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NSF Science of Learning Program (PD 16-004Y) - FAQs

What is the NSF Science of Learning program?

The NSF Science of Learning program is a discretionary grant opportunity that funds potentially transformative, basic research aimed at advancing the fundamental science of how learning works. Its central goal is to deepen scientific understanding of learning itself, including what learning is, how it changes over time, what enables it, what limits it, and how it varies across contexts, individuals, and systems.

What kind of research does this opportunity prioritize?

This opportunity prioritizes foundational, theory-building research on the principles, processes, and constraints that shape learning. The emphasis is on core scientific insights rather than primarily on near-term classroom interventions or specific training programs.

Is this program focused on classroom interventions or training programs?

No. While applicants may describe potential connections to practical challenges (for example in education or workforce development) as broader impacts, the program is designed around advancing basic knowledge first. Near-term interventions or specific training programs are not the primary focus for intellectual merit under this program.

Are interdisciplinary proposals required?

No. NSF explicitly welcomes integrative and interdisciplinary projects and suggests they may be especially valuable when they connect different levels of explanation (for example, linking neural mechanisms to real-world behavior). However, single-discipline projects can also be a strong fit if they address basic scientific questions about learning.

What does NSF mean by "integrating across levels of analysis"?

Integration across levels of analysis refers to connecting insights across different scales of explanation, such as linking molecular or cellular mechanisms, neural circuits and systems, cognitive and affective processes, and social or cultural influences. The intent is to frame questions or develop tools that allow findings at one level to constrain, explain, or meaningfully inform findings at another level.

What levels or domains of learning research are included in the program scope?

The scope is broad and includes learning research across one or multiple levels of analysis, including: molecular and cellular mechanisms; brain circuits and systems; cognitive and affective processes; observable behavior; and social and cultural influences. Proposals may focus on one level or connect multiple levels.

What research methods and approaches are supported?

The program supports a wide range of research approaches, including controlled experiments, field studies in naturalistic settings, surveys, analyses of existing or large-scale secondary datasets, and computational or mathematical modeling.

What are the main topic areas highlighted as examples of "science of learning" research?

NSF highlights several illustrative question areas, including: (1) transfer and generalization (how learning carries over across situations, rule extraction, and why knowledge can be flexible or context-bound); (2) robustness and interference (what makes learning resilient when new experiences compete with older learning); (3) stability over time including consolidation and reconsolidation (how transient experiences become durable memory and when stored knowledge is updated or destabilized); (4) the structure of learning environments (timing, spacing, sequencing, content organization, context, and developmental timing); (5) engagement (active vs passive learning, individual vs group learning); and (6) integration across levels of analysis (linking biology/neuroscience with cognitive, emotional, social, and behavioral processes).

Does the program support research on transfer and generalization?

Yes. One major highlighted theme is transfer and generalization, including how learning generalizes from one situation to another, how general rules are extracted from specific experiences, and why some knowledge remains flexible while other knowledge remains tied to the original context.

Does the program support research on interference, robustness, and competing experiences?

Yes. NSF explicitly includes robustness and interference as a highlighted area, including what makes learning resilient when new experiences compete with, overwrite, or interfere with earlier learning.

Does the program support research on consolidation and reconsolidation?

Yes. Another central topic is how learning becomes stable over time, including consolidation and reconsolidation processes that transform transient experiences into durable memory, and the conditions under which stored knowledge is updated or destabilized.

Can proposals study how the learning environment affects learning?

Yes. NSF highlights the structure of the learning environment as a major theme. This includes timing factors (such as spacing and sequencing), organization of content, the learning context or setting, and developmental timing (including differences across ages or sensitive periods).

Is research on active vs passive learning and individual vs group learning within scope?

Yes. NSF points to engagement as an important dimension, including active learning versus passive exposure and individual versus group learning. The emphasis is on understanding why particular structures produce particular learning dynamics, what mechanisms drive those effects, and under what conditions those patterns hold.

Does NSF encourage connecting neuroscience to behavior or real-world outcomes?

Yes. NSF signals that proposals linking different levels of explanation may be especially valuable, such as connecting mechanisms in neurons to behavior in real-world settings. The program is interested in approaches that can bridge biology and neuroscience with higher-level cognitive, emotional, social, and behavioral processes.

Can learning be studied beyond humans or beyond biological systems?

Yes. The program encourages thinking about learning as a phenomenon that can appear in biological, non-biological, and social systems. NSF highlights opportunities to connect insights from biological learners to computational learning systems and areas such as neuromorphic engineering, materials science, and nanotechnology, and also to use computational or engineered perspectives to sharpen theories of learning in brains and behavior.

Are practical applications required as the main contribution?

No. Practical relevance can be described as beneficial broader impacts, but the core requirement emphasized here is advancing basic scientific understanding of learning. Real-world applications are welcomed but are not positioned as mandatory for the main intellectual contribution.

What is the Funding Opportunity Number for this program?

The Funding Opportunity Number listed is PD 16-004Y.

What is the CFDA number and category associated with this opportunity?

The opportunity is listed under CFDA 47.075 and is categorized within science and technology and other research and development.

Who is eligible to apply?

Eligibility is listed as unrestricted, meaning it is broadly open to applicant types, subject to any additional clarifications in the full solicitation text.

What is the maximum award amount (award ceiling)?

The listed award ceiling is $600,000.

How many awards does NSF expect to make under the referenced competition cycle?

NSF anticipated making about 25 awards under the competition cycle referenced in the provided information.

When was the opportunity created and what was the original closing date mentioned?

The source material notes the opportunity was created on September 13, 2016, with an original closing date of January 18, 2017.

How should an applicant think about overall fit with this program?

A strong fit is a project that advances foundational knowledge about the mechanisms, structures, and principles of learning across scales and systems using rigorous empirical and/or modeling approaches. Interdisciplinary integration is strongly encouraged but not required, and practical relevance can be included as broader impacts rather than as the primary focus.