Tamas Madl

The University of Manchester · School of Computer Science

We propose that human cognition consists of cascading cycles of recurring brain events. Each cognitive cycle senses the current situation, interprets it with reference to ongoing goals, and then selects an internal or external action in response. While most aspects of the cognitive cycle are unconscious, each cycle also yields a momentary "ignition...

We describe a cognitive architecture (LIDA) that affords attention, action selection and human-like learning intended for use in controlling cognitive agents that replicate human experiments as well as performing real-world tasks. LIDA combines sophisticated action selection, motivation via emotions, a centrally important attention mechanism, and m...

Accurate spatial localization requires a mechanism that corrects for errors, which might arise from inaccurate sensory information or neuronal noise. In this paper, we propose that Hippocampal place cells might implement such an error correction mechanism by integrating different sources of information in an approximately Bayes-optimal fashion. We...

Pages 22 - 28 is my paper. It describes how we can use an ultra-delayed quantum eraser experiment to figure out when and where the wavefunction collapses.

Artificial intelligence (AI) deep learning protocols offer solutions to complex data processing and analysis. Increasingly these solutions are being applied in the healthcare field, most commonly in processing complex medical imaging data used for diagnosis. Current models apply AI to screening populations of patients for markers of disease and rep...

Atrial fibrillation is increasingly prevalent, especially in the elderly, and challenging to detect due paroxysmal nature. Here, we propose novel computational methods based on heart beat intervals to facilitate rapid and robust discrimination between atrial fibrillation and sinus rhythm. We used low-cost Android smartphones, and recorded short, 30...

Computational cognitive models of spatial memory often neglect difficulties posed by the real world, such as sensory noise, uncertainty, and high spatial complexity. On the other hand, robotics is unconcerned with understanding biological cognition. Here, we describe a computational framework for robotic architectures aiming to function in realisti...

In several domains obtaining class annotations is expensive while at the same time unlabelled data are abundant. While most semi-supervised approaches enforce restrictive assumptions on the data distribution, recent work has managed to learn semi-supervised models in a non-restrictive regime. However, so far such approaches have only been proposed...

Clinical diagnoses of age-related conditions (e.g. frailty) are based on specific signs and symptoms of the disease state. The assumption that preclinical states of these conditions display milder versions of the same clinical signs and symptoms is fundamentally flawed and lacks empirical evidence. More importantly, such assumptions preclude the de...

Despite of the pain and limited accuracy of blood tests for early recognition of cardiovascular disease, they dominate risk screening and triage. On the other hand, heart rate variability is non-invasive and cheap, but not considered accurate enough for clinical practice. Here, we tackle heart beat interval based classification with deep learning....

Heart beat interval time series contain information predictive of heart disease, but most current predictors do not provide sufficient reliability for clinical use. Using several predictors improves predictive power, but the limit is not yet known, suggesting that not all the information in interbeat interval series has been captured by previous wo...

Existing computational cognitive models of spatial memory often neglect difficulties posed by the real world, such as sensory noise, uncertainty, and high spatial complexity. On the other hand, robotics is unconcerned with understanding biological cognition. This thesis takes an interdisciplinary approach towards developing cognitively plausible sp...

It has been suggested that the map-like representations that support human spatial memory are fragmented into sub-maps with local reference frames, rather than being unitary and global. However, the principles underlying the structure of these 'cognitive maps' are not well understood. We propose that the structure of the representations of navigati...

Methodological details. (PDF)

Over a decade in the making and described in some seventy-five published papers, the LIDA cognitive model is comprehensive, complex, and hard to "wrap one's head around". Here we offer, in tutorial fashion, a current, relatively complete and somewhat detailed, description of the conceptual LIDA model, with pointers to more complete accounts of indi...

The ability to represent and utilize spatial information relevant to their goals is vital for intelligent agents. Doing so in the real world presents significant challenges, which have so far mostly been addressed by robotics approaches neglecting cognitive plausibility; whereas existing cognitive models mostly implement spatial abilities in simpli...

Modern tools and methods of cognitive science, such as brain imaging or computational modeling, can provide new insights for age-old philosophical questions regarding the nature of temporal experience. This chapter aims to provide an overview of functional consciousness and time perception in brains and minds (Section 8.2), and to describe a comput...

Although most cognitive architectures, in general, and LIDA, in particular, are still in the early stages of development and still far from being adequate bases for implementations of human-like ethics, we think that they can contribute to the understanding, design, and implementation of constrained ethical systems for robots, and we hope that the...

Accurate spatial localization requires a mechanism that corrects for errors, which might arise from inaccurate sensory information or neuronal noise. In this paper, we propose that Hippocampal place cells might implement such an error correction mechanism by integrating different sources of information in an approximately Bayes-optimal fashion. We...

Spatial memory refers to the part of the memory system that encodes, stores, recognizes and recalls spatial information about the environment and the agent’s orientation within it. Such information is required to be able to navigate to goal locations, and is vitally important for any embodied agent, or model thereof, for reaching goals in a spatial...

Human spatial representations are known to be remarkably robust and efficient, and to be structured hierarchically. In this paper, we describe a biologically inspired computational model of spatial working memory attempting to account for these properties, based on the LIDA cognitive architecture. We also present preliminary results regarding a virt...

The attentional blink (AB) refers to the impairment in consciously perceiving the second of two targets presented in close temporal proximity (200 – 500ms) in a rapid serial visual presentation paradigm. The present paper is a preliminary report describing a conceptual and partially computational model of the AB based on the LIDA (Learning Intellig...