Challenges and promises for translating computational tools into clinical practice

Ahn, Woo-Young; Busemeyer, Jerome R.

doi:10.1016/j.cobeha.2016.02.001

Cited by 45 publications

(49 citation statements)

References 71 publications

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“…There is a growing interest among researchers to develop and apply computational (i.e., cognitive) models to classical assessment tools to help guide clinical decision‐making (e.g., Ahn & Busemeyer, ; Batchelder, ; McFall & Townsend, ; Neufeld, Vollick, Carter, Boksman, & Jetté, ; Ratcliff, Spieler, & Mckoon, ; Treat, McFall, Viken, & Kruschke, ; Wallsten, Pleskac, & Lejuez, ). Despite this interest, clinical assessment has yet to be influenced by the many computational assays available today (see Ahn & Busemeyer, ). There are many potential reasons for this, but two important factors are the lack of (a) precise characterizations of neurocognitive processes and (b) optimal, externally valid paradigms for assessing psychiatric conditions.…”

Section: Introductionmentioning

confidence: 99%

The Outcome‐Representation Learning Model: A Novel Reinforcement Learning Model of the Iowa Gambling Task

2018

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The Iowa Gambling Task (IGT) is widely used to study decision making within healthy and psychiatric populations. However, the complexity of the IGT makes it difficult to attribute variation in performance to specific cognitive processes. Several cognitive models have been proposed for the IGT in an effort to address this problem, but currently no single model shows optimal performance for both short- and long-term prediction accuracy and parameter recovery. Here, we propose the Outcome-Representation Learning (ORL) model, a novel model that provides the best compromise between competing models. We test the performance of the ORL model on 393 subjects’ data collected across multiple research sites, and we show that the ORL reveals distinct patterns of decision making in substance using populations. Our work highlights the importance of using multiple model comparison metrics to make valid inference with cognitive models and sheds light on learning mechanisms that play a role in underweighting of rare events.

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Section: Introductionmentioning

confidence: 99%

The Outcome‐Representation Learning Model: A Novel Reinforcement Learning Model of the Iowa Gambling Task

2018

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“…These studies suggest that, temporal information emerges from neural properties that are naturally time-varying, such as short-term synaptic plasticity24. Our results show, for the first time to our knowledge, how these dynamics can be captured by non-invasive electrophysiological recordings, addressing an important lack of evidence in favour of this population dynamics hypothesis in humans.…”

Section: Discussionmentioning

confidence: 67%

“…For example, the recovered states could be related to the accumulator process: as pulses are accumulated, they generate activity that is reflected across electrodes. However, it is important to mention that, in a broad view, state-dependent models can be extended to be consistent with the majority of timing models26, with the different models imposing specific constraints on what would define the state space. Thus, although our findings open an important and missing first step in how to investigate state dependent models in human EEG, they do not, at the moment, present conclusive evidence in favour of such view.…”

Section: Discussionmentioning

confidence: 99%

Dynamic representation of time in brain states

Bueno

Morita

Camargo

et al. 2017

Sci Rep

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The ability to process time on the scale of milliseconds and seconds is essential for behaviour. A growing number of studies have started to focus on brain dynamics as a mechanism for temporal encoding. Although there is growing evidence in favour of this view from computational and in vitro studies, there is still a lack of results from experiments in humans. We show that high-dimensional brain states revealed by multivariate pattern analysis of human EEG are correlated to temporal judgements. First, we show that, as participants estimate temporal intervals, the spatiotemporal dynamics of their brain activity are consistent across trials. Second, we present evidence that these dynamics exhibit properties of temporal perception, such as scale invariance. Lastly, we show that it is possible to predict temporal judgements based on brain states. These results show how scalp recordings can reveal the spatiotemporal dynamics of human brain activity related to temporal processing.

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“…The benefits of ADO also come with costs. For example, trials that are most informative can be ones that are also difficult for the participant (Ahn & Busemeyer, 2016). Repeated presentation of difficult trials can frustrate and fatigue participants.…”

Section: Discussionmentioning

confidence: 99%

Rapid, precise, and reliable measurement of delay discounting using a Bayesian learning algorithm

Ahn

Shen

et al. 2019

Preprint

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Number of words in the abstract: 135Number of words in the main text (including references): 7,600 AbstractMachine learning has the potential to facilitate the development of computational methods that improve the measurement of cognitive and mental functioning. In three populations (college students, patients with a substance use disorder, and Amazon Mechanical Turk workers), we evaluated one such method, Bayesian adaptive design optimization (ADO), in the area of delay discounting by comparing its test-retest reliability, precision, and efficiency with that of a conventional staircase method. In all three populations tested, the results showed that ADO led to 0.95 or higher test-retest reliability of the discounting rate within 10-20 trials (under 1-2 minutes of testing), captured approximately 10% more variance in test-retest reliability, was 3-5 times more precise, and was 3-8 times more efficient than the staircase method. The ADO methodology provides efficient and precise protocols for measuring individual differences in delay discounting.

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Challenges and promises for translating computational tools into clinical practice

Cited by 45 publications

References 71 publications

The Outcome‐Representation Learning Model: A Novel Reinforcement Learning Model of the Iowa Gambling Task

The Outcome‐Representation Learning Model: A Novel Reinforcement Learning Model of the Iowa Gambling Task

Dynamic representation of time in brain states

Rapid, precise, and reliable measurement of delay discounting using a Bayesian learning algorithm

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