The Reno Methodology 2.0: AI and Qualitative Insights in Verbal Behavior

Guest Blog Authored by: Yors Garcia

Pontificia Universidad Javeriana

Yors Garcia, Ph.D., is an Assistant Professor at Pontificia Universidad Javeriana in Bogotá, Colombia. Dr. Garcia began his career working with individuals with developmental and intellectual disabilities in a private nonprofit program. He earned his doctorate in Rehabilitation Services with a specialization in Behavior Analysis from Southern Illinois University and has since held academic and supervisory roles across the United States, Colombia, and Saudi Arabia. His primary research interests include acceptance and commitment training, derived relational responding, international supervision, and diversity. Dr. Garcia is the incoming Editor-in-Chief of The Psychological Record (2025–2028) and serves as president of the Culture & Diversity Special Interest Group for ABAI. He is also a founding member of ABAI’s Colombia chapter and remains dedicated to advancing behavior analysis worldwide.

B. F. Skinner once remarked, “Behaviorism, as we know it, will eventually die—not because it is a failure, but because it is a success. As a critical philosophy of science, it will necessarily change as a science of behavior changes…” (Skinner, 1969, p. 267).

This insightful statement underscores the ever-evolving and dynamic nature of behavior analysis—a field that continually adapts as new methodologies and technologies emerge. In this blog, I examine one such progression: integrating qualitative methods into behavior-analytic research, specifically focusing on their application to the study of verbal behavior.

Historically, behavior analysis has focused on quantitative methods, such as single-case designs, to study and predict behavior through precise and continuous data collection. However, qualitative approaches—present in the field for over 40 years—are being rediscovered for their potential to enhance our understanding of complex phenomena (Burney et al., 2023, 2024). By integrating both approaches, behavior analysts can gain a deeper understanding of verbal behavior, making their findings more accessible and applicable to broader audiences.

This blog revisits an early but often-overlooked example of qualitative research in behavior analysis: the Reno Methodology (McCorkle, 1991; Moore, 1991; Xavier et al., 2017). I discuss its relevance today and explore how modern advancements, including artificial intelligence (AI), can enhance these methods. By embracing diverse analytic tools, behavior analysts can strengthen their science and expand their reach to meet the needs of an evolving world.

The Value of Qualitative Research in Behavior Analysis

Qualitative research is an exploratory approach designed to understand complex phenomena by examining context, participants’ perspectives, and lived experiences. Unlike quantitative methods, which focus on measuring and explaining behavior, qualitative research emphasizes identifying patterns, capturing contextual nuances, and uncovering insights often missed in traditional experimental designs (Aspers & Corte, 2019).

Key features of qualitative research include:

• Studying behavior in natural settings,
• Prioritizing participants’ voices and experiences and
• Flexibility to uncover emerging themes or unexpected findings.

Common qualitative methods include interviews, focus groups, and participant observation. These methods enable researchers to study behavior in real-world contexts, highlighting situational influences and centering the perspectives of those directly involved.

For behavior analysts, qualitative research complements traditional data-driven methods by deepening the understanding of private experiences, contextual influences, and cultural factors (Burney et al., 2023). For example, qualitative measures during behavioral assessments can uncover private experiences, including individuals’ feelings of empowerment or discomfort with interventions; contextual influences, including the impact of classroom size or availability of school resources on behavior plans; and cultural factors, where family values or language barriers shape participation and acceptance. Furthermore, qualitative methods facilitate social validity assessments, ensuring that interventions are meaningful, relevant, and acceptable to individuals and communities. By integrating qualitative approaches, behavior analysis can become more responsive to diverse populations, settings, and needs—ultimately enhancing its societal impact.

Revisiting Key Milestones in Qualitative Behavior Analysis

The integration of qualitative methods into behavior analysis is not new. Two pivotal moments illustrate this evolution:

1. The Emergence of Social Validity. Seminal works by Wolf (1978), Kazdin (1977), and Van Houten (1979) introduced the concept of social validity, emphasizing that interventions should not only be effective but also socially meaningful and acceptable. This laid the groundwork for incorporating participants’ perspectives into behavior analysis, marking an early step toward qualitative inquiry.

• Willard Day’s Groundbreaking Contributions: Willard Day bridged behavior-analytic concepts with phenomenology (Day, 1969) and hermeneutics (Day, 1988; Dougher, 1993). His innovative work advanced the study of verbal behavior by analyzing language within its natural context and focusing on meaning-making processes. For instance, when a child with autism says, “I don’t want to” during therapy, the phrase may initially seem straightforward. However, examining it across contexts—such as during challenging tasks versus preferred activities—can reveal distinct functions, including escape, avoidance, or a lack of reinforcement history. By combining functional analysis with qualitative techniques, including clinical interviews and naturalistic observations, Day demonstrated the importance of understanding how verbal behavior adapts to environmental and motivational variables. Although he never formally published his methodology, his students’ work provided valuable insights into these processes (McCorkle, 1991; Moore, 1991; Xavier et al., 2017).

The Reno Methodology

The Reno Methodology, developed by Willard Day and collaborators at the University of Nevada, is a qualitative approach tailored to the functional analysis of verbal behavior. Rooted in Skinner’s radical behaviorism and influenced by phenomenology, it bridges experimental and interpretative methods. The Reno Method is particularly effective for analyzing complex verbal interactions within their historical and environmental contexts. It examines the interplay between speaker, listener, and environment, focusing on how verbal responses are shaped by historical reinforcement contingencies (McCorkle, 1991; Xavier et al., 2017).

Key Steps in the Reno Methodology

1. Define the Research Focus: Identify the specific verbal interactions you want to study (e.g., conversations between parents and children, behavior analysts and clients, or therapist and clients).
2. Data Collection: Record verbal interactions in natural or controlled settings. Transcribe the recordings, noting what is said, nonverbal cues (e.g., gestures, tone of voice), and relevant context (e.g., home, school, clinic, availability of preferred items, other people).
3. Identify Types of Language (Verbal Operants): Verbal operants, as described in Skinner’s taxonomy, are categorized based on their functional purpose in communication. Each type of verbal operant is defined by the antecedent conditions (what happens before the utterance) and the consequences (what happens after the utterance). Here is a breakdown:
4. Functional Analysis: Functional analysis is essential for confirming the classification of a verbal utterance as one operant or another. While the taxonomy provides a framework for categorization, the true distinction between verbal operants lies in their function—their effect on the environment—within a specific context (e.g., was the request followed by receiving a snack?).
   • Repeats (Echoics): These involve repeating what is heard. The antecedent is another person’s vocal behavior, and the response is reinforced by matching it (e.g., a child repeats “Say please” and is reinforced for accurately echoing the phrase.
   • Requests (Mands): These are utterances or signs made to fulfill a need or want, reinforced by the delivery of the requested item or action (e.g., “Can I have a snack?” is reinforced when the snack is provided).
   • Labels (Tacts): These are verbal responses to a non-verbal stimulus, such as naming or describing objects, events, or features of the environment (e.g., saying “dog” when seeing a dog). The reinforcement is often social, such as agreement or acknowledgment.
   • Conversations (Intraverbals): These occur when someone responds with language that is thematically related but not identical to the antecedent stimulus (e.g., answering “What’s your favorite color?” with “Blue.”). Reinforcement typically comes through maintaining the conversation.
   • Clarifying Words (Autoclitics): These are modifiers that add meaning, specify relationships, or indicate the speaker’s intent (e.g., “I think it’s raining” or “It’s really hot”). They are reinforced by improving the clarity or precision of communication.
5. Thematic Categorization: Group similar verbal behaviors into common themes by identifying patterns, such as recurring phrases or consistent responses to specific situations.
6. Refine Observations: Revisit the data repeatedly to improve accuracy and clarity. Review transcripts, refine unclear utterances, and adjust thematic groupings as necessary.
7. Interpret and Apply: Use these insights to design strategies that improve communication, such as helping parents respond more effectively to their child’s requests, improving behavior analysts’ communication with clients, or supporting clients in expressing their needs more clearly.

A Brief Example of the Reno Methodology in Practice

Let’s consider how the Reno Methodology might be used to study the impact of parents’ verbal operants on their child’s communication skills. Specifically, we’ll explore parent-child training sessions focused on children with autism.

STEP 1: Define the Research Focus.

The goal is to analyze how parents’ use of different types of language—such as requests (mands), labels (tacts), conversational exchanges (intraverbals), and clarifying words (autoclitics)—affects their child’s communication skills. This involves identifying how these types of language encourage or prompt the child to respond and how their behaviors are reinforced (rewarded) during structured play sessions.

STEP 2: Data Collection

• Participants: Parents of children with autism, with a range of verbal abilities to ensure diverse data.
• Recording Interactions: Sessions occur in naturalistic settings (e.g., the child’s home). Parents use toys, books, or other familiar materials to engage their children in spontaneous communication. Audio and video equipment records these interactions to capture verbal and nonverbal behavior.
• Transcription: Transcripts include parent and child verbalizations, contextual factors (e.g., materials used), and nonverbal cues (e.g., gestures, tone).

Example Interaction:

• Parent (Mand): “Can you show me the blue ball?”
• Child (Tact): “Ball!”
• Parent: “Great job! That’s the blue ball!” (Positive reinforcement)
• Parent (Mand): “Now, can you give me the red ball?”
• Child: (No response.) The parent repeats the mand and gestures to prompt the child.

STEP 3: Identify Types of Language (Verbal Operants)

Transcripts are analyzed using verbal behavior taxonomy:

1. Mands: Requests to evoke a response (e.g., “What’s this?” holding a toy car).
2. Tacts: Labels for stimuli (e.g., Child says, “Car!” for a toy car).
3. Intraverbals: Conversational exchanges involving related but distinct responses (e.g., Parent asks, “What do you like to play with?” Child responds, “Blocks!”).
4. Autoclitics: Words that modify or clarify other verbal responses (e.g., “I think you’re holding your favorite toy” adds intent).

STEP 4: Functional Analysis

Each verbal operant is analyzed in terms of antecedents, behaviors, and consequences to understand its function in context.

Example Analysis:

• Antecedent: Parent points to a picture book and asks, “What’s this animal?”
• Behavior: Child responds, “Dog!” (a tact)
• Consequence: The parent says, “Yes, it’s a dog! Great job!” and gives a favorite toy.
• Result: The tact is reinforced, increasing the future likelihood of similar responses.

STEP 5: Thematic Categorization

Themes are identified to highlight effective strategies and challenges observed in the data:

1. Effective Use of Mands: Clear, consistent mands improve child responses and participation.
2. Reinforcement Strategies: Immediate, specific reinforcement (e.g., verbal praise, rewards) sustains engagement and builds verbal skills.
3. Inconsistent Reinforcement: Intermittent reinforcement may slow down progress.

Theme Example: Parents who consistently reinforce tacts report increased spontaneous labeling during playtime.

STEP 6: Refine Observations

Findings are revisited to refine insights and inform more effective strategies.

• Initial Insight: Parents often reinforce mands more frequently than other verbal operants, such as tacts (labels) or intraverbals (conversational responses).
• Refinement: Parents reinforce mands (requests) more frequently than other verbal operants, such as intraverbals, which could limit their child’s development of conversational turn-taking skills. Balancing reinforcement of mands with intraverbals may encourage more natural and reciprocal interactions.
• Outcome: Based on this refinement, behavior analysts may help parents modify their strategies in subsequent sessions, actively promoting conversational exchanges alongside requests and fostering a more balanced communication repertoire.

STEP 7: Interpretation and Application

Findings inform actionable parent training interventions:

1. Parent Training Protocol: Teach parents to mix mands, tacts, and intraverbals during communication.
   • Example: Replace “Give me a car” (mand) with “What color is the car?” or “What sound does it make?”
2. Reinforcement Coaching: Teach parents to provide specific, descriptive feedback.
   • Example: Replace “Good job!” with “Yes, it’s a red ball! You remembered the color!”
3. Feedback Loops: Provide recorded interaction reviews, highlighting effective mands and areas for improvement, such as consistent and continuous reinforcement.

Revolutionizing the Reno Methodology with Artificial Intelligence

The Reno Methodology offers a valuable qualitative approach to analyzing complex verbal interactions within their historical and environmental contexts. While this method can provide profound insights into verbal behavior, its time-consuming nature can be a significant barrier. However, advancements in AI are poised to revitalize this methodology.

AI can automate critical tasks such as transcribing and categorizing single and multiple verbal operants within natural interactions between a practitioner and a parent, staff member, or individual with autism. Speech-to-text systems and natural language processing tools can quickly and accurately generate transcripts and classify verbal responses (Cox & Jennings, 2024; see Using Technology to Quantify Verbal Behavior). This automation minimizes human error and allows researchers to focus on deeper analysis and intervention. Tools such as NVivo and Atlas.ti streamline qualitative data analysis by identifying patterns and relationships between antecedents, behaviors, and consequences. NVivo uses AI-assisted auto-coding to tag triggers (antecedents), responses (behaviors), and outcomes (consequences), while Atlas.ti highlights sequences and themes through text mining. Additionally, free AI-driven, open-source qualitative analysis tools, such as Taguette, Voyant Tools, or QDA Miner Lite, help uncover meaningful insights into verbal behavior, supporting more targeted and effective interventions.

AI also offers significant potential to enhance the predictive capabilities of the Reno Methodology. Machine learning (ML) algorithms can analyze historical and real-time data to predict the effectiveness of interventions and design personalized treatment plans with greater precision (Turgeon & Lanovaz, 2020). For instance, ML can identify the most effective reinforcement strategies for a child with autism, considering their unique behavioral patterns and environmental context. This aligns with recent advancements in ABA, where AI models have successfully optimized treatment plans based on patient data (Maharjan et al., 2023).

By integrating AI with the Reno Methodology, behavior analysts can deepen their understanding of verbal behavior, refine functional analyses, and develop more effective and socially relevant interventions. This partnership could mark a significant advancement in the study and applications of verbal behavior.

Looking to the Future

Building on Skinner’s insight, behavior analysis continues to evolve by integrating new methodologies, revitalizing older ones, and leveraging emerging technologies to address modern scientific and societal challenges. Qualitative approaches, such as the Reno Methodology, add depth to behavioral analyses by uncovering complex interactions and contextual variables often overlooked by traditional methods. At the same time, AI tools automate processes such as transcription, coding, and pattern recognition, enabling researchers to identify patterns and relationships more efficiently.

Let me conclude with a passage from Willard Day that captures the essence of this discussion:

“The aim of a functional analysis of verbal behavior remains the specification of controlling contingencies. Methodological difficulties arise because as yet we have no well-established techniques for assessing controlling contingencies outside of the context of an interest in rate of responding, and the rate of verbal responding is perhaps one of its most tedious parameters. What is needed are techniques for establishing baselines which concentrate on other aspects of verbal responding than rate.” (Day, 1976, p. 195, emphasis added).

Put simply, by combining qualitative methods (e.g., the Reno Methodology) with AI, behavior analysts can move beyond rate—defined as the average number of responses made over a specific period—as the sole measure of verbal behavior. This integrated approach offers a deeper, more nuanced understanding of verbal behavior, enhancing the field’s ability to address the complexities of human communication in meaningful, socially significant, and impactful ways.

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