Centre for Cognitive Science (COGS)


COGS Seminars provide a forum for internationally recognised researchers from all corners of cognitive science research to present and discuss their latest findings. All are welcome to attend.

Summer 2020

Tuesdays 16:00-17:30


Feb 2

As Soon as There Was Life There Was Danger
Joseph Ledoux
New York

Abstract: Organisms face challenges to survival throughout life. When we freeze or flee in danger, we often feel fear. Tracing the deep history of danger give a different perspective. The first cells living billions of years ago had to detect and respond to danger in order to survive. Life is about not being dead, and behavior is a major way that organisms hold death off. Although behavior does not require a nervous system, complex organisms have brain circuits for detecting and responding to danger, the deep roots of which go back to the first cells. But these circuits do not make fear, and fear is not the cause of why we freeze or flee. Fear a human invention; a construct we use to account for what happens in our minds when we become aware that we are in harm’s way. This requires a brain that can personally know that it existed in the past, that it is the entity that might be harmed in the present, and that it will cease to exist it the future. If other animals have conscious experiences, they cannot have the kinds of conscious experiences we have because they do not have the kinds of brains we have. This is not meant as a denial of animal consciousness; it is simply a statement about the fact that every species has a different brain. Nor is it a declaration about the wonders of the human brain, since we have done some wonderful, but also horrific, things with our brains.



feb 16

Chemistry vs neurones: pre- and post-natal (or post-hatching) spatial intelligence, in chickens, foals, and mathematicians!
Prof Aaron Sloman

Abstract:I'll present a new, biology-based, line of defence for an expanded version of Immanuel Kant's anti-Hume view of ancient discoveries in geometry, with implications regarding spatial consciousness in humans and other animals, including requirements for spatial intelligence in newly-hatched or newborn animals with sophisticated, but unlearned, competences available soon after hatching or birth, such as chicks that walk, peck for food, and follow a hen, and foals that can walk to a mare to suckle, and run with the herd to escape a predator within hours of birth, without any time to learn how find the nipple, how to suck or how to run, avoiding obstacles, etc.

Many animals need complex spatial competences before they have had time to learn them. I'll suggest (following Kant) that those competences, including some spatial reasoning competences in young humans that also support ancient mathematical competences, cannot be based on varieties of formal reasoning used in modern mathematics and logic-based theorem provers, nor on learning by statistics-based neural nets -- since they are incapable of discovering, or even representing mathematical impossibility or necessity. No amount of statistical evidence can prove either impossibility or necessity.

Instead of neural nets, biological evolution seems to have found ways of re-using *chemical* control mechanisms required for increasingly complex assembly processes in developing organisms, which differ across different types of organism, e.g. microbes, plants of many types including grasses, climbers, giant redwood trees, insects, and egg-laying and live birth vertebrates, etc. and also differ over time within individuals.

As complexity in a developing pre-natal/unhatched organism increases, the complexity of the chemical control mechanisms used in assembling and connecting new components must also increase, and later mechanisms, instead of using only direct chemical reactions (which suffice for the earliest processes based on DNA), will increasingly need to use *information* about what has been constructed so far and what needs to be added or modified next. For much of the time, only chemical information is available. (Compare the uses of information in extending meccano or tinkertoy constructions.)

At later stages of development the information will be more complex and the construction mechanisms will be more complex, performing more complex tasks, e.g. creating a skeleton, muscles, circulatory systems, bones, nervous systems, digestive systems and increasingly complex control mechanisms, which must be chemistry based before brains are available.

In control processes that precede construction of brains (and are needed for construction of brains) the information processing mechanisms cannot be neural mechanisms, because they don't yet exist.

The only alternative is use of chemical control. Developing organisms will require increasingly complex chemical information processing mechanisms as complexity of the new organism increases. The intelligence of newly hatched or newborn animals must be chemistry-based. I'll report on some surprising implications of these ideas and identify work still to be done, requiring deep multi-disciplinary collaboration. Among the implications are limitations of neural nets that collect statistics and derive probabilities. They are incapable of replicating or explaining ancient mathematical competences and related forms of spatial reasoning.

These ideas also point to gaps in the work of Penrose and Hameroff on mathematical consciousness, e.g. as recently explained in their presentations in January 2020: https://www.youtube.com/watch?v=xGbgDf4HCHU. They seem to ignore the possible roles of ancient chemistry-based information processing mechanisms in brains and in organisms without brains. Hameroff emphasises microtubules in brains, but microtubules are involved in early gene expression processes long before brains are created. Perhaps they are most important in information processing mechanisms preceding production of brains?

For more information and ideas for further developments, see the 'chemneuro' web page -- which will be expanded after the talk: https://www.cs.bham.ac.uk/research/projects/cogaff/misc/sloman-chemneuro-sussex.html



Mar 2

Assessing the presence of consciousness in non-human systems
Henry Shevlin

Abstract: TThe scientific study of consciousness has made considerable progress in the last three decades, notably among cognitive theories of consciousness such as the Global Neuronal Workspace account, Higher-order Thought theory, and Attention Schema theory. While such theories are typically concerned to identify correlates of conscious and unconscious processing in human beings, in light of heightened recent interest in the evolution of consciousness and determining the presence of consciousness in animals and even systems, a key question for researchers is whether and how we can apply these frameworks to non-human subjects. In this talk, I review the prospects of this endeavour and discuss some challenges. I focus in particular on what I call the Specificity Problem, which concerns how we can determine an appropriate level of fineness of grain to adopt when moving from human to non-human cases. In light of this and other problems, I argue that most theories of consciousness currently lack the theoretical resources to allow for their straightforward application to non-humans. I go on to consider whether the 'Theory-Light' approach to non-human consciousness recently developed by Jonathan Birch (forthcoming, Noûs) might constitute a plausible alternative method for assessing consciousness in non-human cases. While it has impressive utility, I suggest it is unlikely to give clear answers to all the important examples we may be interested in, especially in non-biological systems. Finally, I argue for a Modest Theoretical Approach, that aims to find a middle ground between the two strategies, combining behavioural and theoretical approaches to offer a powerful but robust approach to the problem. Full paper available at: http://dx.doi.org/10.1111/mila.12338



Apr 20

Dr Fernando E Rosas




May 11

Value Transparency in Science and Machine Learning
Rune Nyrup

Abstract:Several philosophers of science have highlighted value transparency as a plausible approach to managing value-ladenness in science. When policymakers and other non-experts use scientific results which rely on value-laden decisions, the values guiding those decisions should be made explicit and transparent. This is supposed to compensate for a lack of epistemic transparency, i.e. the fact that non-experts cannot fully assess the often-complex chains of uncertainty and justification involved in establishing a given scientific result. A similar debate has recently emerged over value-ladenness and transparency in machine learning (ML). Similar to scientific research, the complexity of ML models and training processes makes it difficult to assess the uncertainties and justifications underlying their recommendations. While much current technical research focuses on making machine learning systems more "interpretable" or "explainable", law and policy scholars have argued that governance frameworks should instead focus on making transparent the goals that the systems are designed to achieve. This paper highlights and explores the parallel between these two, currently unconnected debates. First, I argue that the debate over transparency in machine learning can be interpreted as an instance of the argument for value transparency in science. Second, I use examples of different types of value-ladenness in machine learning as test cases to critically evaluate to what extent value transparency provides a feasible approach to managing value-ladenness.



May 18

Panpsychism as a Theory of Consciousness
Dr Philip Gough

Abstract: What do we need out of a theory of consciousness? I will argue that the task of accounting for consciousness is partly experimental and partly philosophical. The experimental task is to establish the neural correlates of consciousness. The philosophical task is to choose among the various theories philosophers have formulated for explaining why brain activity is correlated with consciousness: materialism, dualism, panpsychism, etc. These theories should aim to: (A) fit the empirical data, (B) eliminate explanatory gaps, (C) be as simple as possible. On the basis of this methodology, I will argue that panpsychism is the most plausible philosophical component of a theory of consciousness.



May 25

Introspection versus Metacognition
Wayne Wu
Carnegie Mellon




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