The Common Time Course of Memory Processes Revealed

Anderson, John R.; Borst, Jelmer P.; Fincham, Jon M.; Ghuman, Avniel Singh; Tenison, Caitlin; Zhang, Qiong

doi:10.1177/0956797618774526

Cited by 21 publications

(27 citation statements)

References 29 publications

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“…In combination with the interpretation of the other stages, and given that it is longer for more difficult associations (fan-2 pairs) and foils, this stage was interpreted as a memory retrieval stage. This was later corroborated by an HsMM-MVPA analysis of a MEG dataset of the same task, which showed hippocampal and prefrontal activation during this stage (Anderson et al 2018;Borst et al 2016).…”

Section: Resultsmentioning

confidence: 58%

“…For targets and re-paired foils, HsMMs with 1-8 bumps were estimated. LOOCV showed a log-likelihood increase up to 6 bumps, but it was argued in Anderson et al (2016) that the 5-bump accounted better for the data (see also Anderson et al 2018 for a similar analysis and conclusion based on MEG data). We will therefore use a 5-bump HsMM for the current analysis; this model is shown in Fig.…”

Section: Resultsmentioning

confidence: 88%

“…Recently, Anderson and colleagues developed a powerful method that can discover processing stages in M/EEG data: HsMM-MVPA analysis (Anderson et al 2016). By combining hidden semi-Markov models (HsMMs) with multivariatepattern analysis (MPVA), this machine-learning method has been successful in detecting latent sequences of processing stages in tasks ranging from associative recognition (Anderson et al 2016;Portoles et al 2018;Zhang et al 2017) to working memory (Zhang et al 2018) and mathematical problem-solving (Anderson et al 2018;Zhang et al 2017).…”

Section: Introductionmentioning

confidence: 99%

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The Discovery and Interpretation of Evidence Accumulation Stages

2021

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To improve the understanding of cognitive processing stages, we combined two prominent traditions in cognitive science: evidence accumulation models and stage discovery methods. While evidence accumulation models have been applied to a wide variety of tasks, they are limited to tasks in which decision-making effects can be attributed to a single processing stage. Here, we propose a new method that first uses machine learning to discover processing stages in EEG data and then applies evidence accumulation models to characterize the duration effects in the identified stages. To evaluate this method, we applied it to a previously published associative recognition task (Application 1) and a previously published random dot motion task with a speed-accuracy trade-off manipulation (Application 2). In both applications, the evidence accumulation models accounted better for the data when we first applied the stage-discovery method, and the resulting parameter estimates where generally in line with psychological theories. In addition, in Application 1 the results shed new light on target-foil effects in associative recognition, while in Application 2 the stage discovery method identified an additional stage in the accuracy-focused condition — challenging standard evidence accumulation accounts. We conclude that the new framework provides a powerful new tool to investigate processing stages.

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Section: Resultsmentioning

confidence: 58%

Section: Resultsmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

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The Discovery and Interpretation of Evidence Accumulation Stages

2021

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“…For example, when it was applied to EEG data from the classic Sternberg task, five stages were identified: 1) pre-attention, 2) encoding, 3) memory retrieval, 4) decision, and 5) response (Anderson, Zhang, Borst, & Walsh, 2016). The HsMM-MVPA method has been proved to be a versatile method for detecting processing stages in a range of tasks (Anderson et al, 2018;Borst & Anderson, 2015;Portoles, Borst, & van Vugt, 2018;Walsh, Gunzelmann, & Anderson, 2017;Zhang, Borst, Kass, & Anderson, 2017;Zhang, van Vugt, Borst, & Anderson, 2018;Zhang, Walsh, & Anderson, 2017. At the same time, the interpretation of discovered stages remains a challenge.…”

Section: Introductionmentioning

confidence: 99%

EEG-based Identification of Evidence Accumulation Stages in Decision-Making

Berberyan

Maanen

Rijn

et al. 2021

Journal of Cognitive Neuroscience

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Dating back to the 19th century, the discovery of processing stages has been of great interest to researchers in cognitive science. The goal of this paper is to demonstrate the validity of a recently developed method, hidden semi-Markov model multivariate pattern analysis (HsMM-MVPA), for discovering stages directly from EEG data, in contrast to classical reaction-time-based methods. To test the validity of stages discovered with the HsMM-MVPA method, we applied it to two relatively simple tasks where the interpretation of processing stages is straightforward. In these visual discrimination EEG data experiments, perceptual processing and decision difficulty were manipulated. The HsMM-MVPA revealed that participants progressed through five cognitive processing stages while performing these tasks. The brain activation of one of those stages was dependent on perceptual processing, whereas the brain activation and the duration of two other stages were dependent on decision difficulty. In addition, evidence accumulation models (EAMs) were used to assess to what extent the results of HsMM-MVPA are comparable to standard reaction-time-based methods. Consistent with the HsMM-MVPA results, EAMs showed that nondecision time varied with perceptual difficulty and drift rate varied with decision difficulty. Moreover, nondecision and decision time of the EAMs correlated highly with the first two and last three stages of the HsMM-MVPA, respectively, indicating that the HsMM-MVPA gives a more detailed description of stages discovered with this more classical method. The results demonstrate that cognitive stages can be robustly inferred with the HsMM-MVPA.

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“…The onset of each stage is defined as a multivariate pattern across the EEG electrodes (when averaged, parts of these patterns form traditional ERPs). This HsMM-MVPA method has been successfully applied to a range of cognitive tasks including associative memory recognition, arithmetic retrieval, and classical short-term memory tasks (Anderson et al, 2018(Anderson et al, , 2016Portoles, Borst, & van Vugt, 2018;van Maanen, Portoles, & Borst, in press;Walsh et al, 2017;Zhang, Borst, Kass, & Anderson, 2017;Zhang, van Vugt, Borst, & Anderson, 2018;Zhang, Walsh, et al, 2018). In a recent validation study, it was found that HsMM-MVPA allows for zooming into processes identified with RT-based methods by finding a range of corresponding cognitive stages (Berberyan, van Maanen, van Rijn, & Borst, 2021).…”

Section: 1lexical Decision Taskmentioning

confidence: 99%

Discovering the Brain Stages of Lexical Decision: Behavioral Effects Originate from a Single Neural Decision Process

Berberyan¹,

Rijn²,

Borst³

2021

Preprint

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Lexical decision (LD) – judging whether a sequence of letters constitutes a word – has been widely investigated. In a typical lexical decision task (LDT), participants are asked to respond whether a sequence of letters is an actual word or a nonword. Although behavioral differences between types of words/nonwords have been robustly detected in LDT, there is an ongoing discussion about the exact cognitive processes that underlie the word identification process in this task. To obtain data-driven evidence on the underlying processes, we recorded electroencephalographic (EEG) data and applied a novel machine-learning method, hidden semi-Markov model multivariate pattern analysis (HsMM-MVPA). In the current study, participants performed an LDT in which we varied the frequency of words (high, low frequency) and “wordlikeness” of non-words (pseudowords, random non-words). The results revealed that models with six processing stages accounted best for the data in all conditions. While most stages were shared, Stage 5 differed between conditions. Together, these results indicate that the differences in word frequency and lexicality effects are driven by a single cognitive processing stage. Based on its latency and topology, we interpret this stage as a Decision process during which participants discriminate between words and nonwords using activated lexical information.

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The Common Time Course of Memory Processes Revealed

Cited by 21 publications

References 29 publications

The Discovery and Interpretation of Evidence Accumulation Stages

The Discovery and Interpretation of Evidence Accumulation Stages

EEG-based Identification of Evidence Accumulation Stages in Decision-Making

Discovering the Brain Stages of Lexical Decision: Behavioral Effects Originate from a Single Neural Decision Process

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