Membership in ACM includes a subscription to Communications of the ACM (CACM), the computing industry's most trusted source for staying connected to the world of advanced computing. Can AI Expand the Human Mind? LLMs could represent a new layer of human cognition, which researchers call'System 0.' Giuseppe Riva first started to think about the role that artificial intelligence (AI) can play in human cognition when he and a colleague were trying to find someplace to have dinner in Los Angeles. Both pulled out their phones and started perusing Google Maps for suggestions of nearby restaurants. Riva quickly noticed that the list of possibilities on his phone was very different from what his companion was seeing.

Contemporary human - AI interaction research currently faces a significant limitation: existing frameworks are inadequate to explain how artificial intelligence systems fundamentally reshape human cognition before conscious awareness occurs. This preprocessi ng influence, which operates beneath the threshold of deliberate thought, represents a crucial missing layer in the understanding of distributed cognition. This paper introduces "Cognitive Infrastructure Studies" (CIS) as a new interdisciplinary domain to reconceptualize AI s as "cognitive infrastructures" . C ognitive infrastructures - e.g., search engines, recommender systems, algorithmic curation platforms, and large language models - exhibit classic infrastructural properties: they are invisible in normal operation, becoming visible only upon breakdown; they are embedded in social and technical arrangements; they are learned as part of membership in digital communities; they link with conventions of practice; and they embody standards that shape what counts as appropriate, relevant, or true . Yet cognitive infrastructures possess distinctive characteristics that distinguish them from traditional infrastructures. Unlike physical infrastru ctures that passively transport matter or energy, cognitive infrastructures have agency, filtering and curating individuals' perception of reality before it reaches human consciousness. Through narrative scenarios spanning individual (cognitive dependency), collective (democratic deliberation), and societal (governance) scales, we describe how cognitive infrastructures reshape human cognition, public reasoning, and social epistemologies. CIS also provides methodological innovations for studying invisible algorithmic influence: " infrastructure breakdown methodologies ", experimental approaches that reveal cognitive dependencies by systematically withdrawing AI preprocessing after periods of habituation.

The "Thinking, Fast and Slow" paradigm of Kahneman proposes that we use two different styles of thinking -- a fast and intuitive System 1 for certain tasks, along with a slower but more analytical System 2 for others. While the idea of using this two-system style of thinking is gaining popularity in AI and robotics, our work considers how to interleave the two styles of decision-making, i.e., how System 1 and System 2 should be used together. For this, we propose a novel and general framework which includes a new System 0 to oversee Systems 1 and 2. At every point when a decision needs to be made, System 0 evaluates the situation and quickly hands over the decision-making process to either System 1 or System 2. We evaluate such a framework on a modified version of the classic Pac-Man game, with an already-trained RL algorithm for System 1, a Monte-Carlo tree search for System 2, and several different possible strategies for System 0. As expected, arbitrary switches between Systems 1 and 2 do not work, but certain strategies do well. With System 0, an agent is able to perform better than one that uses only System 1 or System 2.