Weekly QuEST Discussion Topics and News, 18 April

April 18, 2014 Leave a comment

First a note – there will be NO QuEST meeting next week (25 April, 2014) – Capt Amerika has a commitment and will be out of town all week!

Topics this week, 18 April include:
1.) Context – we can define context in many ways but one way we’ve pursued in the past: Anything (experience / knowledge – could define experience as a type of knowledge than just say knowledge instead of anything) that contributes to the reduction of uncertainty in the representation of an agent that isn’t supplied explicitly in the sensor input at that moment (prior sensory data, expected data, computations by other agents, relevant domain knowledge, .. – note could use sensors to capture stimuli from other agents vs the environment, …)
2.) Goal for use of context is to reduce ambiguity (our definition of generating information) in object or situation recognition (correct assignment of object / situation labels requires consideration of other objects / prior-future situations, model seems to fit if the context is used to disambiguate between multiple competing alternatives / narratives)
3.) Common to think of context use as a post process to max agreement between parallel processes
a. In this sense you might imagine Context Agents – possibly all Type 2 agents that generate Qualia are these Context Agents – where their sensors are capturing aspects of the representation of a set of agents looking to maximize the agreement between the parallel computations from those agents – a means to choose the most plausible narrative!
4.) Sources of context
a. Learning from training (co-occurrence – can be from other agents)
b. Pre-programmed in (Google sets examples)
c. Derived information (includes agent’s current and prior informational states includes Environment (city, weather, location, orientation, proximity, change of proximity, time) User’s own activity User’s own physiological states)
5.) One reason context can be important to consider is the statement: Total reliance on sensor data is metaphorically equivalent to trying to solve a set of equations when there exist more unknowns than equations
6.) Topics I would like to discuss include Context and Big Data – are current approaches to Big Data looking to account for just one aspect of Context – co-occurrence? If so can we look as another value added path for QuEST to provide a path to incorporate other aspects of Context (like relevant domain knowledge)?
7.) Another topic is the relationship of current proposed means to use context and compliance with QuEST tenets – Context provides the means to ‘situate’ new sensory representations – it is all the other stuff in the representation that is being experienced – thus situating a representation is a big step towards QuEST compliance – let’s look at some examples and discuss what is missing and what is accounted for in terms of our Theory of Consciousness

WeeklyQuESTDiscussionTopicsandNews18Apr

Weekly QuEST Discussion Topics and News, 11 April

April 10, 2014 Leave a comment

This week’s topics
Article – a preprint related to a recent news story:
Neural portraits of perception: Reconstructing face images from evoked brain activity
Q13Q3 Alan S. Cowen a, MarvinM. Chunb, Brice A. Kuhl c,d
Q2 a Department of Psychology, University of California Berkeley, USA
b Department of Psychology, Yale University, USA
Q4 c Department of Psychology, New York University, USA
d Center for Neural Science, New York University, US
• Recent neuroimaging advances have allowed visual experience to be reconstructed from patterns of brain activity.
• While neural reconstructions have ranged in complexity, they have relied almost exclusively on retinotopic mappings between visual input and activity in early visual cortex.
• However, subjective perceptual information is tied more closely to higher-level cortical regions that have not yet been used as the primary basis for neural reconstructions.
• Furthermore, no reconstruction studies to date have reported reconstructions of face images, which activate a highly distributed cortical network.
• Thus, we investigated
• (a) whether individual face images could be accurately reconstructed from distributed patterns of neural activity, and
• (b) whether this could be achieved even when excluding activity within occipital cortex.
• Our approach involved four steps.
• (1) Principal component analysis (PCA) was used to identify components that efficiently represented a set of training faces.
• (2) The identified components were then mapped, using a machine learning algorithm, to fMRI activity collected during viewing of the training faces.
• (3) Based on activity elicited by a new set of test faces, the algorithm predicted associated component scores.
• (4) Finally, these scores were transformed into reconstructed images. Using both objective and subjective validation measures, we show that our methods yield strikingly accurate neural reconstructions of faces even when excluding occipital cortex.
An Article from our colleague Prof Mills:
Representation and Recognition of
Situations in Sensor Networks
Rachel Cardell-Oliver and Wei Liu, The University of Western Australia

IEEE Communications Magazine • March 2010

Their use of “situation” is different from ours but there are
some interesting nuggets.

Abstract: A situation is an abstraction for a pattern of observations made
by a distributed system such as a sensor network. Situations have previously
been studied in different domains, as composite events in distributed event
based systems, service composition in multi-agent systems, and
macro-programming in sensor networks. However, existing languages do not
address the specific challenges posed by sensor networks. This article
presents a novel language for representing situations in sensor networks
that addresses these challenges. Three algorithms for recognizing situations
in relevant fields are reviewed and adapted to sensor networks. In
particular, distributed commitment machines are introduced and demonstrated
to be the most suitable algorithm among the three for recognizing situations
in sensor networks.

The last topic if we get to it is a discussion of the white paper – what sections are you personally associated with – who owns that section – what is your respective plans to advance those thoughts – do we need QuEST meetings dedicated to discussions of the respective sections?

WeeklyQuESTDiscussionTopicsandNews11April

Weekly QuEST Discussion Topics and News, 4 April

• Visual Recognition
As Soon as You Know It Is There, You Know What It Is
Kalanit Grill-Spector1 and Nancy Kanwisher2
1Department of Psychology, Stanford University, and 2Department of Brain and Cognitive Sciences, Massachusetts
Institute of Technology – an article that attempts to advance a Theory of Object Recognition – ABSTRACT—What is the sequence of processing steps involved in visual object recognition?

We varied the exposure duration of natural images and measured subjects’ performance on three different tasks, each designed to tap a different candidate component process of object recognition.
For each exposure duration,
– accuracy was lower and reaction time longer on a within-category identification task (e.g., distinguishing pigeons from other birds)

– than on a perceptual categorization task (e.g., birds vs. cars).

However, strikingly, at each exposure duration, subjects performed just as quickly and accurately on the categorization task as they did on a task requiring only object detection:
– By the time subjects knew an image contained an object at all, they already knew its category.

These findings place powerful constraints on theories of object recognition.

Second Article – a preprint related to a recent news story:

Neural portraits of perception: Reconstructing face images from evoked brain activity
Q13Q3 Alan S. Cowen a, MarvinM. Chunb, Brice A. Kuhl c,d
Q2 a Department of Psychology, University of California Berkeley, USA
b Department of Psychology, Yale University, USA
Q4 c Department of Psychology, New York University, USA
d Center for Neural Science, New York University, US
• Recent neuroimaging advances have allowed visual experience to be reconstructed from patterns of brain activity.

• While neural reconstructions have ranged in complexity, they have relied almost exclusively on retinotopic mappings between visual input and activity in early visual cortex.

• However, subjective perceptual information is tied more closely to higher-level cortical regions that have not yet been used as the primary basis for neural reconstructions.

• Furthermore, no reconstruction studies to date have reported reconstructions of face images, which activate a highly distributed cortical network.

• Thus, we investigated

• (a) whether individual face images could be accurately reconstructed from distributed patterns of neural activity, and

• (b) whether this could be achieved even when excluding activity within occipital cortex.

• Our approach involved four steps.

• (1) Principal component analysis (PCA) was used to identify components that efficiently represented a set of training faces.

• (2) The identified components were then mapped, using a machine learning algorithm, to fMRI activity collected during viewing of the training faces.

• (3) Based on activity elicited by a new set of test faces, the algorithm predicted associated component scores.

• (4) Finally, these scores were transformed into reconstructed images. Using both objective and subjective validation measures, we show that our methods yield strikingly accurate neural reconstructions of faces even when excluding occipital cortex.

WeeklyQuESTDiscussionTopicsandNews4April

Weekly QuEST Discussion Topics 28 Mar

March 28, 2014 Leave a comment

Prof Ron Hartung from our QuEST group will give a talk and lead a discussion on:

Non-Axiomatic Logic

When a reasoner is mentioned in AI work it denotes one of two types of systems. The longest running examples are based on first order logic. Recently, Bayesian reasoners have come into vogue. Pei Wang brings this into question by proposing NARS – a non-axiomatic reasoning system and is not based on first order logic or probability. Truth in this system is grounded in experience. Wang is interested in Artificial General Intelligence and did his PhD under D. Hofstadter. Should QUEST consider this system?

Weekly QuEST Discussion Topics and News, 21 Mar

March 20, 2014 Leave a comment

QuEST March 21, 2014

The first topic is a discussion of Qualia as a vocabulary for conscious deliberation. Specifically I want to tee up a discussion on the 50 bits/sec bandwidth limitation tenet (now a sub tenet) of our Theory of consciousness. Igor was asking how to convert the human cognition type 2 processing to bits/sec in a Shannon sense – below is a cut/paste from an old discussion Matt /Adam and I had some time back when we were adding the 50 bits/sec tenet (now a sub tenet in the simulation tenet)

An example of where the 50 bits/sec comes from is requiring a subject to read unfamiliar text (like a newspaper article) as fast as he can. At about 2.5 bits/letter => about 7 or 8 bits/five letter words and about 300 words/minute, you get around 40 bits/second. If you memorize the text, you can speak faster than that, but then the listener has trouble understanding what has been said (sounds like the lawyer-speak at the end of a TV contest offer). We used to extend this the vision too, by making an assumption about the number of pictures any person could identify, and rate at which he could do it (we would use a camera shutter and flash slides up and ask the observer to identify the object) ; it also comes out at about 50 bits/sec.

Suppose that the receiver (in Shannon’s formal channel) is a qualia decoder (like the human visual system is) and is therefore looking for only a VERY small subset of all the possible signals (formally, an infinite number of possible world events).

I think that this channel, which consists of the real world as a transmitter [of photons] to the receiver [which is the 50 bit/sec visual system] turns out to have an extremely high information transmission rate for the things that it cares about. In this way, the HVS evades Shannon rate limits (so much for that physics stuff).

For QUEST to work this way and exploit the power of qualia matching as a detector, it will have to have some efficient way of selecting what the qualia need to be for any specific task.

How can a 50 bit/second comm channel (like the human visual channel) enable construction of an exquisitely detailed model of the real world, in real time (with a slight 200 ms delay), inside the mind?

Only because hardly any of the sensed world data are needed to cue up the already stored internal qualia out of which the world model gets constructed. ONLY A QUALIA BASED SYSTEM CAN WORK the way animal sensory channels do. Once in a while, the wrong qualia are triggered into the mind and we get neckered (as in Necker cube); that’s small price to pay for a very fast sensory analysis system.

We spend the first years of our lives generating all the qualia we will use to internally compose the Cartesean theatre in our mind for the rest of our lives. We spend our night dreaming to modulate that set to continually refine the set for more efficient use.

What that means for QUEST is that we must be able to construct a set of internal qualia sufficient to span the entire set of things we expect to have to identify (make a list of statements about). Notice that fovea based visual systems avoid having to generate lots of possible qualia (that would be needed to compensate for PREDICTABLE variations in the real world, namely scale and rotation transformations), by building log r/theta hardware.

I don’t think the web 3.0 folks have the least idea of things like this; it would be like us trying to do PR or ATR in pixel space. Their approaches will never scale – will never be able to handle the Biederman issues. This is the purpose of QuEST.

Other topics on the plate we might discuss are –

definition of Chunks and relating that to our definition of qualia and our definition of situations.

Dreaming – see above – Bob E was asking about a comment Capt Amerika made about the purpose of dreaming so we might revisit our prior discussion of the use of dreaming as a means of refinement of our Qualia vocabulary.

An article sent by Robert P on ‘making fingers and words count in a cognitive robot

WeeklyQuESTDiscussionTopicsandNews21Mar

Weekly QUEST Discussion Topics and News, 14 March

March 13, 2014 Leave a comment

For our QUEST discussion this week, we are happy to have our colleagues from the Human Performance Wing Dr. Kevin Gluck and Dr. Matthew Walsh speak to us about their work on ‘Mechanisms for robust behavior’. Please see below for an abstract of the subject, and you may visit the QUEST VDL page or contact Cathy Griffith to obtain a copy of the presentation.

Title: Mechanisms for robust behavior

Dr. Matthew M. Walsh, 711 HPW/RH
Dr. Kevin A. Gluck, 711 HPW/RH

Manpower costs consume a significantly growing fraction of the Air Force
budget, driving the need for technologies that enable mission effectiveness
while reducing manpower requirements. The Air Force has identified the
increased use of autonomy as a key enabler for meeting this need. Although
progress has been made in this area, autonomous systems remain notoriously
brittle. In this presentation we propose that robustness should be taken
seriously as a selection criterion in the emerging human-machine system
research, development, evaluation, and acquisition agenda. To that end, we
advocate the adoption of a particular domain-general definition of
robustness, as well as a formal quantification methodology for measuring and
comparatively evaluating the robustness of systems. Further, we ask, how do
existing natural and engineered systems achieve a high degree of robustness?
Examples from the domains of biology, engineering, and cognitive science
reveal three general mechanisms for enabling robustness: system control,
redundancy, and adaptability. These mechanisms will be important in the
development of successful human-machine systems, just as they are known to
be in other natural and engineered systems.

WeeklyQUESTDiscussionTopicsandNews14March

Weekly QUEST Discussion Topics and News, 7 Mar 2014

Weekly QUEST Discussion Topics and News
7 March 2014

The topics this week include:
1.) Types of Qualia – capt Amerika has become more and more uncomfortable with the equating of the terms Qualia ~ situations ~ chunks ~ events ~ entities ~ narratives … so we want to have a discussion on what we want the ‘Q-word’ to mean to QuEST. To facilitate that discussion we can resurrect our prior discussions on Types of Qualia. The goal is to define what we will mean by Qualia and distinguish the other terms like ‘situations’ / ‘chunks’ / …
2.) I have also spent some time this week re-visiting the issues of blending – how do decisions get formulated when there are a range of cognitive engines/processes being applied to the stimuli. How do you either use Type 1 or Type 2 or blend inputs from the two? So I spent some time to go back and dig out an article we referenced in the past: IEEE TRANSJ,CTIONS ON SYSTEMS, MAN, AND CYBERNETICS, VOL. SMC-17, NO. 5, SEPTEMBER/OCTOBER 1987 753”Direct Comparison of the Efficacy of Intuitive and Analytical Cognition in Expert Judgment ‘ by KENNETH R. HAMMOND, ROBERT M. HAMM, JANET GRASSIA, AND TAMRA PEARSON Abstract – In contrast to the usual indirect comparison of intuitive cognitive activity with a normative model, direct comparisons were made of expert highway engineers’ use of analytical, quasi-rational, and intuitive cognition on three different tasks, each displayed in three different ways. Use of a systems approach made it possible to develop indices for measuring the location of each of the nine information display conditions on a continuum ranging from intuition inducing to analysis inducing and for measuring the location of each expert engineer’s cognition on a continuum ranging from intuition to analysis. Individual analyses of each expert’s performance over the nine conditions showed that the location of the task on the task index induced cognition to be located at the corresponding region on the cognitive continuum index. Surprisingly, intuitive and quasi-rational cognition frequently outperformed analytical cognition in terms of the empirical accuracy of judgments. Judgmental accuracy was related to the degree of correspondence between the type of task (intuition inducing versus analysis inducing) and the type of the experts’ cognitive activity (intuition versus analysis) on the cognitive continuum. — I did not spend bandwidth attempting to find more recent articles on the topic and will leave that as an exercise to the team but I did want to use the work as a means for us to again discuss the sorts of engineering decisions that will have to be made in any of our models.
3.) Along the same lines of blending I also attempted to revisit the phronetic rules provided by the Black Swan article to see if they provide any insight into decision making wisdom. Although I found this very difficult some of the rules might be worth re-visiting.
4.) We also this week (thanx to Cathy) had an email exchange with La Rue – she provided a couple of articles (we had seen them before) and has queried about the opportunity for her to work in the area after she finishes her Doctoral work. we might mention the articles she sent and discuss them.
5.) A follow on to the Hammond work : NURSING THEORY AND CONCEPT DEVELOPMENT OR ANALYSIS Cognitive Continuum Theory in nursing decision-making Raffik Cader BA MSc DN CertEd RGN RMN Senior Lecturer, School of Health, Community and Education Studies, Northumbria University, Newcastle Upon Tyne, UK Abstract: Findings. There is empirical evidence to support many of the concepts and propositions of Cognitive Continuum Theory. The theory has been applied to the decision-making process of many professionals, including medical practitioners and nurses. Existing evidence suggests that Cognitive Continuum Theory can provide the framework to explain decision-making in nursing. Conclusion. Cognitive Continuum Theory has the potential to make major contributions towards understanding the decision-making process of nurses in the clinical environment. Knowledge of the theory in nursing practice has become crucial.

Weekly QUEST Discussion Topics and News 7 March 2014

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