Internal-Clock Models and Misguided Views of Mechanistic Explanations: A Reply to Eckard & Lattal (2020)
Talk about timing in probability language, not gear-language, so your claims stay testable.
01Research in Context
What this study did
Sanabria (2020) wrote a reply to Eckard & Lattal. They said internal-clock models are bad science. Sanabria said no, they still help us understand timing data.
The paper is pure theory. No kids, no rats, no trials. It is a map for how to talk about timing without sounding like old-school mechanists.
What they found
The big idea: stop saying clocks 'cause' behavior like gears. Say clocks 'change the odds' of a response. This keeps the model testable and humble.
Sanabria showed that stochastic talk keeps the internal-clock story alive and useful for future experiments.
How this fits with other research
Craig (2023) did the same rescue job for behavioral momentum theory. Both papers admit the data hurt, but keep the lens because it guides next steps.
Cook et al. (2020) and Wilder et al. (2023) also push richer metrics over single numbers. Sanabria adds richer language to the same fight.
King et al. (2025) warn that sloppy time labels swing meta-analyses. Sanabria’s tighter causal talk helps avoid that trap.
Why it matters
When you write or speak about timing data, swap 'the clock made the bird peck' for 'the clock raised the probability of pecking.' This small word shift keeps your claims falsifiable and aligns with modern measurement papers. It also trains your team to think in probabilities, not gears, which is how we actually graph data.
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02At a glance
03Original abstract
Eckard and Lattal’s Perspectives on Behavior Science, 43(1), 5–19 (2020) critique of internal clock (IC) mechanisms is based on narrow concepts of clocks, of their internality, of their mechanistic nature, and of scientific explanations in general. This reply broadens these concepts to characterize all timekeeping objects—physical and otherwise—as clocks, all intrinsic properties of such objects as internal to them, and all simulatable explanations of such properties as mechanisms. Eckard and Lattal’s critique reflects a restrictive billiard-ball view of causation, in which environmental manipulations and behavioral effects are connected by a single chain of contiguous events. In contrast, this reply offers a more inclusive stochastic view of causation, in which environmental manipulations are probabilistically connected to behavioral effects. From either view of causation, computational ICs are hypothetical and unobservable, but their heuristic value and parsimony can only be appreciated from a stochastic view of causation. Billiard-ball and stochastic views have contrasting implications for potential explanations of interval timing. As illustrated by accounts of the variability in start times in fixed-interval schedules of reinforcement, of the two views of causality examined, only the stochastic account supports falsifiable predictions beyond simple replications. It is thus not surprising that the experimental analysis of behavior has progressively adopted a stochastic view of causation, and that it has reaped its benefits. This reply invites experimental behavior analysts to continue on that trajectory.
Perspectives on Behavior Science, 2020 · doi:10.1007/s40614-020-00268-6