Adaptation to criticality through organizational invariance in embodied agents

Many biological and cognitive systems do not operate deep within one or other regime of activity. Instead, they are poised at critical points located at phase transitions in their parameter space. The pervasiveness of criticality suggests that there may be general principles inducing this behaviour, yet there is no well-founded theory for understanding how criticality is generated at a wide span of levels and contexts. In order to explore how criticality might emerge from general adaptive mechanisms, we propose a simple learning rule that maintains an internal organizational structure from a specific family of systems at criticality. We implement the mechanism in artificial embodied agents controlled by a neural network maintaining a correlation structure randomly sampled from an Ising model at critical temperature. Agents are evaluated in two classical reinforcement learning scenarios: the Mountain Car and the Acrobot double pendulum. In both cases the neural controller appears to reach a point of criticality, which coincides with a transition point between two regimes of the agent's behaviour. These results suggest that adaptation to criticality could be used as a general adaptive mechanism in some circumstances, providing an alternative explanation for the pervasive presence of criticality in biological and cognitive systems.Comment: arXiv admin note: substantial text overlap with arXiv:1704.0525

A consistent test of significance

This paper presents a test of significance consistent under nonparametric alternatives. Under the null hypothesis, a regressor has no effect on the regression model. Our statistic does not require to estimate the model on the alternative hypothesis, which is left unspecified. Hence, no smoothing techniques are required. The statistic is a weighted empirical process which resembles the Cram~r-von Mises. The asymptotic test is consistent under Pitman's alternatives converging to the null at arate n-1/2. A Monte-Cario experiment illustrates the performance ofthe test in small samples. We also inelude two applications involving biomedical and acid rain data

Further results on dissimilarity spaces for hyperspectral images RF-CBIR

Content-Based Image Retrieval (CBIR) systems are powerful search tools in image databases that have been little applied to hyperspectral images. Relevance feedback (RF) is an iterative process that uses machine learning techniques and user's feedback to improve the CBIR systems performance. We pursued to expand previous research in hyperspectral CBIR systems built on dissimilarity functions defined either on spectral and spatial features extracted by spectral unmixing techniques, or on dictionaries extracted by dictionary-based compressors. These dissimilarity functions were not suitable for direct application in common machine learning techniques. We propose to use a RF general approach based on dissimilarity spaces which is more appropriate for the application of machine learning algorithms to the hyperspectral RF-CBIR. We validate the proposed RF method for hyperspectral CBIR systems over a real hyperspectral dataset.Comment: In Pattern Recognition Letters (2013

Ecological psychology is radical enough: A reply to radical enactivists

Ecological psychology is one of the most influential theories of perception in the embodied, anti-representational, and situated cognitive sciences. However, radical enactivists claim that Gibsonians tend to describe ecological information and its ‘pick up’ in ways that make ecological psychology close to representational theories of perception and cognition. Motivated by worries about the tenability of classical views of informational content and its processing, these authors claim that ecological psychology needs to be “RECtified” so as to explicitly resist representational readings. In this paper, we argue against this call for RECtification. To do so, we offer a detailed analysis of the notion of perceptual information and other related notions such as specificity and meaning, as they are presented in the specialized ecological literature. We defend that these notions, if properly understood, remain free of any representational commitment. Ecological psychology, we conclude, does not need to be RECtified

How should we measure the return on public investment in a VAR?

A new method of empirically computing the macroeconomic returns to public investment is proposed. Pereira's (2000) technique is modified, and a measure which accounts for both public and private investment costs is suggested. An empirical application to US data shows that differences between alternative ways of measuring rates of return are non-trivial - taking into consideration the full investment effort halves estimated returns when partial public costs only are considered.