Risk-sensitive foraging theory and operant psychology.

Houston (1991) wrote a theory paper. The topic was risk-sensitive foraging.

The paper asked: how does survival-time horizon change the model’s guess about what animals will do?

No new data were collected. The goal was to tell future researchers exactly what to measure.

The old foraging models forgot one thing: how long the animal must stay alive.

When survival time is short, a risky choice that might pay off soon looks smart.

When survival time is long, safe but steady payoffs look smarter.

To test this in an operant lab, you must first pick and state the survival-time horizon you are using.

Lalli et al. (1995) showed that food density, not clock time, drives VI response curves. Houston (1991) adds that the animal’s “time left to live” also matters. The two papers fit: density sets the local payoff, while survival horizon sets the long game.

Nasr et al. (2000) argued that clients resist therapy when short-term relief fights long-term gain. The same time-horizon clash Houston (1991) uses for pigeons shows up in people. Both papers say: state the horizon or your prediction will miss.

Newland (2024) fixes how we calculate risk ratios. Houston (1991) fixes how we define the time window that those ratios sit inside. Use both: pick the horizon first, then compute the ratio correctly.

Next time you run a token economy or a gambling-choice task, write down the survival-time horizon you want the client to use. Is the session ending in five minutes? Will they return tomorrow? State it aloud or in the protocol. Then arrange reinforcers so the safe versus risky choice matches that horizon. You will see cleaner data and fewer surprises.