Brain Processes Research Articles

Towards Human Sensory Augmentation: A Cognitive Neuroscience Framework for Evaluating Integration of New Signals within Perception, Brain Representations, and Subjective Experience

New wearable devices and technologies provide unprecedented scope to augment or substitute human perceptual abilities. However, the flexibility to reorganize brain processing to use novel sensory signals during early sensitive periods in infancy is much less evident at later ages, making integration of new signals into adults’ perception a significant challenge. We believe that an approach informed by cognitive neuroscience is crucial for maximizing the true potential of new sensory technologies. Here, we present a framework for measuring and evaluating the extent to which new signals are integrated within existing structures of perception and experience. As our testbed, we use laboratory tasks in which healthy volunteers learn new, augmented perceptual-motor skills. We describe a suite of measures of (i) perceptual function (psychophysics), (ii) neural representations (fMRI/decoding), and (iii) subjective experience (qualitative interview/micro-phenomenology) targeted at testing hypotheses about how newly learned signals become integrated within perception and experience. As proof of concept, we provide example data showing how this approach allows us to measure changes in perception, neural processing, and subjective experience. We argue that this framework, in concert with targeted approaches to optimizing training and learning, provides the tools needed to develop and optimize new approaches to human sensory augmentation and substitution.

Modeling PET Data Acquired During Nonsteady Conditions: What If Brain Conditions Change During the Scan?

Researchers use dynamic PET imaging with target-selective tracer molecules to probe molecular processes. Kinetic models have been developed to describe these processes. The models are typically fitted to the measured PET data with the assumption that the brain is in a steady-state condition for the duration of the scan. The end results are quantitative parameters that characterize the molecular processes. The most common kinetic modeling endpoints are estimates of volume of distribution or the binding potential of a tracer. If the steady state is violated during the scanning period, the standard kinetic models may not apply. To address this issue, time-variant kinetic models have been developed for the characterization of dynamic PET data acquired while significant changes (e.g., short-lived neurotransmitter changes) are occurring in brain processes. These models are intended to extract a transient signal from data. This work in the PET field dates back at least to the 1990s. As interest has grown in imaging nonsteady events, development and refinement of time-variant models has accelerated. These new models, which we classify as belonging to the first, second, or third generation according to their innovation, have used the latest progress in mathematics, image processing, artificial intelligence, and statistics to improve the sensitivity and performance of the earliest practical time-variant models to detect and describe nonsteady phenomena. This review provides a detailed overview of the history of time-variant models in PET. It puts key advancements in the field into historical and scientific context. The sum total of the methods is an ongoing attempt to better understand the nature and implications of neurotransmitter fluctuations and other brief neurochemical phenomena.

How Bud Craig's Insights Reshape the Research on Pain and Mind-Body Therapies.

With his elegant studies, Bud Craig determined the structural neural basis for interoception and critically expanded our conceptual understanding of it. Importantly, he placed pain in the framework of interoception and redefined pain as a homeostatic emotion. Craig understood emotions and pain as experiences based on inferential brain processes within the theoretical model of prediction processing. This chapter aims to give a brief overview of relevant research. Mind-body therapies, such as meditation, mindfulness, yoga, Tai Chi, and others, are included as first-line non-pharmacological approaches in clinical guidelines for the management of chronic pain. Craig's groundbreaking work provided the background for our contemporary understanding of mind-body therapies and for the key role that interoceptive processes play in these therapies as they apply to a wide range of clinical conditions, including pain. This chapter reviews the tremendous influence that Craig's work had on the current state of research on mind-body therapies for managing chronic pain and how it led to new directions for cutting-edge clinical and neuroscientific research.

A subcortical feeding circuit linking an interoceptive node to jaw movement.

The brain processes an array of stimuli, enabling the selection of appropriate behavioural responses, but the neural pathways linking interoceptive inputs to outputs for feeding are poorly understood1-3. Here we delineate a subcortical circuit in which brain-derived neurotrophic factor (BDNF)-expressing neurons in the ventromedial hypothalamus (VMH) directly connect interoceptive inputs to motor centres, controlling food consumption and jaw movements. VMHBDNF neuron inhibition increases food intake by gating motor sequences of feeding through projections to premotor areas of the jaw. When food is unavailable, VMHBDNF inhibition elicits consummatory behaviours directed at inanimate objects such as wooden blocks, and inhibition of perimesencephalic trigeminal area (pMe5) projections evokes rhythmic jaw movements. The activity of these neurons is decreased during food consumption and increases when food is in proximity but not consumed. Activity is also increased in obese animals and after leptin treatment. VMHBDNF neurons receive monosynaptic inputs from both agouti-related peptide (AgRP) and proopiomelanocortin neurons in the arcuate nucleus (Arc), and constitutive VMHBDNF activation blocks the orexigenic effect of AgRP activation. These data indicate an Arc → VMHBDNF → pMe5 circuit that senses the energy state of an animal and regulates consummatory behaviours in a state-dependent manner.

Unveiling the Human Brain on a Chip: An Odyssey to Reconstitute Neuronal Ensembles and Explore Plausible Applications in Neuroscience.

The brain is an incredibly complex structure that consists of millions of neural networks. In developmental and cellular neuroscience, probing the highly complex dynamics of the brain remains a challenge. Furthermore, deciphering how several cues can influence neuronal growth and its interactions with different brain cell types (such as astrocytes and microglia) is also a formidable task. Traditional in vitro macroscopic cell culture techniques offer simple and straightforward methods. However, they often fall short of providing insights into the complex phenomena of neuronal network formation and the relevant microenvironments. To circumvent the drawbacks of conventional cell culture methods, recent advancements in the development of microfluidic device-based microplatforms have emerged as promising alternatives. Microfluidic devices enable precise spatiotemporal control over compartmentalized cell cultures. This feature facilitates researchers in reconstituting the intricacies of the neuronal cytoarchitecture within a regulated environment. Therefore, in this review, we focus primarily on modeling neuronal development in a microfluidic device and the various strategies that researchers have adopted to mimic neurogenesis on a chip. Additionally, we have presented an overview of the application of brain-on-chip models for the recapitulation of the blood-brain barrier and neurodegenerative diseases, followed by subsequent high-throughput drug screening. These lab-on-a-chip technologies have tremendous potential to mimic the brain on a chip, providing valuable insights into fundamental brain processes. The brain-on-chip models will also serve as innovative platforms for developing novel neurotherapeutics to address several neurological disorders.

Time courses of brain plasticity underpinning visual motion perceptual learning

Visual perceptual learning (VPL) refers to a long-term improvement of visual task performance through training or experience, reflecting brain plasticity even in adults. In human subjects, VPL has been mostly studied using functional magnetic resonance imaging (fMRI). However, due to the low temporal resolution of fMRI, how VPL affects the time course of visual information processing is largely unknown. To address this issue, we trained human subjects to perform a visual motion direction discrimination task. Their behavioral performance and magnetoencephalography (MEG) signals responding to the motion stimuli were measured before, immediately after, and two weeks after training. Training induced a long-lasting behavioral improvement for the trained direction. Based on the MEG signals from occipital sensors, we found that, for the trained motion direction, VPL increased the motion direction decoding accuracy, reduced the motion direction decoding latency, enhanced the direction-selective channel response, and narrowed the tuning profile. Following the MEG source reconstruction, we showed that VPL enhanced the cortical response in early visual cortex (EVC) and strengthened the feedforward connection from EVC to V3A. These VPL-induced neural changes co-occurred in 160–230 ms after stimulus onset. Complementary to previous fMRI findings on VPL, this study provides a comprehensive description on the neural mechanisms of visual motion perceptual learning from a temporal perspective and reveals how VPL shapes the time course of visual motion processing in the adult human brain.

Generalization in perceptual learning across stimuli and tasks

Perceptual learning, known to improve visual perception, demonstrates the plasticity of brain processes underlying vision. Early studies, using the backward-masked texture discrimination task (TDT), focused on the lack of generalizing learning to stimulus features, relating learning specificity to the selectivity of the brain networks involved in the visual task. Learning was found to be highly specific to the stimulus features, as expected from the processing selectivity found in early visual areas as well as to the task employed in training, pointing to top-down effects. More recent studies demonstrate the generalization of learning to untrained features under specifically designed training procedures. Here we suggest that transfer of learning takes place when the trained and untrained stimuli and task activate overlapping brain processes. We tested the effect of TDT learning, under conditions with and without visual adaptation, on the contrast detection (CD) of localized Gabor targets, either alone or backward masked (BM). At the TDT peripheral-target location, we found that the transfer of learning between TDT to CD and BM occurs under the TDT adaptation condition, but not under the no-adaptation condition, whereas at the TDT center-target location we found that transfer occurs for both conditions. Our results suggest that learning generalization across experimental conditions depends on overlapping neural processes within brain networks, here dominated by the inhibitory effects involved in adaptation and in spatiotemporal masking. Importantly, increased adaptation during training, due to increased stimulus consistency, enabled the transfer of learning to other tasks limited by sensory adaptation.

From Solid to Fluid: Novel Approaches in Neuromorphic Engineering.

Neuromorphic engineering is rapidly developing as an approach to mimicking processes in brains using artificial memristors, devices that change conductivity in response to the electrical field (resistive switching effect). Memristor-based neuromorphic systems can overcome the existing problems of slow and energy-inefficient computing that conventional processors face. In the Introduction, the basic principles of memristor operation and its applications are given. The history of switching in sandwich structures and granular metals is reviewed in the Historical Overview. Particular attention is paid to the fundamental articles from the pre-memristor era (the 1960s-70s), which demonstrated the first evidence of resistive switching and predicted the filamentary mechanism of switching. Multi-dimensionality in neuromorphic systems: Despite the powerful computational abilities of traditional memristor arrays, they cannot repeat many organizational characteristics of biological neural networks, i.e., their multi-dimensionality. This part reviews the unconventional nanowire- and nanoparticle-based neuromorphic systems that demonstrate incredible potential for use in reservoir computing due to the unique spiking change in conductance similar to firing in neurons. Liquid-based neuromorphic devices: The transition of neuromorphic systems from solid to liquid state broadens the possibilities for mimicking biological processes. In this section, ionic current memristors are reviewed and, the working principles of which bring us closer to the mechanisms of information transmittance in real synapses. Nanofluids: A novel direction in neuromorphic engineering linked to the application of nanofluids for the formation of reconfigurable nanoparticle networks with memristive properties is given in this section. The Conclusion t summarizes the bullet points of the Review and provides an outlook on the future of liquid-state neuromorphic systems.

Health beliefs model to explore older adults' dementia prevention and health promotion from 2021 to 2022 in Taiwan: A cross-sectional survey study.

One person suffers from dementia every 3 seconds globally. Thirteen older adults aged 65 and older will have dementia, and 1 in 5 older adults over the age of 80 years will have dementia in Taiwan. Older adults should be equipped with demonstrated health beliefs regarding dementia prevention and health promotion about Ascertain Dementia 8-item Questionnaire (AD8), cues to action, health beliefs, self-efficacy, and behavioral intention in daily life. The purpose of this study was to survey older adults' demographic background, AD8, cues to action, health beliefs, self-efficacy, and behavioral intention for dementia prevention and health promotion. A cross-sectional survey design was used. Convenience sampling was performed. A total of 330 older adults participated in the study. The questionnaire used in this study included questions on older adults' demographic background, AD8, cues to action, health beliefs, self-efficacy, and behavioral intention. The researcher collected complete data by receiving the sampling on paper or by interview from October 8, 2021, to February 12, 2022. The SPSS 23.0 statistical package was employed for quantitative analysis. Data analysis included frequency, percentage, mean, standard deviation (SD), Spearman's rho correlation, and simple regression analysis. The findings showed that older adults had the following mean scores on health beliefs (perceived susceptibility 13.45 ± SD 2.34, perceived severity 13.54 ± SD 2.69, perceived benefits 16.57 ± SD 2.84, perceived barriers 8.20 ± SD 3.69), self-efficacy 16.96 ± SD3.52, and behavioral intention 19.56 ± SD 3.51. Older adults' demographic background, perceived susceptibility, perceived severity, perceived benefits, perceived barriers, and self-efficacy explained 56.1% of the variance in behavioral intention. The conclusions of the study indicated that older adults' demographic background, AD8, cues to action, health beliefs, self-efficacy, and behavioral intention constituted the main factors for effective dementia prevention and health promotion. In the future, the research team will continue to explore older adults' dementia prevention and develop many strategies on health promotion, as well as slowing the aging brain process.

An Insight into Medicinal Chemistry and SAR Studies of Cholinesterase and BACE-1 Inhibitors for Alzheimer's Disease.

Alzheimer's Disease (AD) is a serious neurodegenerative condition that predominantly impacts the cholinergic neurons of the entorhinal cortex and hippocampal regions, playing a critical role in learning, navigation, and brain processing. This paper aims to discuss the three main hypotheses of Alzheimer's disease, focusing on neurotoxicity and neurodegeneration caused by mitochondrial dysfunction and ROS production, particularly analyzing the susceptibility differences between genders. Our comprehensive review focuses on significant findings from the past five years, particularly on Cholinesterase (ChE) and BACE-1 inhibitors. Researchers have conducted a detailed analysis of in vitro, in silico, and in vivo data, incorporating extensive Structure-Activity Relationship (SAR) studies. The reviewed papers have been sourced from platforms, such as Google Scholar, Semantic Scholar, and ClinicalTrials.gov, and have been selected based on their AChE and BACE-1 inhibitory activity and structural motif similarity. The review identifies the most effective compounds targeting ChE and BACE-1, highlighting acridine, dihydropyridine, and thiazole-coumarin hybrids for ChE inhibition, and oxadiazole, benzofuran, and dihydropyrimidinone for BACE-1 inhibition. This demonstrates a diverse array of potent heterocyclic hybrids. The review presents a varied compilation of scaffolds showing promise in treating Alzheimer's disease, highlighting the potential of specific compounds against ChE and BACE-1. Given the critical insights derived from our analysis, we posit that this compilation will substantially contribute to the ongoing efforts to combat neurodegeneration and prolong dementia, underscoring the importance of continuous research in this domain.

A Review on Phytochemical Constituents Used as Current Treatment Strategies for Neurodegenerative Disease.

In today's time, a diversity of neurodegenerative diseases that widely affect the CNS causing insufficiency in particular brain processes such as memory, mobility, and cognition due to the moderate loss of CNS neurons. This review emphasizes different phytochemical constituents used widely for the prevention or treatment of various neurodegenerative diseases such as Alzheimer's disease (AD) and Parkin-son's disease (PD). Berberin (BBR), which is an isoquinoline class of alkaloid and isolated from the plant Hydrastis condenses and Berberis aaristata, has both acetylcholine esterase (AChE) inhibiting properties as well as monoamine oxidase (MAO) inhibiting properties involved in the betterment of AD by decreasing the production of reactive oxygen species (ROS). Like BBR, Physostigmine, isolated from the Physostigma venenosum / Calabar bean and belongs to the family Leguminosae, and Morphine, isolated from the plant Papaver somniferum / Opium poppy or Breadseed poppy, also has a significant impact on the management and treatment of AD and PD by reducing both neuroinflammation and pro-inflammatory cytokines production. Morphine bineurodegenerative diseases with μ-opioid receptor (MOR) in CNS elevate GABA levels in the synaptic cleft of the brain and reduces the neurotoxicity via stimulation of MOR. It has been discovered that physostigmine improves cognitive function in AD patients and reduces α-synu-clein expression in PD neural cell lines. Isorhyncophylline (IRN) is a Chinese herbal medicine isolated from the plant Uncaria rhyncophylla which provides neuroprotective efficiency against neurotoxicity that occurs by amyloid β (the main component of amyloid plaques) found in the brain of people with AD.

Scale matters: Large language models with billions (rather than millions) of parameters better match neural representations of natural language.

Recent research has used large language models (LLMs) to study the neural basis of naturalistic language processing in the human brain. LLMs have rapidly grown in complexity, leading to improved language processing capabilities. However, neuroscience researchers haven't kept up with the quick progress in LLM development. Here, we utilized several families of transformer-based LLMs to investigate the relationship between model size and their ability to capture linguistic information in the human brain. Crucially, a subset of LLMs were trained on a fixed training set, enabling us to dissociate model size from architecture and training set size. We used electrocorticography (ECoG) to measure neural activity in epilepsy patients while they listened to a 30-minute naturalistic audio story. We fit electrode-wise encoding models using contextual embeddings extracted from each hidden layer of the LLMs to predict word-level neural signals. In line with prior work, we found that larger LLMs better capture the structure of natural language and better predict neural activity. We also found a log-linear relationship where the encoding performance peaks in relatively earlier layers as model size increases. We also observed variations in the best-performing layer across different brain regions, corresponding to an organized language processing hierarchy.

An exploratory study on teachers’ perspective on applicability of neurolinguistic programming in developing language learning (at the high school level)

Neuro Linguistic Programming (NLP) is a fast-emerging set of tools and techniques for developing linguistic competencies among learners and teachers as well. NLP enables us to better understand the way our brain processes the words that we use and how that can impact our past, present and future. Learning second language offers high level of motivation and satisfaction to the learners. Similarly, teaching second language requires lots of planning and innovation in its application. In this context, NLP could be very helpful for teachers to improve their teaching skills, and similarly, it could facilitate learners to learn second language in a congenial environment. It could provide a roadmap for teachers to enhance the linguistic competence of their learners. On the other hand, it could enable the learners to identify their grey areas and provide solutions to work on their weaknesses. At this backdrop, the present paper attempts an exploratory study of teachers' perspective on the applicability of Neuro Linguistic Programming in developing Language Skills at the High School Level. / Keywords: neuro linguistic programming (NLP), second language (l2), teachers, learners, classroom, LSRW skills.

The 2024 Richardson Lecture: Prosopagnosia - A Classic Neurologic Deficit Meets the Modern Era.

Acquired prosopagnosia is a rare disorder, but it serves as a model for impairments in expert-level visual processing. This review discusses five key observations made over the past 30 years. First, there are variants, an apperceptive type linked to damage to the inferior occipitotemporal cortex and an amnestic type associated with anterior temporal lesions, both either right or bilateral. Second, these variants are clustered in syndromes with other perceptual deficits, the apperceptive type with field defects, dyschromatopsia and topographagnosia, and the amnestic type with topographagnosia and the auditory disorders of phonagnosia and acquired amusia. Third, extensive testing often shows additional problems with recognizing exemplars of other objects, especially when degrees of expertise are taken into account. Fourth, the prosopagnosic impairment does not affect all facial information. For example, the perception of expression and lip-reading likely depends on other neural substrates than those for processing facial identity. Last, face perception in prosopagnosia is not immutable but can improve with extensive training, though as yet this does not represent a cure for the condition. Continuing work with neural networks and animal models will enhance our understanding of this intriguing condition and what it tells us about how our brains process vision.

Combination of motor, sensory and affective tasks in an EEG paradigm for children with developmental disabilities

Individuals with neurodevelopmental disorders exhibit overlapping emotional, somatosensory and motor deficits. Although brain processes underlying these impairments have been extensively studied in a separate way, the brain interaction of these inputs is an innovative line of research. Here we present a new EEG methodology for exploring the interactive brain activity of sensorimotor and affective stimuli. The task consists in presenting affective stimuli of different modalities (e.g. affective pictures, affective touch) while simultaneously an arthromotor performs passive joint movements, unseen by the participant. Participants were then required to press one of two buttons to indicate if their joint position agreed with a picture shown in a screen. Pilot data of electroencephalography recordings revealed distinct somatosensory event-related potentials (SEP) when movement was subsequent to affective stimuli, compared to neutral stimuli, as well as a differentiation of SEPs for different neurodevelopmental conditions. Behavioral responses further showed that children with cerebral palsy had more errors to identify their hand position when they were exposed to affective stimuli. This paradigm is a valuable tool to explore the modulative influence of emotion in the sensorimotor brain processing of different populations with joint emotional and sensorimotor impairments, such as children with neurodevelopmental disorders or patients with stroke.•This method allows exploring the interaction between affective and sensoriomotor inputs in an EEG paradigm.

Processing of social closeness in the human brain

Healthy social life requires relationships in different levels of personal closeness. Based on ethological, sociological, and psychological evidence, social networks have been divided into five layers, gradually increasing in size and decreasing in personal closeness. Is this division also reflected in brain processing of social networks? During functional MRI, 21 participants compared their personal closeness to different individuals. We examined the brain volume showing differential activation for varying layers of closeness and found that a disproportionately large portion of this volume (80%) exhibited preference for individuals closest to participants, while separate brain regions showed preference for all other layers. Moreover, this bipartition reflected cortical preference for different sizes of physical spaces, as well as distinct subsystems of the default mode network. Our results support a division of the neurocognitive processing of social networks into two patterns depending on personal closeness, reflecting the unique role intimately close individuals play in our social lives.

Single-neuron representations of odours in the human brain

Olfaction is a fundamental sensory modality that guides animal and human behaviour1,2. However, the underlying neural processes of human olfaction are still poorly understood at the fundamental—that is, the single-neuron—level. Here we report recordings of single-neuron activity in the piriform cortex and medial temporal lobe in awake humans performing an odour rating and identification task. We identified odour-modulated neurons within the piriform cortex, amygdala, entorhinal cortex and hippocampus. In each of these regions, neuronal firing accurately encodes odour identity. Notably, repeated odour presentations reduce response firing rates, demonstrating central repetition suppression and habituation. Different medial temporal lobe regions have distinct roles in odour processing, with amygdala neurons encoding subjective odour valence, and hippocampal neurons predicting behavioural odour identification performance. Whereas piriform neurons preferably encode chemical odour identity, hippocampal activity reflects subjective odour perception. Critically, we identify that piriform cortex neurons reliably encode odour-related images, supporting a multimodal role of the human piriform cortex. We also observe marked cross-modal coding of both odours and images, especially in the amygdala and piriform cortex. Moreover, we identify neurons that respond to semantically coherent odour and image information, demonstrating conceptual coding schemes in olfaction. Our results bridge the long-standing gap between animal models and non-invasive human studies and advance our understanding of odour processing in the human brain by identifying neuronal odour-coding principles, regional functional differences and cross-modal integration.

The expectations humans have of a pleasurable sensation asymmetrically shape neuronal responses and subjective experiences to hot sauce.

Expectations shape our perception, profoundly influencing how we interpret the world. Positive expectations about sensory stimuli can alleviate distress and reduce pain (e.g., placebo effect), while negative expectations may heighten anxiety and exacerbate pain (e.g., nocebo effect). To investigate the impact of the (an)hedonic aspect of expectations on subjective experiences, we measured neurobehavioral responses to the taste of hot sauce among participants with heterogeneous taste preferences. By identifying participants who "liked" versus those who strongly "disliked" spicy flavors and by providing contextual cues about the spiciness of the sauce to be tasted, we dissociated the effects of positive and negative expectations from sensory stimuli (i.e., visual and gustatory stimuli), which were the same across all participants. Our results indicate that positive expectations lead to modulations in the intensity of subjective experience. These modulations were accompanied by increased activity in brain regions previously linked to information integration and the placebo effect, including the anterior insula, dorsolateral prefrontal cortex, and dorsal anterior cingulate cortex, as well as a predefined "pleasure signature." In contrast, negative expectations decreased hedonic experience and increased neural activity in the previously validated "Neurological Pain Signature" network. These findings demonstrate that hedonic aspects of one's expectations asymmetrically shape how the brain processes sensory input and associated behavioral reports of one's subjective experiences of intensity, pleasure, and pain. Our results suggest a dissociable impact of hedonic information: positive expectations facilitate higher-level information integration and reward processing, while negative expectations prime lower-level nociceptive and affective processes. This study demonstrates the powerful role of hedonic expectations in shaping subjective reality and suggests potential avenues for consumer and therapeutic interventions targeting expectation-driven neural processes.

Distinct visual processing networks for foveal and peripheral visual fields

Foveal and peripheral vision are two distinct modes of visual processing essential for navigating the world. However, it remains unclear if they engage different neural mechanisms and circuits within the visual attentional system. Here, we trained macaques to perform a free-gaze visual search task using natural face and object stimuli and recorded a large number of 14588 visually responsive units from a broadly distributed network of brain regions involved in visual attentional processing. Foveal and peripheral units had substantially different proportions across brain regions and exhibited systematic differences in encoding visual information and visual attention. The spike-local field potential (LFP) coherence of foveal units was more extensively modulated by both attention and visual selectivity, thus indicating differential engagement of the attention and visual coding network compared to peripheral units. Furthermore, we delineated the interaction and coordination between foveal and peripheral processing for spatial attention and saccade selection. Together, the systematic differences between foveal and peripheral processing provide valuable insights into how the brain processes and integrates visual information from different regions of the visual field.

Metaethics: A neuro-geometric proposal

Metaethics explores the relationship between types of ethics. The field of moral psychology has increasingly attempted to explain types of ethical thinking based on brain processes. This paper proposes a metaethical typology of seven distinct types of natural thinking processes rooted in how visual images are translated into abstract thinking. These seven ethical thinking types are Transcendent, Unitary, Hierarchical, Equitable, Vectoral, Analogic, and Topographical. Human beings then ladder concepts upon these processes using the construction of increasingly abstract metaphors. As proposed, these seven thinking processes appear to effectively form the foundation for current ethical theories such as Utilitarianism and Kantian Deontology, or other forms of moral reasoning. They may also be combined to explain the operations of more complex forms of moral reasoning such as religious ethics. Finally, they reveal that the possibility of natural thinking types has normative implications for ethics.