How can we understand how our brain works since we are using the brain to do that process

Understanding how our brain works, despite the apparent circularity of using the brain to study itself, is a core challenge in neuroscience. This 'self-reference problem' or 'introspection problem' is not a dead-end but rather a self-referential scientific problem that requires sophisticated strategies. We approach this by treating the brain both as a physical object measurable from the outside and as a system capable of internal self-modeling.

Approaches to Overcome the Self-Reference Challenge

1. External-Perspective Methods (Treating the Brain as a Physical Object): These methods probe, perturb, and measure the brain from an objective, third-person perspective.

   • Neuroimaging Techniques: Tools such as fMRI (functional Magnetic Resonance Imaging), PET (Positron Emission Tomography), MEG (Magnetoencephalography), EEG (Electroencephalography), and fNIRS allow us to observe brain structures and activity in real-time or near real-time. They provide whole-brain maps of metabolic or electrical correlates of activity while subjects perform tasks. Sensors are external to the neurons, interpreting signals directly.
   • Electrophysiology: Techniques like EEG, MEG, and single-unit recordings measure the electrical activity of neurons or neuronal populations. Deep Brain Stimulation (DBS) can also be used to measure activity from within the brain or to activate specific regions to observe effects.
   • Lesion and Stimulation Studies: Observing behavioral changes after destroying (e.g., stroke cases, Broca's aphasia) or stimulating (e.g., TMS, DBS) specific brain regions helps establish causal links between regions and functions. The cause is externally imposed, linking region to function objectively.
   • Animal Studies (e.g., Optogenetics/Chemogenetics): In animal models, precise control over defined neuronal types allows researchers to program neural activity and observe resulting behaviors. This establishes strong causal relationships, with findings often generalized to humans due due to conserved fundamental mechanisms.
   • Connectomics: Techniques like diffusion MRI and neural tracing provide structural wiring diagrams, offering an objective blueprint of potential information flow independent of subjective introspection.

2. Internal-Perspective Tools (Leveraging the Brain's Self-Modeling Capacity): These tools leverage the brain's unique ability to generate internal models of itself.

   • Introspection / Verbal Reports: While limited by language, memory, and reporting biases, first-person accounts of perception, decisions, and emotions provide direct access to conscious content. This gives us what can be described as the 'conscious experience' of the brain at work.
   • Metacognition Tasks: Tasks involving confidence judgments or error monitoring reveal how the brain assesses its own performance and models its own processes.
   • Computational Modeling: Formal descriptions of hypothesized neural computations (e.g., Bayesian brain, predictive coding) allow us to simulate the brain's inference mechanisms and compare predictions to observed data. Discrepancies highlight areas where our self-knowledge or current models are incomplete.

3. Theoretical Frameworks and Scientific Methods: These bridge the gap between external measurements and internal experiences.

   • The "Third-Person" Scientific Method: Treats the brain as any other physical system, using instrumental variables (e.g., external stimulation) to infer causality without requiring the brain to explain itself.
   • Predictive Coding / Bayesian Brain: Proposes that the brain constantly generates predictions about sensory data and updates them via error signals. This provides a testable computational view.
   • Neural Network / AI Analogs: Deep learning models, built outside the brain but sharing architectural motifs, allow for the development of testable hypotheses about brain circuits by training them on tasks humans perform.
   • Philosophical Analyses: While concepts like the "Hard Problem of Consciousness" (Chalmers, 1995) highlight limits to empirically explaining subjective experience, the "Easy Problems" (perception, cognition) are amenable to scientific investigation.

Practical Workflow for Brain Science Studies

A typical scientific workflow involves defining a cognitive function, formulating a computational hypothesis, selecting external measures (fMRI for spatial, MEG/EEG for temporal, TMS for perturbation), collecting behavioral and introspective data, and then iteratively refining the model against the data. This process uses the brain as a tool for inquiry while simultaneously probing it with independent, observable evidence.

Limits and Uncertainties

Challenges persist due to factors like measurement noise, circular inference (mitigated by cross-validation), the subjectivity of introspection, species differences (addressed by comparative anatomy and modeling), and the "Hard Problem" of consciousness itself. The brain's complex interconnectedness and individual variability also pose hurdles.

Despite these complexities, the combination of objective external data with subjective internal reports, along with the testing of computational theories, allows us to progressively uncover how the brain implements perception, cognition, and even its own self-monitoring. This robust framework, supported by multiple converging methods, ensures that the self-referential nature of the problem does not prevent scientific understanding.