Modeling the Effect of Speed Emphasis in Probabilistic Category Learning

Sewell, David K.; Stallman, Alexander

doi:10.1007/s42113-019-00067-6

Cited by 13 publications

(18 citation statements)

References 63 publications

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“…Empirical work draws a more complex picture. Several papers suggest that in addition to thresholds, drift rates (Arnold et al, 2015; Heathcote and Love, 2012; Ho et al, 2012; Rae et al, 2014; Sewell and Stallman, 2020) and sometimes even non-decision times (Arnold et al, 2015; Voss et al, 2004) can be affected. Increases in drift rates in a race model could indicate an urgency signal, implemented by drift gain modulation, with qualitatively similar effects to collapsing thresholds over the course of a decision (Cisek et al, 2009; Hawkins et al, 2015; Miletić, 2016; Miletić and Van Maanen, 2019; Murphy et al, 2016; Thura and Cisek, 2016; Trueblood et al, 2020; van Maanen et al, 2019).…”

Section: Resultsmentioning

confidence: 99%

“…Next, we compared the RL-DDM and RL-ARD, and in light of the multiple psychological mechanisms potentially affected by the SAT manipulation, we allowed different combinations of threshold, drift rate, and for the RL-ARD urgency, to vary with the SAT manipulation. We fit three RL-DDM models, varying either threshold, the Q-value weighting on the drift rates parameter (Sewell and Stallman, 2020), or both. For the RL-ARD, we fit all seven possible models with different combinations of the threshold, urgency, and drift rate parameters free to vary between SAT conditions.…”

Section: Resultsmentioning

confidence: 99%

“…Next, we fit three RL-DDMs and seven RL-ARDs. The three RL-DDM models varied either threshold, the Q-value weighting on the drift rates parameter (Sewell and Stallman, 2020), or both. The seven RL-ARD allowed all unique combinations of the threshold, urgency, and drift rate parameters free to vary between the speed and accuracy conditions.…”

Section: Methodsmentioning

confidence: 99%

“…Recent advances (Fontanesi et al, 2019a, 2019b; Luzardo et al, 2017; McDougle and Collins, 2020; Miletić et al, 2020; Millner et al, 2018; Pedersen et al, 2017; Pedersen and Frank, 2020; Sewell et al, 2019; Sewell and Stallman, 2020; Shahar et al, 2019; Turner, 2019) have emphasized how both modelling traditions can be combined in joint models of reinforcement learning (RL) and evidence-accumulation decision-making processes, providing mutual benefits for both fields. Combined models generally propose that value-based decisionmaking and learning interact as follows: For each decision a subject gradually accumulates evidence for each choice option by sampling from a distribution of memory representations of the subjective value (or expected reward ) associated with each choice option (known as Q-values ).…”

mentioning

confidence: 99%

“…The DDM is the dominant EAM as currently used in reinforcement learning(Fontanesi et al, 2019a, 2019b; Millner et al, 2018; Pedersen et al, 2017; Pedersen and Frank, 2020; Sewell et al, 2019; Sewell and Stallman, 2020; Shahar et al, 2019), but this choice is without experimental justification. Furthermore, the DDM has several theoretical drawbacks, such as its inability to explain multi-alternative decision-making and its strong commitment to the accumulation of the evidence difference , which leads to difficulties in explaining behavioral effects of absolute stimulus and reward magnitudes without additional mechanisms (Fontanesi et al, 2019a; Ratcliff et al, 2018; Teodorescu et al, 2016).…”

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confidence: 99%

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A new model of decision processing in instrumental learning tasks

Miletić

Boag

Trutti

et al. 2020

Preprint

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Learning and decision making are interactive processes, yet cognitive modelling of error-driven learning and decision making have largely evolved separately. Recently, evidence accumulation models (EAMs) of decision making and reinforcement learning (RL) models of error-driven learning have been combined into joint RL-EAMs that can in principle address these interactions. However, we show that the most commonly used combination, based on the diffusion decision model (DDM) for binary choice, consistently fails to capture crucial aspects of response times observed during reinforcement learning. We propose a new RL-EAM based on an advantage racing diffusion (ARD) framework for choices among two or more options that not only addresses this problem but captures stimulus difficulty, speed-accuracy trade-off, and stimulus-response-mapping reversal effects. The RL-ARD avoids fundamental limitations imposed by the DDM on addressing effects of absolute values of choices, as well as extensions beyond binary choice, and provides a computationally tractable basis for wider applications.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A new model of decision processing in instrumental learning tasks

Miletić

Boag

Trutti

et al. 2020

Preprint

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Reinforcement learning with associative or discriminative generalization across states and actions: fMRI at 3 T and 7 T

Colas

Dundon

Gerraty

et al. 2022

Human Brain Mapping

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The model-free algorithms of "reinforcement learning" (RL) have gained clout across disciplines, but so too have model-based alternatives. The present study emphasizes other dimensions of this model space in consideration of associative or discriminative generalization across states and actions. This "generalized reinforcement learning" (GRL) model, a frugal extension of RL, parsimoniously retains the single rewardprediction error (RPE), but the scope of learning goes beyond the experienced state and action. Instead, the generalized RPE is efficiently relayed for bidirectional counterfactual updating of value estimates for other representations. Aided by structural information but as an implicit rather than explicit cognitive map, GRL provided the most precise account of human behavior and individual differences in a reversallearning task with hierarchical structure that encouraged inverse generalization across both states and actions. Reflecting inference that could be true, false (i.e., overgeneralization), or absent (i.e., undergeneralization), state generalization distinguished those who learned well more so than action generalization. With highresolution high-field fMRI targeting the dopaminergic midbrain, the GRL model's RPE signals (alongside value and decision signals) were localized within not only the striatum but also the substantia nigra and the ventral tegmental area, including specific effects of generalization that also extend to the hippocampus. Factoring in generalization as a multidimensional process in value-based learning, these findings shed light on complexities that, while challenging classic RL, can still be resolved within the bounds of its core computations.

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Exemplifying “Us”: Integrating social identity theory of leadership with cognitive models of categorization

Sewell

Ballard

Steffens

2022

The Leadership Quarterly

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Identity leadership theorizing suggests that leadership effectiveness derives from a potential leader's perceived ability to create, embody, promote, and embed a shared group identity. However, little is known about how people integrate this information to form a judgment of a leader. We use cognitive modeling to operationalize leadership judgments as exemplar-and prototype-based categorization processes. Analysis of attribute rating data for 80 highly recognizable Americans revealed that leadership judgments were well-characterized by an exemplar-based model. Judgments were based overwhelmingly on promoting shared collective interests and embedding group identity. The pattern of attribute weightings was consistent for judgments of a general leadership role (i.e., as a competent leader) as well as judgments for a specific leadership role (i.e., as an effective US president). We discuss the implications of these findings for our understanding of identity leadership as well as for integrated social-cognitive models of individuals' judgements of and responses to leaders.

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Modeling the Effect of Speed Emphasis in Probabilistic Category Learning

Cited by 13 publications

References 63 publications

A new model of decision processing in instrumental learning tasks

A new model of decision processing in instrumental learning tasks

Reinforcement learning with associative or discriminative generalization across states and actions: fMRI at 3 T and 7 T

Exemplifying “Us”: Integrating social identity theory of leadership with cognitive models of categorization

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