developed an online game—Spot the Spy—where players are required to find a hidden spy amidst a crowded room, by asking questions that guide them in their investigation. Thus, we dive into the very essence of how creative and strategic thinking collaborate to shape the queries we formulate. We find that players' gameplay correlates with their cognitive abilities, especially with intelligence measures. As such, our game captures insights into the profound ways creative cognition shapes the questions we articulate and navigate within dynamic gaming environments.

applied contexts, focusing on online shopping with consumer feedback. The richness of crowdsourced information and the growth of e-commerce highlight the importance of understanding how different consumer feedback signals are weighted across product types. Participants allocated 100 points among eight common consumer feedback signals for products differing in emotional, commitment, and monetary values. A pilot study confirmed the product choice validity, assessing a separate group of participants' inclination to purchase target products for emotional needs and long-term use. Results reveal an increased reliance on crowdsourced information weight heightened decision stakes. While the overall signal importance ranking remains consistent across products, negative information gains significance, and average ratings diminish in importance for high-stakes decisions. The findings carry theoretical and practical implications, shedding light on the nuanced decision dynamics in applied contexts.

The scientific potential of digital game studies in psychology is limited by the observational nature of the data that they investigate. However, digital environments present us with a perfect opportunity to incorporate experimental paradigms in complex interactive and multivariate worlds where each decision made by participants can be tracked and recorded. In this study, we demonstrate an industry-academic research collaboration that offers a proof-of-the-concept on how minor modifications of the game settings could be used to test psychological research questions. We modify the settings of the Zool platform game, where players allocated to the experimental group are provided with more information when in danger of dying in the game. Results of the study show that manipulation does not influence behaviour in the game, such as achieved score or number of deaths, but it changes the overall player's response of whether they will continue playing the game after the disappointing event of losing all their lives, game over event. In line with previous studies, the additional information provided through the experimental manipulation made death in the game more informative to the players.

Interleaving, as opposed to blocking, information improves learning of categories, such as artists' painting styles. The current study examined whether presentation schedules also impact memory for specific items. 179 participants studied paintings from 12 different artists on either a blocked or interleaved schedule. In Study 1 (N = 84), participants were then asked to either identify the artists of a series of paintings (style recognition task) or determine whether they had previously seen a specific painting (item recognition task). In Study 2 (N = 93), participants completed both tasks. Results showed that the interleaved schedule led to better learning of the painting styles, but did not impact item memory. However, when participants had to recognize the style and the painting for an artist on the interleaved schedule, they incorrectly thought that they had previously seen the painting. This finding illustrates the dynamic relationship between item memory and category learning.

In Experiment 1, we conducted a survey in which we asked a sample of N = 762 participants explicitly about their preferences regarding reimbursement and experimental duration of web-based experiments. Participants significantly prefer donations and raffles over other forms of reimbursement in 5-minute experiments. When experiments take 30 minutes or longer, participants significantly prefer direct payment. This finding applies to 15-minute experiments, too, if only data of PayPal account holders is analyzed (75.23% of our sample). In Experiment 2, we implicitly measured the preferences of N = 189 participants by letting them choose between experiments with different durations and forms of reimbursements. As in Experiment 1, direct payment was the preferred reimbursement in longer studies. The most popular choice of duration and reimbursement was to receive direct payment for an experiment of 60 minutes, which was selected by 57% of all participants.

The current study used a real-time interactive “advisor-decider” task, in which advice given by one participant results in an onerous workload for another participant, to show that self-conscious affect based on performance in one domain shapes decisions to engage in prosocial behavior in an unrelated domain: Advisors that performed at or worse than the norm, in terms of giving incorrect advice, made more frequent subsequent charity donations. Intriguingly, when advisors were given social information about their performance relative to the norm, this pattern was reversed, such that advisors that performed worse than the norm made less frequent donations. We interpret this finding as reflecting a shift in the emotion driving the behavior, from guilt to shame. Consistent with this interpretation, trait measures of guilt proneness but not of shame proneness predicted an increase in both the probability and magnitude of donations.

Exploration and exploitation decision-making are crucial cognitive processes, often guided by an individual's knowledge state. The information gap theory posits that lesser knowledge enhances exploration, yet the differential impacts of factual versus perceived knowledge on exploration preferences are not thoroughly understood. This research aims to bridge this gap by independently manipulating factual and perceived knowledge to assess their separate effects on exploration behaviors. Through three studies, we discovered that individuals with less factual knowledge explored more intensely and for longer durations, but only when they were explicitly aware of their information gaps. Furthermore, our findings reveal that the perception of insufficient knowledge can trigger increased exploration, independent of the factual knowledge possessed. Our studies illuminate the significant impact of metacognitive states on exploration preferences, advancing our understanding of how people decide whether to explore or exploit.

Memory for the QWERTY keyboard has been shown to be a good experimental paradigm to test the relationship between explicit and implicit memory as, despite high typing proficiency in young students nowadays, explicit knowledge of the keyboard seems to remain scarce. In our experiment, we investigate the relationship between implicit and explicit keyboard memory by asking participants to find the 21 letters of the Italian alphabet on a blank QWERTY keyboard (explicit task) and then perform a procedural (implicit) task by typing short paragraphs. Results showed significantly lower explicit (compared to implicit) accuracy. To investigate the role of linguistic context in the implicit task, we compared these results with a subset from Experiment 1 in Ianì et al. (2024), who used a single letter procedural task, illustrating a decline in implicit performance between the two experiments. Our findings suggest the importance of linguistic and sensorimotor contextual factors for procedural knowledge.

LLMs can interact as though they understand language, yet they remain algorithms and can be used as such. This study explores a novel guided interaction design for modeling users' information foraging behavior when navigating GPT-generated content and the role of Computational Thinking skills in shaping such behavior. Conducted with nine educational researchers in a doctoral-level AIEd course, our research used editable prompt templates and keywords to structure the prompt crafting process. We modeled and analyzed participants' behaviors in terms of \textit{exploration} (to generate and explore various information landscapes) and \textit{exploitation} (to delve deeper in a specific landscape). Our data, including responses from the Computational Thinking Scale, suggests that Algorithmic Thinking and Creativity might encourage exploitation behavior, leaning more on AI-generated information rather than pre-defined design elements.

Leveraging people's proficiency in extracting summary statistics from ensembles, we conducted two studies in which we presented rating information from consumer feedback systems through color-coded icon arrays. The investigation aims to explore how different icon array arrangements (ascending, descending, random) influence decision making and average estimation across varying levels of ensemble means (average ratings) and ensemble sizes (review volume). Our results revealed four key insights: 1) Preferences for rating variance differed, particularly at the extremes of the average rating spectrum when ensembled were dominated by one or two rating categories. 2) Structured information yielded greater certainty in responses, with confidence increasing when the task setup aligned with task goals. 3) Ensemble size prompted individuals to adapt strategies based on contextual needs. 4) Unstructured presentations led to higher estimation accuracy, suggesting that a lack of structure may encourage heightened processing effort.

The Green IT approach questions the environmental, social and economic impact of digital technology. Design fiction is a discipline that can guide behaviors in favor of Green IT by building new imaginaries. However, to our knowledge, few studies assess the impact of narratives of possible future on behavioral intentions. To answer this question, we conducted a study with 388 participants, examining the impact of 14 different scenarios of possible digital futures on individual perceptions. The results showed that, for individuals with already a high level of Green IT practices, the fear dimension of scenarios has a preeminent impact on their intention to further increase these behaviors, whereas people with little practice of Green IT are more sensitive to confidence and the presence of solutions. These results pave the way for the integration of specific technological narratives within the construction of future public policies in favor of Green IT.

In simple judgment tasks, it is generally assumed that thinking for longer leads to more accurate judgments, providing better benefits as suggested by the speed-accuracy tradeoff framework. However, human cognitive resources are limited, and longer thinking induces cognitive costs such as subjective workload. Therefore, a total benefit should be considered under the tradeoff between thinking benefits (i.e., improving accuracy) and thinking costs (i.e., increasing cognitive load) as suggested by the resource rationality framework. We examined this issue using computer simulations and behavioral experiments. Our simulations showed that, if a thinking cost was introduced based on resource-rational approaches, there was an optimal length of time for maximizing a total benefit and the total benefit gradually decreased there. In addition, our experiments demonstrated that judgment accuracy did not always improve even if participants were provided a longer thinking time; conversely, longer thinking time was likely to increase their subjective workload. These results are consistent with resource rationality rather than speed-accuracy tradeoff. The importance of considering cognitive load is suggested to further understand human intelligence in the context of a speed-accuracy tradeoff.

Judgements about others' behavior is often made based on the relative rank of that behavior. We investigated this in the new domain of pro-environmental behavior, specifically for the categories of energy and water consumption, food (meat) consumption and transport choice. Using unimodal and bimodal distributions, we experimentally manipulated three fictional individuals' (common points) rank positions while keeping their absolute frequency of behaviors constant. Consistent with previous literature, participants' judgements about these people's pro-environmental behavior differed based on their rank position. Rank effects were not moderated by the perceived Importance of others' behavior, the perceived Visibility of the behavior, or the perceived level of Control. The results of this experiment are in line with a Decision by Sampling account of judgments of pro-environmental behavior, and set a foundation for future research seeking to conduct behavioral interventions (such as rank-based nudges) within this domain. Prior to this, however, future studies should investigate whether the smaller effect sizes found in this experiment, compared to those seen in previous research, are attributable to methodological differences, or the domain itself.

Intellectual humility (IH) is the ability to understand the limits of one's knowledge. It is important to maximize the benefits and mitigate the threats of IH. We explored the impact of perspective taking (PT) on IH and its connection to confirmation bias (CB). In a mixed pretest-posttest experiment with 174 participants randomly assigned to self- or other-perspective, IH was higher in the other-perspective (vs self-perspective). Also, exposure to other-perspective boosted IH (vs baseline) and exposure to self-perspective inhibited IH (vs baseline). Interestingly, IH was not correlated with CB, challenging the notion that IH is a protective factor against CB. The study illustrates a clear distinction between other- and self-perspective and their impact on IH. Practicing other-perspective, allows to transcend from one's egocentric views, fostering IH. While self-perspective, reinforces egocentric views, leading to intellectual arrogance. Lastly, both intellectually humble and arrogant are susceptible to CB, emphasizing the need for more research.

This study looks at the benefits and functions of bilingualism in Indian schoolchildren with low vision impairment. Bilingualism, particularly in a multilingual country like India, can have considerable cognitive, social, and educational benefits. The study focuses on a sample group of N=60 (monolingual and bilingual) school-aged children with varying degrees of low vision impairment and analyses how bilingual (L1-Telugu and L2-English and L1-Hindi and L2-English) education effects their learning and social integration. Using the Language Experience and Proficiency Questionnaire (LEAP-Q), the study employs both qualitative and quantitative methods to assess cognitive development, language competency, and social interaction abilities in a bilingual situation. The findings indicate that bilingualism improves not only verbal abilities, but also cognitive flexibility, problem-solving ability, and social empathy in early children. This study suggests that bilingual education should be an integral part of the curriculum for visually impaired pupils in India, encouraging their overall development and integration into society. The findings have significant implications for educational policies and practices affecting special-needs children in diverse environments.

Conformity refers to phenomena where people match their behavior to others. Much research has focused on cases where people observe others in identical situations, saying little about its depth or generalizability. When conforming, do people revise behaviors only in that specific situation, or do they update more deeply to maintain consistent behaviors across situations? Using simulations, we first show that deep and shallow conformity leads to contrasting group dynamics; only with deep conformity can groups accumulate improvements beyond individual lifespans. We further conduct an experiment using an estimation task to examine the depths of conformity in humans. People generally extended conformist social influence to new situations without direct reference to others. However, those who simply averaged their answer with that of the direct reference showed notable failures in this generalization. Collectively, our research highlights the importance of distinguishing different depths of conformity when studying social influence and resulting group outcomes.

Acute stress triggers a cascade of physiological and psychological changes including heightened cortisol levels, perspiration, and anxiety. Existing research has focused on acute stress's effect on cognition in basic tasks of executive functioning, but its effect on computationally harder tasks is not well understood. Here, in a within-participants laboratory experiment (n=42, mostly college students), we test for an effect of acute stress on decision-making at varying levels of computational hardness in the 0-1 Knapsack Decision Problem. We find that acute stress, induced via the Trier Social Stress Test, leads to impaired decision quality irrespective of the level of computational hardness. Among cortisol responders, higher cortisol levels were associated with lower decision quality and higher time on task. Our findings help bridge the gap between research on executive functioning tasks and `real-world decisions', building a more nuanced understanding of how acute stress affects decision-making.

Conversations between people are characterized by complex nonlinear combinations of nonverbal and neurocognitive responses complementing the words that are spoken. New tools are needed to integrate these multimodal components into coherent models of conversation. We present a study and analysis pipeline for integrating multimodal measures of conversation. Data were collected using video recordings and functional near-infrared spectroscopy (fNIRS), a portable neuroimaging technology, during dyadic conversations among strangers (N=70 dyads). Rather than running discrete analyses of neural and nonverbal data, we introduce a pipeline to combine time series data from each modality into multimodal deep neural networks (DNNs) – including channel-based fNIRS signals and OpenFace data that quantifies facial expressions over time – using S2S-RNN-Autoencoders. We explored two measures to examine the resulting t-SNE space: distance and synchrony. We found that across the dimensions integrating neural and nonverbal input features, conversing dyads tend to stay closer together than permuted pairs. Dyads exhibit significantly higher synchrony in their covariation in this space compared to permuted pairs. The results suggest a mixed methodological integration may contribute to a deeper understanding of the dynamics of communication.

When individuals are pressed to make decisions quickly, their accuracy tends to decline, which is termed as speed-accuracy trade-off. But does this phenomenon extend to perceptual rating? In other words, do rapid judgments result in more extreme outcomes? To address this question, the study analyzed a global dataset covering 11,481 adult participants' ratings of 120 targets across 45 countries. The hypothesis posited that the rating became more extreme if it took less time. The study firstly identified response time as a significant predictor in extremity of social judgments through a machine learning algorithm, XGBoost, with cultural variables emerging as the second most important predictor. Given the importance of response time, the study employed hierarchical general linear models to investigate whether faster decision-making correlates with more extreme ratings and how this effect varies across diverse cultural contexts. The findings revealed a significant global level effect, also showing considerable variance across eleven regions. This observed phenomenon is termed as the “speed-extremity trade-off,” and is strongest in the Middle East and weakest in East Southeast Asia and Scandinavia.

This study investigates the differential impacts of virtual and human influencers on consumer purchase intentions, focusing particularly on the roles of perceived psychological distance and credibility. Utilizing image recognition algorithms, two influencers with facial similarities were stringently selected, and surveys from 427 consumers on their perceptions of the products endorsed by these influencers were analyzed. Results show human influencers outperform virtual ones, yet the latter still positively affect purchase intentions, revealing their potential as effective marketing tools. The study further reveals that perceived psychological distance can independently mediate the relationship between influencer type and purchase intention, and also acts in tandem with perceived credibility in this mediation. This research not only offers empirical insights into the comparative effectiveness of virtual versus human influencers in digital marketing but also advances understanding of the psychological mechanisms underpinning consumer behavior in the digital era.

Previous research about the intensity of emotional facial expressions has relied on stimulus sets of morphed facial expressions that have been generated artificially. Ecologically valid open-access facial stimulus sets with varying intensities of multiple different expressions are rare. However, there is a growing need for a validated facial stimulus set that would include multiple levels of intensities. This study aimed to develop and test the psychometric properties of a stimulus set with real facial expressions (8 men and 8 women) with 11 intensity levels for five facial expression categories: angry, happy, neutral, surprised and sad. 52 individuals rated the valence, arousal and intensity of the 656 stimuli. Descriptive statistics, internal consistency of the rating for each stimulus, emotion category, and intensity level were described. The stimuli and summary data are available upon request (https://osf.io/f8ews/).