Built on evidence

Errorless learning (Terrace, 1963) is a teaching strategy that structures the environment so that learners make few or no errors during acquisition. Rather than presenting full-difficulty trials from the outset, the procedure fades stimulus supports gradually — ensuring the learner experiences success at each stage before supports are removed.

INTERLAZA uses a 6-phase distractor fading procedure: distractors begin at near-zero opacity (effectively invisible) and gradually increase to full visibility across phases. After any error, difficulty drops immediately back one phase — protecting against the frustration and emotional responses that errors can produce in young children. Crucially, each concept tracks its own phase independently, so mastery of one concept never artificially inflates or deflates difficulty on another.

Mueller et al. (2007) reviewed the errorless learning literature for children with pervasive developmental disorders and confirmed the approach as best practice for this population. The frustration-free progression through success — rather than through repeated correction — is particularly important for maintaining motivation and approach behavior toward learning tasks.

In 1971, Murray Sidman discovered something surprising: teaching a small number of conditional discriminations produces untrained relations for free. Train A→B and B→C, and the learner spontaneously demonstrates A→C (transitivity), C→A, B→A, and C→B (symmetry) — without any explicit instruction on those pairs. Stimuli that have never appeared together during training act as if they belong to the same equivalence class.

Sidman and Tailby (1982) formalized three defining properties of equivalence: Reflexivity (A matches A), Symmetry (if A→B then B→A), and Transitivity (if A→B and B→C then A→C). Training just two conditional discriminations produces up to seven derived relations — a teaching efficiency that is foundational to language and concept formation. This is why match-to-sample training is a gold standard for teaching language concepts in applied behavior analysis.

INTERLAZA builds on this paradigm. The core exercise levels (identity → symbolic → auditory) are based on Sidman's training sequence, while the equivalence probe module tests all three equivalence properties without feedback. The efficiency is built in: teach 2 relations, get 7.

Bayesian Knowledge Tracing (Corbett & Anderson, 1995) is a probabilistic model that estimates a learner's mastery of a concept in real time. After each trial response, the model applies Bayes' theorem to update P(mastery) — the probability that the learner has acquired the underlying knowledge — accounting for the possibility of lucky guesses and careless errors. Four parameters govern the model: P(L₀) initial mastery, P(T) probability of learning on any given trial, P(G) guess rate, and P(S) slip rate.

The result is a dynamic mastery estimate that responds to actual performance patterns rather than simple trial counts. A learner who answers correctly three times in a row but has shown fragile performance previously will have a lower P(mastery) than one who has been consistently accurate — and the algorithm adjusts accordingly. When P(mastery) exceeds 0.95, INTERLAZA advances difficulty; when it drops below 0.40 or errors cluster, difficulty decreases. This creates individualized learning trajectories for each child and each concept, without requiring instructor manual adjustment.

INTERLAZA uses BKT as the primary mastery criterion for advanced exercises, and also to drive adaptive difficulty adjustment (adding or removing comparison stimuli) and errorless learning phase transitions. Per-student parameter adaptation (Yudelson et al., 2013) is on the development roadmap, which will further improve the model's precision for individual learners.

Julio Varela and Claudia Quintana's transfer taxonomy classifies how learned discriminations transfer to new situations by varying four independent factors: Dimension (what property is relevant), Relation (the logical relationship between stimuli), Modality (the sensory channel), and Instance (the specific stimulus objects used). Each factor can be Constant (K) or Variable (Var) between training and test, producing 15 distinct transfer levels ranging from no generalization to full abstract transfer.

The practical insight is powerful: you can teach a child to match red circles to red squares (training) and then systematically test whether that learning transfers when you change the shape, the color dimension, the modality, or use entirely new objects. Each transfer level requires different instruction — and failing at level 5 but succeeding at level 3 tells the instructor exactly where to intervene.

INTERLAZA's transfer module is based on this taxonomy. Instructors select which factors vary between training and probe trials, and the engine generates the correct trial structure automatically. This turns a sophisticated research paradigm into a routine clinical tool, making Varela and Quintana's framework accessible without requiring deep expertise in the underlying theory.

Read the full guide: The 4 Factors, 15 Levels & Clinical Protocol →

Relational Frame Theory (Hayes, Barnes-Holmes & Roche, 2001) extends stimulus equivalence to a much richer repertoire of relations. Where Sidman showed that stimuli can be learned as equivalent (same as), RFT demonstrates that humans learn to apply arbitrarily applicable relational responding across many frame types: not just coordination (sameness), but also distinction (different from), opposition (opposite of), comparison (more/less than), hierarchy (part of/contains), temporal, spatial, and deictic (perspective-taking) frames.

Relations in RFT are governed by two types of contextual cues: Crel — a cue that specifies the type of relation (e.g., "opposite of" vs. "same as") — and Cfunc — a cue that selects which function of a stimulus is relevant in a given context (e.g., size, temperature, category). This Crel/Cfunc structure is what makes language so flexible: the word "more" changes the relational frame, and the context determines whether we're comparing size, quantity, or temperature.

INTERLAZA incorporates multiple RFT frame types — coordination, distinction, opposition, and comparison — with configurable Crel cues and Cfunc dimensions. Peer-reviewed research across clinical populations (Dunne et al., 2014; Gibbs & Tullis, 2021) confirms that these frame types are trainable and clinically meaningful — not just theoretical constructs.

Applied Behavior Analysis (ABA) is the scientific discipline that studies behavior as a function of its environment and applies those findings to socially significant problems. Its roots are in B. F. Skinner's experimental analysis of behavior — The Behavior of Organisms (1938) laid out the operant framework, and Verbal Behavior (1957) extended it to language as learned functional classes (mand, tact, echoic, intraverbal). Sidney W. Bijou then carried the operant program into child development: Child Development: A Systematic and Empirical Theory (Bijou & Baer, 1961) reframed development itself as progressive interactions between the child and the environment, and Bijou's later work on experimental studies with typically developing and atypically developing children became the template for modern behavioral learning research. Baer, Wolf and Risley (1968) then defined the seven dimensions that still guide ABA practice today: applied, behavioral, analytic, technological, conceptually systematic, effective, and generality.

For INTERLAZA, ABA contributes the core mechanics that the platform relies on every session: operationally defined target behaviors, discrete-trial teaching, prompting and prompt fading, differential reinforcement, systematic data collection, and mastery criteria. Match-to-sample itself is a conditional discrimination procedure native to this tradition — which is why the trial structure, reinforcement schedules, and phase transitions in the platform map directly onto standard ABA protocols (Cooper, Heron & Heward, 2020).

ABA gives the platform its measurement and teaching machinery: what a trial is, how to reinforce, how to fade, how to score mastery, and how to generalize. The interbehavioral lens in the next section gives the platform its conceptual vocabulary — what counts as a stimulus in the first place.

Where standard ABA treats the stimulus as a relatively stable environmental event, Kantor's (1959) interbehavioral field theory reframes it as a functional role that emerges during the interaction between an organism and its environment. The same physical object can serve completely different stimulus functions depending on context, history, and the organism's current state. The unit of analysis is not the response to a stimulus, but the full behavioral field in which the two are inseparable.

This distinction underlies INTERLAZA's three-layer data model: Concepts represent abstract ideas (e.g., "dog" as a category). Instances are concrete representations — specific images, sounds, or words that instantiate a concept. Stimuli are the functional roles those instances play during a particular trial — sample, comparison, correct, incorrect. A JPEG of a dog is not a stimulus. It becomes one when it is presented to a child in a specific trial context with a specific relational function.

This precision matters clinically. When a child fails to respond correctly, the question is not "which image was wrong?" but "which stimulus function failed to control the response?" Varela (2008) extends this framework into a full interbehavioral account of intelligent behavior — the theoretical foundation for the transfer matrix module. ABA and interbehaviorism are complementary here: ABA tells us how to teach a trial; interbehaviorism tells us what a trial actually is.