Common and Distinct Functional Brain Networks for Intuitive and Deliberate Decision Making

Reinforcement learning studies in rodents and primates demonstrate that goal-directed and habitual choice behaviors are mediated through different fronto-striatal systems, but the evidence is less clear in humans. In this study, functional magnetic resonance imaging (fMRI) data were collected whilst participants ( n = 20) performed a conditional associative learning task in which blocks of novel conditional stimuli (CS) required a deliberate choice, and blocks of familiar CS required an intuitive choice. Using standard subtraction analysis for fMRI event-related designs, activation shifted from the dorso-fronto-parietal network, which involves dorsolateral prefrontal cortex (DLPFC) for deliberate choice of novel CS, to ventro-medial frontal (VMPFC) and anterior cingulate cortex for intuitive choice of familiar CS. Supporting this finding, psycho-physiological interaction (PPI) analysis, using the peak active areas within the PFC for novel and familiar CS as seed regions, showed functional coupling between caudate and DLPFC when processing novel CS and VMPFC when processing familiar CS. These findings demonstrate separable systems for deliberate and intuitive processing, which is in keeping with rodent and primate reinforcement learning studies, although in humans they operate in a dynamic, possibly synergistic, manner particularly at the level of the striatum.Peer reviewedFinal Published versio

Psychological factors affecting equine performance

For optimal individual performance within any equestrian discipline horses must be in peak physical condition and have the correct psychological state. This review discusses the psychological factors that affect the performance of the horse and, in turn, identifies areas within the competition horse industry where current behavioral research and established behavioral modification techniques could be applied to further enhance the performance of animals. In particular, the role of affective processes underpinning temperament, mood and emotional reaction in determining discipline-specific performance is discussed. A comparison is then made between the training and the competition environment and the review completes with a discussion on how behavioral modification techniques and general husbandry can be used advantageously from a performance perspective

A transdisciplinary view on curiosity beyond linguistic humans: animals, infants, and artificial intelligence

ABSTRACTCuriosity is a core driver for life‐long learning, problem‐solving and decision‐making. In a broad sense, curiosity is defined as the intrinsically motivated acquisition of novel information. Despite a decades‐long history of curiosity research and the earliest human theories arising from studies of laboratory rodents, curiosity has mainly been considered in two camps: ‘linguistic human’ and ‘other’. This is despite psychology being heritable, and there are many continuities in cognitive capacities across the animal kingdom. Boundary‐pushing cross‐disciplinary debates on curiosity are lacking, and the relative exclusion of pre‐linguistic infants and non‐human animals has led to a scientific impasse which more broadly impedes the development of artificially intelligent systems modelled on curiosity in natural agents. In this review, we synthesize literature across multiple disciplines that have studied curiosity in non‐verbal systems. By highlighting how similar findings have been produced across the separate disciplines of animal behaviour, developmental psychology, neuroscience, and computational cognition, we discuss how this can be used to advance our understanding of curiosity. We propose, for the first time, how features of curiosity could be quantified and therefore studied more operationally across systems: across different species, developmental stages, and natural or artificial agents

Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework

In this paper, we argue that the future of Artificial Intelligence research resides in two keywords: integration and embodiment. We support this claim by analyzing the recent advances of the field. Regarding integration, we note that the most impactful recent contributions have been made possible through the integration of recent Machine Learning methods (based in particular on Deep Learning and Recurrent Neural Networks) with more traditional ones (e.g. Monte-Carlo tree search, goal babbling exploration or addressable memory systems). Regarding embodiment, we note that the traditional benchmark tasks (e.g. visual classification or board games) are becoming obsolete as state-of-the-art learning algorithms approach or even surpass human performance in most of them, having recently encouraged the development of first-person 3D game platforms embedding realistic physics. Building upon this analysis, we first propose an embodied cognitive architecture integrating heterogenous sub-fields of Artificial Intelligence into a unified framework. We demonstrate the utility of our approach by showing how major contributions of the field can be expressed within the proposed framework. We then claim that benchmarking environments need to reproduce ecologically-valid conditions for bootstrapping the acquisition of increasingly complex cognitive skills through the concept of a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017 conference (Lisbon, Portugal

A transdisciplinary view on curiosity beyond linguistic humans:animals, infants, and artificial intelligence

Curiosity is a core driver for life-long learning, problem-solving and decision-making. In a broad sense, curiosity is defined as the intrinsically motivated acquisition of novel information. Despite a decades-long history of curiosity research and the earliest human theories arising from studies of laboratory rodents, curiosity has mainly been considered in two camps: ‘linguistic human’ and ‘other’. This is despite psychology being heritable, and there are many continuities in cognitive capacities across the animal kingdom. Boundary-pushing cross-disciplinary debates on curiosity are lacking, and the relative exclusion of pre-linguistic infants and non-human animals has led to a scientific impasse which more broadly impedes the development of artificially intelligent systems modelled on curiosity in natural agents. In this review, we synthesize literature across multiple disciplines that have studied curiosity in non-verbal systems. By highlighting how similar findings have been produced across the separate disciplines of animal behaviour, developmental psychology, neuroscience, and computational cognition, we discuss how this can be used to advance our understanding of curiosity. We propose, for the first time, how features of curiosity could be quantified and therefore studied more operationally across systems: across different species, developmental stages, and natural or artificial agents

Is conditioning a useful framework for understanding the development and treatment of phobias?

Despite the prevalence of therapeutic interventions based on conditioning models of fear acquisition, conditioning has been seen by many as a poor explanation of how fears develop: partly because research on conditioning has become less mainstream and models of teaming have become increasingly more complex. This article reviews some of what is now known about conditioning/associative teaming and describes how these findings account for some early criticisms of conditioning models of fear acquisition. It also describes how pathways to fear such as vicarious teaming and fear information can be conceptualised as forms of associative teaming that obey the same teaming rules. Some popular models of conditioning are then described with a view to highlighting the important components in teaming. Finally, suggestions are made about how what we know about conditioning can be applied to improve therapeutic interventions and prevention programs for child anxiety. (c) 2006 Elsevier Ltd. All rights reserved

The propositional nature of human associative learning

The past 50 years have seen an accumulation of evidence suggesting that associative learning depends oil high-level cognitive processes that give rise to propositional knowledge. Yet, many learning theorists maintain a belief in a learning mechanism in which links between mental representations are formed automatically. We characterize and highlight the differences between the propositional and link approaches, and review the relevant empirical evidence. We conclude that learning is the consequence of propositional reasoning processes that cooperate with the unconscious processes involved in memory retrieval and perception. We argue that this new conceptual framework allows many of the important recent advances in associative learning research to be retained, but recast in a model that provides a firmer foundation for both immediate application and future research