Multi-feature Bottom-up Processing and Top-down Selection for an Object-based Visual Attention Model

Artificial vision systems can not process all the information that they receive from the world in real time because it is highly expensive and inefficient in terms of computational cost. However, inspired by biological perception systems, it is possible to develop an artificial attention model able to select only the relevant part of the scene, as human vision does. This paper presents an attention model which draws attention over perceptual units of visual information, called proto-objects, and which uses a linear combination of multiple low-level features (such as colour, symmetry or shape) in order to calculate the saliency of each of them. But not only bottom-up processing is addressed, the proposed model also deals with the top-down component of attention. It is shown how a high-level task can modulate the global saliency computation, modifying the weights involved in the basic features linear combination.Ministerio de Economía y Competitividad (MINECO), proyectos: TIN2008-06196 y TIN2012-38079-C03-03. Campus de Excelencia Internacional Andalucía Tech

A half century of progress towards a unified neural theory of mind and brain with applications to autonomous adaptive agents and mental disorders

Invited article for the book Artificial Intelligence in the Age of Neural Networks and Brain Computing R. Kozma, C. Alippi, Y. Choe, and F. C. Morabito, Eds. Cambridge, MA: Academic PressThis article surveys some of the main design principles, mechanisms, circuits, and architectures that have been discovered during a half century of systematic research aimed at developing a unified theory that links mind and brain, and shows how psychological functions arise as emergent properties of brain mechanisms. The article describes a theoretical method that has enabled such a theory to be developed in stages by carrying out a kind of conceptual evolution. It also describes revolutionary computational paradigms like Complementary Computing and Laminar Computing that constrain the kind of unified theory that can describe the autonomous adaptive intelligence that emerges from advanced brains. Adaptive Resonance Theory, or ART, is one of the core models that has been discovered in this way. ART proposes how advanced brains learn to attend, recognize, and predict objects and events in a changing world that is filled with unexpected events. ART is not, however, a “theory of everything” if only because, due to Complementary Computing, different matching and learning laws tend to support perception and cognition on the one hand, and spatial representation and action on the other. The article mentions why a theory of this kind may be useful in the design of autonomous adaptive agents in engineering and technology. It also notes how the theory has led to new mechanistic insights about mental disorders such as autism, medial temporal amnesia, Alzheimer’s disease, and schizophrenia, along with mechanistically informed proposals about how their symptoms may be ameliorated

Introduction: The Fourth International Workshop on Epigenetic Robotics

As in the previous editions, this workshop is trying to be a forum for multi-disciplinary research ranging from developmental psychology to neural sciences (in its widest sense) and robotics including computational studies. This is a two-fold aim of, on the one hand, understanding the brain through engineering embodied systems and, on the other hand, building artificial epigenetic systems. Epigenetic contains in its meaning the idea that we are interested in studying development through interaction with the environment. This idea entails the embodiment of the system, the situatedness in the environment, and of course a prolonged period of postnatal development when this interaction can actually take place. This is still a relatively new endeavor although the seeds of the developmental robotics community were already in the air since the nineties (Berthouze and Kuniyoshi, 1998; Metta et al., 1999; Brooks et al., 1999; Breazeal, 2000; Kozima and Zlatev, 2000). A few had the intuition – see Lungarella et al. (2003) for a comprehensive review – that, intelligence could not be possibly engineered simply by copying systems that are “ready made” but rather that the development of the system fills a major role. This integration of disciplines raises the important issue of learning on the multiple scales of developmental time, that is, how to build systems that eventually can learn in any environment rather than program them for a specific environment. On the other hand, the hope is that robotics might become a new tool for brain science similarly to what simulation and modeling have become for the study of the motor system. Our community is still pretty much evolving and “under construction” and for this reason, we tried to encourage submissions from the psychology community. Additionally, we invited four neuroscientists and no roboticists for the keynote lectures. We received a record number of submissions (more than 50), and given the overall size and duration of the workshop together with our desire to maintain a single-track format, we had to be more selective than ever in the review process (a 20% acceptance rate on full papers). This is, if not an index of quality, at least an index of the interest that gravitates around this still new discipline

Tools for Thought: The Case of Mathematics

The objective of this article is to take into account the functioning of representational cognitive tools, and in particular of notations and visualizations in mathematics. In order to explain their functioning, formulas in algebra and logic and diagrams in topology will be presented as case studies and the notion of manipulative imagination as proposed in previous work will be discussed. To better characterize the analysis, the notions of material anchor and representational affordance will be introduced

Assessing the perceived realism of agent crowd behaviour within virtual urban environments using psychophysics

Inhabited virtual environments feature in a growing number of graphical applications. Simulated crowds are employed for different purposes; ranging from evaluation of evacuation procedures to driving interactable elements in video games. For many applications, it is important that the displayed crowd behaviour is perceptually plausible to the intended viewers. Crowd behaviour is inherently in flux, often depending upon many different variables such as location, situation and crowd composition. Researchers have, for a long time, attempted to understand and reason about crowd behaviour, going back as far as famous psychologists such as Gustave Le Bon and Sigmund Freud who applied theories of mob psychology with varying results. Since then, various other methods have been tried, from articial intelligence to simple heuristics, for crowd simulation. Even though the research into methods for simulating crowds has a long history, evaluating such simulations has received less attention and, as this thesis will show, increased complexity and high-delity recreation of recorded behaviours does not guarantee improvement in the plausibility for a human observer. Actual crowd data is not always perceived more real than simulation, making it dicult to identify gold standards, or a ground truth. This thesis presents new work on the use of psychophysics for perceptual evaluation of crowd simulation in order to develop methods and metrics for tailoring crowd behaviour for target applications. Psychophysics itself is branch of psychology dedicated to studying the relationship between a given stimuli and how it is perceived. A three-stage methodology of analysis, synthesis and perception is employed in which crowd data is gathered from the analysis of real instances of crowd behaviour and then used to synthesise behavioural features for simulation before being perceptually evaluated using psychophysics. Perceptual thresholds are calculated based on the psychometric function and key congurations are identied that appear the most perceptually plausible to human viewers. The method is shown to be useful for the initial application and it is expected that it will be applicable to a wide range of simulation problems in which human perception and acceptance is the ultimate measure of success

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

A false colouring real time visual saliency algorithm for reference resolution in simulated 3-D environments

In this paper we present a novel false colouring visual saliency algorithm and illustrate how it is used in the Situated Language Interpreter system to resolve natural language references

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

From holism to compositionality: memes and the evolution of segmentation, syntax, and signification in music and language

Steven Mithen argues that language evolved from an antecedent he terms “Hmmmmm, [meaning it was] Holistic, manipulative, multi-modal, musical and mimetic”. Owing to certain innate and learned factors, a capacity for segmentation and cross-stream mapping in early Homo sapiens broke the continuous line of Hmmmmm, creating discrete replicated units which, with the initial support of Hmmmmm, eventually became the semantically freighted words of modern language. That which remained after what was a bifurcation of Hmmmmm arguably survived as music, existing as a sound stream segmented into discrete units, although one without the explicit and relatively fixed semantic content of language. All three types of utterance – the parent Hmmmmm, language, and music – are amenable to a memetic interpretation which applies Universal Darwinism to what are understood as language and musical memes. On the basis of Peter Carruthers’ distinction between ‘cognitivism’ and ‘communicativism’ in language, and William Calvin’s theories of cortical information encoding, a framework is hypothesized for the semantic and syntactic associations between, on the one hand, the sonic patterns of language memes (‘lexemes’) and of musical memes (‘musemes’) and, on the other hand, ‘mentalese’ conceptual structures, in Chomsky’s ‘Logical Form’ (LF)

Assessing the perceived realism of agent grouping dynamics for adaptation and simulation

Virtual crowds are a prominent feature for a range of applications; from simulations for cultural heritage, to interactive elements in video games. A body of existing research seeks to develop and improve algorithms for crowd simulation, typically with a goal of achieving more realistic behaviours. For applications targeting human interaction however, what is judged as realistic crowd behaviour can be subjective, leading to situations where actual crowd data is not always perceived to be more real than simulation, making it difficult to identify a ground truth. We present a novel method using psychophysics to assess the perceived realism of behavioural features with respect to virtual crowds. In this instance, a focus is given to the grouping dynamics feature, whereby crowd composition in terms of group frequency and density is evaluated through thirty-six conditions based on crowd data captured from three pedestrianised real-world locations. The study, conducted with seventy-eight healthy participants, allowed for the calculation of perceptual thresholds, with configurations identified that appear most real to human viewers. The majority of these configurations correlate with the values extracted from the crowd data, with results suggesting that viewers have more perceptual flexibility when group frequency and density are increased, rather than decreased.</p