Implicit Temporal Priming in Delayed-Response Decision Tasks

Abstract

We examined whether implicit temporal cues influence decision latencies in delayed-response tasks. Participants (N=32) responded to target stimuli following variable inter-stimulus intervals. Results show that response times decrease by ~150 ms when temporal structure is implicitly predictable, suggesting automatic temporal expectancy mechanisms in decision-making.

Introduction

The temporal structure of stimulus sequences has long been recognized as a factor in perceptual and cognitive performance. While early research focused on explicit timing tasks, recent investigations hint at implicit mechanisms by which observers anticipate stimulus timing and adjust response readiness accordingly. The present study extends this line of inquiry into decision-making contexts, where temporal predictability may modulate both response initiation and choice confidence.

Classical dual-process theories posit that decision systems integrate both deliberative and automatic signals. Temporal regularities in the environment could constitute one such automatic signal, operating outside conscious awareness. Understanding how implicit temporal cues influence decision latencies may illuminate the automaticity of human judgment and inform models of real-world decision-making in temporally structured domains such as driving, athletic performance, and linguistic processing.

Method

Participants

Thirty-two undergraduate students (18 female, 14 male; mean age = 20.3 years) from the local university participated in exchange for course credit. All participants reported normal or corrected-to-normal vision and had no history of neurological disorder.

Procedure

Participants completed a computerized delayed-response decision task implemented in PsyScope running on a Macintosh Quadra. On each trial, a warning tone preceded a visual target (a randomly chosen colored square) by either 0.5, 1.0, 1.5, or 2.0 seconds. Half of the trials followed a predictable sequence (alternating 0.5–1.5 s intervals), while the remaining trials were random. Participants pressed one of two buttons corresponding to the target color as quickly and accurately as possible.

Each session consisted of 200 trials divided into two blocks of 100. Before each block, participants were shown a visual schedule indicating which inter-stimulus intervals would occur (explicit condition) or were simply instructed to respond as quickly as possible (implicit condition). The order of explicit and implicit blocks was counterbalanced across participants. Reaction time and accuracy were recorded for each response.

Results

Mean reaction times and error rates were submitted to a 2 (block type: explicit, implicit) × 4 (inter-stimulus interval: 0.5, 1.0, 1.5, 2.0 s) repeated-measures ANOVA. A significant main effect of inter-stimulus interval was observed, F(3, 93) = 8.41, p < .001, with faster responses following predictable intervals (M = 425 ms, SD = 87) compared to random intervals (M = 575 ms, SD = 102). Block type did not interact with interval predictability, F(3, 93) = 1.15, p = .33, suggesting that implicit temporal structure influenced decisions even when participants were unaware of the pattern.

Post-hoc paired t-tests (α = .05) revealed that reaction times decreased significantly from random to predictable conditions in both explicit, t(31) = 3.89, p < .001, and implicit blocks, t(31) = 3.67, p < .001. Accuracy was high across all conditions (mean = 96.2%, SD = 3.4%) and did not differ by interval type or block, F(1, 31) = 0.78, p = .38.

Discussion

These findings extend prior work on temporal expectancy to the domain of decision-making. The implicit condition is particularly noteworthy: participants showed reduced response latencies under temporal predictability despite explicit instructions to treat all trials identically. This dissociation suggests that temporal regularities are processed automatically and influence decision readiness without conscious intention.

The mechanisms underlying implicit temporal priming remain unclear. Possible accounts include automatic timing circuits in the cerebellum or dorsal prefrontal cortex, or more general associative learning mechanisms by which the nervous system tracks environmental regularities. Future work employing neuroimaging (e.g., fMRI or EEG) may clarify the neural substrates, and investigations manipulating task relevance and conscious attention could further delineate implicit from explicit temporal processes.

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