Contours of Cognition

This thesis concerns the nature of cognition. It posits that cognitive processes primarily are means to maintain allostasis in organisms whose ecological niches require movement to approach food-resources and avoid predation. Hence triggering, or motivation, of behaviours are a consequence of prediction errors from the body resulting from biological variables moving away from homeostasis. Depending on circumstance and the nature of the particulars of the ecological niche, an organism may require the ability to find the way to a goal-site containing food or water, perceive its surroundings in order to trigger allostatic behaviour, make choices and priorities, and predict outcomes. Hence, cognition is situated in a larger context of staying alive, but efforts are also made to zoom in on exactly how some important cognitive processes may plausibly work, on the level of neural units and networks. These processes include visual perception, spatial cognition, predictive simulation processes (intelligence), and familiarity based trust, as well as reflection, decision-making, and memory

From focused thought to reveries: A memory system for a conscious robot

© 2018 Balkenius, Tjøstheim, Johansson and Gärdenfors. We introduce a memory model for robots that can account for many aspects of an inner world, ranging from object permanence, episodic memory, and planning to imagination and reveries. It is modeled after neurophysiological data and includes parts of the cerebral cortex together with models of arousal systems that are relevant for consciousness. The three central components are an identification network, a localization network, and a working memory network. Attention serves as the interface between the inner and the external world. It directs the flow of information from sensory organs to memory, as well as controlling top-down influences on perception. It also compares external sensations to internal top-down expectations. The model is tested in a number of computer simulations that illustrate how it can operate as a component in various cognitive tasks including perception, the A-not-B test, delayed matching to sample, episodic recall, and vicarious trial and error

Gender differences in allocation of attention and read time in an educational history game

Previous research has shown that female students sometimes benefit more than males when it comes to interacting with pedagogical agents. In our analysis we examined students' tendency to attend to and read feedback text that were visually signalled by a teachable agent (TA), or by an arrow (AR), or non-signalled in a control condition (CN). The results indicate that male learners may benefit from having a TA signalling such feedback texts. The female learners in the study allocated their attention quite similarly between the three different signaling conditions whereas the male learners were most likely to attend to the feedback when presented by their TA. However, for reading the feedback text, both male and female students were more inclined to read the feedback texts when presented by their TA, compared to in the two other conditions

Intelligence as Accurate Prediction

This paper argues that intelligence can be approximated by the ability to produce accurate predictions. It is further argued that general intelligence can be approximated by context dependent predictive abilities combined with the ability to use working memory to abstract away contextual information. The flexibility associated with general intelligence can be understood as the ability to use selective attention to focus on specific aspects of sensory impressions to identify patterns, which can then be used to predict events in novel situations and environments. The argumentation synthesizes Godfrey-Smith’s environmental complexity theory, adding the notion of niche broadness as well as changes concerning the view of cognition and control, and Hohwy’s predictive mind theory, making explicit the significance of accuracy as a composite of trueness and precision where the nervous system acts as a distributed controller motivating actions that keep the body in homeostasis

Communicating emotional state and personality with eye-color and light intensity

We conducted two experiments where subjects rated images of a robot head with different eye colors and light intensities on how well they communicate emotions like anger, enjoyment, and surprise, as well as personality traits like friendliness, intelligence, and level of trust. Results indicate e.g. that green and turquoise eye colors were more associated with agreeable personality traits. We found also that for sadness and disgust, dimming light intensity appears to communicate more intense feeling. Finally, red communicates negative emotions most saliently

Ikaros : A framework for controlling robots with system-level brain models

Ikaros is an open framework for system-level brain modeling and real-time robot control. Version 2 of the system includes a range of computational components that implements various algorithms and methods ranging from models of neural circuits to control systems and hardware interfaces for robot. Ikaros supports the design and implementation of large-scale computation models using a flow programming paradigm. Version 2 includes a number of new features that support complex networks of hierarchically arranged components as well as a web-based interactive editor. More than 100 persons have contributed to the code base and over 100 scientific publications report on work that has used Ikaros for simulations or robot control

Arousal and awareness in a humanoid robot

We describe how an arousal system that controls the levels of awareness can be implemented in a robot. The different levels of awareness correspond to different states of consciousness and we argue that an artificial arousal system modeled after its biological counterpart has a useful function in controlling the cognitive processing of a brain-like cognitive architecture. The level of awareness depends on arousal that in turn is controlled by novel or emotionally charged stimuli as well as by a circadian clock. Arousal is also modulated during cognitive tasks to control the randomness of decision processes and to select between exploration and exploitation

Adaptive Inhibition for Optimal Energy Consumption by Animals, Robots and Neurocomputers

In contrast to artificial systems, animals must forage for food. In biology, the availability of energy is typically both precarious and highly variable. Most importantly, the very structure of organisms is dependent on the continuous metabolism of nutrients into ATP, and its use in maintaining homeostasis. This means that energy is at the centre of all biological processes, including cognition. So far, in computational neuroscience and artificial intelligence, this issue has been overlooked. In simulations of cognitive processes, whether at the neural level, or the level of larger brain systems, the constant and ample supply of energy is implicitly assumed. However, studies from the biological sciences indicate that much of the brain’s processes are in place to maintain allostasis, both of the brain itself and of the organism as a whole. This also relates to the fact that different neural populations have different energy needs. Many artificial systems, including robots and laptop computers, have circuitry in place to measure energy consumption. However, this information is rarely used in controlling the details of cognitive processing to minimize energy consumption. In this work, we make use of some of this circuitry and explicitly connect it to the processing requirements of different cognitive subsystems and show first how a cognitive model can learn the relation between cognitive ‘effort’, the quality of the computations and energy consumption, and second how an adaptive inhibitory mechanism can learn to only use the amount of energy minimally needed for a particular task. We argue that energy conservation is an important goal of central inhibitory mechanisms, in addition to its role in attentional and behavioral selection

Epi : An open humanoid platform for developmental robotics

Epi is a humanoid robot developed by Lund University Cognitive Science Robotics Group. It was designed to be used in experiments in developmental robotics and has proportions to give a childlike impression while still being decidedly robotic. The robot head has two degrees of freedom in the neck and each eye can independently move laterally. There is a camera in each eye to make stereovision possible. The arms are designed to resemble those of a human. Each arm has five degrees of freedom, three in the shoulder, one in the elbow and one in the wrist. The hands have four movable fingers and a stationary thumb. A force distribution mechanism inside the hand connect a single servo to the movable fingers and makes sure the hand closes around an object regardless of its shape. The rigid parts of the hands are 3D printed in PLA and HIPS while the flexible parts, including the joints and the tendons, are made from polyurethane rubber. The control system for Epi is based on neurophysiological data and is implemented using the Ikaros system. Most of the sensory and motor processing is done at 40 Hz to allow smooth movements. The irises of the eyes can change colour and the pupils can dilate and contract. There is also a grid of LEDs that resembles a mouth that can be animated by changing colour and intensity

Direct Approach or Detour : A Comparative Model of Inhibition and Neural Ensemble Size in Behavior Selection

Organisms must cope with different risk/reward landscapes in their ecological niche. Hence, species have evolved behavior and cognitive processes to optimally balance approach and avoidance. Navigation through space, including taking detours, appears also to be an essential element of consciousness. Such processes allow organisms to negotiate predation risk and natural geometry that obstruct foraging. One aspect of this is the ability to inhibit a direct approach toward a reward. Using an adaptation of the well-known detour paradigm in comparative psychology, but in a virtual world, we simulate how different neural configurations of inhibitive processes can yield behavior that approximates characteristics of different species. Results from simulations may help elucidate how evolutionary adaptation can shape inhibitive processing in particular and behavioral selection in general. More specifically, results indicate that both the level of inhibition that an organism can exert and the size of neural populations dedicated to inhibition contribute to successful detour navigation. According to our results, both factors help to facilitate detour behavior, but the latter (i.e., larger neural populations) appears to specifically reduce behavioral variation