Integrating principles · a line of research

Life is complex and multiply controlled.

Behavior is never under a single influence. At any moment, what an organism does is jointly controlled by many variables at once (e.g., the size of an outcome, how long until it arrives, how likely it is, what it costs, and what came just before). Behavior analysis has named dozens of principles that each shape behavior on their own. The harder, more honest question is how they combine.

A principle in isolation describes a slice

Studying a principle alone tells you what one variable does when everything else is held still. Real behavior is rarely so tidy. Consider a single choice: a smaller reward now, or a larger one later. Its value depends on the amount, the delay, the probability, the effort to obtain it, whether it is a gain or a loss, and the budget the organism is working within, all at the same time. Study any one of these alone and you describe a slice. Study how two of them interact and you begin to describe behavior.

The map

The combination landscape

To see the whole problem at once, it helps to lay it out. Below is every behavioral process paired with every other. There is, tellingly, no settled list of these principles to begin from. Miltenberger and Flores (2024) surveyed 144 behavior analysts and found consensus on only 16 operant and 5 respondent terms, which is close to the catalog-derived set used here.

A cell is shaded where the field has co-studied a pair (drawn from the 11,920-paper Behavioral Process Catalog), highlighted where our lab has taken one on, and left dark where no one has. Hover for a count; click any cell to read the studies, or to propose an open one with us.

studied by the field studied by our lab open (no one yet)

26 processes shown. Process list and lab mappings are working drafts.

Two things stand out. First, the field has worked densely in one corner (the classic operant processes and their pairwise combinations) and sparsely everywhere else. Second, of all the pairings on the board, only a handful have ever been co-studied. The combination space is almost entirely unexplored.

What our lab has taken on

Our combination studies start in the value of choice. We have crossed delay with probability, effort with probability, amount with delay, and gains with losses across both. We have asked how the economic context an organism sits in (its earnings budget) reshapes those functions, and how verbal stimuli move risky choice. Each study adds one cell, and each cell narrows the gap between what we can model in isolation and how behavior actually allocates.

2026

Effort × Probability ▸ hover for the graph

Combined effects of effort and probability on monetary discounting · Drugan-Eppich & Cox · read paper →

With N = 61, probability discounting steepened at higher effort and effort discounting steepened at lower probability. Effort and probability interact to set choice, and of nine candidate models the general theory of discounting fit best (AICc).

2023

Economic context × Delay, Probability, and Amount ▸ hover for the graph

Effects of economic context and reward amount on discounting · Anderson et al. · read paper →

With n = 213, participants discounted more when the bank amount was small relative to the outcome (a low economic context). Economic context also attenuated the magnitude effect in probability discounting.

2020

Gains vs losses × Delay and Probability ▸ hover for the graph

Multiplicative vs additive hyperbolic and hyperboloid discounting · Bialaszek et al. · read paper →

Across delayed lotteries spanning gains and losses, choice was best described by a three-parameter multiplicative model in which delay and probability are jointly (psychophysically) scaled, rather than added.

2018

Multiple outcomes × Delay × Probability × Gains vs Losses ▸ hover for the graph

Influence of second outcome on monetary discounting · Cox & Dallery · read paper →

Each choice involved two outcomes, each delayed and probabilistic and spanning gains and losses. The second outcome's probability mattered more than its delay, raising a future loss's probability shifted preference toward the larger-later option, and a multiplicative model best described the discounting of the two outcomes.

2018

Verbal behavior × Probability ▸ hover for the graph

Verbal behavior and risky choice in humans · Cox & Dallery · read paper →

Contacting gains in isolation reversed the description-experience gap (uncertain gains preferred when described). Intermixing gains with losses restored the typical gap, so verbal framing and outcome context jointly move risky choice.

2016

Delay × Probability ▸ hover for the graph

Effects of delay and probability combinations on discounting in humans · Cox & Dallery · read paper →

Across n = 212 and n = 98, delay and probability interact to set the value of money. A magnitude effect appeared for probabilistic gains and losses but not for delayed losses, and the 5-trial and adjusting-amount methods agreed in 6 of 7 comparisons.

Scaling with AI and machine learning

Cell by cell is slow, and the space is vast. So the second half of this line moves from studying combinations one at a time to modeling them at scale. We took the generalized matching law from an N of one to an N of a million (Cox et al., 2022), used natural language processing to read behavior from text, and integrated reinforcement-learning models with behavior science to predict the next response (Cox & Santos, 2025). The aim is to let computational and AI methods explore the combination space faster than hand-run experiments can, and to feed what they find back into the lab. This is where this line meets the artificial-organisms work.

The open frontier

The point of the map is the dark. Every open cell is a study no one has run, and an invitation. If one of them is yours, we would like to run it with you. Click an open cell above to propose it, or reach out directly.

Propose a study with us →