Do infants have a sense of numerosity? A p‐curve analysis of infant numerosity discrimination studies

Research demonstrating that infants discriminate between small (e.g., 1 vs. 3 dots) and large numerosities (e.g., 8 vs. 16 dots) is central to theories concerning the origins of human numerical abilities. To date, there has been no quantitative meta‐analysis of the infant numerical competency data. Here, we quantitatively synthesize the evidential value of the available literature on infant numerosity discrimination using a meta‐analytic tool called p‐curve. In p‐curve the distribution of available p‐values is analyzed to determine whether the published literature examining particular hypotheses contains evidential value. p‐curves demonstrated evidential value for the hypotheses that infants can discriminate between both small and large unimodal and cross‐modal numerosities. However, the analyses also revealed that the published data on infants’ ability to discriminate between large numerosities is less robust and statistically powered than the data on their ability to discriminate small numerosities. We argue there is a need for adequately powered replication studies to enable stronger inferences in order to use infant data to ground theories concerning the ontogenesis of numerical cognition.     

Analysis of driver’s decision distance and merging distance in work zone area based on parametric survival models: With the aid of a driving simulator experiment

Work zones affect traffic safety and efficiency by changing the road condition and drivers’ maneuver. Therefore, it is important to fully understand drivers’ merging behavior in work zone related areas. In this study, a model framework composed of decision-distance analysis and merging-distance analysis was proposed, which could describe both decision-making and lane-changing process of merging behavior. A road environment with work zone was developed based on a driving simulator, and six scenarios composed of two levels of traffic volume and three different lane-end sign’s locations were designed. Forty-two licensed participants, including 21 females (10 professional drivers vs. 11 normal drivers) and 21 males (15 professional drivers vs. 6 normal drivers) finally completed the experiment. Based on the experimental data, parametric survival models were established to analyze the effects of traffic sign location, traffic situation and driver characteristics on drivers’ decision distance and merging distance. The results showed that: (i) the lane-end sign’s location affected the decision point of lane changing and further affected the merging distance. However, the effect was weakened when the sign was placed far away from the work zone; (ii) merging distance in high traffic volume condition was shorter than that in low traffic volume condition; (iii) work zone posed greater challenges for female drivers as they merged later than males, and females were reluctant to adjust their merging distance according to different gap conditions. The findings shed some light on the future improvement of traffic design and management in work zones.     

Buying an electric car: A rational choice or a norm-directed behavior?

The diffusion of electric cars can contribute to more sustainability in the transport sector, but diffusion rates in most countries are still low. We investigated motives for electric car adoption in German households from an environmental psychology perspective. The public debate focuses on rational aspects such as the purchase price or new technological demands (e.g., limited range and a new charging system). Psychological research on energy-related investment decisions in households confirms the relevance of rational motives, but additionally points to the importance of norm-directed motives (moral and social norms). We investigated the relevance of different motives in an online questionnaire with n = 220 members of German households interested in buying a new car. The questionnaire included possible rational and norm-related predictors of electric car adoption. We tested three action models to explain adoption intention: An adjusted technology acceptance model (TAM), an adjusted norm activation model (NAM), and an integrative model with predictors from both models. We analyzed the hypothesized models with path analyses. All models explained a substantial share of variance in adoption intention. The explained share of variance in the NAM was higher than in the TAM and comparably high to the integrative model. The results demonstrate the important role of moral and social motives for households’ investment decisions. Additionally, the technology’s perceived usefulness was an important rational motive. We discuss the context dependency of the results, as household members might have little knowledge about the new technology during the early stages of a technology’s diffusion process. The results strongly suggest broadening political support schemes, such as informational and image campaigns, as a way to more effectively foster electric car diffusion. More comprehensive assessments appear to be necessary in future analyses of electric car adoption as well as energy-related investment decisions.