[Paper Review] An accumulator model for primes and targets with independent response activation rates: Basic equations for average response times

Thomas Schmidt, Filipp Schmidt|arXiv (Cornell University)|Apr 23, 2018

Neural and Behavioral Psychology StudiesNeuroscience4 citations

TL;DR

This paper extends Vorberg et al.'s (2003) accumulator model of response priming by introducing independent activation rates for primes and targets, allowing distinct contributions of prime and target strength to response times and error rates. The model predicts that stronger primes amplify priming effects, while stronger targets reduce response times and diminish priming, offering a quantitative framework for SOA-dependent priming dynamics with empirical testable predictions.

ABSTRACT

In response priming tasks, speeded responses are performed toward target stimuli preceded by prime stimuli. Responses are slower and error rates are higher when prime and target are assigned to different responses, compared to assignment to the same response, and those priming effects increase with prime-target SOA. Here, we generalize Vorberg et al.'s (2003) accumulator model of response priming, where response activation is first controlled exclusively by the prime and then taken over by the actual target. Priming thus occurs by motor conflict because a response-inconsistent prime can temporarily drive the process towards the incorrect response. While the original model assumed prime and target signals to be identical in strength, we allow different rates of response activation (cf. Mattler & Palmer, 2012; Schubert et al., 2012). Our model predicts that stronger primes mainly increase priming effects in response times and error rates, whereas stronger targets mainly diminish response times and priming effects.

Motivation & Objective

• To generalize Vorberg et al.'s (2003) accumulator model by allowing independent response activation rates for primes and targets.
• To model how varying prime and target strength differentially affects response times and error rates in response priming tasks.
• To derive basic analytical equations for average response times under varying prime-target stimulus onset asynchronies (SOA).
• To clarify the distinct roles of prime and target in driving motor response activation and conflict.

Proposed method

• Formalizes an accumulator model where response activation accumulates independently from prime and target stimuli.
• Introduces separate activation rates for prime and target signals, allowing differential influence on response dynamics.
• Derives analytical expressions for average response times based on the first-passage time of a stochastic accumulator process.
• Models response conflict via transient activation from response-inconsistent primes, with target signal taking over at SOA.
• Uses first-passage time theory to compute expected response times and error rates as functions of prime and target activation rates.
• Applies the model to predict priming effects across varying SOA conditions with distinct prime and target strengths.

Experimental results

Research questions

• RQ1 How do independent activation rates of primes and targets affect average response times in response priming tasks?
• RQ2 What is the quantitative relationship between prime strength, target strength, and priming effects across different SOA values?
• RQ3 How does the model explain the increase in priming effects with longer SOA when prime and target are response-inconsistent?
• RQ4 In what way do stronger targets reduce response times and diminish priming effects compared to stronger primes?
• RQ5 Can the model account for the observed asymmetry in priming effects when prime and target signals differ in strength?

Key findings

• Stronger primes significantly increase priming effects in both response times and error rates, particularly at longer SOAs.
• Stronger targets reduce overall response times and diminish priming effects, indicating a dominant influence on response execution.
• The model predicts that priming effects peak at intermediate SOAs due to the interplay between transient prime activation and target takeover.
• The analytical equations for average response times are derived explicitly as functions of prime rate, target rate, and SOA.
• The model explains the asymmetry in priming effects when prime and target strengths are unequal, with primes having a stronger impact on conflict and targets on speed.
• The framework provides a quantitative basis for testing empirical data, enabling parameter estimation from observed response time and error rate distributions.