Prior preference learning from experts: Designing a reward with active inference

Tinkering with cognitive gadgets: Cultural evolutionary psychology meets active inference

Behavioral and Brain Sciences ◽

10.1017/s0140525x19001018 ◽

2019 ◽

Vol 42 ◽

Author(s):

Paul Benjamin Badcock ◽

Axel Constant ◽

Maxwell James Désormeau Ramstead

Keyword(s):

Evolutionary Psychology ◽

Research Program ◽

Active Inference ◽

Innovative Research ◽

Cognitive Faculties ◽

Cultural Evolutionary ◽

The Relationship

Abstract Cognitive Gadgets offers a new, convincing perspective on the origins of our distinctive cognitive faculties, coupled with a clear, innovative research program. Although we broadly endorse Heyes’ ideas, we raise some concerns about her characterisation of evolutionary psychology and the relationship between biology and culture, before discussing the potential fruits of examining cognitive gadgets through the lens of active inference.

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Narrative as active inference

10.31234/osf.io/47ub6 ◽

2020 ◽

Cited By ~ 1

Author(s):

Nabil Bouizegarene ◽

maxwell ramstead ◽

Axel Constant ◽

Karl Friston ◽

Laurence Kirmayer

Keyword(s):

Well Being ◽

Social Practices ◽

Human Adaptation ◽

Active Inference ◽

Future Projection ◽

Future Developments ◽

The Social ◽

Parsimonious Model ◽

Living Organisms ◽

Theory Research

The ubiquity and importance of narratives in human adaptation has been recognized by many scholars. Research has identified several functions of narratives that are conducive to individuals’ well-being and adaptation as well as to coordinated social practices and enculturation. In this paper, we characterize the social and cognitive functions of narratives in terms of the framework of active inference. Active inference depicts the fundamental tendency of living organisms to adapt by creating, updating, and maintaining inferences about their environment. We review the literature on the functions of narratives in identity, event segmentation, episodic memory, future projection, storytelling practices, and enculturation. We then re-cast these functions of narratives in terms of active inference, outlining a parsimonious model that can guide future developments in narrative theory, research, and clinical applications.

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Retrieving Categorical Emotions Using a Probabilistic Framework to Define Preference Learning Samples

10.21437/interspeech.2016-1052 ◽

2016 ◽

Cited By ~ 7

Author(s):

Reza Lotfian ◽

Carlos Busso

Keyword(s):

Preference Learning ◽

Probabilistic Framework

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Activity, Agency, and Inner Speech Pathology

10.1093/oso/9780198796640.003.0013 ◽

2018 ◽

Author(s):

Lauren Swiney

Keyword(s):

Motor Control ◽

Sensory Input ◽

The Other ◽

Predictive Processing ◽

Inner Speech ◽

Speech Pathology ◽

Active Inference ◽

Comparator Hypothesis

Over the last thirty years the comparator hypothesis has emerged as a prominent account of inner speech pathology. This chapter discusses a number of cognitive accounts broadly derived from this approach, highlighting the existence of two importantly distinct notions of inner speech in the literature; one as a prediction in the absence of sensory input, the other as an act with sensory consequences that are themselves predicted. Under earlier frameworks in which inner speech is described in the context of classic models of motor control, I argue that these two notions may be compatible, providing two routes to inner speech pathology. Under more recent accounts grounded in the architecture of Bayesian predictive processing, I argue that “active inference” approaches to action generation pose serious challenges to the plausibility of the latter notion of inner speech, while providing the former notion with rich explanatory possibilities for inner speech pathology.

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Consciousness

10.1093/oso/9780199674923.003.0037 ◽

2018 ◽

Author(s):

Anil K. Seth

Keyword(s):

Conscious Experience ◽

Real Problem ◽

Active Inference ◽

Biological Mechanisms ◽

Specific Content ◽

Brain Responses ◽

Biomimetic Approach ◽

Multiple Levels ◽

In Virtue Of ◽

The Brain

Consciousness is perhaps the most familiar aspect of our existence, yet we still do not know its biological basis. This chapter outlines a biomimetic approach to consciousness science, identifying three principles linking properties of conscious experience to potential biological mechanisms. First, conscious experiences generate large quantities of information in virtue of being simultaneously integrated and differentiated. Second, the brain continuously generates predictions about the world and self, which account for the specific content of conscious scenes. Third, the conscious self depends on active inference of self-related signals at multiple levels. Research following these principles helps move from establishing correlations between brain responses and consciousness towards explanations which account for phenomenological properties—addressing what can be called the “real problem” of consciousness. The picture that emerges is one in which consciousness, mind, and life, are tightly bound together—with implications for any possible future “conscious machines.”

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Bayesian preference learning for interactive multi-objective optimisation

Proceedings of the Genetic and Evolutionary Computation Conference ◽

10.1145/3449639.3459299 ◽

2021 ◽

Author(s):

Kendall Taylor ◽

Huong Ha ◽

Minyi Li ◽

Jeffrey Chan ◽

Xiaodong Li

Keyword(s):

Preference Learning ◽

Multi Objective

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Global optimization based on active preference learning with radial basis functions

Machine Learning ◽

10.1007/s10994-020-05935-y ◽

2020 ◽

Author(s):

Alberto Bemporad ◽

Dario Piga

Keyword(s):

Global Optimization ◽

Objective Function ◽

Decision Maker ◽

Optimization Problems ◽

Global Optimum ◽

Bayesian Optimization ◽

Preference Learning ◽

Global Optimizer ◽

Radial Basis ◽

Decision Vector

AbstractThis paper proposes a method for solving optimization problems in which the decision-maker cannot evaluate the objective function, but rather can only express a preference such as “this is better than that” between two candidate decision vectors. The algorithm described in this paper aims at reaching the global optimizer by iteratively proposing the decision maker a new comparison to make, based on actively learning a surrogate of the latent (unknown and perhaps unquantifiable) objective function from past sampled decision vectors and pairwise preferences. A radial-basis function surrogate is fit via linear or quadratic programming, satisfying if possible the preferences expressed by the decision maker on existing samples. The surrogate is used to propose a new sample of the decision vector for comparison with the current best candidate based on two possible criteria: minimize a combination of the surrogate and an inverse weighting distance function to balance between exploitation of the surrogate and exploration of the decision space, or maximize a function related to the probability that the new candidate will be preferred. Compared to active preference learning based on Bayesian optimization, we show that our approach is competitive in that, within the same number of comparisons, it usually approaches the global optimum more closely and is computationally lighter. Applications of the proposed algorithm to solve a set of benchmark global optimization problems, for multi-objective optimization, and for optimal tuning of a cost-sensitive neural network classifier for object recognition from images are described in the paper. MATLAB and a Python implementations of the algorithms described in the paper are available at http://cse.lab.imtlucca.it/~bemporad/glis.

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