Large-N Rat Data Enables Phenotyping of Risky Decision-Making: A Retrospective Analysis of Brain Injury on the Rodent Gambling Task

Understanding Individual Subject Differences through Large Behavioral Datasets: Analytical and Statistical Considerations

A core feature of behavior analysis is the single-subject design, in which each subject serves as its own control. This approach is powerful for identifying manipulations that are causal to behavioral changes but often fails to account for individual differences, particularly when coupled with a small sample size. It is more common for other subfields of psychology to use larger-N approaches; however, these designs also often fail to account for the individual by focusing on aggregate-level data only. Moving forward, it is important to study individual differences to identify subgroups of the population that may respond differently to interventions and to improve the generalizability and reproducibility of behavioral science. We propose that large-N datasets should be used in behavior analysis to better understand individual subject variability. First, we describe how individual differences have been historically treated and then outline practical reasons to study individual subject variability. Then, we describe various methods for analyzing large-N datasets while accounting for the individual, including correlational analyses, machine learning, mixed-effects models, clustering, and simulation. We provide relevant examples of these techniques from published behavioral literature and from a publicly available dataset compiled from five different rat experiments, which illustrates both group-level effects and heterogeneity across individual subjects. We encourage other behavior analysts to make use of the substantial advancements in online data sharing to compile large-N datasets and use statistical approaches to explore individual differences.

Statistical power and false positive rates for interdependent outcomes are strongly influenced by test type: Implications for behavioral neuroscience

Statistical errors in preclinical science are a barrier to reproducibility and translation. For instance, linear models (e.g., ANOVA, linear regression) may be misapplied to data that violate assumptions. In behavioral neuroscience and psychopharmacology, linear models are frequently applied to interdependent or compositional data, which includes behavioral assessments where animals concurrently choose between chambers, objects, outcomes, or types of behavior (e.g., forced swim, novel object, place/social preference). The current study simulated behavioral data for a task with four interdependent choices (i.e., increased choice of a given outcome decreases others) using Monte Carlo methods. 16,000 datasets were simulated (1000 each of 4 effect sizes by 4 sample sizes) and statistical approaches evaluated for accuracy. Linear regression and linear mixed effects regression (LMER) with a single random intercept resulted in high false positives (>60%). Elevated false positives were attenuated in an LMER with random effects for all choice-levels and a binomial logistic mixed effects regression. However, these models were underpowered to reliably detect effects at common preclinical sample sizes. A Bayesian method using prior knowledge for control subjects increased power by up to 30%. These results were confirmed in a second simulation (8000 datasets). These data suggest that statistical analyses may often be misapplied in preclinical paradigms, with common linear methods increasing false positives, but potential alternatives lacking power. Ultimately, using informed priors may balance statistical requirements with ethical imperatives to minimize the number of animals used. These findings highlight the importance of considering statistical assumptions and limitations when designing research studies.

Exploring decision-making strategies in the Iowa gambling task and rat gambling task

Decision-making requires that individuals perceive the probabilities and risks associated with different options. Experimental human and animal laboratory testing provide complimentary insights on the psychobiological underpinnings of decision-making. The Iowa gambling task (IGT) is a widely used instrument that assesses decision-making under uncertainty and risk. In the task participants are faced with a choice conflict between cards with varying monetary reinforcer/loss contingencies. The rat gambling task (rGT) is a pre-clinical version using palatable reinforcers as wins and timeouts mimicking losses. However, interspecies studies elaborating on human and rat behavior in these tasks are lacking. This study explores decision-making strategies among young adults (N = 270) performing a computerized version of the IGT, and adult outbred male Lister Hooded rats (N = 72) performing the rGT. Both group and individual data were explored by normative scoring approaches and subgroup formations based on individual choices were investigated. Overall results showed that most humans and rats learned to favor the advantageous choices, but to a widely different extent. Human performance was characterized by both exploration and learning as the task progressed, while rats showed relatively consistent pronounced preferences for the advantageous choices throughout the task. Nevertheless, humans and rats showed similar variability in individual choice preferences during end performance. Procedural differences impacting on the performance in both tasks and their potential to study different aspects of decision-making are discussed. This is a first attempt to increase the understanding of similarities and differences regarding decision-making processes in the IGT and rGT from an explorative perspective.