In search for an alternative to the computer metaphor of the mind and brain (2206.04603v1)

Abstract: The brain-as-computer metaphor has anchored the professed computational nature of the mind, wresting it down from the intangible logic of Platonic philosophy to a material basis for empirical science. However, as with many long-lasting metaphors in science, the computer metaphor has been explored and stretched long enough to reveal its boundaries. These boundaries highlight widening gaps in our understanding of the brain's role in an organism's goal-directed, intelligent behaviors and thoughts. In search of a more appropriate metaphor that reflects the potentially noncomputable functions of mind and brain, eight author groups answer the following questions: (1) What do we understand by the computer metaphor of the brain and cognition? (2) What are some of the limitations of this computer metaphor? (3) What metaphor should replace the computational metaphor? (4) What findings support alternative metaphors? Despite agreeing about feeling the strain of the strictures of computer metaphors, the authors suggest an exciting diversity of possible metaphoric options for future research into the mind and brain.

• The paper challenges the dominant computer metaphor by highlighting its failure to represent the brain's complex, emergent dynamics.
• It proposes alternative metaphors such as control systems, dissipative structures, and fractal antennas to capture adaptive neural processes.
• The findings suggest a paradigm shift that promotes interdisciplinary research and paves the way for innovative experimental frameworks in cognitive science.

On Alternatives to the Computer Metaphor in Understanding Mind and Brain

The current paradigms in cognitive science and neuroscience have long been dominated by the computer metaphor, primarily positioning the brain as an analog to computational devices. This metaphor, deeply entrenched since the mid-20th century, conceptualizes cognitive processes as akin to information processing akin to that of a digital computer. However, recent scholarly discourse has identified significant limitations to this framework, prompting a call for alternative metaphors to better elucidate the nature of the mind and brain.

The paper in question underscores multiple dimensions and facets of the computer metaphor and critiques its efficacy in fully addressing the complexities of brain function and cognition. The authors articulate that while the metaphor has facilitated a rigorous empirical foundation and exploration of cognitive processes, it fails to capture the adaptive, emergent, and self-organizing features of biological systems. This inadequacy becomes particularly pronounced in the context of understanding non-computational aspects that are intricate to the mind-brain complex.

Key Critiques and Alternative Propositions

Limitations of the Current Metaphor: The authors argue that understanding the brain merely as a computation system results in a disconnect with many observed neurological phenomena. For example, while computers are built to perform predictable symbolic manipulations, the brain's processes often involve complex, non-linear dynamics that cannot be simplistically reduced to algorithmic rules or discrete input-output mappings.

Exploration of New Metaphors: The paper discusses a series of metaphoric alternatives that attempt to fill the explanatory gaps left by the computer analogy. Key propositions include viewing the brain as a control system, emphasizing its role in adaptive behavior regulation, or as a dissipative structure, capturing its far-from-equilibrium dynamics that facilitate self-organization. Additionally, the metaphor of fractal antennas introduces the notion of the brain as capable of resonating and potentially amplifying the fractal-like structures of perception and cognition across multiple scales and contexts.

Multiplicative Cascades and Complexity: Among the discussed alternatives, the notion of multiplicative cascade dynamics is highlighted as an integrative framework that suggests cascading instabilities underpin coordinated neural and cognitive activity. The cascade metaphor, inspired by Turing’s work on morphogenesis, suggests that the brain’s adaptive capacities might be accounted for by a power-law-driven fluctuation that allows for flexible and context-sensitive behavior.

Theoretical and Practical Implications

The shift from computational to more physically or biologically grounded metaphors holds substantial implications for future research and applications in understanding the mind and brain:

1. Theoretical Paradigm Shift: Moving beyond computation may necessitate a paradigm shift in how cognitive and neural phenomena are conceptualized, studied, and operationalized. This shift should account for the inherent non-linearity, context-dependence, and emergent properties of brain activity.
2. Interdisciplinary Approach: Embracing metaphors like dissipative structures may foster interdisciplinary research, bridging insights from physics, biology, and complexity science to develop more holistic models of cognition.
3. Towards Real-World Applications: The insights drawn from these alternative metaphors could inform technological innovations in AI and machine learning by modeling adaptive systems that mimic biological flexibility more closely than traditional artificial intelligence architectures.
4. Novel Experimental Frameworks: Empirical research might benefit from being structured around these metaphors. This includes designing experiments to explore and validate the proposed dynamics, like self-organized criticality or fractal scaling behaviors, in cognitive performance.

As cognitive science seeks to move beyond the established computational paradigm, it is critical that it embraces metaphoric frameworks that better account for the inherent complexity and adaptability of living systems. The discussed paper effectively opens this discourse, providing potential pathways toward more integrated and insightful models of the mind and brain. This paradigm shift promises not only to enrich theoretical understandings but also to deliver practical advancements across cognitive science, neuroscience, and related fields.