PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 18, 2026Pacific philosophical quarterly0 citationsOpen Access

An Emergentist Approach to Phenomenal Causality

View Full Paper
LZLei Zhong

Key Points

  • The aim is to develop an emergentist approach that attributes causal roles to phenomenal properties while challenging physicalism.
  • Develop a theoretical framework for emergentism in the context of phenomenal causality.
  • Compare emergentism with compatibilism regarding causal roles of phenomenal properties.
  • Critically engage with existing literature on physicalism and emergentism.
  • Emergentism offers novel causal powers for phenomenal properties.
  • Challenges the view that emergentism is inherently incompatible with physicalism.
  • Advocates for a physicalist version of emergentism that grounds phenomena in physical facts.

Abstract

ABSTRACT Philosophers have long debated whether phenomenal properties can play genuine causal roles. In this article, I aim to develop an emergentist approach to phenomenal causality, an approach that attributes novel causal powers to phenomenal properties and rejects the causal closure of physics. I also compare this emergentist approach with an influential competing approach to phenomenal causality, the compatibilist account, which postulates a model of causal overdetermination understood broadly. There is a widespread view in the philosophy of mind that emergentism is physicalistically unacceptable (whereas compatibilism is consistent with physicalism). However, the article attempts to challenge this consensus by arguing for a physicalist version of emergentism, according to which phenomenal properties as well as their novel causal powers are grounded by (noncausal) physical properties and facts.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Lei Zhong (2026) studied this question.

synapsesocial.com/papers/69e31fcb40886becb653ef9chttps://doi.org/10.1111/papq.70014
Ask AI
Helpful
Bookmark
Share
View Full Paper