scholarly journals Planning and navigation as active inference

2017 ◽  
Author(s):  
Raphael Kaplan ◽  
Karl J Friston
Keyword(s):  
Target Location ◽  
Place Cells ◽  
Process Theory ◽  
Active Inference ◽  
Interesting Phenomenon ◽  
Underlying Process ◽  
Context Sensitive ◽  
Electrophysiological Responses ◽  
The World ◽  
Maze Problem

AbstractThis paper introduces an active inference formulation of planning and navigation. It illustrates how the exploitation–exploration dilemma is dissolved by acting to minimise uncertainty (i.e., expected surprise or free energy). We use simulations of a maze problem to illustrate how agents can solve quite complicated problems using context sensitive prior preferences to form subgoals. Our focus is on how epistemic behaviour – driven by novelty and the imperative to reduce uncertainty about the world – contextualises pragmatic or goal-directed behaviour. Using simulations, we illustrate the underlying process theory with synthetic behavioural and electrophysiological responses during exploration of a maze and subsequent navigation to a target location. An interesting phenomenon that emerged from the simulations was a putative distinction between ‘place cells’ – that fire when a subgoal is reached – and ‘path cells’ – that fire until a subgoal is reached.

Towards a process theory of learning: Feeling the beauty of the world

Interchange ◽  
1995 ◽  
Vol 26 (4) ◽  
pp. 347-364 ◽  
Author(s):  
Howard Woodhouse
Keyword(s):  
Process Theory ◽  
The World

scholarly journals Dissociation of exteroceptive and idiothetic orientation cues⋮ effect on hippocampal place cells and place navigation

1997 ◽  
Vol 352 (1360) ◽  
pp. 1515-1524 ◽  
Author(s):  
J. Bures ◽  
A. A. Fenton ◽  
Yu. Kaminsky ◽  
J. Rossier ◽  
B. Sacchetti ◽  
...  
Keyword(s):  
Spatial Cognition ◽  
Reference Frames ◽  
Target Location ◽  
Place Cells ◽  
Related Information ◽  
Place Navigation ◽  
Preference Task ◽  
Spatial Memories ◽  
Place Avoidance ◽  
Orientation Cues

Navigation by means of cognitive maps appears to require the hippocampus; hippocampal place cells (PCs) appear to store spatial memories because their discharge is confined to cell–specific places called firing fields (FFs). Experiments with rats manipulated idiothetic and landmark–related information to understand the relationship between PC activity and spatial cognition. Rotating a circular arena in the light caused a discrepancy between these cues. This discrepancy caused most FFs to disappear in both the arena and room reference frames. However, FFs persisted in the rotating arena frame when the discrepancy was reduced by darkness or by a card in the arena. The discrepancy was increased by ’field clamping’the rat in a room–defined FF location by rotations that countered its locomotion. Most FFs dissipated and reappeared an hour or more after the clamp. Place–avoidance experiments showed that navigation uses independent idiothetic and exteroceptive memories. Rats learned to avoid the unmarked footshock region within a circular arena. When acquired on the stable arena in the light, the location of the punishment was learned by using both room and idiothetic cues; extinction in the dark transferred to the following session in the light. If, however, extinction occurred during rotation, only the arena–frame avoidance was extinguished in darkness; the room–defined location was avoided when the lights were turned back on. Idiothetic memory of room–defined avoidance was not formed during rotation in light; regardless of rotation, there was no avoidance when the lights were turned off, but room–frame avoidance reappeared when the lights were turned back on. The place–preference task rewarded visits to an allocentric target location with a randomly dispersed pellet. The resulting behaviour alternated between random pellet searching and target–directed navigation, making it possible to examine PC correlates of these two classes of spatial behaviour. The independence of idiothetic and exteroceptive spatial memories and the disruption of PC firing during rotation suggest that PCs may not be necessary for spatial cognition; this idea can be tested by recordings during the place–avoidance and preference tasks.

scholarly journals The Organization of Political Violence by Insurgencies

2012 ◽  
Vol 18 (3) ◽  
Author(s):  
Roos Haer
Keyword(s):  
Political Violence ◽  
Probit Model ◽  
Ordered Probit ◽  
Process Theory ◽  
Ordered Probit Model ◽  
Armed Groups ◽  
Human Right ◽  
Armed Group ◽  
The World ◽  
The Relationship

AbstractA range of theories have attempted to explain the variation in civilian abuse of warring parties. Most of these theories have been focused on the strategic environment in which these acts take place. Less attention is devoted to the perpetrators of these human right abuses themselves: the armed groups. This study tries to fill this niche by using the organizational process theory in which it is assumed that armed groups, like every organization, struggles for survival. The leader tries to ensure the maintenance of her armed group by increasing her control over her troops. The relationship between the level of control and the perpetrated civilian abuse is examined with a new dataset on the internal structure of more than 70 different armed groups around the world. With the help of a Bayesian Ordered Probit model, this new dataset on civilian abuse is analyzed. The results show that especially particular incentives play an important role.

scholarly journals Dynamical Latent State Computation in the Posterior Parietal Cortex

2022 ◽  
Author(s):  
Kaushik J Lakshminarasimhan ◽  
Eric Avila ◽  
Xaq Pitkow ◽  
Dora E Angelaki
Keyword(s):  
Parietal Cortex ◽  
Posterior Parietal Cortex ◽  
Target Location ◽  
Neural Representation ◽  
World Model ◽  
Population Activity ◽  
The World ◽  
Neural Interactions ◽  
Posterior Parietal ◽  
Hidden States

Success in many real-world tasks depends on our ability to dynamically track hidden states of the world. To understand the underlying neural computations, we recorded brain activity in posterior parietal cortex (PPC) of monkeys navigating by optic flow to a hidden target location within a virtual environment, without explicit position cues. In addition to sequential neural dynamics and strong interneuronal interactions, we found that the hidden state -- monkey's displacement from the goal -- was encoded in single neurons, and could be dynamically decoded from population activity. The decoded estimates predicted navigation performance on individual trials. Task manipulations that perturbed the world model induced substantial changes in neural interactions, and modified the neural representation of the hidden state, while representations of sensory and motor variables remained stable. The findings were recapitulated by a task-optimized recurrent neural network model, suggesting that neural interactions in PPC embody the world model to consolidate information and track task-relevant hidden states.

scholarly journals Computational mechanisms of curiosity and goal-directed exploration

2018 ◽  
Author(s):  
Philipp Schwartenbeck ◽  
Johannes Passecker ◽  
Tobias U Hauser ◽  
Thomas H B FitzGerald ◽  
Martin Kronbichler ◽  
...  
Keyword(s):  
Information Gain ◽  
Task Structure ◽  
Active Inference ◽  
Computational Framework ◽  
The World ◽  
Different Types ◽  
Types Of Information ◽  
The Right ◽  
High Uncertainty ◽  
Optimal Behaviour

AbstractSuccessful behaviour depends on the right balance between maximising reward and soliciting information about the world. Here, we show how different types of information-gain emerge when casting behaviour as surprise minimisation. We present two distinct mechanisms for goal-directed exploration that express separable profiles of active sampling to reduce uncertainty. ‘Hidden state’ exploration motivates agents to sample unambiguous observations to accurately infer the (hidden) state of the world. Conversely, ‘model parameter’ exploration, compels agents to sample outcomes associated with high uncertainty, if they are informative for their representation of the task structure. We illustrate the emergence of these types of information-gain, termed active inference and active learning, and show how these forms of exploration induce distinct patterns of ‘Bayes-optimal’ behaviour. Our findings provide a computational framework to understand how distinct levels of uncertainty induce different modes of information-gain in decision-making.

scholarly journals Semiosis and pragmatism: Toward a dynamic concept of meaning

2006 ◽  
Vol 34 (1) ◽  
pp. 37-65
Author(s):  
João Queiroz ◽  
Floyd Merrell
Keyword(s):  
Dynamic Process ◽  
Social Scientists ◽  
Cognitive Sciences ◽  
Context Sensitive ◽  
Central Focus ◽  
The World ◽  
Extended Dynamic ◽  
Pragmatic Philosophy

Philosophers and social scientists of diverse orientations have suggested that the pragmatics of semiosis is germane to a dynamic account of meaning as process. Semiosis, the central focus of C. S. Peirce’s pragmatic philosophy, may hold a key to perennial problems regarding meaning. Indeed, Peirce’s thought should be deemed seminal when placed within the cognitive sciences, especially with respect to his concept of the sign. According to Peirce’s pragmatic model, semiosis is a triadic, time-bound, context-sensitive, interpreter-dependent, materially extended dynamic process. Semiosis involves inter-relatedness and inter-action between signs, their objects, acts and events in the world, and the semiotic agents who are in the process of making and taking them.

scholarly journals Epistemic Communities under Active Inference

2022 ◽  
Author(s):  
Mahault Albarracin ◽  
Daphne Demekas ◽  
Maxwell Ramstead ◽  
Conor Heins
Keyword(s):  
Social Networks ◽  
Confirmation Bias ◽  
Community Formation ◽  
Epistemic Communities ◽  
Local Network ◽  
Active Inference ◽  
Shared Beliefs ◽  
The World ◽  
Echo Chambers ◽  
Degree Of Certainty

The spread of ideas is a fundamental concern of today’s news ecology. Understanding the dynamics of the spread of information and its co-option by interested parties is of critical importance. Research on this topic has shown that individuals tend to cluster in echo-chambers and are driven by confirmation bias. In this paper, we leverage the active inference framework to provide an in silico model of confirmation bias and its effect on echo-chamber formation. We build a model based on active inference, where agents tend to sample information in order to justify their own view of reality, which eventually leads to them to have a high degree of certainty about their own beliefs. We show that, once agents have reached a certain level of certainty about their beliefs, it becomes very difficult to get them to change their views. This system of self-confirming beliefs is upheld and reinforced by the evolving relationship between agent's beliefs and its observations, which over time will continue to provide evidence for their ingrained ideas about the world. The epistemic communities that are consolidated by these shared beliefs, in turn, tend to produce perceptions of reality that reinforce those shared beliefs. We provide an active inference account of this community formation mechanism. We postulate that agents are driven by the epistemic value that they obtain from sampling or observing the behaviors of other agents. Inspired by digital social networks like Twitter, we build a generative model in which agents generate observable social claims or posts (e.g. `tweets') while reading the socially-observable claims of other agents, that lend support towards one of two mutually-exclusive abstract topics. Agents can choose which other agent they pay attention to at each timestep, and crucially who they attend to and what they choose to read influences their beliefs about the world. Agents also assess their local network’s perspective, influencing which kinds of posts they expect to see other agents making. The model was built and simulated simulated using the freely-available Python package pymdp. The proposed active inference model can reproduce the formation of echo-chambers over social networks, and gives us insight into the cognitive processes that lead to this phenomenon.

scholarly journals Praktik Jurnalisme Kloning di Kalangan Wartawan Online

2018 ◽  
Vol 11 (1) ◽  
pp. 1-9
Author(s):  
Tsana Garini ◽  
Abie Besman
Keyword(s):  
Mass Media ◽  
Research Method ◽  
Interesting Phenomenon ◽  
The World ◽  
Individual Regulation ◽  
Media Company

The practice of cloning journalism is one of the most interesting phenomenon in the world of journalism. This practice has been considered as a common thing among journalists, especially online journalists. Whereas the practice of cloning journalism is closely related to plagiarism which is clearly incompatible with the ethics of journalism. Through this research the writer will discuss about the reasons behind this practice and what ethics have been violated by journalists who did it. The writer also discuss what ethics have been violated by journalists who do cloning journalism and why is this practice rife among online journalists using an autoetnography research method. The result of this research shows that the practice of cloning journalism among journalists is done because of several factors that include cooperation and solidarity among fellow journalists, the demand of online journalists’ work to write as many news as possible in the shortest period of time, the performance of journalists as an individual, regulation of mass media company, and the development of technology. The practice of cloning journalism is proven to be incompatible with the ethics of journalism because it is a form of plagiarism in the realm of mass media. It is also incompatible with regulations about accuracy and verification.

scholarly journals Introducing a Bayesian model of selective attention based on active inference

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
M. Berk Mirza ◽  
Rick A. Adams ◽  
Karl Friston ◽  
Thomas Parr
Keyword(s):  
Selective Attention ◽  
Formal Model ◽  
Information Gain ◽  
Active Inference ◽  
Context Sensitive ◽  
State Dependent ◽  
Expected Information Gain ◽  
Context Specific ◽  
Hidden States ◽  
Task Irrelevant

Abstract Information gathering comprises actions whose (sensory) consequences resolve uncertainty (i.e., are salient). In other words, actions that solicit salient information cause the greatest shift in beliefs (i.e., information gain) about the causes of our sensations. However, not all information is relevant to the task at hand: this is especially the case in complex, naturalistic scenes. This paper introduces a formal model of selective attention based on active inference and contextual epistemic foraging. We consider a visual search task with a special emphasis on goal-directed and task-relevant exploration. In this scheme, attention modulates the expected fidelity (precision) of the mapping between observations and hidden states in a state-dependent or context-sensitive manner. This ensures task-irrelevant observations have little expected information gain, and so the agent – driven to reduce expected surprise (i.e., uncertainty) – does not actively seek them out. Instead, it selectively samples task-relevant observations, which inform (task-relevant) hidden states. We further show, through simulations, that the atypical exploratory behaviours in conditions such as autism and anxiety may be due to a failure to appropriately modulate sensory precision in a context-specific way.

scholarly journals Generalised free energy and active inference

2019 ◽  
Vol 113 (5-6) ◽  
pp. 495-513 ◽  
Author(s):  
Thomas Parr ◽  
Karl J. Friston
Keyword(s):  
Free Energy ◽  
Generative Model ◽  
Free Energy Function ◽  
Active Inference ◽  
Future Expectations ◽  
Perceptual Inference ◽  
Energy Functionals ◽  
Sensory Data ◽  
The World ◽  
The Brain

Abstract Active inference is an approach to understanding behaviour that rests upon the idea that the brain uses an internal generative model to predict incoming sensory data. The fit between this model and data may be improved in two ways. The brain could optimise probabilistic beliefs about the variables in the generative model (i.e. perceptual inference). Alternatively, by acting on the world, it could change the sensory data, such that they are more consistent with the model. This implies a common objective function (variational free energy) for action and perception that scores the fit between an internal model and the world. We compare two free energy functionals for active inference in the framework of Markov decision processes. One of these is a functional of beliefs (i.e. probability distributions) about states and policies, but a function of observations, while the second is a functional of beliefs about all three. In the former (expected free energy), prior beliefs about outcomes are not part of the generative model (because they are absorbed into the prior over policies). Conversely, in the second (generalised free energy), priors over outcomes become an explicit component of the generative model. When using the free energy function, which is blind to future observations, we equip the generative model with a prior over policies that ensure preferred (i.e. priors over) outcomes are realised. In other words, if we expect to encounter a particular kind of outcome, this lends plausibility to those policies for which this outcome is a consequence. In addition, this formulation ensures that selected policies minimise uncertainty about future outcomes by minimising the free energy expected in the future. When using the free energy functional—that effectively treats future observations as hidden states—we show that policies are inferred or selected that realise prior preferences by minimising the free energy of future expectations. Interestingly, the form of posterior beliefs about policies (and associated belief updating) turns out to be identical under both formulations, but the quantities used to compute them are not.

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