Salience Not Status: How Category Labels Influence Feature Inference

Two main uses of categories are classification and feature inference, and category labels have been widely shown to play a dominant role in feature inference. However, the nature of this influence remains unclear, and we evaluate two contrasting hypotheses formalized as mathematical models: the label special‐mechanism hypothesis and the label super‐salience hypothesis. The special‐mechanism hypothesis is that category labels, unlike other features, trigger inference decision making in reference to the category prototypes. This results in a tendency for prototype‐compatible inferences because the labels trigger a special mechanism rather than because of any influences they have on similarity evaluation. The super‐salience hypothesis assumes that the large label influence is due to their high salience and corresponding impact on similarity without any need for a special mechanism. Application of the two models to a feature inference task based on a family resemblance category structure yields strong support for the label super‐salience hypothesis and in particular does not support the need for a special mechanism based on prototypes.     

L’entraînement cognitivo-perceptif basé sur la vidéo et ses modes d’instructions

This literature review examines both the effectiveness of simulation training video to improve decision-making of athletes and secondly, the benefits of explicit, implicit and guided instructions used during video-based perceptual training. Thus, it is shown that video-based perceptual training improves cognitive and perceptual skills resulting in decrease in decision time and increase in decision's accuracy in simulations and field. Furthermore, the benefits of explicit approaches in early stage of learning to promote rapid acquisition of knowledge is highlighted. On the other hand, it is suggested that implicit approaches will be more conducive to the experts to ensure that they “reinvest” their knowledge in stressful situations. Finally, it is envisaged that guided approaches are relevant alternatives to explicit and implicit approaches.     

Social influence on metacognitive evaluations: The power of nonverbal cues

Metacognitive evaluations refer to the processes by which people assess their own cognitive operations with respect to their current goal. Little is known about whether this process is susceptible to social influence. Here we investigate whether nonverbal social signals spontaneously influence metacognitive evaluations. Participants performed a two-alternative forced-choice task, which was followed by a face randomly gazing towards or away from the response chosen by the participant. Participants then provided a metacognitive evaluation of their response by rating their confidence in their answer. In Experiment 1, the participants were told that the gaze direction was irrelevant to the task purpose and were advised to ignore it. The results revealed an effect of implicit social information on confidence ratings even though the gaze direction was random and therefore unreliable for task purposes. In addition, nonsocial cues (car) did not elicit this effect. In Experiment 2, the participants were led to believe that cue direction (face or car) reflected a previous participant's response to the same question—that is, the social information provided by the cue was made explicit, yet still objectively unreliable for the task. The results showed a similar social influence on confidence ratings, observed with both cues (car and face) but with an increased magnitude relative to Experiment 1. We additionally showed in Experiment 2 that social information impaired metacognitive accuracy. Together our results strongly suggest an involuntary susceptibility of metacognitive evaluations to nonverbal social information, even when it is implicit (Experiment 1) and unreliable (Experiments 1 and 2).     

Driving examiners’ views on data-driven assessment of test candidates: An interview study

Vehicles are increasingly equipped with sensors that capture the state of the driver, the vehicle, and the environment. These developments are relevant to formal driver testing, but little is known about the extent to which driving examiners would support the use of sensor data in their job. This semi-structured interview study examined the opinions of 37 driving examiners about data-driven assessment of test candidates. The results showed that the examiners were supportive of using data to explain their pass/fail verdict to the candidate. According to the examiners, data in an easily accessible form such as graphs of eye movements, headway, speed, or braking behavior, and color-coded scores, supplemented with camera images, would allow them to eliminate doubt or help them convince disagreeing test-takers. The examiners were skeptical about higher levels of decision support, noting that forming an overall picture of the candidate’s abilities requires integrating multiple context-dependent sources of information. The interviews yielded other possible applications of data collection and sharing, such as selecting optimal routes, improving standardization, and training and pre-selecting candidates before they are allowed to take the driving test. Finally, the interviews focused on an increasingly viable form of data collection: simulator-based driver testing. This yielded a divided picture, with about half of the examiners being positive and half negative about using simulators in driver testing. In conclusion, this study has provided important insights regarding the use of data as an explanation aid for examiners. Future research should consider the views of test candidates and experimentally evaluate different forms of data-driven support in the driving test.     

An Actor's Knowledge and Intent Are More Important in Evaluating Moral Transgressions Than Conventional Transgressions

An actor's mental states—whether she acted knowingly and with bad intentions—typically play an important role in evaluating the extent to which an action is wrong and in determining appropriate levels of punishment. In four experiments, we find that this role for knowledge and intent is significantly weaker when evaluating transgressions of conventional rules as opposed to moral rules. We also find that this attenuated role for knowledge and intent is partly due to the fact that conventional rules are judged to be more arbitrary than moral rules; whereas moral transgressions are associated with actions that are intrinsically wrong (e.g., hitting another person), conventional transgressions are associated with actions that are only contingently wrong (e.g., wearing pajamas to school, which is only wrong if it violates a dress code that could have been otherwise). Finally, we find that it is the perpetrator's belief about the arbitrary or non‐arbitrary basis of the rule—not the reality—that drives this differential effect of knowledge and intent across types of transgressions.     

Inference in the Wild: A Framework for Human Situation Assessment and a Case Study of Air Combat

Situation awareness is a key construct in human factors and arises from a process of situation assessment (SA). SA comprises the perception of information, its integration with existing knowledge, the search for new information, and the prediction of the future state of the world, including the consequences of planned actions. Causal models implemented as Bayesian networks (BNs) are attractive for modeling all of these processes within a single, unified framework. We elicited declarative knowledge from two Royal Australian Air Force (RAAF) fighter pilots about the information sources used in the identification (ID) of airborne entities and the causal relationships between these sources. This knowledge was represented in a BN (the declarative model) that was evaluated against the performance of 19 RAAF fighter pilots in a low‐fidelity simulation. Pilot behavior was well predicted by a simple associative model (the behavioral model) with only three attributes of ID. Search for information by pilots was largely compensatory and was near‐optimal with respect to the behavioral model. The average revision of beliefs in response to evidence was close to Bayesian, but there was substantial variability. Together, these results demonstrate the value of BNs for modeling human SA.     

Coparenting and Children's School Readiness: A Mediational Model

We examined the long‐term direct and indirect links between coparenting (conflict, communication, and shared decision‐making) and preschoolers' school readiness (math, literacy, and social skills). The study sample consisted of 5,650 children and their biological mothers and fathers who participated in the Early Childhood Longitudinal Study‐Birth Cohort. Using structural equation modeling and controlling for background characteristics, we found that our conceptual model of the pathways from coparenting to child outcomes is structurally the same for cohabiting and married families. Controlling for a host of background characteristics, we found that coparenting conflict and shared decision‐making were negatively and positively, respectively, linked to children's academic and social skills and co‐parental communication was indirectly linked to academic and social skills through maternal supportiveness. Coparenting conflict was also indirectly linked to children's social skills through maternal depressive symptoms. The overall findings suggest that for both cohabiting and married families, the context of conflicted coparenting may interfere with the development of children's social competencies and academic skills, whereas collaborative coparenting promotes children's school readiness because mothers are more responsive to their children's needs. These findings have implications for programs aimed at promoting positive family processes in cohabiting and married families.