The discovery of processing stages: Extension of Sternberg’s method.

Abstract

We introduce a method for measuring the number and durations of processing stages from the electroencephalographic (EEG) signal and apply it to the study of associative recognition. Using an extension of past research that combines multivariate pattern analysis (MVPA) with hidden semi-Markov models (HSMMs), the approach identifies on a trial-by-trial basis where brief sinusoidal peaks (called bumps) are added to the ongoing EEG signal. We propose that these bumps mark the onset of critical cognitive stages in processing. The results of the analysis can be used to guide the development of detailed process models. Applied to the associative recognition task, the HSMM-MVPA method indicates that the effects of associative strength and probe type are localized to a memory retrieval stage and a decision stage. This is in line with a previously developed ACT-R process model of the task. As a test of the generalization of our method we also apply it to a data set on the Sternberg working memory task collected by Jacobs et al. (2006). The analysis generalizes robustly, and localizes the typical set size effect in a late comparison/decision stage. In addition to providing information about the number and durations of stages in associative recognition, our analysis sheds light on the ERP components implicated in the study of recognition memory.

The Impact of Inserting an Additional Mental Process

“…Originally developed as a method to uncover the sequence of information processing stages and their durations in a task (Anderson et al 2016), we propose in the current paper that HSMM-MVPA can also be applied as a novel way to test the assumption of pure insertion. Once EEG signal is decomposed into a sequence of latent stages interleaved with distinct bumps, we can estimate the number, timings, and topographical distributions of each bump and durations of each stage.…”

“…The estimation process has to consider all possible combinations of bump locations and this is what is efficiently calculated by the dynamic programming associated with hidden semi-Markov models (Yu 2010). We follow closely the model selection and estimation procedure described in Anderson et al (2016). In the current study, there are additional constraints in bump locations in order to fit an HSMM-MVPA to four conditions simultaneously.…”

“…The HSMM-MVPA method identifies brief, distinctive profiles of scalp activity (i.e., bumps) at variable latencies in each trial (Anderson et al 2016). A bump is modeled as a half-sine multidimensional peak across the scalp that signifies a significant change in information processing, followed by a flat period where the mean of the task-related, ongoing sinusoidal noise is assumed to be 0.…”

“…recovering the durations of the underlying processing stages (e.g., recollection, decision) and showed predictable changes with experimental factors (Anderson et al 2016;Walsh et al 2017;Zhang et al 2017Zhang et al , 2018. The HSMM-MVPA method identifies brief, distinctive profiles of scalp activity (i.e., bumps) at variable latencies in each trial (Anderson et al 2016).…”

“…To model the trial-to-trial variability of ERPs, we have developed an approach (referred as HSMM-MVPA, combining hidden semi-Markov models with multivariate pattern analysis; Anderson et al 2016) that allows us to identify ERP-like components as distinctive profiles of scalp activity (i.e., bumps) at variable latencies in each trial. We can estimate the number of bumps using a model selection procedure and identify the durations of the stages bounded by these bumps.…”