Some psychological models explain behavior through intermediate representational, behavioral, or biological processes, or through formal variables assigned an intermediate explanatory role. The traditional mediational–nonmediational distinction captures an important contrast but obscures differences among these explanatory roles. We distinguish lineages, research traditions, and explanatory models, and reconstruct five lineages whose recurrent arguments constrain autonomous, empirically unanchored psychological mediation: phenomenological–enactive, ecological–dynamic, pragmatic–grammatical, behaviorist, and material–dynamic. Their positive ontological commitments differ, but their characteristic alternatives include mediation through independently constrained, materially specifiable processes, formally disciplined variables not reified as causes, and nonmediational explanations grounded in environmental, behavioral, or organism–environment relations. We distinguish architectural mediation from empirical anchoring and propose four model-level criteria: process autonomy, necessity under a prior adequacy standard, rewriting resistance, and empirical anchoring. Applications to temporal behavior, behavior systems, and verbal mediation show that predictive warrant may exceed ontological warrant: a weakly anchored mediator may remain useful or indispensable without being independently established as the causal process attributed to it. The framework therefore refines the mediational–nonmediational distinction while separating explanatory success from ontological commitment.
In one experiment with rats, we investigated the impact of a retrieval-extinction procedure on response-independent reinstatement of instrumental learning. All rats were trained to run down an alley for alcoholic beer during the first phase. Then, the instrumental response underwent extinction. Half of the rats received a brief exposure to the beer prior to each extinction session, whereas this beer exposure was omitted for the rest of the animals. Then, all rats were tested twice to measure reinstatement. The first test was carried out immediately after the last extinction session for all rats; whereas the second test took place after a single session of reexposure to the beer. We found reinstatement of beer seeking in those animals that did not experience beer exposure prior to extinction. By and large, reinstatement was prevented or reduced when rats received exposure to beer before extinction. Our data suggest that the reinstatement of alcohol seeking may be lessened by means of the retrieval-extinction paradigm.
This study investigated serial learning in albino rats subjected to a task that evaluated anticipatory behavior, behavioral variability, and serial learning in the Multiple Choice Maze proposed by Dashiell (1930). In the first phase, three consecutive reinforced trials (RRR type) were presented, followed by a second phase in which one group of rats faced an RNN type sequence, whereas another group faced an RNR type sequence. All subjects subsequently underwent a redetermination phase under RRR conditions. The results showed that rats exhibited anticipatory behavior, reducing their speed during no-rewarded trials but showing an increase in path variability. In general, the results successfully replicated previous studies on serial learning (Capaldi et al., 1990) and orientation behavior in mazes. In addition, new findings were identified, such as behavioral variability depending on the presence or absence of reinforcers and the time and distance of the runs in each trial. Taken together, this study contributes to the understanding of how organisms adapt and modify their behavior in response to environmental stimuli.
Recent research has shown that large language models (LLMs) exhibit rule-based insensitivity to contingencies, a functional analog of a well-documented phenomenon in human behavior. The present study extended these findings by examining whether tracking inductions can attenuate this insensitivity. Six frontier LLMs (GPT 5.2, Gemini 3 Flash, Grok 4.1 Fast, DeepSeek V3.2, Qwen3 235B, and Kimi K2) completed a modified rock-paper-scissors paradigm with manipulated outcome probabilities in a 3 (Rule: No Rule, Correct Rule, Incorrect Rule) × 3 (Induction: No Induction, Direct Tracking, Metaphorical Tracking) factorial design across 2,160 sessions. All models demonstrated In-Context Learning and strong rule adherence. Replicating and extending prior findings, rule provision produced significant contingency insensitivity across all six models. Tracking inductions significantly reduced this insensitivity when rules were provided, while having minimal effects in the absence of rules. Direct tracking inductions consistently enhanced contingency sensitivity across all models. Metaphorical tracking showed greater variability, being equally or more effective than direct tracking in some models, but ineffective or paradoxically detrimental in others. These findings demonstrate that prompt-based tracking inductions can modulate rule-based contingency insensitivity in LLMs, with implications for understanding and improving the behavioral flexibility of artificial agents.
To reply to the two commentaries, an overview of social contextual analysis is given focusing more on the overall goals and expected outcomes based on 50 years of studying human behavior and 35 years of field or community observations. Misinterpretations occur because there are different goals in play between behavior analysis and SCA. In particular, we need to prioritize getting “off the page” and away from theories and models and focus instead on better observing both the behaviors of humans and the many contexts that shape those behavior in different ways. Behavior analysis has done the first but not the second of these, but we can use the social sciences as our initial starting points for making our observations. We resort to theories when we have poor or no observations and the recent behavior analysis of human behavior has seen both poor observations of context and an upsurge in abstract theorizing and modeling. Getting “off the page,” however, requires new methodologies for observation and how we interact and engage with our subject matter, and less time spent on how we write observations into theories. Like all “science,” our written versions are there only to sensitize readers as to where they might make observations, not to record an accumulation of truths.
