Dictionaries commonly refer to cognition as a set of mental processes for acquiring knowledge ^[1][2][1,2]. However, this view originates from the assignment of mental processes to the act of thinking and is anchored in philosophical descriptions of the mind, including the concepts of consciousness and intentionality ^[1][3][4][1,3,4]. This also presumes that objects of nature are reflections of true and determined forms, and creates a division between the substances of matter and that of the mind.

Instead, a material description of cognition is restricted to the physical processes available to nature. An example is the study of primate face recognition, where the measurements of facial features serve as the basis of object recognition [5]. This perspective also excludes the concept that there is an innate and prior knowledge of objects, so therefore, cognition would form a representation of objects from their constituent parts ^[6][7][6,7]. Likewise, there is not an expectation that the physical processes of cognition are functionally deterministic.

The following sections on cognition focus on an informational perspective. For example, information flow as a physical process is a fundamental cause of cognition, so this scale of interest is insightful in forming expectations about cognitive processes. These expectations do not exclude the other levels of the biological hierarchy which yield an insight into brain function, such as in the action of individual neurons of regions in the primate brain and their effect on motor function ^[8][9][8,9].

Scientific work generally acknowledges a mechanical description of information and the physical processes as drivers of cognition. However, a perspective based on the duality of physical and mental processing is retained to a small degree in the academic world. For example, there is a conjecture about the relationship between the human mind and a simulation of it [10]. This idea is based on assumptions about intentionality and the act of thinking. In contrast with this view, a physical process of cognition is defined by the generation of an action in neuronal cells without dependence on non-material processes [11].

Another result of physical limits on cognition is observed in the intention of moving a body limb, such as a person reaching for an object across a table. Instead, studies have replaced the assignment of intentionality with a material interpretation of this action, and have shown that the relevant neural activity occurs before awareness of the associated motor action [12].

Across the natural sciences, the neural system has been studied at various biological scales, including at the molecular level and at the higher level in the case of information processing ^[13][14][13,14]. At this higher-level perspective, the neural systems are functionally analogous to the deep learning models of computer science, as both are based on information and the flow of information ^[15][16][15,16]. This allows for a comparative approach for understanding cognitive processes. However, at the lower scale, the artificial neural system is dependent on an abstract model of neurons and the network, so here, the animal neural system is not likely comparable.

The definition of cognition as used here is restricted to a set of mechanical processes [1]. Moreover, cognitive processing is described from a broad perspective, with some examples from the visual system along with insights from the deep learning approaches in computer science.

This is an informational perspective of cognition, since this scale has explanatory power in explaining the causes of knowledge. The other scales, both the large and the small, seem less tractable for constructing explanations of cognition. At the larger scale, if we consider the mental processes as occurrences of the mind, then the phenomena of cognition are subject to mere interpretation, guided by perception and impression, and are not restricted to the true designs and processes of nature. At the lower scale, modeling cognition is less tractable as an activity at the level of individual neuronal cells, given the complexity of the corresponding experiments. These notions on scale and perspective are pivotal in finding explanations for the phenomena of nature [17].

There are also insights from other perspectives, but the definition that follows is not a systematic review of studies of the mind or a broad survey of empirical knowledge across the cognitive sciences; instead, it is a narrow survey of the physical processes and the phenomena of information of higher cognition. This definition is also for a general audience and academic workers outside the science of cognition. However, within the practice of cognitive science, the technical terms may be defined in a different context, consistent with the stricter definitions as recommended by this entry ^[8][18][8,18].

The following sections represent the categories of cognition and its processes. However, they do not reflect the true divisions in cognition, since the cognitive processes are not fully understood at a mechanistic level. Instead, the divisions are based on commonly used boundaries in thinking about cognition, such as in the division between sensory perception and higher reasoning regarding concepts.

The last section on conceptual knowledge is a synthesis of ideas from the previous sections, and serves the purpose of yielding an insight into the general properties of higher cognition. The overarching theme of the sections is that the neural network and its information flow are the foundation for a deeper understanding of the cognitive processes.

This entry uses terminology from science and engineering that requires further clarification. An example is a (mental) representation. In this case, a representation is commonly defined as information that corresponds to an idea or image. This is a particular case where there is reference to the mind, but this term is also a reminder that the origin of these phenomena is in the brain itself. They are encoded in the neural network of the brain.

Another term is “probabilistic”, as a description of a process. This refers to a process that is expected to vary and potentially lead to different outcomes. Representations are expected to occur by this process, so their properties will vary among individuals.

The reference to deep learning originates in the field of engineering. This is a many-layered neural network that is particularly suited for learning about the abovementioned representations. These artificial neural networks share a network-like organization with that of the brain in animals. The other terms and their use are expected to follow their commonly accepted meanings as found in a dictionary of scientific or common words. For example, the biosphere of the Earth refers to that portion of the planet shaped by biological and geological processes. These processes are dynamic, so they have changed over time and across the surface of the Earth.

Lastly, the informational processes have been described as physical processes because information flow is a phenomenon of the physical world. Therefore, matter and energy are required for this phenomenon to occur. The proximate mechanism of the flow of information in the brain is in the electrochemical dynamics that occur among neuronal cells, involving the movement of the ions of chemical elements that generate an electromotive force (voltage), and the diffusion of molecular-level neurotransmitters. The neural system is also influenced by humoral factors, such as the chemical messengers known as hormones.