Revisiting the video deficit in technology-saturated environments: Successful imitation from people, screens, and social robots

The "video deficit" is a well-documented effect whereby children learn less well about information delivered via a screen than the same information delivered in person. Research suggests that increasing social contingency may ameliorate this video deficit. The current study instantiated social contingency to screen-based information by embodying the screen within a socially interactive robot presented to urban Australian children with frequent exposure to screen-based communication. We failed to document differences between 22- to 26-month-old children's (N = 80) imitation of screen-based information embedded in a social robot and in-person humans. Furthermore, we did not replicate the video deficit with children imitating at similar levels regardless of the presentation medium. This failure to replicate supports the findings of a recent meta-analysis of video deficit research whereby there appears to be a steady decrease over time in the magnitude of the video deficit effect. We postulate that, should the video deficit effect be truly dwindling in effect size, the video deficit may soon be a historical artifact as children begin perceiving technology as relevant and meaningful in everyday life more and more. This research finds that observational-based learning material can be successfully delivered in person, via a screen, or via a screen embedded in a social robot.

Interactive neurorobotics: Behavioral and neural dynamics of agent interactions

Interactive neurorobotics is a subfield which characterizes brain responses evoked during interaction with a robot, and their relationship with the behavioral responses. Gathering rich neural and behavioral data from humans or animals responding to agents can act as a scaffold for the design process of future social robots. This research seeks to study how organisms respond to artificial agents in contrast to biological or inanimate ones. This experiment uses the novel affordances of the robotic platforms to investigate complex dynamics during minimally structured interactions that would be difficult to capture with classical experimental setups. We then propose a general framework for such experiments that emphasizes naturalistic interactions combined with multimodal observations and complementary analysis pipelines that are necessary to render a holistic picture of the data for the purpose of informing robotic design principles. Finally, we demonstrate this approach with an exemplar rat-robot social interaction task which included simultaneous multi-agent tracking and neural recordings.

Personal and complex: The needs and experiences related to technology use for people living with dementia

BACKGROUND

With increasing focus on living well with dementia, technology has been identified as having potential benefits for safety, independence and wellbeing. Despite a large growth in specialised technology development, there has been limited uptake. There is a need to understand the current use and needs related to technology for people living with dementia and their care partners (informal carers).

METHODS

As part of a participatory design study, a qualitative inquiry into technology experiences and needs was undertaken within an interpretive description approach. A cross-disciplinary team including living experience experts (people living with dementia, unpaid care partners) was involved. Semi-structured interviews, including sharing technology locations and supports, were conducted, audio-recorded and transcribed. Key aspects of the technology use experience were constructed.

FINDINGS

Thirteen people living with dementia and 21 care partners participated in the study. Two core aspects of technology use were identified: Lots of moving parts: the complex context, and A technology cycle: the use and non-use. The findings indicated that the context involved an individual configuration of multiple factors including the user, their technology identity, their supporters, the technology and the environment. The experience is underpinned by shifting foundations created by constantly changing technologies and user characteristics. The cycle of use and non-use identified the processes required to maintain technology engagement.

CONCLUSION

The use of technology for people living with dementia is complex and personal. Future technology development, policies and practices need to consider this complexity and the effort required to keep using technology to realise the benefits.

An Information Theoretic Approach to Symbolic Learning in Synthetic Languages

An important aspect of using entropy-based models and proposed “synthetic languages”, is the seemingly simple task of knowing how to identify the probabilistic symbols. If the system has discrete features, then this task may be trivial; however, for observed analog behaviors described by continuous values, this raises the question of how we should determine such symbols. This task of symbolization extends the concept of scalar and vector quantization to consider explicit linguistic properties. Unlike previous quantization algorithms where the aim is primarily data compression and fidelity, the goal in this case is to produce a symbolic output sequence which incorporates some linguistic properties and hence is useful in forming language-based models. Hence, in this paper, we present methods for symbolization which take into account such properties in the form of probabilistic constraints. In particular, we propose new symbolization algorithms which constrain the symbols to have a Zipf–Mandelbrot–Li distribution which approximates the behavior of language elements. We introduce a novel constrained EM algorithm which is shown to effectively learn to produce symbols which approximate a Zipfian distribution. We demonstrate the efficacy of the proposed approaches on some examples using real world data in different tasks, including the translation of animal behavior into a possible human language understandable equivalent.

Convergent cross sorting for estimating dynamic coupling

Natural systems exhibit diverse behavior generated by complex interactions between their constituent parts. To characterize these interactions, we introduce Convergent Cross Sorting (CCS), a novel algorithm based on convergent cross mapping (CCM) for estimating dynamic coupling from time series data. CCS extends CCM by using the relative ranking of distances within state-space reconstructions to improve the prior methods' performance at identifying the existence, relative strength, and directionality of coupling across a wide range of signal and noise characteristics. In particular, relative to CCM, CCS has a large performance advantage when analyzing very short time series data and data from continuous dynamical systems with synchronous behavior. This advantage allows CCS to better uncover the temporal and directional relationships within systems that undergo frequent and short-lived switches in dynamics, such as neural systems. In this paper, we validate CCS on simulated data and demonstrate its applicability to electrophysiological recordings from interacting brain regions.

The early ontogeny of infants' imitation of on screen humans and robots

Traditionally, infants have learned how to interact with objects in their environment through direct observations of adults and peers. In recent decades these models have been available over different media, and this has introduced non-human agents to infants' learning environments. Humanoid robots are increasingly portrayed as social agents in on screen, but the degree to which infants are capable of observational learning from screen-based robots is unknown. The current study thus investigated how well 1- to 3-year-olds (N = 230) could imitate on-screen robots relative to on-screen and live humans. Participants exhibited an imitation deficit for robots that varied with age. Furthermore, the well-known video deficit did not replicate as expected, and was weak and transient relative to past research. Together, the findings documented here suggest that infants are learning from media in ways that differ from past generations, but that this new learning is nuanced when novel technologies are involved.

"Building the Threads of Connection that We Already Have": The Nature of Connections via Technology for Older People

Objectives: The social connectedness of older people is of increasing concern. Technology has been suggested for enhancing social inclusion. This study aimed to explore the nature and quality of connections via technology.Methods: Qualitative exploration of experiences, stories, and needs was undertaken through semi-structured interviews with older (7) and middle-aged (3) adults with rich experience of connections via technology in Australia and England. Core aspects of connections through technology were constructed through interpretive description analysis.Results: Four key aspects were: 1. The caliber of connections: descriptions of a range of subjective quality of connections and characteristics of good connections; 2. Experiences of poor connection (mis- and dis-connection) including descriptions of experiences creating isolation; 3. Reasons to connect described the purposes of technology-based connections including connecting with others, themselves and places important to them; 4. Making connections work described active strategies to enhance connection.Conclusions: Using technology is part of the social engagement of many people. Considering the related feelings of connection and support strategies and needs could enhance future research and practice with older people.Clinical implications: The different characteristics and potential positive and negative experiences of connection via technology need consideration in measuring social isolation and supporting older adults.

An Automated Approach to Examining Pausing in the Speech of People With Dementia

Dementia is a common neurodegenerative condition involving the deterioration of cognitive and communication skills. Pausing in the speech of people with dementia is a dysfluency that may be used to signal conversational trouble in social interaction. This study aimed to examine the speech-pausing profile within picture description samples from people with dementia and healthy controls (HCs) within the DementiaBank database using the Calpy computational speech processing toolkit. Sixty English-speaking participants between the ages of 53 and 88 years (Mage = 67.43, SD = 8.33; 42 females) were included in the study: 20 participants with mild cognitive impairment, 20 participants with moderate cognitive impairment, and 20 HCs. Quantitative analysis shows a progressive increase in the duration of pausing between HCs, the mild dementia group, and the moderate dementia group, respectively.

P373 Left atrial mechanics and aortic stiffness following high intensity interval training: a randomised controlled study

Background

Physical inactivity is associated with an increased risk of cardiovascular disease. High intensity interval training (HIIT) has been shown to improve important health parameters, including aerobic capacity, arterial blood pressure, cardiac autonomic modulation and left ventricular mechanics. However, adaptations in left atrial (LA) mechanics and aortic stiffness remain unclear. Therefore, the aim of the present study was to assess any left atrial and aortic adaptations to HIIT.

Methods

Forty-one physically inactive males and females (aged 23 ± 2.7 years) volunteered for the study. Participants were randomised to either a 4-week HIIT intervention (n = 21) or 4-week control period (n = 20). The HIIT protocol consisted of 3 x 30-second maximal cycle ergometer sprints with a resistance of 7.5% body weight, interspersed with 2-minutes of active unloaded recovery. Speckle tracking imaging of the left atrium and M-Mode tracing of the aorta was performed pre and post HIIT and control period using commercially available software (EchoPac; GE Medical Systems). Analysis of covariance, with baseline measures as the covariate, was used to explore any differences in left atrial mechanics and aortic stiffness between the intervention and control groups. Stepwise linear regression analysis using LA stiffness as the dependent variable was conducted.

Results

Following 4-weeks of HIIT, there was significant improvement in LA mechanics, including LA reservoir (13.9 ± 13.4%, p = 0.033) and LA stiffness (-0.05 ± 0.04%-1, p = 0.032) compared to the control condition. In addition, improvements were observed in aortic distensibility (2.1 ± 2.7 cm2 × dyn×-1 × 103, p = 0.031) and aortic stiffness index (-2.6 ± 4.6, p = 0.041) compared to the control condition. In stepwise linear regression analysis, aortic distensibility change was significantly associated with LA stiffness change (p = 0.002), with an R2 of 0.613.

Conclusion

A short-term programme of HIIT was associated with a significant improvement in LA mechanics and aortic stiffness. These adaptations may have important health implications and contribute to the improved left ventricular diastolic and systolic mechanics, aerobic capacity and reduced arterial blood pressure previously documented following HIIT.

Toward the Development of SMART Communication Technology: Automating the Analysis of Communicative Trouble and Repair in Dementia

BACKGROUND AND OBJECTIVES

Communication difficulties have been reported as one of the most stress-inducing aspects of caring for people with dementia. Notably, with disease progression comes an increase in the frequency of communication difficulty and a reduction in the effectiveness of attempts to remedy breakdowns in communication. The aim of the current research was to evaluate the utility of an automated discourse analysis tool (i.e., Discursis) in distinguishing between different types of trouble and repair signaling behaviors, demonstrated within conversations between people with dementia and their professional care staff.

RESEARCH DESIGN AND METHODS

Twenty conversations between people with dementia and their professional care staff were human-coded for instances of interactive/noninteractive trouble and typical/facilitative repair behaviors. Associations were then examined between these behaviors and recurrence metrics generated by Discursis.

RESULTS

Significant associations were identified between Discursis metrics, trouble-indicating, and repair behaviors.

DISCUSSION AND IMPLICATIONS

These results suggest that discourse analysis software is capable of discriminating between different types of trouble and repair signaling behavior, on the basis of term recurrence calculated across speaker turns. The subsequent recurrence metrics generated by Discursis offer a means of automating the analysis of episodes of conversational trouble and repair. This achievement represents the first step toward the future development of an intelligent assistant that can analyze conversations in real time and offers support to people with dementia and their carers during periods of communicative trouble.

Social semantic networks: Measuring topic management in discourse using a pyramid of conceptual recurrence metrics

Studies of conversational encounters typically employ manual discourse analysis methods to reveal participants' topic management patterns, usually focusing on turn-by-turn interactions within a specific social context. These analyses, while powerful, are time-consuming to apply and can prove difficult to generalize. Recurrence analysis has recently been applied to discourse datasets to model how individual terms and concepts recur over whole conversation time scales and relate patterns of recurrence to topic management practices by individuals. In this paper, we propose a new multi-level quantitative method for modelling the topical interaction dynamics in conversation based on conceptual recurrence quantification analysis. The new protocol develops a hierarchy of speakers and their interactions, and partitions recurrence based on these groups. The new protocol is evaluated against expert human coding of television broadcast interviews. Our analysis reveals topic use patterns and networks of conceptual engagement (person-person and group-group) that show experts preferentially engaging with other experts rather than with laypeople, findings that are consistent with prior expectations for this discourse, although never before expressed as metrics. The studies provide a starting point for new computational protocols to provide fast, semi-automated methods for measuring the degree of conceptual interaction between individuals and groups.

Temporally selective contextual encoding in the dentate gyrus of the hippocampus

A recent model of the hippocampus predicts that the unique properties of the dentate gyrus allow for temporal separation of events. This temporal separation is accomplished in part through the continual generation of new neurons, which, due to a transient window of hyperexcitability, could allow for preferential encoding of information present during their development. Here we obtain in vivo electrophysiological recordings and identify a cell population exhibiting activity that is selective to single contexts when rats experience a long temporal separation between context exposures during training. This selectivity is attenuated as the temporal separation between context exposures is shortened and is further attenuated when neurogenesis is reduced. Our data reveal the existence of a temporal orthogonalizing neuronal code within the dentate gyrus, a hallmark feature of episodic memory.

Acquired Codes of Meaning in Data Visualization and Infographics: Beyond Perceptual Primitives

While information visualization frameworks and heuristics have traditionally been reluctant to include acquired codes of meaning, designers are making use of them in a wide variety of ways. Acquired codes leverage a user's experience to understand the meaning of a visualization. They range from figurative visualizations which rely on the reader's recognition of shapes, to conventional arrangements of graphic elements which represent particular subjects. In this study, we used content analysis to codify acquired meaning in visualization. We applied the content analysis to a set of infographics and data visualizations which are exemplars of innovative and effective design. 88% of the infographics and 71% of data visualizations in the sample contain at least one use of figurative visualization. Conventions on the arrangement of graphics are also widespread in the sample. In particular, a comparison of representations of time and other quantitative data showed that conventions can be specific to a subject. These results suggest that there is a need for information visualization research to expand its scope beyond perceptual channels, to include social and culturally constructed meaning. Our paper demonstrates a viable method for identifying figurative techniques and graphic conventions and integrating them into heuristics for visualization design.

Hello Harlie: Enabling Speech Monitoring Through Chat-Bot Conversations

People with neurological conditions such as Parkinson's disease and dementia are known to have difficulties in language and communication. This paper presents initial testing of an artificial conversational agent, called Harlie. Harlie runs on a smartphone and is able to converse with the user on a variety of topics. A description of the application and a sample dialog are provided to illustrate the various roles chat-bots can play in the management of neurological conditions. Harlie can be used for measuring voice and communication outcomes during the daily life of the user, and for gaining information about challenges encountered. Moreover, it is anticipated that she may also have an educational and support role.

An Automated Approach to Examining Conversational Dynamics between People with Dementia and Their Carers

The progressive neuropathology involved in dementia frequently causes a gradual decline in communication skills. Communication partners who are unaware of the specific communication problems faced by people with dementia (PWD) can inadvertently challenge their conversation partner, leading to distress and a reduced flow of information between speakers. Previous research has produced an extensive literature base recommending strategies to facilitate conversational engagement in dementia. However, empirical evidence for the beneficial effects of these strategies on conversational dynamics is sparse. This study uses a time-efficient computational discourse analysis tool called Discursis to examine the link between specific communication behaviours and content-based conversational engagement in 20 conversations between PWD living in residential aged-care facilities and care staff members. Conversations analysed here were baseline conversations recorded before staff members underwent communication training. Care staff members spontaneously exhibited a wide range of facilitative and non-facilitative communication behaviours, which were coded for analysis of conversation dynamics within these baseline conversations. A hybrid approach combining manual coding and automated Discursis metric analysis provides two sets of novel insights. Firstly, this study revealed nine communication behaviours that, if used by the care staff member in a given turn, significantly increased the appearance of subsequent content-based engagement in the conversation by PWD. Secondly, the current findings reveal alignment between human- and computer-generated labelling of communication behaviour for 8 out of the total 22 behaviours under investigation. The approach demonstrated in this study provides an empirical procedure for the detailed evaluation of content-based conversational engagement associated with specific communication behaviours.

Global segregation of cortical activity and metastable dynamics

Cortical activity exhibits persistent metastable dynamics. Assemblies of neurons transiently couple (integrate) and decouple (segregate) at multiple spatiotemporal scales; both integration and segregation are required to support metastability. Integration of distant brain regions can be achieved through long range excitatory projections, but the mechanism supporting long range segregation is not clear. We argue that the thalamocortical matrix connections, which project diffusely from the thalamus to the cortex and have long been thought to support cortical gain control, play an equally-important role in cortical segregation. We present a computational model of the diffuse thalamocortical loop, called the competitive cross-coupling (CXC) spiking network. Simulations of the model show how different levels of tonic input from the brainstem to the thalamus could control dynamical complexity in the cortex, directing transitions between sleep, wakefulness and high attention or vigilance. The model also explains how mutually-exclusive activity could arise across large portions of the cortex, such as between the default-mode and task-positive networks. It is robust to noise but does not require noise to autonomously generate metastability. We conclude that the long range segregation observed in brain activity and required for global metastable dynamics could be provided by the thalamocortical matrix, and is strongly modulated by brainstem input to the thalamus.

Detecting contaminated birthdates using generalized additive models

Background

Erroneous patient birthdates are common in health databases. Detection of these errors usually involves manual verification, which can be resource intensive and impractical. By identifying a frequent manifestation of birthdate errors, this paper presents a principled and statistically driven procedure to identify erroneous patient birthdates.

Results

Generalized additive models (GAM) enabled explicit incorporation of known demographic trends and birth patterns. With false positive rates controlled, the method identified birthdate contamination with high accuracy. In the health data set used, of the 58 actual incorrect birthdates manually identified by the domain expert, the GAM-based method identified 51, with 8 false positives (resulting in a positive predictive value of 86.0% (51/59) and a false negative rate of 12.0% (7/58)). These results outperformed linear time-series models.

Conclusions

The GAM-based method is an effective approach to identify systemic birthdate errors, a common data quality issue in both clinical and administrative databases, with high accuracy.

Rodent scope: a user-configurable digital wireless telemetry system for freely behaving animals

This paper describes the design and implementation of a wireless neural telemetry system that enables new experimental paradigms, such as neural recordings during rodent navigation in large outdoor environments. RoSco, short for Rodent Scope, is a small lightweight user-configurable module suitable for digital wireless recording from freely behaving small animals. Due to the digital transmission technology, RoSco has advantages over most other wireless modules of noise immunity and online user-configurable settings. RoSco digitally transmits entire neural waveforms for 14 of 16 channels at 20 kHz with 8-bit encoding which are streamed to the PC as standard USB audio packets. Up to 31 RoSco wireless modules can coexist in the same environment on non-overlapping independent channels. The design has spatial diversity reception via two antennas, which makes wireless communication resilient to fading and obstacles. In comparison with most existing wireless systems, this system has online user-selectable independent gain control of each channel in 8 factors from 500 to 32,000 times, two selectable ground references from a subset of channels, selectable channel grounding to disable noisy electrodes, and selectable bandwidth suitable for action potentials (300 Hz-3 kHz) and low frequency field potentials (4 Hz-3 kHz). Indoor and outdoor recordings taken from freely behaving rodents are shown to be comparable to a commercial wired system in sorting for neural populations. The module has low input referred noise, battery life of 1.5 hours and transmission losses of 0.1% up to a range of 10 m.

Improved gross efficiency during long duration submaximal cycling following a short-term high carbohydrate diet

To assess the effect of dietary manipulation on gross efficiency (GE), 15 trained male cyclists completed 3×2 h tests at submaximal exercise intensity (60% Maximal Minute Power). Using a randomized, crossover design participants consumed an isoenergetic diet (~4 000 kcal.day-1) in the 3 days preceding each test, that was either high in carbohydrate (HighCHO, [70% of the total energy derived from carbohydrate, 20% fat, 10% protein]), low in carbohydrate (LowCHO, [70% fat, 20% carbohydrate, 10% protein]) or contained a moderate amount of carbohydrate (ModCHO, [45% carbohydrate, 45% fat, 10% protein]). GE along with blood lactate and glucose were assessed every 30 min, and heart rate was measured at 5 s intervals throughout. Mean GE was significantly greater following the HighCHO than the ModCHO diet (HighCHO=20.4%±0.1%, ModCHO=19.6±0.2%; P<0.001). Additionally, HighCHO GE was significantly greater after 25 min (P=0.015) and 85 min (P=0.021) than in the LowCHO condition. Heart rate responses in the HighCHO condition were significantly lower than during the LowCHO tests (P=0.005). Diet had no effect on blood glucose or lactate (P>0.05). This study suggests that before the measurement of gross efficiency, participants' diet should be controlled and monitored to ensure the validity of the results obtained.

Repeat-encoded poly-Q tracts show statistical commonalities across species

BACKGROUND

Among repetitive genomic sequence, the class of tri-nucleotide repeats has received much attention due to their association with human diseases. Tri-nucleotide repeat diseases are caused by excessive sequence length variability; diseases such as Huntington's disease and Fragile X syndrome are tied to an increase in the number of repeat units in a tract. Motivated by the recent discovery of a tri-nucleotide repeat associated genetic defect in Arabidopsis thaliana, this study takes a cross-species approach to investigating these repeat tracts, with the goal of using commonalities between species to identify potential disease-related properties.

RESULTS

We find that statistical enrichment in regulatory function associations for coding region repeats - previously observed in human - is consistent across multiple organisms. By distinguishing between homo-amino acid tracts that are encoded by tri-nucleotide repeats, and those encoded by varying codons, we show that amino acid repeats - not tri-nucleotide repeats - fully explain these regulatory associations. Using this same separation between repeat- and non-repeat-encoded homo-amino acid tracts, we show that poly-glutamine tracts are disproportionately encoded by tri-nucleotide repeats, and those tracts that are encoded by tri-nucleotide repeats are also significantly longer; these results are consistent across multiple species.

CONCLUSION

These findings establish similarities in tri-nucleotide repeats across species at the level of protein functionality and protein sequence. The tendency of tri-nucleotide repeats to encode longer poly-glutamine tracts indicates a link with the poly-glutamine repeat diseases. The cross-species nature of this tendency suggests that unknown repeat diseases are yet to be uncovered in other species. Future discoveries of new non-human repeat associated defects may provide the breadth of information needed to unravel the mechanisms that underpin this class of human disease.

Action potential waveform variability limits multi-unit separation in freely behaving rats

Extracellular multi-unit recording is a widely used technique to study spontaneous and evoked neuronal activity in awake behaving animals. These recordings are done using either single-wire or mulitwire electrodes such as tetrodes. In this study we have tested the ability of single-wire electrodes to discriminate activity from multiple neurons under conditions of varying noise and neuronal cell density. Using extracellular single-unit recording, coupled with iontophoresis to drive cell activity across a wide dynamic range, we studied spike waveform variability, and explored systematic differences in single-unit spike waveform within and between brain regions as well as the influence of signal-to-noise ratio (SNR) on the similarity of spike waveforms. We also modelled spike misclassification for a range of cell densities based on neuronal recordings obtained at different SNRs. Modelling predictions were confirmed by classifying spike waveforms from multiple cells with various SNRs using a leading commercial spike-sorting system. Our results show that for single-wire recordings, multiple units can only be reliably distinguished under conditions of high recording SNR (≥4) and low neuronal density (≈20,000/ mm³). Physiological and behavioural changes, as well as technical limitations typical of awake animal preparations, reduce the accuracy of single-channel spike classification, resulting in serious classification errors. For SNR <4, the probability of misclassifying spikes approaches 100% in many cases. Our results suggest that in studies where the SNR is low or neuronal density is high, separation of distinct units needs to be evaluated with great caution.

Conceptual recurrence plots: revealing patterns in human discourse

Human discourse contains a rich mixture of conceptual information. Visualization of the global and local patterns within this data stream is a complex and challenging problem. Recurrence plots are an information visualization technique that can reveal trends and features in complex time series data. The recurrence plot technique works by measuring the similarity of points in a time series to all other points in the same time series and plotting the results in two dimensions. Previous studies have applied recurrence plotting techniques to textual data; however, these approaches plot recurrence using term-based similarity rather than conceptual similarity of the text. We introduce conceptual recurrence plots, which use a model of language to measure similarity between pairs of text utterances, and the similarity of all utterances is measured and displayed. In this paper, we explore how the descriptive power of the recurrence plotting technique can be used to discover patterns of interaction across a series of conversation transcripts. The results suggest that the conceptual recurrence plotting technique is a useful tool for exploring the structure of human discourse.

Using strategic movement to calibrate a neural compass: a spiking network for tracking head direction in rats and robots

The head direction (HD) system in mammals contains neurons that fire to represent the direction the animal is facing in its environment. The ability of these cells to reliably track head direction even after the removal of external sensory cues implies that the HD system is calibrated to function effectively using just internal (proprioceptive and vestibular) inputs. Rat pups and other infant mammals display stereotypical warm-up movements prior to locomotion in novel environments, and similar warm-up movements are seen in adult mammals with certain brain lesion-induced motor impairments. In this study we propose that synaptic learning mechanisms, in conjunction with appropriate movement strategies based on warm-up movements, can calibrate the HD system so that it functions effectively even in darkness. To examine the link between physical embodiment and neural control, and to determine that the system is robust to real-world phenomena, we implemented the synaptic mechanisms in a spiking neural network and tested it on a mobile robot platform. Results show that the combination of the synaptic learning mechanisms and warm-up movements are able to reliably calibrate the HD system so that it accurately tracks real-world head direction, and that calibration breaks down in systematic ways if certain movements are omitted. This work confirms that targeted, embodied behaviour can be used to calibrate neural systems, demonstrates that ‘grounding’ of modelled biological processes in the real world can reveal underlying functional principles (supporting the importance of robotics to biology), and proposes a functional role for stereotypical behaviours seen in infant mammals and those animals with certain motor deficits. We conjecture that these calibration principles may extend to the calibration of other neural systems involved in motion tracking and the representation of space, such as grid cells in entorhinal cortex.

Solving navigational uncertainty using grid cells on robots

To successfully navigate their habitats, many mammals use a combination of two mechanisms, path integration and calibration using landmarks, which together enable them to estimate their location and orientation, or pose. In large natural environments, both these mechanisms are characterized by uncertainty: the path integration process is subject to the accumulation of error, while landmark calibration is limited by perceptual ambiguity. It remains unclear how animals form coherent spatial representations in the presence of such uncertainty. Navigation research using robots has determined that uncertainty can be effectively addressed by maintaining multiple probabilistic estimates of a robot's pose. Here we show how conjunctive grid cells in dorsocaudal medial entorhinal cortex (dMEC) may maintain multiple estimates of pose using a brain-based robot navigation system known as RatSLAM. Based both on rodent spatially-responsive cells and functional engineering principles, the cells at the core of the RatSLAM computational model have similar characteristics to rodent grid cells, which we demonstrate by replicating the seminal Moser experiments. We apply the RatSLAM model to a new experimental paradigm designed to examine the responses of a robot or animal in the presence of perceptual ambiguity. Our computational approach enables us to observe short-term population coding of multiple location hypotheses, a phenomenon which would not be easily observable in rodent recordings. We present behavioral and neural evidence demonstrating that the conjunctive grid cells maintain and propagate multiple estimates of pose, enabling the correct pose estimate to be resolved over time even without uniquely identifying cues. While recent research has focused on the grid-like firing characteristics, accuracy and representational capacity of grid cells, our results identify a possible critical and unique role for conjunctive grid cells in filtering sensory uncertainty. We anticipate our study to be a starting point for animal experiments that test navigation in perceptually ambiguous environments.

Navigating robots face similar challenges to wild rodents in creating useable maps of their environments. Both must learn about their environments through experience, and in doing so face similar problems dealing with ambiguous and noisy information from their sensory inputs. Navigation research using robots has determined that uncertainty can be effectively addressed by maintaining multiple probabilistic estimates of a robot's pose. Neural recordings from navigating rats have revealed cells with grid-like spatial firing properties in the entorhinal cortex region of the rodent brain. Here we show how a robot equipped with conjunctive grid-cell-like cells can maintain multiple estimates of pose and solve a navigation task in an environment with no uniquely identifying cues. We propose that grid cells in the entorhinal cortex provide a similar ability for rodents. Robotics has learned much from biological systems. In a complementary way, in this study our understanding of neural systems is enhanced by insights from engineered solutions to a common problem faced by mobile robots and navigating animals.

Calibration of the head direction network: a role for symmetric angular head velocity cells

Continuous attractor networks require calibration. Computational models of the head direction (HD) system of the rat usually assume that the connections that maintain HD neuron activity are pre-wired and static. Ongoing activity in these models relies on precise continuous attractor dynamics. It is currently unknown how such connections could be so precisely wired, and how accurate calibration is maintained in the face of ongoing noise and perturbation. Our adaptive attractor model of the HD system that uses symmetric angular head velocity (AHV) cells as a training signal shows that the HD system can learn to support stable firing patterns from poorly-performing, unstable starting conditions. The proposed calibration mechanism suggests a requirement for symmetric AHV cells, the existence of which has previously been unexplained, and predicts that symmetric and asymmetric AHV cells should be distinctly different (in morphology, synaptic targets and/or methods of action on postsynaptic HD cells) due to their distinctly different functions.

Computational influence of adult neurogenesis on memory encoding

Adult neurogenesis in the hippocampus leads to the incorporation of thousands of new granule cells into the dentate gyrus every month, but its function remains unclear. Here, we present computational evidence that indicates that adult neurogenesis may make three separate but related contributions to memory formation. First, immature neurons introduce a degree of similarity to memories learned at the same time, a process we refer to as pattern integration. Second, the extended maturation and change in excitability of these neurons make this added similarity a time-dependent effect, supporting the possibility that temporal information is included in new hippocampal memories. Finally, our model suggests that the experience-dependent addition of neurons results in a dentate gyrus network well suited for encoding new memories in familiar contexts while treating novel contexts differently. Taken together, these results indicate that new granule cells may affect hippocampal function in several unique and previously unpredicted ways.

A generative bias towards average complexity in artificial cell lineages

The evolution of life on earth has been characterized by generalized long-term increases in phenotypic complexity. Although natural selection is a plausible cause for these trends, one alternative hypothesis--generative bias--has been proposed repeatedly based on theoretical considerations. Here, we introduce a computational model of a developmental system and use it to test the hypothesis that long-term increasing trends in phenotypic complexity are caused by a generative bias towards greater complexity. We use our model to generate random organisms with different levels of phenotypic complexity and analyse the distributions of mutational effects on complexity. We show that highly complex organisms are easy to generate but there are trade-offs between different measures of complexity. We also find that only the simplest possible phenotypes show a generative bias towards higher complexity, whereas phenotypes with high complexity display a generative bias towards lower complexity. These results suggest that generative biases alone are not sufficient to explain long-term evolutionary increases in phenotypic complexity. Rather, our finding of a generative bias towards average complexity argues for a critical role of selective biases in driving increases in phenotypic complexity and in maintaining high complexity once it has evolved.

LinMap: visualizing complexity gradients in evolutionary landscapes

This article describes an interactive visualization tool, LinMap, for exploring the structure of complexity gradients in evolutionary landscapes. LinMap is a computationally efficient and intuitive tool for visualizing and exploring multidimensional parameter spaces. An artificial cell lineage model is presented that allows complexity to be quantified according to several different developmental and phenotypic metrics. LinMap is applied to the evolutionary landscapes generated by this model to demonstrate that different definitions of complexity produce different gradients across the same landscape; that landscapes are characterized by a phase transition between proliferating and quiescent cell lineages where both complexity and diversity are maximized; and that landscapes defined by adaptive fitness and complexity can display different topographical features.

Robustness and state-space structure of Boolean gene regulatory models

Robustness to perturbation is an important characteristic of genetic regulatory systems, but the relationship between robustness and model dynamics has not been clearly quantified. We propose a method for quantifying both robustness and dynamics in terms of state-space structures, for Boolean models of genetic regulatory systems. By investigating existing models of the Drosophila melanogaster segment polarity network and the Saccharomyces cerevisiae cell-cycle network, we show that the structure of attractor basins can yield insight into the underlying decision making required of the system, and also the way in which the system maximises its robustness. In particular, gene networks implementing decisions based on a few genes have simple state-space structures, and their attractors are robust by virtue of their simplicity. Gene networks with decisions that involve many interacting genes have correspondingly more complicated state-space structures, and robustness cannot be achieved through the structure of the attractor basins, but is achieved by larger attractor basins that dominate the state space. These different types of robustness are demonstrated by the two models: the D. melanogaster segment polarity network is robust due to simple attractor basins that implement decisions based on spatial signals; the S. cerevisiae cell-cycle network has a complicated state-space structure, and is robust only due to a giant attractor basin that dominates the state space.

The application of machine learning techniques as an adjunct to clinical decision making in alcohol dependence treatment

With few exceptions, research in the addictive sciences has relied on linear statistics and methodologies. Addiction involves a complex array of nonlinear behaviors. This study applies two machine learning techniques, Bayesian and decision tree classifiers, in the assessment of outcome of an alcohol dependence treatment program. These nonlinear approaches are compared to a standard linear analysis. Seventy-three alcohol-dependent subjects undertaking a 12-week cognitive-behavioral therapy (CBT) program and 66 subjects undertaking an identical program but also prescribed the relapse prevention agent Acamprosate were employed in this study. Demographic, alcohol use, dependence severity, craving, health-related quality of life, and psychological measures at baseline were used to predict abstinence at 12 weeks. Decision trees had a 77% predictive accuracy across both data sets, Bayesian networks 73%, and discriminant analysis 42%. Combined with clinical experience, machine learning approaches offer promise in understanding the complex relationships that underlie treatment outcome for abstinence-based alcohol treatment programs.

Potential role for adult neurogenesis in the encoding of time in new memories

The dentate gyrus in the hippocampus is one of two brain regions with lifelong neurogenesis in mammals. Despite an increasing amount of information about the characteristics of the newborn granule cells, the specific contribution of their robust generation to memory formation by the hippocampus remains unclear. We describe here a possible role that this population of young granule cells may have in the formation of temporal associations in memory. Neurogenesis is a continuous process; the newborn population is only composed of the same cells for a short period of time. As time passes, the young neurons mature or die and others are born, gradually changing the identity of this young population. We discuss the possibility that one cognitive impact of this gradually changing population on hippocampal memory formation is the formation of the temporal clusters of long-term episodic memories seen in some human psychological studies.

Hyperspace geography: visualizing fitness landscapes beyond 4D

Human perception is finely tuned to extract structure about the 4D world of time and space as well as properties such as color and texture. Developing intuitions about spatial structure beyond 4D requires exploiting other perceptual and cognitive abilities. One of the most natural ways to explore complex spaces is for a user to actively navigate through them, using local explorations and global summaries to develop intuitions about structure, and then testing the developing ideas by further exploration. This article provides a brief overview of a technique for visualizing surfaces defined over moderate-dimensional binary spaces, by recursively unfolding them onto a 2D hypergraph. We briefly summarize the uses of a freely available Web-based visualization tool, Hyperspace Graph Paper (HSGP), for exploring fitness landscapes and search algorithms in evolutionary computation. HSGP provides a way for a user to actively explore a landscape, from simple tasks such as mapping the neighborhood structure of different points, to seeing global properties such as the size and distribution of basins of attraction or how different search algorithms interact with landscape structure. It has been most useful for exploring recursive and repetitive landscapes, and its strength is that it allows intuitions to be developed through active navigation by the user, and exploits the visual system's ability to detect pattern and texture. The technique is most effective when applied to continuous functions over Boolean variables using 4 to 16 dimensions.

Towards more biological mutation operators in gene regulation studies

Genetic regulation is often viewed as a complex system whose properties emerge from the interaction of regulatory genes. One major paradigm for studying the complex dynamics of gene regulation uses directed graphs to explore structure, behaviour and evolvability. Mutation operators used in such studies typically involve the insertion and deletion of nodes, and the insertion, deletion and rewiring of links at the network level. These network-level mutational operators are sufficient to allow the statistical analysis of network structure, but impose limitations on the way networks are evolved. There are a wide variety of mutations in DNA sequences that have yet to be analysed for their network-level effects. By modelling an artificial genome at the level of nucleotide sequences and mapping it to a regulatory network, biologically grounded mutation operators can be mapped to network-level mutations. This paper analyses five such sequence level mutations (single-point mutation, transposition, inversion, deletion and gene duplication) for their effects at the network level. Using analytic and simulation techniques, we show that it is rarely the case that nodes and links are cleanly added or deleted, with even the simplest point mutation causing a wide variety of network-level modifications. As expected, the vast majority of simple (single-point) mutations are neutral, resulting in a neutral plateau from which a range of functional behaviours can be reached. By analysing the effects of sequence-level mutations at the network level of gene regulation, we aim to stimulate more careful consideration of mutation operators in gene regulation models than has previously been given.

A gene network model for developing cell lineages

Biological development is a remarkably complex process. A single cell, in an appropriate environment, contains sufficient information to generate a variety of differentiated cell types, whose spatial and temporal dynamics interact to form detailed morphological patterns. While several different physical and chemical processes play an important role in the development of an organism, the locus of control is the cell's gene regulatory network. We designed a dynamic recurrent gene network (DRGN) model and evaluated its ability to control the developmental trajectories of cells during embryogenesis. Three tasks were developed to evaluate the model, inspired by cell lineage specification in C. elegans, describing the variation in gene activity required for early cell diversification, combinatorial control of cell lineages, and cell lineage termination. Three corresponding sets of simulations compared performance on the tasks for different gene network sizes, demonstrating the ability of DRGNs to perform the tasks with minimal external input. The model and task definition represent a new means of linking the fundamental properties of genetic networks with the topology of the cell lineages whose development they control.

Transient phenomena in learning and evolution: genetic assimilation and genetic redistribution

Deacon has recently proposed that complexes of genes can be integrated into functional groups as a result of environmental changes that mask and unmask selection pressures. For example, many animals endogenously synthesize ascorbic acid (vitamin C), but anthropoid primates have only a nonfunctional version of the crucial gene for this pathway. It is hypothesized that the loss of functionality occurred in the evolutionary past when a diet rich in vitamin C masked the effect of the gene, and its loss effectively trapped the animals in a fruit-eating lifestyle. As a result, the complex of abilities that support this lifestyle were evolutionarily bound together, forming a multilocus complex. In this study we use evolutionary computation simulations to explore the thesis that masking and unmasking can transfer dependence from one set of genes to many sets, and thereby integrate the whole complex of genes. We used a framework based on Hinton and Nowlan's 1987 simulation of the Baldwin effect. Additional gene complexes and an environmental parameter were added to their basic model, and the fitness function extended. The simulation clearly demonstrates that the genetic redistribution effect can occur in silico, showing an initial advantage of endogenously synthesized vitamin C, followed by transfer of the fitness contribution to the complex of genes that together allow the acquisition of vitamin C from the environment. As is well known in the modeling community, the Baldwin effect only occurs in simulations when the population of agents is ''poised on the brink'' of discovering the genetically specified solution. Similarly, the redistribution effect occurs in simulations under specific initial conditions: too little vitamin C in the environment, and its synthesis it is never fully masked; too much vitamin C, and the abilities required to acquire it are not tightly integrated. The Baldwin effect has been hypothesized as a potential mechanism for developing language-specific adaptations like innate universal grammar and other highly modular capacities. We conclude with a discussion of the relevance of genetic assimilation and genetic redistribution to the evolution of language and other cognitive adaptations.

Effective affective user interface design in games

It is proposed that games, which are designed to generate positive affect, are most successful when they facilitate flow (Csikszentmihalyi 1992). Flow is a state of concentration, deep enjoyment, and total absorption in an activity. The study of games, and a resulting understanding of flow in games can inform the design of non-leisure software for positive affect. The paper considers the ways in which computer games contravene Nielsen's guidelines for heuristic evaluation (Nielsen and Molich 1990) and how these contraventions impact on flow. The paper also explores the implications for research that stem from the differences between games played on a personal computer and games played on a dedicated console. This research takes important initial steps towards defining how flow in computer games can inform affective design.

Mapping the royal road and other hierarchical functions

In this paper we present a technique for visualising hierarchical and symmetric, multi-modal fitness functions that have been investigated in the evolutionary computation literature. The focus of this technique is on landscapes in moderate-dimensional, binary spaces (i.e., fitness functions defined over [0,1](n), for n < or = 16). The visualisation approach involves an unfolding of the hyperspace into a two-dimensional graph, whose layout represents the topology of the space using a recursive relationship, and whose shading defines the shape of the cost surface defined on the space. Using this technique we present case-study explorations of three fitness functions: royal road, hierarchical-if-and-only-if (H-IFF), and hierarchically decomposable functions (HDF). The visualisation approach provides an insight into the properties of these functions, particularly with respect to the size and shape of the basins of attraction around each of the local optima.