Cognition Without Neural Representation: Dynamics of a Complex System

This paper proposes an account of neurocognitive activity without leveraging the notion of neural representation. Neural representation is a concept that results from assuming that the properties of the models used in computational cognitive neuroscience (e.g., information, representation, etc.) must literally exist the system being modelled (e.g., the brain). Computational models are important tools to test a theory about how the collected data (e.g., behavioural or neuroimaging) has been generated. While the usefulness of computational models is unquestionable, it does not follow that neurocognitive activity should literally entail the properties construed in the model (e.g., information, representation). While this is an assumption present in computationalist accounts, it is not held across the board in neuroscience. In the last section, the paper offers a dynamical account of neurocognitive activity with Dynamical Causal Modelling (DCM) that combines dynamical systems theory (DST) mathematical formalisms with the theoretical contextualisation provided by Embodied and Enactive Cognitive Science (EECS).

Volition and “free will”

Philosophers have debated the “free will” for centuries, yet it is only in recent years that voluntary actions have become an object of investigation for cognitive neuroscience. This review begins by attempting a definition of volition (i.e., the mental state associated specifically with voluntary actions) that could be relevant for cognitive neuroscience. We then review the neuropsychology of volition. Alterations in voluntary behaviour in neurological and psychiatric patients first suggested the possibility that specific cognitive processes of volition have specific bases in the brain. These findings counter traditional dogmas that human volition is somehow ineffable, and instead suggest that voluntary actions depend on specific brain circuitry that is accessible to scientific investigation.The second part of the review focuses on the experimental psychology of volition. A number of studies have combined a systematic manipulation of experimental conditions, and recording of brain processes associated with voluntary action. We argue that this combination is most likely to identify the brain processes specifically associated with volition, and we therefore review these studies systematically. For example, several studies link the Readiness Potential of the EEG to preparatory conscious preplanning of actions. Further, a meta-analysis of neuroimaging studies (PET/ fMRI) reveals a distinctive pattern of activations for choosing one among many possible actions - a key element of volition. The medial frontal cortex appears to make a key contribution to both these biomarkers of volition.

Demand Forecast on the Orchard Cold Chain Logistics in Hunan Province Based on Cognitive Neuroscience

How to scientifically and effectively predict the cold chain logistics demand and provide basis for decision making has always been the focus of forestry and orchard logistics research. From the learning environment of neurons, cognitive neuroscience provides a new perspective for forecasting the demand for cold chain logistics. This paper uses the cognitive neuroscience theory to construct a BP neural network model containing two hidden layers to predict the cold chain logistics demand of the forestry and orchard industry in Hunan province in 2017-2021. Suggestions are then given from the aspects of cold chain logistics construction, transportation infrastructure construction, government policy, enterprise and industry according to the prediction results, thus, providing a theoretical basis for the planning of the cold chain logistics system of Hunan province in a certain period of time, as well as references for the development of cold chain logistics in other parts of the country.

Demand Forecast on the Orchard Cold Chain Logistics in Hunan Province Based on Cognitive Neuroscience

How to scientifically and effectively predict the cold chain logistics demand and provide basis for decision making has always been the focus of forestry and orchard logistics research. From the learning environment of neurons, cognitive neuroscience provides a new perspective for forecasting the demand for cold chain logistics. This paper uses the cognitive neuroscience theory to construct a BP neural network model containing two hidden layers to predict the cold chain logistics demand of the forestry and orchard industry in Hunan province in 2017-2021. Suggestions are then given from the aspects of cold chain logistics construction, transportation infrastructure construction, government policy, enterprise and industry according to the prediction results, thus, providing a theoretical basis for the planning of the cold chain logistics system of Hunan province in a certain period of time, as well as references for the development of cold chain logistics in other parts of the country.

A cognitive neuroscience perspective on insight as a memory process: encoding the solution

This is one of two chapters on "A cognitive neuroscience perspective on insight as a memory process" to be published in the "Routledge International Handbook of Creative Cognition" by L. J. Ball & F. Valleé-Tourangeau (Eds.). While the previous chapter discussed the role of long-term memory for solving problems by insight [https://psyarxiv.com/zv4dk], the current chapter focuses on the role of insight problem solving for long-term memory formation. Insight in problem solving has long been assumed to facilitate memory formation for the problem and its solution. Here, we discuss cognitive, affective, and neurocognitive candidate mechanisms that may underlie learning in insight problem solving. We conclude that insight appears to combine several beneficial effects that each on their own have been found to facilitate long-term memory formation: the generation effect, subjective importance of the discovery of the solution, intrinsic reward, schema congruence, and level-of-processing. A distributed set of brain regions is identified that is associated with these processes. On the one hand, the more affective response related to pleasure, surprise, and novelty detection is linked to amygdala, ventral striatum, and dopaminergic midbrain activity, supporting an important role of reward learning. On the other hand, insight as completing a schema is associated with prior knowledge dependent and medial prefrontal cortex mediated memory formation. Thus, learning by insight may reflect a fast route to cortical memory representations. However, many open questions remain, which we explicitly point out during this review.

A cognitive neuroscience perspective on insight as a memory process: Searching for the solution.

What are the cognitive and brain processes that lead to an insight? This is one of two chapters on "A cognitive neuroscience perspective on insight as a memory process" to be published in the "Routledge International Handbook of Creative Cognition" by L. J. Ball & F. Valleé-Tourangeau (Eds.). In this chapter, we will describe the insight solution process from a neurocognitive perspective. Inspired by cognitive theories, we translate some of insight's main cognitive subprocesses (problem representation, search, representational change, solution) into related neurocognitive ones and summarize them in a descriptive framework. Those described processes focus primarily on verbal insight and are explained using the remote associates task. In this task, the solver is provided with several problem elements (e.g. drop, coat, summer) and needs to find the (remotely related) target that matches those cues (e.g., rain). In a nutshell, insight is the consequence of a problem-solving process where the target is encoded in long-term memory but cannot be retrieved at first because the relationship between the problem elements and the target is unknown, precluding a simple memory search. Upon problem display, the problem elements and a whole network of associated concepts are automatically activated in long-term memory in distinct areas of the brain representing those concepts (=problem representation). Insight is assumed to occur when automatic processes suddenly activate the target after control processes associated with inferior frontal gyrus and anterior cingulate cortex activation manage to overcome prior knowledge and/or perceptual constraints by revising the current activation pattern (=representational change). The next chapter (https://psyarxiv.com/bevjm) will focus on the role of insight problem solving for long-term memory formation.