Promoting Erroneous Divergent Opinions Increases the Wisdom of Crowds.

The aggregation of many lay judgments generates surprisingly accurate estimates. This phenomenon, called the "wisdom of crowds," has been demonstrated in domains such as medical decision-making and financial forecasting. Previous research identified two factors driving this effect: the accuracy of individual assessments and the diversity of opinions. Most available strategies to enhance the wisdom of crowds have focused on improving individual accuracy while neglecting the potential of increasing opinion diversity. Here, we study a complementary approach to reduce collective error by promoting erroneous divergent opinions. This strategy proposes to anchor half of the crowd to a small value and the other half to a large value before eliciting and averaging all estimates. Consistent with our mathematical modeling, four experiments (N = 1,362 adults) demonstrated that this method is effective for estimation and forecasting tasks. Beyond the practical implications, these findings offer new theoretical insights into the epistemic value of collective decision-making.

Comparing experience- and description-based economic preferences across 11 countries.

Recent evidence indicates that reward value encoding in humans is highly context dependent, leading to suboptimal decisions in some cases, but whether this computational constraint on valuation is a shared feature of human cognition remains unknown. Here we studied the behaviour of n = 561 individuals from 11 countries of markedly different socioeconomic and cultural makeup. Our findings show that context sensitivity was present in all 11 countries. Suboptimal decisions generated by context manipulation were not explained by risk aversion, as estimated through a separate description-based choice task (that is, lotteries) consisting of matched decision offers. Conversely, risk aversion significantly differed across countries. Overall, our findings suggest that context-dependent reward value encoding is a feature of human cognition that remains consistently present across different countries, as opposed to description-based decision-making, which is more permeable to cultural factors.

Experimental and theoretical investigation of the influence of post-curing on mixed mode fracture properties of 3d-printed polymer samples.

This study explores the mechanical properties and fracture characteristics of additively manufactured acrylonitrile butadiene styrene specimens, focusing on the impact of raster angle and post-process heat treatment. To this end, a large number of tensile and semi-circular bending samples with three distinct raster angles of 0/90°, 22/ - 68°, and 45/ - 45° were prepared and exposed to four types of heat treatments with different temperature and pressure conditions. Simultaneously, theoretical models of maximum tangential stress (MTS) and generalized MTS (GMTS) were developed to estimate the onset of specimen fracture under mixed-mode in-plane loading conditions. Recognizing the non-linear behavior within the stress-strain curve of tensile test samples, particularly in the annealed samples, an effort was undertaken to transform the original ductile material into a virtual brittle material through the application of the equivalent material concept (EMC). This approach serves the dual purpose of bypassing intricate and tedious elastoplastic analysis, while concurrently enhancing the precision of the GMTS criterion. The experimental findings have revealed that while the annealing process has a minimal effect on the yield strength, it considerably enhances energy absorption capacity, increases fracture toughness, and reduces the anisotropy. Additionally, the combined EMC-GMTS criterion has demonstrated its capability to predict the failure of the additively manufactured parts with an acceptable level of accuracy.

Co-perceiving: Bringing the social into perception.

Humans and other animals possess the remarkable ability to effectively navigate a shared perceptual environment by discerning which objects and spaces are perceived by others and which remain private to themselves. Traditionally, this capacity has been encapsulated under the umbrella of joint attention or joint action. In this comprehensive review, we advocate for a broader and more mechanistic understanding of this phenomenon, termed co-perception. Co-perception encompasses the sensitivity to the perceptual engagement of others and the capability to differentiate between objects perceived privately and those perceived commonly with others. It represents a distinct concept from mere simultaneous individual perception. Moreover, discerning between private and common objects doesn't necessitate intricate mind-reading abilities or mutual coordination. The act of perceiving objects as either private or common provides a comprehensive account for social scenarios where individuals simply share the same context or may even engage in competition. This conceptual framework encourages a re-examination of classical paradigms that demonstrate social influences on perception. Furthermore, it suggests that the impacts of shared experiences extend beyond affective responses, also influencing perceptual processes. This article is categorized under: Psychology > Attention Philosophy > Foundations of Cognitive Science Philosophy > Psychological Capacities.

The roots of polarization in the individual reward system.

Polarization raises concerns for democracy and society, which have expanded in the internet era where (mis)information has become ubiquitous, its transmission faster than ever, and the freedom and means of opinion expressions are expanding. The origin of polarization however remains unclear, with multiple social and emotional factors and individual reasoning biases likely to explain its current forms. In the present work, we adopt a principled approach and show that polarization tendencies can take root in biased reward processing of new information in favour of choice confirmatory evidence. Through agent-based simulations, we show that confirmation bias in individual learning is an independent mechanism and could be sufficient for creating polarization at group level independently of any additional assumptions about the opinions themselves, a priori beliefs about them, information transmission mechanisms or the structure of social relationship between individuals. This generative process can interact with polarization mechanisms described elsewhere, but constitutes an entrenched biological tendency that helps explain the extraordinary resilience of polarization against mitigating efforts such as dramatic informational change in the environment.

Interpersonal alignment of neural evidence accumulation to social exchange of confidence.

Private, subjective beliefs about uncertainty have been found to have idiosyncratic computational and neural substrates yet, humans share such beliefs seamlessly and cooperate successfully. Bringing together decision making under uncertainty and interpersonal alignment in communication, in a discovery plus pre-registered replication design, we examined the neuro-computational basis of the relationship between privately held and socially shared uncertainty. Examining confidence-speed-accuracy trade-off in uncertainty-ridden perceptual decisions under social vs isolated context, we found that shared (i.e. reported confidence) and subjective (inferred from pupillometry) uncertainty dynamically followed social information. An attractor neural network model incorporating social information as top-down additive input captured the observed behavior and demonstrated the emergence of social alignment in virtual dyadic simulations. Electroencephalography showed that social exchange of confidence modulated the neural signature of perceptual evidence accumulation in the central parietal cortex. Our findings offer a neural population model for interpersonal alignment of shared beliefs.

Interindividual differences in Pavlovian influence on learning are consistent.

Pavlovian influences impair instrumental learning. It is easier to learn to approach reward-predictive signals and avoid punishment-predictive cues than their contrary. Whether the interindividual variability in this Pavlovian influence is consistent across time has been examined by a number of recent studies and met with mixed results. Here we introduce an open-source, web-based instance of a well-established Go-NoGo paradigm for measuring Pavlovian influence. We closely replicated the previous laboratory-based results. Moreover, the interindividual differences in Pavlovian influence were consistent across a two-week time window at the level of (i) raw measures of learning (i.e. performance accuracy), (ii) linear, descriptive estimates of Pavlovian bias (test-retest reliability: 0.40), and (iii) parameters obtained from reinforcement learning model fitting and model selection (test-retest reliability: 0.25). Nonetheless, the correlations reported here are still lower than the standards (i.e. 0.7) employed in psychometrics and self-reported measures. Our results provide support for trusting Pavlovian bias as a relatively stable individual characteristic and for using its measure in the computational understanding of human mental health.

Intelligence brings responsibility - Even smart AI assistants are held responsible.

People will not hold cars responsible for traffic accidents, yet they do when artificial intelligence (AI) is involved. AI systems are held responsible when they act or merely advise a human agent. Does this mean that as soon as AI is involved responsibility follows? To find out, we examined whether purely instrumental AI systems stay clear of responsibility. We compared AI-powered with non-AI-powered car warning systems and measured their responsibility rating alongside their human users. Our findings show that responsibility is shared when the warning system is powered by AI but not by a purely mechanical system, even though people consider both systems as mere tools. Surprisingly, whether the warning prevents the accident introduces an outcome bias: the AI takes higher credit than blame depending on what the human manages or fails to do.

Social interoception: Perceiving events during cardiac afferent activity makes people more suggestible to other people's influence.

Our judgements are often influenced by other people's views and opinions. Interoception also influences decision making, but little is known about its role in social influence and particularly, the extent to which other people may influence our decisions. Across two experiments, using different forms of social influence, participants judged the trustworthiness of faces presented either during the systolic phase of the cardiac cycle, when baroreceptors convey information from the heart to the brain, or during diastolic phase, when baroreceptors are quiescent. We quantified the extent to which participants changed their minds (as an index of social influence) following the social feedback, in order to compare two competing hypotheses. According to the Arousal-Confidence Hypothesis, cardiac signals create a context of heightened bodily arousal that increases confidence in perceptual judgements. People should, therefore, be less subject to social influence during systole. By contrast, according to the Uncertainty-Conformity Hypothesis, cardiac signals increase neural noise and sensory attenuation, such that people should display greater effects of social influence during systole, as they then underweight private interoceptive signals in favour of the external social information. Across two studies that used different kind of social interactions, we found that participants changed their minds more when faces were presented at systole. Our results, therefore, support the Uncertainly-Conformity hypothesis and highlight how cardiac afferent signals contribute to shape our social decision-making in different types of social interactions.

Prosocial learning: Model-based or model-free?

Prosocial learning involves the acquisition of knowledge and skills necessary for making decisions that benefit others. We asked if, in the context of value-based decision-making, there is any difference between learning strategies for oneself vs. for others. We implemented a 2-step reinforcement learning paradigm in which participants learned, in separate blocks, to make decisions for themselves or for a present other confederate who evaluated their performance. We replicated the canonical features of the model-based and model-free reinforcement learning in our results. The behaviour of the majority of participants was best explained by a mixture of the model-based and model-free control, while most participants relied more heavily on MB control, and this strategy enhanced their learning success. Regarding our key self-other hypothesis, we did not find any significant difference between the behavioural performances nor in the model-based parameters of learning when comparing self and other conditions.

Compromising improves forecasting.

Predicting the future can bring enormous advantages. Across the ages, reliance on supernatural foreseeing was substituted by the opinion of expert forecasters, and now by collective intelligence approaches which draw on many non-expert forecasters. Yet all of these approaches continue to see individual forecasts as the key unit on which accuracy is determined. Here, we hypothesize that compromise forecasts, defined as the average prediction in a group, represent a better way to harness collective predictive intelligence. We test this by analysing 5 years of data from the Good Judgement Project and comparing the accuracy of individual versus compromise forecasts. Furthermore, given that an accurate forecast is only useful if timely, we analyze how the accuracy changes through time as the events approach. We found that compromise forecasts are more accurate, and that this advantage persists through time, though accuracy varies. Contrary to what was expected (i.e. a monotonous increase in forecasting accuracy as time passes), forecasting error for individuals and for team compromise starts its decline around two months prior to the event. Overall, we offer a method of aggregating forecasts to improve accuracy, which can be straightforwardly applied in noisy real-world settings.

Outcome context-dependence is not WEIRD: Comparing reinforcement- and description-based economic preferences worldwide.

Recent evidence indicates that reward value encoding in humans is highly context-dependent, leading to suboptimal decisions in some cases. But whether this computational constraint on valuation is a shared feature of human cognition remains unknown. To address this question, we studied the behavior of individuals from across 11 countries of markedly different socioeconomic and cultural makeup using an experimental approach that reliably captures context effects in reinforcement learning. Our findings show that all samples presented evidence of similar sensitivity to context. Crucially, suboptimal decisions generated by context manipulation were not explained by risk aversion, as estimated through a separate description-based choice task (i.e., lotteries) consisting of matched decision offers. Conversely, risk aversion significantly differed across countries. Overall, our findings suggest that context-dependent reward value encoding is a hardcoded feature of human cognition, while description-based decision-making is significantly sensitive to cultural factors.

Does decision confidence reflect effort?

Goal directed behaviour requires transformation of sensory input to decision, and then to output action. How the sensory input is accumulated to form the decision has been extensively studied, however, the influence of output action on decision making has been largely dismissed. Although the recent emerging view postulates the reciprocal interaction between action and decision, still little is known about how the parameters of an action modulates the decision. In this study, we focused on the physical effort which necessarily entails with action. We tested if the physical effort during the deliberation period of the perceptual decision, not the effort required after deciding a particular option, can impact on the process of forming the decision. Here, we set up an experimental situation where investing effort is necessary for the initiation of the task, but importantly, is orthogonal to success in task performance. The study was pre-registered to test the hypothesis that the increased effort will decrease the metacognitive accuracy of decision, without affecting the decision accuracy. Participants judged the direction of a random-dot motion stimuli, while holding and maintaining the position of a robotic manipulandum with their right hand. In the key experimental condition, the manipulandum produced force to move away from its position, requiring the participants to resist the force while accumulating the sensory evidence for the decision. The decision was reported by a key-press using the left-hand. We found no evidence that such incidental (i.e., non-instrumental) effort may influence the subsequent decision process and most importantly decision confidence. The possible reason for this result and the future direction of the research are discussed.

Diffusion of punishment in collective norm violations.

People assign less punishment to individuals who inflict harm collectively, compared to those who do so alone. We show that this arises from judgments of diminished individual causal responsibility in the collective cases. In Experiment 1, participants (N = 1002) assigned less punishment to individuals involved in collective actions leading to intentional and accidental deaths, but not failed attempts, emphasizing that harmful outcomes, but not malicious intentions, were necessary and sufficient for the diffusion of punishment. Experiments 2.a compared the diffusion of punishment for harmful actions with 'victimless' purity violations (e.g., eating a dead human's flesh as a group; N = 752). In victimless cases, where the question of causal responsibility for harm does not arise, diffusion of collective responsibility was greatly reduced-an outcome replicated in Experiment 2.b (N = 479). Together, the results are consistent with discounting in causal attribution as the underlying mechanism of reduction in proposed punishment for collective harmful actions.

Determination of Translaminar Notch Fracture Toughness for Laminated Composites Using Brazilian Disk Test.

This paper evaluates the fracture of notched epoxy matrix composites using the Brazilian disk (BD) test from both numerical and experimental points of view. The study began with a comprehensive experimental program covering three different composite lay-ups (quasi-isotropic, unidirectional, and cross-ply) and various geometries of U and V notches. Specifically, the BD samples combined the three layouts, four different notch angles, and three notch radii with three specimens per combination, leading to an overall number of 108 fracture tests. The experiments showed the appropriateness of the BD test for the study of the fracture behavior of composite materials and provided a good pool of data for further investigations. Subsequently, the virtual isotropic material concept (VIMC) was applied in combination with two fracture criteria to theoretically predict the experimentally acquired fracture loads. This study demonstrated that using the VIMC approach can provide robust predictions while incurring much lower computational costs compared to the conventional approaches found in the literature.

Democratic forecast: Small groups predict the future better than individuals and crowds.

Predictions pose unique problems. Experts regularly get them wrong, and collective solutions (such as prediction markets and super-forecaster schemes) do better but remain selective and costly. Contrary to the idea that face-to-face discussion hinders collective intelligence, social deliberation improves the resolution of general knowledge problems, with four consensually agreed answers outperforming the aggregate knowledge of 5,000 nondeliberating individuals. Could discussion help predict the future in an efficient, cheap, and inclusive way? We show that smaller groups of lay individuals, when organized, come up with better predictions than those they provide alone. Deliberation and consensus made individual predictions significantly more accurate. Aggregating as few as two consensual predictions did better than classical "wisdom of crowds" aggregation of 100 individual ones. Against the view that discussion can impair decision-making, our results demonstrate that collective intelligence of small groups and consensus-seeking improves accuracy about yet unknown facts, opening the avenue for efficient, inclusive, and inexpensive group forecasting solutions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Shared Responsibility Decreases the Sense of Agency in the Human Brain.

Sharing responsibility in social decision-making helps individuals use the flexibility of the collective context to benefit themselves by claiming credit for good outcomes or avoiding the blame for bad outcomes. Using magnetoencephalography, we examined the neuronal basis of the impact that social context has on this flexible sense of responsibility. Participants performed a gambling task in various social contexts and reported feeling less responsibility when playing as a member of a team. A reduced magnetoencephalography outcome processing effect was observed as a function of decreasing responsibility at 200 msec post outcome onset and was centered over parietal, central, and frontal brain regions. Before outcome revelation in socially made decisions, an attenuated motor preparation signature at 500 msec after stimulus onset was found. A boost in reported responsibility for positive outcomes in social contexts was associated with increased activity in regions related to social and reward processing. Together, these results show that sharing responsibility with others reduces agency, influencing pre-outcome motor preparation and post-outcome processing, and provides opportunities to flexibly claim credit for positive outcomes.

An Active Sensing Paradigm for Studying Human Auditory Perception.

Our perception is based on active sensing, i.e., the relationship between self-motion and resulting changes to sensory inputs. Yet, traditional experimental paradigms are characterized by delayed reactions to a predetermined stimulus sequence. To increase the engagement of subjects and potentially provide richer behavioral responses, we developed Sensory Island Task for humans (SITh), a freely-moving search paradigm to study auditory perception. In SITh, subjects navigate an arena in search of an auditory target, relying solely on changes in the presented stimulus frequency, which is controlled by closed-loop position tracking. A "target frequency" was played when subjects entered a circular sub-area of the arena, the "island", while different frequencies were presented outside the island. Island locations were randomized across trials, making stimulus frequency the only informative cue for task completion. Two versions of SITh were studied: binary discrimination, and gradual change of the stimulus frequency. The latter version allowed determining frequency discrimination thresholds based on the subjects' report of the perceived island location (i.e., target frequency). Surprisingly, subjects exhibited similar thresholds as reported in traditional "stationary" forced-choice experiments after performing only 30 trials, highlighting the intuitive nature of SITh. Notably, subjects spontaneously employed a small variety of stereotypical search patterns, and their usage proportions varied between task versions. Moreover, frequency discrimination performance depended on the search pattern used. Overall, we demonstrate that the use of an ecologically driven paradigm is able to reproduce established findings while simultaneously providing rich behavioral data for the description of sensory ethology.

Diversity of opinions promotes herding in uncertain crowds.

Classic and recent studies demonstrate how we fall for the 'tyranny of the majority' and conform to the dominant trend when uncertain. However, in many social interactions outside of the laboratory, there is rarely a clearly identified majority and discerning who to follow might be challenging. Here, we asked whether in such conditions herding behaviour depends on a key statistical property of social information: the variance of opinions in a group. We selected a task domain where opinions are widely variable and asked participants (N = 650) to privately estimate the price of eight anonymous paintings. Then, in groups of five, they discussed and agreed on a shared estimate for four paintings. Finally, they provided revised individual estimates for all paintings. As predicted (https://osf.io/s89w4), we observed that group members converged to each other and boosted their confidence following social interaction. We also found evidence supporting the hypothesis that the more diverse groups show greater convergence, suggesting that the variance of opinions promotes herding in uncertain crowds. Overall, these findings empirically examine how, in the absence of a clear majority, the distribution of opinions relates to subjective feelings of confidence and herding behaviour.