The shift from life in water to life on land advantaged planning in visually-guided behavior

Mugan, Ugurcan; MacIver, Malcolm A.

doi:10.1101/585760

Cited by 6 publications

(7 citation statements)

References 128 publications

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“…The transition of aquatic animals to land resulting in increased capacity for long-distance vision approximately 380 million years ago, and so increased adaptive significance for being able to plan ahead ( MacIver et al, 2017 ; Mugan and MacIver, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Integrated world modeling theory expanded: Implications for the future of consciousness

Safron

2022

Front. Comput. Neurosci.

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Integrated world modeling theory (IWMT) is a synthetic theory of consciousness that uses the free energy principle and active inference (FEP-AI) framework to combine insights from integrated information theory (IIT) and global neuronal workspace theory (GNWT). Here, I first review philosophical principles and neural systems contributing to IWMT’s integrative perspective. I then go on to describe predictive processing models of brains and their connections to machine learning architectures, with particular emphasis on autoencoders (perceptual and active inference), turbo-codes (establishment of shared latent spaces for multi-modal integration and inferential synergy), and graph neural networks (spatial and somatic modeling and control). Future directions for IIT and GNWT are considered by exploring ways in which modules and workspaces may be evaluated as both complexes of integrated information and arenas for iterated Bayesian model selection. Based on these considerations, I suggest novel ways in which integrated information might be estimated using concepts from probabilistic graphical models, flow networks, and game theory. Mechanistic and computational principles are also considered with respect to the ongoing debate between IIT and GNWT regarding the physical substrates of different kinds of conscious and unconscious phenomena. I further explore how these ideas might relate to the “Bayesian blur problem,” or how it is that a seemingly discrete experience can be generated from probabilistic modeling, with some consideration of analogies from quantum mechanics as potentially revealing different varieties of inferential dynamics. I go on to describe potential means of addressing critiques of causal structure theories based on network unfolding, and the seeming absurdity of conscious expander graphs (without cybernetic symbol grounding). Finally, I discuss future directions for work centered on attentional selection and the evolutionary origins of consciousness as facilitated “unlimited associative learning.” While not quite solving the Hard problem, this article expands on IWMT as a unifying model of consciousness and the potential future evolution of minds.

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Section: Discussionmentioning

confidence: 99%

Integrated world modeling theory expanded: Implications for the future of consciousness

Safron

2022

Front. Comput. Neurosci.

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“…Footnote 42 (continued) advantages in complex environments (Buzsáki et al 2014;Mugan and MacIver 2019;Redish 2016).…”

Section: Model 3: Counterfactual Active Inferencementioning

confidence: 99%

From allostatic agents to counterfactual cognisers: active inference, biological regulation, and the origins of cognition

2020

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What is the function of cognition? On one influential account, cognition evolved to co-ordinate behaviour with environmental change or complexity (Godfrey-Smith in Complexity and the function of mind in nature, Cambridge Studies in Philosophy and Biology, Cambridge University Press, Cambridge, 1996). Liberal interpretations of this view ascribe cognition to an extraordinarily broad set of biological systems-even bacteria, which modulate their activity in response to salient external cues, would seem to qualify as cognitive agents. However, equating cognition with adaptive flexibility per se glosses over important distinctions in the way biological organisms deal with environmental complexity. Drawing on contemporary advances in theoretical biology and computational neuroscience, we cash these distinctions out in terms of different kinds of generative models, and the representational and uncertainty-resolving capacities they afford. This analysis leads us to propose a formal criterion for delineating cognition from other, more pervasive forms of adaptive plasticity. On this view, biological cognition is rooted in a particular kind of functional organisation; namely, that which enables the agent to detach from the present and engage in counterfactual (active) inference.

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“…Animals seek many goals, but two of the most common are evading predators and seeking food. The cognitive challenges of these goals may have driven the evolution of multi-step planning [68] . The neural mechanisms of naturalistic food-seeking have been studied using patch foraging tasks, in which an animal makes sequential decisions about whether to continue harvesting food from a depleting patch vs. search for a new patch.…”

Section: Multi-step Planning Shares Neural Mechanisms With Foragingmentioning

confidence: 99%

Multi-step planning in the brain

Miller

Venditto

2021

Current Opinion in Behavioral Sciences

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Decisions in the natural world are rarely made in isolation. Each action that an organism selects will affect the future situations in which it finds itself, and those situations will in turn affect the future actions that are available. Achieving real-world goals often requires successfully navigating a sequence of many actions. An efficient and flexible way to achieve such goals is to construct an internal model of the environment, and use it to plan behavior multiple steps into the future. This process is known as multi-step planning, and its neural mechanisms are only beginning to be understood. Here, we review recent advances in our understanding of these mechanisms, many of which take advantage of multi-step decision tasks for humans and animals. Multi-Step Planning Shares Neural Mechanisms with Associative Learning Work on the neural mechanisms of planning builds upon a rich body of work investigating cognitive capacities upon which planning may depend. One of these is the ability to associate stimuli or actions with specific expected outcomes (e.g. walk south → arrive at lab; walk north → arrive at coffee shop). Behavior guided by such outcome-specific associations can be thought of as exercising a simple one-step form of planning. Researchers have developed several selective assays of this capacity and have used them extensively to identify and characterize the neural structures that support outcome-specific associations [23]. A second cognitive capacity necessary for multi-step planning is "inference": the ability to combine separately-learned associations in order to form new associations between items that may never have been encountered together before (e.g. walk north → arrive at coffee shop; place order → receive coffee; ∴ to obtain coffee, walk north). Researchers have developed assays of this capacity as well, and have used them to identify neural structures necessary for combining separately-learned associations [e.g. 24,25,26]. Mechanisms for outcome-specific associations and for inference are universally present in theoretical accounts of multi-step planning (see box). Recent work has borne out the idea that the neural structures which support outcome-specific associations and inference in associative learning tasks likely support planning in multi-step decision tasks as well. Orbitofrontal Cortex and Model-Based Prediction Regions of frontal cortex belonging to the orbital network (especially areas LO and AIv in rodents and areas 13 and 11l in primates, often referred to as "OFC" or "lateral OFC") play an important role both in outcome-specific associations and in inference. Lesions or inactivations of OFC have been shown to impair performance on behavioral assays of these capacities in rodents [27-29] and to impair use of outcome-specific associations in nonhuman primates [30]. Recent data have extended these findings to humans as well, showing that disruption of OFC activity impairs performance on assays of outcome-specific associations and of inference [31,32]. Correspondingly, neural acti...

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The shift from life in water to life on land advantaged planning in visually-guided behavior

Cited by 6 publications

References 128 publications

Integrated world modeling theory expanded: Implications for the future of consciousness

Integrated world modeling theory expanded: Implications for the future of consciousness

From allostatic agents to counterfactual cognisers: active inference, biological regulation, and the origins of cognition

Multi-step planning in the brain

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