Behavioral learning research has an implementation problem. Decades of peer-reviewed findings on stimulus equivalence, relational frame theory, and transfer of training have produced clear evidence about how to teach language and symbolic thinking to children who benefit from structured learning. But that evidence has never been translated into a practical, affordable tool that instructors and families can use in daily sessions.
INTERLAZA is our attempt to close that gap.
What INTERLAZA Is
INTERLAZA is an adaptive match-to-sample (MTS) training platform for instructors working with children from 18 months and up. Because the engine adapts to each learner, it is suitable for typically developing children and children with learning disabilities alike. It runs on any tablet or computer, works offline, and adjusts its difficulty automatically based on each child’s real-time performance.
The name comes from interlazar — Spanish for “to interweave.” It reflects the platform’s core idea: that different types of stimulus relations — identity, symbolic, auditory, equivalence, relational — are not separate procedures to be taught one at a time, but strands that can be woven together into a comprehensive, adaptive curriculum.
The Four Scientific Pillars
1. Varela Transfer Theory
Julio Varela and Claudia Quintana developed a systematic taxonomy for understanding how stimulus control transfers across training conditions. Their 15-level transfer matrix describes how changing any of four factors — dimension, relation, modality, and instance — affects how much a learner needs to relearn.
INTERLAZA’s transfer module is based on this framework, allowing researchers and advanced instructors to systematically probe whether trained relations transfer to novel contexts. Rather than implementing Varela’s model directly, INTERLAZA adapts it into a practical clinical tool — making transfer assessment accessible without requiring deep expertise in the underlying taxonomy.
For a deeper introduction to the science, visit the Science page.
2. Sidman Stimulus Equivalence
Murray Sidman’s work demonstrated that teaching certain stimulus relations (A→B and A→C) causes children to spontaneously develop untrained relations (B→A, C→A, B↔C) — without further training. This derived relational responding is considered a model of symbolic thinking and is fundamental to language development.
INTERLAZA’s equivalence training exercises are based on Sidman’s MTO (many-to-one) and OTM (one-to-many) training structures, and its probe exercises test for spontaneous emergence of symmetry and transitivity. The system tracks which relations are trained and which have been derived, giving instructors a clear picture of each child’s symbolic repertoire.
3. Relational Frame Theory (RFT)
RFT, developed by Hayes, Barnes-Holmes, and colleagues, extends Sidman’s findings into a comprehensive account of human language and cognition. Where equivalence training focuses on coordination (A = B), RFT describes a range of relational frames including distinction (A ≠ B), opposition (A is the opposite of B), hierarchy (A is a type of B), and more.
INTERLAZA incorporates several RFT frames as exercise types, with configurable cue functions and contextual control. Researchers can define custom dimensions for contextual control exercises, enabling the kind of fine-grained RFT research that has historically required custom-built software.
4. Bayesian Knowledge Tracing (BKT)
BKT is a probabilistic model from educational technology that continuously estimates the probability that a learner has acquired a specific concept. INTERLAZA uses BKT to drive difficulty adjustment — automatically adding or removing distractors as each child’s mastery probability rises or falls.
We have written a full explanation of how BKT works in INTERLAZA in a separate article: How Bayesian Knowledge Tracing Personalizes Every Session.
Key Features
Errorless learning with adaptive prompt fading. Distractors fade in from low to full opacity, giving children an initial period of guaranteed success. Optional prompts (highlighting, arrows, size differences) fade out systematically as the child demonstrates competence.
Smart intervention detection. The system monitors response patterns in real time and alerts instructors to position bias, fast guessing, perseverative errors, and other patterns that suggest the program needs adjustment — before a full session of poor data accumulates.
Clinical reports. After each session, instructors see per-concept accuracy, mastery trajectory, and any adjustments the system made. Optional AI-assisted summaries generate parent-friendly progress reports in seconds.
Offline-first. Sessions run entirely in the browser’s local database. Data syncs to the cloud when a connection is available. A session in a clinic without wifi works identically to one at home.
Who It’s For
Instructors and clinicians use INTERLAZA to run individual sessions, create custom programs, and track mastery across concepts and students. The instructor interface is designed to be configurable without being overwhelming — sensible defaults are available for every setting, with advanced options available when needed.
Parents can run home practice sessions using cloned copies of their child’s clinic programs. The parent interface simplifies the configuration options and guides setup step by step.
Researchers have access to raw data export, custom relational frame dimensions, the full Varela transfer matrix, and probe-based generalization testing — tools designed for those who want to go beyond standard clinical programming.
Getting Started
INTERLAZA is available now at interlaza.app. All paid tiers start with a 7-day free trial. If you are an instructor, the Getting Started guide walks through setting up your first student and running your first session.
If you have questions, the team is reachable at support@interlaza.com or via the contact form.