Social perception and cognition : processing of gestures, postures and facial expressions in the human brain

Humans are a social species with the internal capability to process social information from other humans. To understand others behavior and to react accordingly, it is necessary to infer their internal states, emotions and aims, which are conveyed by subtle nonverbal bodily cues such as postures, gestures, and facial expressions. This thesis investigates the brain functions underlying the processing of such social information. Studies I and II of this thesis explore the neural basis of perceiving pain from another person s facial expressions by means of functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG). In Study I, observing another s facial expression of pain activated the affective pain system (previously associated with self-experienced pain) in accordance with the intensity of the observed expression. The strength of the response in anterior insula was also linked to the observer s empathic abilities. The cortical processing of facial pain expressions advanced from the visual to temporal-lobe areas at similar latencies (around 300 500 ms) to those previously shown for emotional expressions such as fear or disgust. Study III shows that perceiving a yawning face is associated with middle and posterior STS activity, and the contagiousness of a yawn correlates negatively with amygdalar activity. Study IV explored the brain correlates of interpreting social interaction between two members of the same species, in this case human and canine. Observing interaction engaged brain activity in very similar manner for both species. Moreover, the body and object sensitive brain areas of dog experts differentiated interaction from noninteraction in both humans and dogs whereas in the control subjects, similar differentiation occurred only for humans. Finally, Study V shows the engagement of the brain area associated with biological motion when exposed to the sounds produced by a single human being walking. However, more complex pattern of activation, with the walking sounds of several persons, suggests that as the social situation becomes more complex so does the brain response. Taken together, these studies demonstrate the roles of distinct cortical and subcortical brain regions in the perception and sharing of others internal states via facial and bodily gestures, and the connection of brain responses to behavioral attributes.

Dynamics of contour, object and face processing in the human visual cortex

The neural basis of visual perception can be understood only when the sequence of cortical activity underlying successful recognition is known. The early steps in this processing chain, from retina to the primary visual cortex, are highly local, and the perception of more complex shapes requires integration of the local information. In Study I of this thesis, the progression from local to global visual analysis was assessed by recording cortical magnetoencephalographic (MEG) responses to arrays of elements that either did or did not form global contours. The results demonstrated two spatially and temporally distinct stages of processing: The first, emerging 70 ms after stimulus onset around the calcarine sulcus, was sensitive to local features only, whereas the second, starting at 130 ms across the occipital and posterior parietal cortices, reflected the global configuration. To explore the links between cortical activity and visual recognition, Studies II III presented subjects with recognition tasks of varying levels of difficulty. The occipito-temporal responses from 150 ms onwards were closely linked to recognition performance, in contrast to the 100-ms mid-occipital responses. The averaged responses increased gradually as a function of recognition performance, and further analysis (Study III) showed the single response strengths to be graded as well. Study IV addressed the attention dependence of the different processing stages: Occipito-temporal responses peaking around 150 ms depended on the content of the visual field (faces vs. houses), whereas the later and more sustained activity was strongly modulated by the observers attention. Hemodynamic responses paralleled the pattern of the more sustained electrophysiological responses. Study V assessed the temporal processing capacity of the human object recognition system. Above sufficient luminance, contrast and size of the object, the processing speed was not limited by such low-level factors. Taken together, these studies demonstrate several distinct stages in the cortical activation sequence underlying the object recognition chain, reflecting the level of feature integration, difficulty of recognition, and direction of attention.

Processing of Stimulus Repetition and Change in the Somatosensory System: Recordings of Electrical and Magnetic Brain Responses

In the present work, effects of stimulus repetition and change in a continuous stimulus stream on the processing of somatosensory information in the human brain were studied. Human scalp-recorded somatosensory event-related potentials (ERPs) and magnetoencephalographic (MEG) responses rapidly diminished with stimulus repetition when mechanical or electric stimuli were applied to fingers. On the contrary, when the ERPs and multi-unit a ctivity (MUA) were directly recorded from the primary (SI) and secondary (SII) somatosensory cortices in a monkey, there was no marked decrement in the somatosensory responses as a function of stimulus repetition. These results suggest that this rate effect is not due to the response diminution in the SI and SII cortices. Obviously the responses to the first stimulus after a long "silent" period are nhanced due to unspecific initial orientation, originating in more broadly distributed and/or deeper neural structures, perhaps in the prefrontal cortices. With fast repetition rates not only the late unspecific but also some early specific somatosensory ERPs were diminished in amplitude. The fast decrease of the ERPs as a function of stimulus repetition is mainly due to the disappearance of the orientation effect and with faster repetition rates additively due to stimulus specific refractoriness. A sudden infrequent change in the continuous stimulus stream also enhanced somatosensory MEG responses to electric stimuli applied to different fingers. These responses were quite similar to those elicited by the deviant stimuli alone when the frequent standard stimuli were omitted. This enhancement was obviously due to the release from refractoriness because the neural structures generating the responses to the infrequent deviants had more time to recover from the refractoriness than the respective structures for the standards. Infrequent deviant mechanical stimuli among frequent standard stimuli also enhanced somatosensory ERPs and, in addition, they elicited a new negative wave which did not occur in the deviants-alone condition. This extra negativity could be recorded to deviations in the stimulation site and in the frequency of the vibratory stimuli. This response is probably a somatosensory analogue of the auditory mismatch negativity (MMN) which has been suggested to reflect a neural mismatch process between the sensory input and the sensory memory trace.

Integrated semantic processing of complex pictures and spoken sentences - Evidence from event-related potentials

It has been suggested that semantic information processing is modularized according to the input form (e.g., visual, verbal, non-verbal sound). A great deal of research has concentrated on detecting a separate verbal module. Also, it has traditionally been assumed in linguistics that the meaning of a single clause is computed before integration to a wider context. Recent research has called these views into question. The present study explored whether it is reasonable to assume separate verbal and nonverbal semantic systems in the light of the evidence from event-related potentials (ERPs). The study also provided information on whether the context influences processing of a single clause before the local meaning is computed. The focus was on an ERP called N400. Its amplitude is assumed to reflect the effort required to integrate an item to the preceding context. For instance, if a word is anomalous in its context, it will elicit a larger N400. N400 has been observed in experiments using both verbal and nonverbal stimuli. Contents of a single sentence were not hypothesized to influence the N400 amplitude. Only the combined contents of the sentence and the picture were hypothesized to influence the N400. The subjects (n = 17) viewed pictures on a computer screen while hearing sentences through headphones. Their task was to judge the congruency of the picture and the sentence. There were four conditions: 1) the picture and the sentence were congruent and sensible, 2) the sentence and the picture were congruent, but the sentence ended anomalously, 3) the picture and the sentence were incongruent but sensible, 4) the picture and the sentence were incongruent and anomalous. Stimuli from the four conditions were presented in a semi-randomized sequence. Their electroencephalography was simultaneously recorded. ERPs were computed for the four conditions. The amplitude of the N400 effect was largest in the incongruent sentence-picture -pairs. The anomalously ending sentences did not elicit a larger N400 than the sensible sentences. The results suggest that there is no separate verbal semantic system, and that the meaning of a single clause is not processed independent of the context.

Integrating and evaluating neural word embeddings in information retrieval

Recent advances in neural language models have contributed new methods for learning distributed vector representations of words (also called word embeddings). Two such methods are the continuous bag-of-words model and the skipgram model. These methods have been shown to produce embeddings that capture higher order relationships between words that are highly effective in natural language processing tasks involving the use of word similarity and word analogy. Despite these promising results, there has been little analysis of the use of these word embeddings for retrieval. Motivated by these observations, in this paper, we set out to determine how these word embeddings can be used within a retrieval model and what the benefit might be. To this aim, we use neural word embeddings within the well known translation language model for information retrieval. This language model captures implicit semantic relations between the words in queries and those in relevant documents, thus producing more accurate estimations of document relevance. The word embeddings used to estimate neural language models produce translations that differ from previous translation language model approaches; differences that deliver improvements in retrieval effectiveness. The models are robust to choices made in building word embeddings and, even more so, our results show that embeddings do not even need to be produced from the same corpus being used for retrieval.

The influence of pre-processing on the estimation of readability of web documents

This paper investigates the effect that text pre-processing approaches have on the estimation of the readability of web pages. Readability has been highlighted as an important aspect of web search result personalisation in previous work. The most widely used text readability measures rely on surface level characteristics of text, such as the length of words and sentences. We demonstrate that different tools for extracting text from web pages lead to very different estimations of readability. This has an important implication for search engines because search result personalisation strategies that consider users reading ability may fail if incorrect text readability estimations are computed.

Cocowood processing manual: from coconut wood to quality flooring.

This manual describes best practice for producing high-value flooring products from coconut ‘wood’—or cocowood. It meets international standards for flooring products and accounts for the recognised, specific, local conditions of the Pacific Islands. The information is intended for operators skilled in timber processing, who need to work with the unusual properties of cocowood, and specifies where cocowood processes differ from standard practice for timber. For other processes, refer to the relevant standards set by the importing country. These technical guidelines are based on the research outcomes of the ACIAR project, 'Improving value and marketability of coconut wood'. The manual is divided into three chapters. Each chapter adds to different aspects of primary and secondary processing. Chapter 1 provides an overview of the best practice steps for harvesting and processing cocowood. There is also a glossary of terms specifically associated with processing cocowood and a section on managing processing risks. Chapter 2 covers cocowood’s unique properties and how they relate to critical processing techniques. This is followed by sections that set out the processing methods in more detail, explaining why these practices are essential when working with cocowood. Chapter 3 provides more information, including contacts, current timber standards and some useful publications.

Small Log Processing Resource Specification

Sawing studies of mixed species and age plantation hardwoods representing potential early resource flows available to plantation sawlog processing industry. Provide information needed to underpin a business case to invest in small log sawing infrastucture.

Harvesting, packing, postharvest technology, transport and processing

This chapter provides updated information on avocado fruit quality parameters, sensory perception and maturity, production and postharvest factors affecting quality defects, disinfestation and storage (including pre-conditioning), predicting outturn quality and processing.

Enhanced processing of SPOT multispectral satellite imagery for environmental monitoring and modelling

The Taita Hills in southeastern Kenya form the northernmost part of Africa’s Eastern Arc Mountains, which have been identified by Conservation International as one of the top ten biodiversity hotspots on Earth. As with many areas of the developing world, over recent decades the Taita Hills have experienced significant population growth leading to associated major changes in land use and land cover (LULC), as well as escalating land degradation, particularly soil erosion. Multi-temporal medium resolution multispectral optical satellite data, such as imagery from the SPOT HRV, HRVIR, and HRG sensors, provides a valuable source of information for environmental monitoring and modelling at a landscape level at local and regional scales. However, utilization of multi-temporal SPOT data in quantitative remote sensing studies requires the removal of atmospheric effects and the derivation of surface reflectance factor. Furthermore, for areas of rugged terrain, such as the Taita Hills, topographic correction is necessary to derive comparable reflectance throughout a SPOT scene. Reliable monitoring of LULC change over time and modelling of land degradation and human population distribution and abundance are of crucial importance to sustainable development, natural resource management, biodiversity conservation, and understanding and mitigating climate change and its impacts. The main purpose of this thesis was to develop and validate enhanced processing of SPOT satellite imagery for use in environmental monitoring and modelling at a landscape level, in regions of the developing world with limited ancillary data availability. The Taita Hills formed the application study site, whilst the Helsinki metropolitan region was used as a control site for validation and assessment of the applied atmospheric correction techniques, where multiangular reflectance field measurements were taken and where horizontal visibility meteorological data concurrent with image acquisition were available. The proposed historical empirical line method (HELM) for absolute atmospheric correction was found to be the only applied technique that could derive surface reflectance factor within an RMSE of < 0.02 ps in the SPOT visible and near-infrared bands; an accuracy level identified as a benchmark for successful atmospheric correction. A multi-scale segmentation/object relationship modelling (MSS/ORM) approach was applied to map LULC in the Taita Hills from the multi-temporal SPOT imagery. This object-based procedure was shown to derive significant improvements over a uni-scale maximum-likelihood technique. The derived LULC data was used in combination with low cost GIS geospatial layers describing elevation, rainfall and soil type, to model degradation in the Taita Hills in the form of potential soil loss, utilizing the simple universal soil loss equation (USLE). Furthermore, human population distribution and abundance were modelled with satisfactory results using only SPOT and GIS derived data and non-Gaussian predictive modelling techniques. The SPOT derived LULC data was found to be unnecessary as a predictor because the first and second order image texture measurements had greater power to explain variation in dwelling unit occurrence and abundance. The ability of the procedures to be implemented locally in the developing world using low-cost or freely available data and software was considered. The techniques discussed in this thesis are considered equally applicable to other medium- and high-resolution optical satellite imagery, as well the utilized SPOT data.

Medication errors in residential aged care facilities: A distributed cognition analysis of the information exchange process

Background Medication safety is a pressing concern for residential aged care facilities (RACFs). Retrospective studies in RACF settings identify inadequate communication between RACFs, doctors, hospitals and community pharmacies as the major cause of medication errors. Existing literature offers limited insight about the gaps in the existing information exchange process that may lead to medication errors. The aim of this research was to explicate the cognitive distribution that underlies RACF medication ordering and delivery to identify gaps in medication-related information exchange which lead to medication errors in RACFs. Methods The study was undertaken in three RACFs in Sydney, Australia. Data were generated through ethnographic field work over a period of five months (May–September 2011). Triangulated analysis of data primarily focused on examining the transformation and exchange of information between different media across the process. Results The findings of this study highlight the extensive scope and intense nature of information exchange in RACF medication ordering and delivery. Rather than attributing error to individual care providers, the explication of distributed cognition processes enabled the identification of gaps in three information exchange dimensions which potentially contribute to the occurrence of medication errors namely: (1) design of medication charts which complicates order processing and record keeping (2) lack of coordination mechanisms between participants which results in misalignment of local practices (3) reliance on restricted communication bandwidth channels mainly telephone and fax which complicates the information processing requirements. The study demonstrates how the identification of these gaps enhances understanding of medication errors in RACFs. Conclusions Application of the theoretical lens of distributed cognition can assist in enhancing our understanding of medication errors in RACFs through identification of gaps in information exchange. Understanding the dynamics of the cognitive process can inform the design of interventions to manage errors and improve residents’ safety.

Automatic wildfire detection and simulation using optical information from unmanned aerial systems

In many parts of the world, uncontrolled fires in sparsely populated areas are a major concern as they can quickly grow into large and destructive conflagrations in short time spans. Detecting these fires has traditionally been a job for trained humans on the ground, or in the air. In many cases, these manned solutions are simply not able to survey the amount of area necessary to maintain sufficient vigilance and coverage. This paper investigates the use of unmanned aerial systems (UAS) for automated wildfire detection. The proposed system uses low-cost, consumer-grade electronics and sensors combined with various airframes to create a system suitable for automatic detection of wildfires. The system employs automatic image processing techniques to analyze captured images and autonomously detect fire-related features such as fire lines, burnt regions, and flammable material. This image recognition algorithm is designed to cope with environmental occlusions such as shadows, smoke and obstructions. Once the fire is identified and classified, it is used to initialize a spatial/temporal fire simulation. This simulation is based on occupancy maps whose fidelity can be varied to include stochastic elements, various types of vegetation, weather conditions, and unique terrain. The simulations can be used to predict the effects of optimized firefighting methods to prevent the future propagation of the fires and greatly reduce time to detection of wildfires, thereby greatly minimizing the ensuing damage. This paper also documents experimental flight tests using a SenseFly Swinglet UAS conducted in Brisbane, Australia as well as modifications for custom UAS.

Tactile processing in human somatosensory and auditory cortices

Tactile sensation plays an important role in everyday life. While the somatosensory system has been studied extensively, the majority of information has come from studies using animal models. Recent development of high-resolution anatomical and functional imaging techniques has enabled the non-invasive study of human somatosensory cortex and thalamus. This thesis provides new insights into the functional organization of the human brain areas involved in tactile processing using magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). The thesis also demonstrates certain optimizations of MEG and fMRI methods. Tactile digit stimulation elicited stimulus-specific responses in a number of brain areas. Contralateral activation was observed in somatosensory thalamus (Study II), primary somatosensory cortex (SI; I, III, IV), and post-auditory belt area (III). Bilateral activation was observed in secondary somatosensory cortex (SII; II, III, IV). Ipsilateral activation was found in the post-central gyrus (area 2 of SI cortex; IV). In addition, phasic deactivation was observed within ipsilateral SI cortex and bilateral primary motor cortex (IV). Detailed investigation of the tactile responses demonstrated that the arrangement of distal-proximal finger representations in area 3b of SI in humans is similar to that found in monkeys (I). An optimized MEG approach was sufficient to resolve such fine detail in functional organization. The SII region appeared to contain double representations for fingers and toes (II). The detection of activations in the SII region and thalamus improved at the individual and group levels when cardiac-gated fMRI was used (II). Better detection of body part representations at the individual level is an important improvement, because identification of individual representations is crucial for studying brain plasticity in somatosensory areas. The posterior auditory belt area demonstrated responses to both auditory and tactile stimuli (III), implicating this area as a physiological substrate for the auditory-tactile interaction observed in earlier psychophysical studies. Comparison of different smoothing parameters (III) demonstrated that proper evaluation of co-activation should be based on individual subject analysis with minimal or no smoothing. Tactile input consistently influenced area 3b of the human ipsilateral SI cortex (IV). The observed phasic negative fMRI response is proposed to result from interhemispheric inhibition via trans-callosal connections. This thesis contributes to a growing body of human data suggesting that processing of tactile stimuli involves multiple brain areas, with different spatial patterns of cortical activation for different stimuli.

Role of information and communication in redefining unmanned aerial vehicle autonomous control levels

Autonomous mission control, unlike automatic mission control which is generally pre-programmed to execute an intended mission, is guided by the philosophy of carrying out a complete mission on its own through online sensing, information processing, and control reconfiguration. A crucial cornerstone of this philosophy is the capability of intelligence and of information sharing between unmanned aerial vehicles (UAVs) or with a central controller through secured communication links. Though several mission control algorithms, for single and multiple UAVs, have been discussed in the literature, they lack a clear definition of the various autonomous mission control levels. In the conventional system, the ground pilot issues the flight and mission control command to a UAV through a command data link and the UAV transmits intelligence information, back to the ground pilot through a communication link. Thus, the success of the mission depends entirely on the information flow through a secured communication link between ground pilot and the UAV In the past, mission success depended on the continuous interaction of ground pilot with a single UAV, while present day applications are attempting to define mission success through efficient interaction of ground pilot with multiple UAVs. However, the current trend in UAV applications is expected to lead to a futuristic scenario where mission success would depend only on interaction among UAV groups with no interaction with any ground entity. However, to reach this capability level, it is necessary to first understand the various levels of autonomy and the crucial role that information and communication plays in making these autonomy levels possible. This article presents a detailed framework of UAV autonomous mission control levels in the context of information flow and communication between UAVs and UAV groups for each level of autonomy.

Mesh Processing for Computerized Facial Anthropometry

Understanding of the shape and size of different features of the human body from scanned data is necessary for automated design and evaluation of product ergonomics. In this paper, a computational framework is presented for automatic detection and recognition of important facial feature regions, from scanned head and shoulder polyhedral models. A noise tolerant methodology is proposed using discrete curvature computations, band-pass filtering, and morphological operations for isolation of the primary feature regions of the face, namely, the eyes, nose, and mouth. Spatial disposition of the critical points of these isolated feature regions is analyzed for the recognition of these critical points as the standard landmarks associated with the primary facial features. A number of clinically identified landmarks lie on the facial midline. An efficient algorithm for detection and processing of the midline, using a point sampling technique, is also presented. The results obtained using data of more than 20 subjects are verified through visualization and physical measurements. A color based and triangle skewness based schemes for isolation of geometrically nonprominent features and ear region are also presented. [DOI: 10.1115/1.3330420]