Introduction   Requirements   Format and 
Papers and
Venue and
 Contacts and 
The workshop will be held over two days. The format will consist of:

   o twenty minute talks with ten minute discussions,
   o a number of invited talks,
   o poster and discussion sessions, and
   o dinners in town.

Talks will be clustered by approach so that researchers unfamiliar with the various aspects of action-selection modeling will have an opportunity to learn a new area. We intend to allow speakers to know the speaking order well in advance so that they can coordinate their talks to maximize content and minimize repetition.

Talks will be chosen from submitted papers. All papers will be peer reviewed. The number of full papers accepted, as determined by review, may exceed the number of talk slots available, in which case the remainder of accepted papers will be offered a special full-paper poster session. The maximum number of participants is limited to 40. If there is room for participants without full papers, a second call will be sent out in May for extended abstracts and ordinary poster submissions.

Invited Speakers:


Drinks & Registration
"" (from 9)

9:30-10:30 Plenary

Tea & posters
Aylett (agents) Houk (ns)
Broekens (agents) Chambers (cn)
Logan (agents) Frank (cn)
2:15 Plenary / (BBSRC)
BBSRC (funding)

2:45 (talk)
Botvinick (network)
Tea & posters
Bogacz (theory)
Humphries (cn)

Houston (theory) Shanahan (cn / theory)
Madden (network/theory) Cisek (cn)
Crabbe (theory)
Discussion: Next Steps
6:00-7:00 Break / travel

Saturday Posters
Sunday Posters
d'Huart Melhuish
Barry Aguilera
Petters Yildirim
Viezzer Hitomi





Paper #1
Cognition, Action Selection, and Inner Rehearsal

Dr. Murray Shanahan
Reader in Computational Intelligence
Dept. Electrical and Electronic Engineering
Imperial College London

This paper presents a large-scale model of the architecture of the mammalian brain, the core circuit of which carries out inner rehearsal of interaction with the environment to realise a form of cognitively mediated action selection. As it alternates between broadcast to and competition between its component neural assemblies, the core circuit exhibits an episodic dynamics suggestive of cortical processing in discrete frames. The implemented architecture is used to control a simulated robot, and a classic experimental paradigm in which rats performed apparently goal-directed action selection is emulated.

No keywords provided.
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Paper #2
How a biological decision network can implement a statistically optimal test

Rafal Bogacz
Department of Computer Science
University of Bristol

Neurophysiological evidence due to Schall, Newsome and others indicates that decision processes in certain cortical areas (e.g. FEF and LIP) involve the integration of noisy evidence. Within this paradigm, we ask which neuronal architectures and parameter values would allow an animal to make the fastest and most accurate decisions. Since evolutionary pressure promotes such optimality (e.g. in prey capture and predator avoidance), it is plausible that biological decision networks realise or approximate optimal performance. We consider a simple decision model consisting of two populations of neurons integrating evidence in support of two alternatives. We show that in order to implement the optimal decision algorithm (sequential probability ratio test) the linearised network must satisfy the following two constraints: (i) it must accumulate the difference between evidence in support of each alternative, as would be implemented by mutual inhibition between the populations; and (ii) the strength of mutual inhibition must be equal to the leak of activity from each population.

decision making, action selection, optimality, sequential probability
ratio test
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Paper #3
When and when not to use your subthalamic nucleus: Lessons from a computational model of the basal ganglia

Michael J Frank
University of Colorado at Boulder

The basal ganglia (BG) are thought to coordinate response selection processes by facilitating adaptive frontal motor commands while suppressing those that are less adaptive. In previous work, a neural network model of the BG accounted for cognitive deficits in reinforcement learning and response selection associated with BG< dopamine depletion in Parkinson's disease. Novel predictions from this model have been subsequently confirmed in medicated and non-medicated patients and in healthy participants taking low doses of dopamine medications. Nevertheless, one clear limitation of the model is in its omission of the subthalamic nucleus (STN), which is a key BG structure that participates in both motor and cognitive processes. Here I include the STN and show that by modulating {\em   when} a response is executed, it can reduce premature responding and therefore have substantial effects on {\em which} response is ultimately selected, particularly when there are multiple competing responses.  The model accurately captures the dynamics of activity in various BG areas during response selection.  Simulated Parkinsonism, by depleting dopamine from the system, results in emergent oscillatory activity in STN and BG output structures, which has been linked with Parkinson's tremor. Finally, the model accounts for the beneficial effects of STN lesions on these oscillations, but suggests that this benefit may come at the expense of impaired decision making.

Neural network models; basal ganglia; subthalamic nucleus; dopamine; response selection
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Paper #4
Predicting violations of transitivity when choices involve fixed or variable delays to food.

Prof. Alasdair I. Houston
Centre for Behavioural Biology
School of Biological Sciences
University of Bristol

Incentive theory, an established model of behaviour, predicts how
animals should choose between alternatives that differ in the amount of food delivered, and the delay until it is delivered. Choice behaviour in such situations may be "irrational", in that it fails to satisfy Strong Stochastic Transitivity (SST). We apply incentive theory within the framework of the standard choice procedure from operant psychology and show that choice behaviour does not satisfy substitutability, and therefore SST does not hold. This occurs due to a change in the context of choice, implicit in the change of experimental conditions necessary to test SST. Our findings show similar patterns to the results of experimental studies of choice behaviour in pigeons. This agreement suggests that behavioural theories may provide insight into other apparent departures from rational behaviour.

Rational choice, intransitivity, choice context
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Paper #5
Estimation of eye-pupil size during blink
by Support Vector Regression

CRADLE(The Center for R&D of Educational Technology)
Tokyo Institute of Technology, Japan

Pupillography can be an index of mental activity and
sleepiness, however blinks prevent its measurability as an artifact. A method of estimation of pupil size from pupillary changes during blinks was developed using a support vector regression technique. Pupil responses for changes in periodic brightness were prepared, and appropriate pupil sizes for blinks were given as a set of training data. The performance of the trained estimation models were compared and an optimized model was obtained. An examination of this revealed that its estimation performance was better than that of the estimation method using MLP. This development helps in the understanding of the behaviour of pupillary change and blink action.

Eye-pupil, blink, estimation, Support Vector Regression
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Paper #6
Learning affordance concepts: some seminal ideas

Manuela Viezzer

Thales Research and Technology

Inspired by the pioneering work of J. J. Gibson, we provide a workable characterisation of the notion of affordance and we explore a possible architecture for an agent that is able to autonomously acquire affordance concepts.

Affordances, cognitive architecture, concept formation
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Paper #7
Routine Action: Combining Familiarity and Goal Orientedness

Nicolas Ruh

School of Psychology, Birkbeck, University of London, Malet Street, London, WC1E 7HX, UK

Two current approaches to modelling naturalistic sequential routine action selection differ along two dimensions: (a) the number of systems required and (b) the nature of the underlying task representation. We present findings from a study that supports a combination of the two computational accounts, namely a familiarity-dependent basic system, interfaced with a higher-level supervisory system to bias it at crucial points in a sequence. In order to elaborate this position, we explore a connectionist reinforcement model of routine action that (a) learns goal-directed action sequences through a combination of exploration and exploitation, and (b) offers the prospect of being interfaced with a supervisory system.

routine sequential action; dual systems; reinforcement learning; recurrent neural networks
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Paper #8

Building agents to understand infant attachment behaviour

Dean Petters
University of Birmingham,
School of Computer Science,
Birmingham B15 2TT, United Kingdom

This paper reports on autonomous agent simulations of infant attachment behaviour. The behaviours simulated have been observed in home environments and in a controlled laboratory procedure called the Strange Situation Experiment. The Avoidant, Secure and Ambivalent styles of behaviour seen in these studies are outlined, and then abstracted to their core elements to act as a specification of requirements for the simulation. A reactive agent architecture demonstrates that these patterns of behaviour can be learnt from reinforcement signals without recourse to deliberative mechanisms. Alternative designs based upon hybrid architectures are also considered.

Attachment infant architecture reactive Bowlby
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Paper #9
Hierarchical reactive planning: Where is its limit? ("Brom_MNAS_Gardener050331.pdf")

Cyril Brom

Charles University in Prague
Faculty of Mathematics and Physics
Malostranské nám. 2/25, Prague, Czech Republic

Nowadays, hierarchical reactive methods are very popular in the field of controlling complex artificial intelligent agents. In this paper, we argue that they can not cope with human-like behaviour.
We propose a detailed analysis of behaviour of a relatively simple human-like artificial agent, an artificial gardener, whose action selection model is based on hierarchical reactive planning with rigid switching. It is shown that although the agent has no troubles with survival in a complex and dynamic environment, it does not behave believable in some situations. However, instead of rejection the judged methodology, we propose how to extend it using certain features of other action selection models.

Action selection, human-like intelligent agent, hierarchical reactive planning
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Paper #10

Action Selection in Subcortical Loops through Basal Ganglia

James C. Houk
Professor of Physiology at Northwestern University Medical School in Chicago, IL, USA

Subcortical loops through the basal ganglia and cerebellum form computationally powerful distributed processing modules (DPMs). This paper relates the computational features of a DPM's loop through the basal ganglia to experimental results for two kinds of natural action selection. First, functional imaging during a serial order recall task was used to study human brain activity during the selection of sequential actions from working memory. Second, microelectrode recordings from monkeys trained in a step-tracking task were used to study the natural selection of corrective submovements. Our DPM-based model assisted in the interpretation of puzzling data from both of these experiments. We come to posit that the many loops through the basal ganglia each regulate the embodiment of pattern formation in a given area of cerebral cortex. This operation serves to instantiate different kinds of action (or thought) mediated by different areas of cerebral cortex.

Basal ganglia, serial order, error correction, functional imaging, neurophysiology
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Paper #11
Forced moves or good tricks in design space?  Great moments in the evolution of the neural substrate for action selection

Tony J. Prescott
Adaptive Behaviour Research Group, University of Sheffield, UK.

This mini-review considers some important landmarks in the evolution of animals, asking to what extent specialised action selection mechanisms play a role in the functional architecture of different nervous system plans, and looking for 'forced moves' or 'good tricks' (Dennett, 1995) that could possibly transfer to the design of control systems for mobile robots. A key conclusion is that while cnidarians (e.g. jellyfish) appear to have discovered some good tricks for the design of behaviour-based control systems-lacking specialised selection mechanisms; the evolution of bilaterality in platyhelminths (flatworms) may have forced the evolution of a central ganglion (or 'archaic brain') whose main function is to resolve conflicts between competing peripheral systems. Whilst vertebrate nervous systems contain many interesting substrates for selection it is likely that here too, the evolution of centralised selection structures such as the basal ganglia and medial reticular formation may have been a forced move due to the need to limit connection costs as brains increased in size.

comparative neurobiology, jellyfish, flatworms, vertebrates, integration, architecture
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Paper #12
Reinforcement Learning of Stable Trajectory for Quasi-Passive-Dynamic Walking

Kentarou HITOMI (1), Tomohiro SHIBATA (1,2), Yutaka NAKAMURA (1), and Shin
(1)Nara Institute of Science and Techcnology
Takayama 8916-5, Ikoma, Nara 630-0192
(2)ATR Computational Neuroscience Laboratories

A class of biped locomotion called Passive Dynamic Walking (PDW) has been recognized to be efficient in energy consumption and a key to understand human walking. Although DW is sensitive to the initial condition and disturbances, some studies of Quasi-PDW, which incorporates supplemental actuators, have been reported to overcome the sensitivity. In this article, we propose a reinforcement learning designed in particular for Quasi-PDW walking. The keys of our approach are a reward function and a learning scheme of a simple feedback controller, both of which utilize the robot’s passive dynamics as much as possible. Computer simulations show that the parameter in a Quasi-PDW controller is quickly tuned by our method, and that the obtained controller is robust against variations in the slope gradient.

2D biped, passive dynamic walk, reinforcement learning, adaptive control
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Paper #13
Combining Action Selection Models with a Five Factor Theory

Mark Witkowski
Intelligent Systems and Networks Group
Department of Electrical and Electronic Engineering
Imperial College
Exhibition Road, London, SW7 2BT, United Kingdom

This paper describes a unifying framework for five highly influential but disparate theories (the five factors) of natural learning and behavioral action selection. These theories are normally considered independently, with their own experimental procedures and results. The framework builds on a structure of connection types, propagation rules and learning rules, which are used in combination to integrate results from each theory into a whole. Exemplar experimental procedures will be used to discuss the areas of genuine difference, and to identify areas where there is overlap and where apparently disparate findings have a common source. The paper focuses on predictive or anticipatory properties inherent in these action selection and learning theories, and uses the Dynamic Expectancy Model and its computer implementation SRS/E as a mechanism to conduct this discussion.

Action selection, animal learning theory, prediction and anticipatory learning, dynamic and static policy maps
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Paper #14
Goal and motor action selection using a hippocampal and prefrontal

Nicolas Cuperlier, Philippe Gaussier, Philippe Laroque, Mathias Quoy
Université de Cergy-Pontoise - ENSEA
6, Avenue du Ponceau \\95014 Cergy-Pontoise France\\

We have developped a mobile robot control system based on hippocampus and prefrontal models. We propose a model that rely on cognitive maps linking transition cells to adress action selection problems encoutered in navigation tasks. The transition cell links two locations with the integrated direction used. We describe a simple neural mechanism able to learn and predict these transitions to perform motivated path planning to reach goals. Furthermore, we propose a dynamical system using a Neural Field for selecting the final movement to perform. Simulations and robotics experiments are carried out.

Action selection, hippocampus, transition cells, dynamical system
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Paper #15
A computational model of reach decisions in the primate cerebral cortex

Paul Cisek, Ph.D.
Department of physiology
University of Montreal
C.P. 6128 Succursale Centre-ville
Montreal QC H3C 3J7 Canada
phone: 514-343-6111 x4355
FAX: 514-343-2111

Neurophysiological evidence suggests that visually-guided reaching movements are produced through "specification" and "selection" processes that overlap both temporally and anatomically (Cisek and Kalaska, 2005). Here, I present a formal computational model which demonstrates how partial specification of several potential movement directions, and the selection of the correct movement, can occur in populations of directionally tuned cells in a distributed cortical network including posterior parietal, premotor, prefrontal, and primary motor cortex. The model reproduces a large set of neurophysiological and psychophysical phenomena, including the behavior of cortical cells during a reach decision task and the spatial and temporal statistics of human reaching choices.

Computational Neuroscience, Decision-Making, Action Selection, Neurophysiology, Cerebral Cortex
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Paper #16
Innate Planning Mechanisms

Sule Yildirim
Complex Adaptive Organically-Inspired Systems Group
Department of Computer Science
The Norwegian University of Science and Technology, Trondheim, Norway

In this paper, we will discuss whether there could be any means to bridge the gap between the Symbolic and Subsymbolic AI. One way to do this is to ask ourselves if human brain executes any planning algorithms. We see that we have taken a series of steps when we are done with planning in a situation. Taking a series of steps during planning might be a result of the execution of an in-nate planning algorithm. If we really are executing a planning al-gorithm, then we believe, its function is very general and is to set the conditions which will trigger a next step to take. A step to take might be the execution of an IF rule as an example. IF rule execu-tions are not the only steps to take while planning, however, for simplicity, they are assumed as the only ones here. There is still not any neuro-scientific evidence against the possibility that human mind incorporates an innate planning algorithm that triggers the next rule to execute (the step to take) yet. For that reason, in this paper we will investigate that possibility.

Action sequencing, action associations, concept-action associations, emergence of association mechanisms
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Paper #17
Building Plans for Household Tasks from Distributed Knowledge

Rakesh Gupta
Honda Research Institute USA Inc

To accomplish a household task, an autonomous system needs a plan with steps. It is desirable to derive this plan dynamically instead of pre-coding it in the system. In this paper, we find a plan by using common sense knowledge collected from volunteers over the web through distributed knowledge capture techniques. This knowledge consists of steps for executing common household tasks. We first pre-process the data with part-of-speech (POS) tagging to identify the actions and objects in the steps in all available plans for the task. We then determine the order of the steps to accomplish the task using discriminative as well as generative models. For the discriminative approach, we cluster the plans using hierarchical agglomerative clustering and choose a plan from the biggest cluster. In the contrasting approach, we make use of generative Markov chain techniques. Using human judgments, we show that the generative model with the first order Markov chain has the best performance. We also show that environmental constraints can be incorporated in the generated plans.

Data Mining, Routine Actions, Task Model
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Paper #18
Prediction of the Behavioural Strategy in a Goal-Oriented Task

Manuel A. Sanchez-Montanes
Escuela Politecnica Superior
Universidad Autonoma de Madrid (Spain)

Tim C. Pearce
Department of Engineering
University of Leicester (UK)

A common approach to understanding the behaviour of animals within their natural habitat involves breaking down complex behavioral sequences into simpler component behavioral units. Here, we propose a theoretical framework based upon Markov state descriptions of animal behaviours which can be optimised in a virtual environment. In this case, the sensory information available to the animal is naturally characterized by nonstationary statistics which depend on the history of action selection. This framework allows us to address many questions, such as how and why behavioral units are arranged in a particular order, the degree of optimality in the overall behavioral strategy, and how much memory is required to efficiently solve a behavioral task. To illustrate our approach, we take a well characterised animal behaviour -- chemotaxis in the moth -- which is performed robustly in turbulent chemical plumes. In this example the optimisation of a two-state model in a virtual plume generates a behavioural strategy, the units of which are directly comparable to those seen in the animal -- 'cast' and 'surge'. Finally, we discuss how our framework may be extended to understand sensory integration and neuronal coding within sensorimotor pathways that supports effective natural action selection.

Chemical search, behaviour prediction, behavioural quantification, partially observable environments, neuroethology
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Paper #19
He/She-You-I Formalism: A Heuristic Model of (En)Action to Make Decisions

Daniel Mellet-d'Huart

This paper presents a heuristic model of (en)action that was developed in order to support the design of virtual environments for learning. The underlying action model is rooted in recent researches in neurosciences and based on a specific conceptual framework. It is organized as a three-pole system within the framework of a "He/She- You-I formalism". A "He/She Pole" deals with understanding, simulation and anticipation of action; a "You Pole" with making decision and taking engagement within action; and a "I Pole" with realizing action within a particular environment.

Acting, action, enaction, model, design method, virtual reality
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Paper #20
Selection Actions and Making Decisions: Lessons from AI Planning

Hector Geffner
ICREA & Universitat Pompeu Fabra, SPAIN

Humans and animals encounter a huge variety of problems which they must solve using general methods.  Even simple problems, however, become computationally hard if the structure of the problems  is not recognized and exploited. Work in Artificial Intelligence  Planning and Problem Solving, faces a similar difficulty, and in recent years has led to well-founded and empirically tested techniques for recognizing and exploiting structure, focusing the search for solutions, and in certain cases, bypassing the need to search altogether. These techniques include the automatic derivation of heuristic functions, the use of limited but effective forms of inference, and the compilation of domains, all of which enable a general problem solver to `adapt' to the task at hand. In this paper, I  present the ideas underlying these techniques, and argue for their relevance to models of natural intelligent behavior. The need for focusing the search for solutions has actually been recognized in  a number of recent works concerned with natural intelligent behavior, where it has been related to the role of emotions. In the paper, we address this issue as well.

Search, Heuristics, Emotion
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Paper #21
Simulation, Emotion and Information Processing: Computational
Investigations of the Regulative Role of Emotion in Adaptive Behavior.

Joost Broekens (corresponding author)  and Fons Verbeek
University of Leiden
Leiden Institute of Advanced Computer Science
Niels Bohrweg 1, 2333CA, Leiden, The Netherlands

Emotion plays an important role in thinking. Integration of emotion at multiple levels of information processing suggests that it contributes to survival. We focus on the low-level regulatory influence of emotion on information processing mechanisms and adaptive behavior, and use a computational reinforcement-learning model to study this. Our model is based upon Interactivism and the simulation hypothesis. We explain how the simulation hypothesis is embedded in the model. We measure emotion as a function of the performance of the agent under interaction with its environment. The emotion is used as feedback to the simulation strategy of the agent. Our main hypothesis tested is: a simulation strategy that is dynamically adapted by emotional feedback provides additional survival value to an agent compared to a static simulation strategy. Experimental results illustrate that this hypothesis holds true. Dynamic adaptation results in a learning performance that at least equals static simulation strategies. Additionally, it results in a major decrease of mental effort required for this performance. This is an important observation in the understanding of the evolutionary plausibility of the simulation hypothesis. In addition, our observation provides evidence of compatibility between the simulation hypothesis and emotion theory, specifically as our concept of pleasure is based on cognitive emotion theory.

Simulation hypothesis, emotional feedback, reinforcement learning.
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Paper #22
Having it both ways the impact of fear on eating and fleeing in virtual flocking animals

Ruth Aylett
Professor of Computer Science
Mathematics and Computer Science, Heriot-Watt University
Edinburgh EH14 4AS, UK      

The paper investigates the role of an affective system as part of an ethologically-inspired action-selection mechanism for virtual animals in a 3D interactive graphics environment. It discusses the integration of emotion with flocking and grazing behaviour and a mechanism for communicating emotion between animals. We develop a metric for analyzing the collective behaviour of the animals and its complexity. We show that emotion reduces the complexity of behaviour and thus mediates between individual and collective behaviour.

Artificial life, autonomous agents, cognitive modeling, lifelike characters, virtual reality
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Paper #23
On Compromise Strategies for Action Selection with Proscriptive Goals

Frederick "Ric" Crabbe, USNA,

Devising a solution to the action selection problem rests on a definition of which actions are preferable over others. Among many properties suggested for action selection mechanisms, one prominent one is the ability to select compromise actions, i.e. actions that are not the best to satisfy any active goal in isolation, but rather compromise between the multiple goals. This paper performs an analysis of compromise actions in situations where the agent has one proscriptive goal. It concludes that optimal compromise behavior looks quite different from what was expected, and, while optimal compromise actions are beneficial to an agent, the benefit is often small compared to greedy algorithms. It goes on to suggest that much of the discussion about compromise behavior is the result of an equivocation on its definition, and it concludes with a new compromise behavior hypothesis.

Action Selection; Compromise behavior; Optimal Behavior.
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Paper #24
Visual Communication and Social Structure – The Group Predation of Lions

Alwyn Barry and Hugo Dalrymple-Smith
University of Bath
Department of Computer Science
Claverton Down, Bath, UK.

[Creel, 1997] in a study of african hunting dogs suggested that, where the maximisation of net energy gain from hunting requires cooperation, cooperative hunting becomes an important part of the sociality of the hunting dogs. When considering Lion cooperative hunting [Scheel and Packer, 1991] suggested, in contrast, that Lions do not form groups to increase the intake of food for the group but for the individual, implying that cooperative behaviour in hunting has very little impact on the formation of prides. In using simulation to investigate the role of visual location in the group hunting behaviour of Lions it is shown that a minimal communication simulation can be derived if the dominance of pride members is taken into account. We conclude that agreed dominance permits the reduction of visual cues required to coordinate complex cooperative simulated behaviour.
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Paper #25:
Action selection in a macroscopic model of the brainstem
reticular formation

Mark Humphries, Kevin Gurney and Tony Prescott
University of Sheffield

The behavioral repertoire of decerebrate and neonatal animals suggests that a relatively self-contained neural substrate of action selection may exist in the brainstem. Here, we develop the hypothesis that the principal component of the substrate is the medial ponto-medullary reticular formation. Our quantitative structural model of this region, which proposes a macroscopic organisation at the level of inter-connected neural clusters, is extended to incorporate sensory input. Evidence is reviewed in support of the proposal that both input and output configurations of this region follow this organisation.
To investigate how this biologically constrained model may be configured to support action selection, a computational neural-population model of the medial reticular formation is outlined, and alternate configurations are assessed in simulation. We conclude that the configuration which most effectively supports action selection is likely to be one which represents compatible sub-actions at the cluster level; thus, co-activation of a set of these clusters would lead to the co-ordinated behavioral response observed in the animal.

Reticular formation, macroscopic models, decerebrate
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Paper #26
Collective Action Selection in Social Insect Colonies

James A. R. Marshall
Department of Computer Science
University of Bristol

Action selection is a problem that is faced not only by individual organisms, but also by collectives of organisms. Colonies of social insects are highly integrated units that frequently perform optimal collective action selection in a decentralised manner. Social insect colonies provide very accessible model systems for studying the mechanisms underlying such action selection processes. This paper discusses models of two action selection mechanisms in insect colonies, and speculates as to the potential for comparing action selection in such colonies to action selection in individuals.

Collective action selection, individual action selection, Temnothorax albipennis, Apis mellifera
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Paper #27
The basal ganglia as a cognitive response mechanism: simulating normal and schizophrenic performance in the Stroop task

Tom Stafford and Kevin Gurney
Department of Psychology
University of Sheffield
Western Bank, Sheffield
S10 2TP, UK

This paper builds on our existing, biologically constrained model of the basal ganglia, originally constructed under the premise that these subcortical structures perform action selection. Here, we show how this same model, when used in conjunction with a connectionist model of processing in the Stroop task, can also provide an account of normal and pathological (schizophrenic) human reaction time performance. Our model accounts for a wide variety of phenomenon and is consistent with the most recent data on the physiology of schizophrenia. This work validates modelling the basal ganglia as the vertebrate solution to the action selection problem. Conversely, it demonstrates the importance of issues of action selection and choice of biologically plausible response mechanisms to performance on cognitive tasks.

Cognitive, response selection, stroop, schizophrenia, basal ganglia
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Paper #28
It's About Time: representing time in cognitive models

Neil Madden and Brian Logan

We outline a cognitive architecture which can be used to model a variety of perceptual phenomena.  The architecture is based on processes operating on collections of time-limited buffers in a parallel model of cognition and draws on aspects of the Multiple Drafts theory \cite{Dennett/Kinsbourne:92a}. We briefly describe the architecture and show how it can be used to model two relevant experiments from the literature: colour phi \cite{Kolers/vonGrunau:76a}, and the cutaneous ``rabbit'' \cite{Geldard/Sherrick:72a}.

Cognitive modelling, philosophical foundations, AI architectures.
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Paper #29
Modelling Primate Task Learning Requires Bad Machine Learning

Joanna J. Bryson and Jonathan C. S. Leong
University of Bath
Artificial models of natural Intelligence
Bath, BA2 7AY United Kingdom

Jonathan C. S. Leong
Harvard University
Primate Cognitive Neuroscience
Cambridge MA 02138, USA

We present a model of transitive inference which is able to account for the performance of monkeys and children on three-item transitive inference tasks. We do this using a modular multi-layer neural architecture which does not integrate error across layers. This system gets trapped in local minimae, and in so doing, generates errors much like those seenin monkeys and children.
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Paper #30
Biorealistic Simulation of Baboon Foraging using Agent-Based Modelling

Brian Logan
School of Computer Science & IT, University of Nottingham
Phone: +44 115 846 6509, Fax: +44 115 951 4254

We present an agent-based model of the key activities of a troop of chacma baboons (Papio hamadryas ursinus) based on data collected at the De Hoop Nature Reserve in South Africa.  We analyse the predictions of the model in terms of how well it was able to duplicate the observed activity patterns of the animals and the relationship between the parameters that control the agent's decision procedure and the model's predictions.
The model predicts reasonable yearly average values for energy intake, time spent socialising and resting, and habitat utilisation, but is unable to account for month by month variation in the field data.  However even at the current stage of model development we are able to show that, across a wide range of decision parameter values, the baboons are easily able to achieve energetic and social time requirements. This suggests that these particular animals are strongly influenced by other factors such as predation risk or thermal load in deciding their activity patterns.

Group action selection, primates
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Paper #31
Contracting model of the basal ganglia

Benoˆýt Girard(1), Nicolas Tabareau(1) and Jean-Jacques Slotine(2) and Alain Berthoz(1)

1. Laboratoire de Physiologie de la Perception et de l’Action, CNRS - Coll`ege de France 11 place Marcelin Berthelot, 75231 Paris Cedex 05, France.

2. Non Linear System Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, USA

It is thought that one role of the basal ganglia is to constitute the neural substrate of action selection. We propose here a modification of the action selection model of the basal ganglia of (Gurney et al., 2001a,b) so as to improve its dynamical features. The dynamic behaviour of this new model is assessed by using the theoretical tool of contraction analysis. We simulate the model in the standard test defined in (Gurney et al., 2001b) and also show that it performs perfect selection when presented a thousand successive random entries. From a biomimetical point of view, our model takes into account a usually neglected projection from GPe to the striatum, which enhances its efficiency.

Contraction analysis, action selection, basal ganglia, computational model
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Paper #32
Tolerance and Sexual Attraction in Despotic Societies: A Replication and Analysis of Hemelrik (2002)

Hagen Lehmann and JingJing Wang and Joanna J. Bryson
University of Bath
Artificial models of natural Intelligence
Bath, BA2 7AY United Kingdom

Most primate societies are characterised by hierarchical structures with more or less despotic value. Males are usually dominant over females, but in periods of sexual attraction (during females period of tumescence) male ‘tolerance’ towards females rises. (5) shows in a model that this ‘tolerance’ is created as a side effect due to the rise of female dominance during periods of sexual attraction. This rise is the consequence of the more frequent approaches of males towards females during these periods. In Hemelrijk’s model the males gain no benefit from ‘tolerating’ females and they only do so at high aggression levels as a kind of ‘respectful timidity’, because some of the females have become dominant over them.
In this paper we discuss and replicate results of Hemelrijk’s study about the effects of sexual attraction and intensity of aggression between sexes in despotic primate societies. We have used the description of her individual-based model called ‘DomWorld’ to create our own version of this model, which simulates an artificial primate society. The replication has been successful, however in implementing it we have found that some results are highly reliant on aspects of the model that are not well supported by the current primate literature.
We discuss which of her results we find most convincing, and relate this to the cognitivist debate with special focus on macaque and chimpanzee societies.
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Paper #33
Toward an Executive without a Homunculus: Computational Models of the Prefrontal Cortex/Basal Ganglia System.

Randall O'Reilly

The prefrontal cortex has long been thought to subserve both working memory (the holding of information online for processing) and ``executive'' functions (deciding how to manipulate working memory and perform processing). Although many computational models of working memory have been developed, the mechanistic basis of executive function remains elusive, often amounting to a homunculus. I present an attempt to deconstruct this homunculus through powerful learning mechanisms that allow a computational model of the prefrontal cortex to control both itself and other brain areas in a strategic, task-appropriate manner. These learning mechanisms are based on subcortical structures in the midbrain, basal ganglia and amygdala, which together form an actor/critic architecture. The model's performance compares favorably with standard backpropagation-based temporal learning mechanisms on the challenging 1-2-AX working memory task, and other benchmark working memory and cognitive control tasks. It also makes a number of testable predictions about the contributions of the basal ganglia and prefrontal cortex in various behavioral tasks, several of which have been tested and confirmed.
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Paper #34
Neurocomputational modeling of human decision-making

Marius Usher

Decision-making is one of the most common and, at the same time, open-ended and effortful human activities. One source of its difficulty resides with the need to evaluate alternatives whose `attractiveness' varies on several incommensurable dimensions. Experimental work in human decision-making has also revealed a series of intriguing behavioral patterns that indicate deviations from normative economic theories and which raise a challenge for the development of a theory of human performance. Here I will review some of these patterns, such as loss-aversion and preference reversals under a series of conditions (time constraints, the introduction of contextual information in the choice set, etc). I will then discuss and contrast a number of neurocomputational theories that have recently been proposed to account for these patterns and to explain the cognitive processes that mediate choice-RT and human decision-making.
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Paper #35
Endogenous Political Parties

Michael Laver, New York University
Michel Schilperoord, Erasmus University, Rotterdam

The spatial model of party competition is one of the dominant paradigms of contemporary political science. Virtually all spatial models of party competition are essentially static: most key model parameters, including the identity of all parties and rules of interaction between them, are set exogenously; the essential solution concept deployed is some form of static equilibrium. However, recent progress has been made with agent-based models that treat party competition as an evolving complex system that may never reach a steady state, (Kollman, Miller and Page 1992; Kollman, Miller and Page 1998; De Marchi 1999; De Marchi 2003; Kollman, Miller and Page 2003; Laver 2005). The central purpose of this paper is to extend the agent-based model of party competition proposed in Laver (2005) to encompass the birth and death of political parties and thereby make the identity of parties in the system an output of, rather than an analyst-specified input to, the model. This is done by modeling party birth as an endogenous change of agent type from citizen to party leader. In order to do this it becomes necessary to model the "memories" of citizens in the system, an issue that has not previously been addressed in agent-based models of party competition, which have hitherto assumed goldfish memories. The birth and death of parties transforms into a dynamic system even an environment where all agents have otherwise non-responsive adaptive behaviors. Substantively, the original purpose of this modeling exercise was to investigate how key system parameters condition the number and identity of political parties in a given system. An unintended but valuable spin-off has been that we are now able to characterize the overall social welfare of the set of citizens, taken as a whole, as a function of party system parameters.
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