Peripheral information extraction: A developmental examination of reading processes

Three experiments examining the information extraction processes are presented. In Experiments I and II, adults and children were presented paragraphs to read and search for specified targets, respectively. Two types of typographical (spatial) manipulations were used: case of the letters within the paragraphs was as normally experienced, or in alternating upper and lower case letters. Spacing between words was either as normal, filled with a neutral symbol, or absent. The response measures were reading and search speed in words per minute. Sensitivity to case (word shape) and space (word boundary) increased as a function of experience and was different for reading compared with search. In Experiment III subjects were asked to judge whether briefly presented pairs of letters were the same or different. Retinal (spatial) location was varied from trial to trial; dependent measure was reaction time. Results showed a developmental progression in response time with complex interactions. Theoretical interpretations of these data are discussed in support of the initial stage of Hochberg's peripheral to cognitive search guidance model.     

Preference for Human (vs. Robotic) Labor is Stronger in Symbolic Consumption Contexts

Advances in robotics, automation, and artificial intelligence increasingly enable firms to replace human labor with technology, thereby fundamentally transforming how goods and services are produced. From both managerial and societal points of view, it is therefore important to understand demand‐side incentives for firms to employ human labor. We begin to address this question by examining for which products and services consumers are more likely to favor human (vs. robotic) labor. In six studies, we demonstrate that consumers prefer human (vs. robotic) labor more for products with higher (vs. lower) symbolic value (e.g., when expressing something about one's beliefs and personality is of greater importance). We theorize that this is because consumers have stronger uniqueness motives in more (vs. less) symbolic consumption contexts (and associate human labor more strongly with product uniqueness). In line with this account, we demonstrate that individual differences in need for uniqueness moderate the interaction between production mode and symbolic motives and that a measure of uniqueness motives mediates the effect of consumption context on preferences for human (vs. robotic) production.     

Evaluating pedestrian interaction preferences with a game theoretic autonomous vehicle in virtual reality

Localisation and navigation of autonomous vehicles (AVs) in static environments are now solved problems, but how to control their interactions with other road users in mixed traffic environments, especially with pedestrians, remains an open question. Recent work has begun to apply game theory to model and control AV-pedestrian interactions as they compete for space on the road whilst trying to avoid collisions. But this game theory model has been developed only in unrealistic lab environments. To improve their realism, this study empirically examines pedestrian behaviour during road crossing in the presence of approaching autonomous vehicles in more realistic virtual reality (VR) environments. The autonomous vehicles are controlled using game theory, and this study seeks to find the best parameters for these controls to produce comfortable interactions for the pedestrians. In a first experiment, participants’ trajectories reveal a more cautious crossing behaviour in VR than in previous laboratory experiments. In two further experiments, a gradient descent approach is used to investigate participants’ preference for AV driving style. The results show that the majority of participants were not expecting the AV to stop in some scenarios, and there was no change in their crossing behaviour in two environments and with different car models suggestive of car and last-mile style vehicles. These results provide some initial estimates for game theoretic parameters needed by future AVs in their pedestrian interactions and more generally show how such parameters can be inferred from virtual reality experiments.     

Comparison of Maternal Perceptions of Preterm and Fullterm Infants

We compared maternal attitudes and feelings in two groups of mother–infant dyads: 25 mothers with preterm newborns (M=30.9 weeks of gestational age) and 25 mothers with fullterm newborns (M=39.7 weeks of gestational age). Both groups were matched for infant sex, age (corrected in preterms) and birth order as well as for maternal age and education. Semi-structured interviews were used to collect data on maternal attitudes and feelings about pregnancy and the first contacts with the newborn. Mothers completed rating scales to indicate the specific behavioural problems they perceived in their infants at 6 weeks and 3 months of (corrected) age. Observations of infant responses to visual and/or auditory stimuli were made at 6 weeks and 3 months in a laboratory setting. At 3 months, each infant was administered the Bayley Scale of Mental Development. There were no differences in maternal attitudes and feelings between the two groups of mothers prior to the birth. However, significant differences appeared after birth and indicated increased anxiety in mothers of preterm infants. Significantly more 6-week-old preterm infants were perceived by their mothers as irritable and to cry more than fullterm infants. At the age of 3 months, both groups of infants differed only in terms of irritability. Differences between the two groups of mother–infant dyads, age-related changes in these differences and relationships between maternal evaluations and the laboratory-based assessments are discussed in the context of contrasts in the stability of behavioural regulation in preterm and fullterm infants.     

Mothers' Perceptions of Infants Labelled ‘Depressed’

Forty-four depressed and non-depressed mothers participated in a videotaped interaction with their own infant and then rated the videotape using the Infant Stereotyping Scale and the Interaction Rating Scale. In addition, one half of the mothers rated a videotape of an unfamiliar infant who was labelled psychologically ‘depressed’ and the other half rated a videotape of the same infant with no label given. Both the depressed and non-depressed mothers rated the ‘depressed’ labelled infant more negatively than the non-labelled infant on the attributes of physical potency, cognitive competence, sociability and difficult behaviour. Physical appearance was the only rating that was not biased by the ‘depressed’ label. Mothers' ratings of their own infants were more positive than their ratings of the non-labelled stimulus infant. Depressed mothers did not see their infants more negatively except on one rating. They rated the physical appearance of their own infant more negatively than non-depressed mothers.     

EARSHOT: A Minimal Neural Network Model of Incremental Human Speech Recognition

Despite the lack of invariance problem (the many-to-many mapping between acoustics and percepts), human listeners experience phonetic constancy and typically perceive what a speaker intends. Most models of human speech recognition (HSR) have side-stepped this problem, working with abstract, idealized inputs and deferring the challenge of working with real speech. In contrast, carefully engineered deep learning networks allow robust, real-world automatic speech recognition (ASR). However, the complexities of deep learning architectures and training regimens make it difficult to use them to provide direct insights into mechanisms that may support HSR. In this brief article, we report preliminary results from a two-layer network that borrows one element from ASR, long short-term memory nodes, which provide dynamic memory for a range of temporal spans. This allows the model to learn to map real speech from multiple talkers to semantic targets with high accuracy, with human-like timecourse of lexical access and phonological competition. Internal representations emerge that resemble phonetically organized responses in human superior temporal gyrus, suggesting that the model develops a distributed phonological code despite no explicit training on phonetic or phonemic targets. The ability to work with real speech is a major advance for cognitive models of HSR.     

Predicting cognitive development and early symptoms of autism spectrum disorder in preterm children: The value of temperament and sensory processing

This study was the first to longitudinally explore the extent to which early temperament and sensory processing were of predictive value for cognitive development and Autism Spectrum Disorder (ASD) symptomatology in a sample of preterm children (N = 50, 22 girls, mean gestational age 27 weeks). At the corrected ages of 10, 18, and 24 months, sensory processing and temperament were assessed, as were cognitive development and ASD symptoms at 36 months. Better cognitive development was predicted by fewer hospitalisation days at birth and by lower Activity Level at 18 months. Temperamental subscales of Negative Affect showed associations with both parent-reported and observational measures of ASD symptomatology, whereas sensory processing only had predictive value for parent-reported symptoms of ASD. The usefulness of temperament and sensory processing for prediction of ASD symptom severity and cognitive outcomes became clear in the second year of life. The results indicate that this area of research is worth additional investigation in the extreme and very preterm population, to explore in further detail whether these two concepts might be able to provide information about which preterms are more likely to develop ASD or cognitive impairments.     

Human factors evaluation of an in-vehicle active traffic and demand management (ATDM) system

This research study focused on the evaluation of an emulated in-vehicle Active Traffic and Demand Management (ATDM) system on Interstate 66 in Northern Virginia. Traditional ATDM systems rely on infrastructure-mounted variable message signs (VMS) to provide information (speed limits, lane availability, etc.) to the traveling public. By providing information about dynamic roadway conditions on an in-vehicle device, the ATDM may improve driving safety and performance by allowing drivers to remain consistently aware of forthcoming traffic conditions and roadway requirements; even when external signage is inaccessible. This study represents an initial investigation of an emulated in-vehicle ATDM to provide developers with design guidance and ensure that unintended consequences, such as distraction, do not undermine the potential benefits.Twenty younger and 20 older participants, accompanied by a member of the research team, experienced the following ATDM features on an in-vehicle device (IVD) mounted to the dashboard: (1) dynamic speed limits, (2) dynamic lane use/shoulder control, (3) High Occupancy Vehicle (HOV) restrictions, and (4) roadway information from variable message signs. The IVD was equipped with auditory and visual alerts notifying the driver when relevant visual information was updated. Research questions addressed distraction and driver behavior associated with use of the system. Qualitative and quantitative participant data was acquired from the instrumented vehicle, various questionnaires, and researcher observation.Several key findings were uncovered: (1) The IVD, as tested, did not warrant classification as a source of distraction according to the NHTSA guidelines; v2) There was a significant difference in eye-glance durations to the IVD when comparing the VMS alerts to both the speed limit and lane management alerts; and (3) The speed limit alert motivated participants to alter their speed (per survey results and participant speed data).     

Statistical learning of multiple speech streams: A challenge for monolingual infants

Language acquisition depends on the ability to detect and track the distributional properties of speech. Successful acquisition also necessitates detecting changes in those properties, which can occur when the learner encounters different speakers, topics, dialects, or languages. When encountering multiple speech streams with different underlying statistics but overlapping features, how do infants keep track of the properties of each speech stream separately? In four experiments, we tested whether 8‐month‐old monolingual infants (N = 144) can track the underlying statistics of two artificial speech streams that share a portion of their syllables. We first presented each stream individually. We then presented the two speech streams in sequence, without contextual cues signaling the different speech streams, and subsequently added pitch and accent cues to help learners track each stream separately. The results reveal that monolingual infants experience difficulty tracking the statistical regularities in two speech streams presented sequentially, even when provided with contextual cues intended to facilitate separation of the speech streams. We discuss the implications of our findings for understanding how infants learn and separate the input when confronted with multiple statistical structures.