The Effects of Probe Similarity on Retrieval and Comparison Processes in Associative Recognition

Zhang, Qiong; Walsh, Matthew M.; Anderson, John R.

doi:10.1162/jocn_a_01059

Cited by 24 publications

(31 citation statements)

References 42 publications

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“…Based on a different EEG experiment, Zhang et al . () hypothesized that in this stage the words that are on the screen are compared to a pair (or triplet in this experiment) retrieved from memory, word by word. Additionally, MEG data have shown that word pairs were retrieved from temporal regions and subsequently represented pre‐frontally, while the comparison itself was associated with activation in the parietal cortex (Borst et al ., ).…”

Section: Discussionmentioning

confidence: 99%

Characterizing synchrony patterns across cognitive task stages of associative recognition memory

Portoles

Borst

Vugt

2018

Eur J of Neuroscience

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Numerous studies seek to understand the role of oscillatory synchronization in cognition. This problem is particularly challenging in the context of complex cognitive behavior, which consists of a sequence of processing steps with uncertain duration. In this study, we analyzed oscillatory connectivity measures in time windows that previous computational models had associated with a specific sequence of processing steps in an associative memory recognition task (visual encoding, familiarity, memory retrieval, decision making, and motor response). The timing of these processing steps was estimated on a single‐trial basis with a novel hidden semi‐Markov model multivariate pattern analysis (HSMM‐MVPA) method. We show that different processing stages are associated with specific patterns of oscillatory connectivity. Visual encoding is characterized by a dense network connecting frontal, posterior, and temporal areas as well as frontal and occipital phase locking in the 4–9 Hz theta band. Familiarity is associated with frontal phase locking in the 9–14 Hz alpha band. Decision making is associated with frontal and temporo‐central interhemispheric connections in the alpha band. During decision making, a second network in the theta band that connects left‐temporal, central, and occipital areas bears similarity to the neural signature for preparing a motor response. A similar theta band network is also present during the motor response, with additionally alpha band connectivity between right‐temporal and posterior areas. This demonstrates that the processing stages discovered with the HSMM‐MVPA method are indeed linked to distinct synchronization patterns, leading to a closer understanding of the functional role of oscillations in cognition.

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Section: Discussionmentioning

confidence: 99%

Characterizing synchrony patterns across cognitive task stages of associative recognition memory

Portoles

Borst

Vugt

2018

Eur J of Neuroscience

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“…The last two stages correspond to the Mapping stage and the Response stage described in the process model, as one needs to select and execute a motor response at the end of each trial. The Preattention stage, the Encoding stage, and the Response stage were also identified in previous studies using an associative recognition task (Anderson et al 2016;Zhang et al 2017). The longer-duration Mapping stage, during which subjects prepare motor response by mapping two digits to the corresponding key presses, was not required in our earlier tasks where participants simply made a yes/no response.…”

Section: Identifying the Stage Durations And The Bump Profiles In Hsmmentioning

confidence: 78%

“…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).…”

Section: Hsmm-mvpa Applied To Eegmentioning

confidence: 99%

The Impact of Inserting an Additional Mental Process

2018

Self Cite

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Pure insertion describes a scenario where a mental process is inserted within a sequence of other processes without altering the other processes. Under the assumption of pure insertion, the duration of the inserted process can be identified by calculating the difference in overall response times when the process is present versus absent (i.e., Donder's subtraction method). Additionally, under the assumption of pure insertion, brain regions associated with the inserted process can be identified in fMRI studies by contrasting activation when the process is present versus absent. However, the assumption of pure insertion does not hold in many situations. In this study, we adopted a novel approach for identifying the impact of insertion by decomposing the EEG signal into a sequence of latent stages, each with a distinct topographical distribution and duration. Based on these latent stages, it is possible to identify when, and for how long, a process occurred. We crossed two factors in the experiment: whether the trial required substituting a letter with a number from memory and whether the trial required calculating the product of two numbers. By crossing these factors, we could examine whether inserting substitution and calculation processes affected the durations of other mental processing stages. Behavioral data in the form of response latencies, and averaged EEG signal in the form of eventrelated potentials (ERPs), provided no evidence of violations of pure insertion. However, our analysis of single-trial EEG signal allowed us both to show that inserting substitution or calculation did affect other stages and to understand why.

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“…There is also a recent line of research focused on constructing fully analytic ACT-R models (Said et al, 2016), which could allow more efficient parameter inference using, for example, gradient-based methods. Some recent ACT-R studies have also used neuroimaging data to isolate active mental stages during task completion and used these measurements to infer specific model parameters (Anderson & Fincham, 2014a;Zhang, Walsh, & Anderson, 2016).…”

Section: Traditional Parameter Inferencementioning

confidence: 99%

Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation

et al. 2019

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This paper addresses a common challenge with computational cognitive models: identifying parameter values that are both theoretically plausible and generate predictions that match well with empirical data. While computational models can offer deep explanations of cognition, they are computationally complex and often out of reach of traditional parameter fitting methods. Weak methodology may lead to premature rejection of valid models or to acceptance of models that might otherwise be falsified. Mathematically robust fitting methods are, therefore, essential to the progress of computational modeling in cognitive science. In this article, we investigate the capability and role of modern fitting methods—including Bayesian optimization and approximate Bayesian computation—and contrast them to some more commonly used methods: grid search and Nelder–Mead optimization. Our investigation consists of a reanalysis of the fitting of two previous computational models: an Adaptive Control of Thought—Rational model of skill acquisition and a computational rationality model of visual search. The results contrast the efficiency and informativeness of the methods. A key advantage of the Bayesian methods is the ability to estimate the uncertainty of fitted parameter values. We conclude that approximate Bayesian computation is (a) efficient, (b) informative, and (c) offers a path to reproducible results.

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The Effects of Probe Similarity on Retrieval and Comparison Processes in Associative Recognition

Cited by 24 publications

References 42 publications

Characterizing synchrony patterns across cognitive task stages of associative recognition memory

Characterizing synchrony patterns across cognitive task stages of associative recognition memory

The Impact of Inserting an Additional Mental Process

Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation

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