Logic, passion and the problem of convergence

Area of Science:

• Cognitive Science
• Philosophy of Mind
• Artificial Intelligence

Background:

• Current definitions of intelligence often conflate reason and emotion.
• This confusion complicates estimates of intelligence convergence in non-human species.
• David Hume's theory of motivation and action offers an alternative framework.

Purpose of the Study:

• To propose a revised framework for understanding intelligence by separating affective and non-affective capacities.
• To re-evaluate the likelihood of convergence on human-style intelligence based on this new framework.
• To explore the implications for artificial intelligence and the search for extraterrestrial intelligence.

Main Methods:

• Revival and application of David Hume's theory of motivation and action.
• Decomposition of intelligence into distinct non-affective (logic, calculation, problem-solving) and affective (wants, preferences, emotions) capacities.
• Analysis of the likelihood of convergence for each capacity set in diverse contexts.

Main Results:

• The conventional understanding of intelligence, mixing reason and emotion, is conceptually flawed.
• A clearer distinction between non-affective and affective capacities is proposed.
• Convergence of non-affective capacities in thinking species appears probable.
• Convergence of affective capacities seems significantly less likely.

Conclusions:

• Estimating intelligence convergence requires distinguishing between its affective and non-affective components.
• Non-affective cognitive abilities may converge more readily across different evolutionary or ecological contexts.
• Affective capacities, being more context-dependent and potentially idiosyncratic, are less likely to show convergence.