Frequency-Sensitivity and Magnitude-Sensitivity in Decision-Making: Predictions of a Theoretical Model-Based Study

Thomas Bose,Andreagiovanni Reina,James A R Marshall,Freya Bottom

doi:10.1007/s42113-019-00031-4

Thomas Bose, Andreagiovanni Reina + Show 2 more

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https://doi.org/10.1007/s42113-019-00031-4

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Journal: Computational brain & behavior	Publication Date: May 20, 2019
Citations: 3	License type: open-access

Affiliation: University of Sheffield

Abstract

We theoretically study decision-making behaviour in a model-based analysis related to binary choices with pulsed stimuli. Assuming a strong coupling between external stimulus and its internal representation, we argue that the frequency of external periodic stimuli represents an important degree of freedom in decision-making which may modulate behavioural responses. We consider various different stimulus conditions, including varying overall magnitudes and magnitude ratios as well as varying overall frequencies and frequency ratios, and different duty cycles of the pulsed stimuli. Decision time distributions, mean decision times and choice probabilities are simulated and compared for two different models—a leaky competing accumulator model and a diffusion-type model with multiplicative noise. Our results reveal an interplay between the sensitivity of the model systems to both frequency and magnitude of the stimuli. In particular, we find that periodic stimuli may shape the decision time distributions resulting from both models by resembling the frequencies of the pulsed stimuli. We obtain significant frequency-sensitive effects on mean decision time and choice probability for a range of overall frequencies and frequency ratios. Our simulation analysis makes testable predictions that frequencies comparable with typical sensory processing and decision-making timescales may influence choice and response times in perceptual decisions. A possible experimental implementation is proposed.

Highlights

When the brain makes decisions, it accumulates evidence to compute a decision variable that is evaluated against a decision criterion (Gold and Shadlen 2007)
It was shown that both models the leaky competing accumulator (LCA) and the mDDM could be used to fit the same set of magnitude-sensitive data (Teodorescu et al 2016)
Assuming a strong coupling between external stimulus input and internal stimulus representation, we have simulated and compared two decision-making models, LCA and mDDM, under periodically oscillating stimuli. Variants of both models are widely used to explain the computation of a decision variable in the brain which reflects choice behaviour in two alternative task settings (e.g. see Shadlen and Newsome (1996, 2001), Usher and McClelland (2001), Ditterich et al (2003), Bogacz et al (2006) and Teodorescu et al (2016) for perceptual decisions, Krajbich et al (2010, 2015), Basten et al (2010) and Hunt et al (2012) for value-based decisions, and Feng et al (2009) and Afacan-Seref et al (2018) where perceptual decisions are based on the integration of rewards associated with options presented)

Summary

Introduction

When the brain makes decisions, it accumulates evidence to compute a decision variable that is evaluated against a decision criterion (Gold and Shadlen 2007) This concept has been tested and verified in binary decision-making tasks in a variety of different settings: from perceptual decisionmaking (Shadlen and Newsome 1996, 2001; Usher and McClelland 2001; Ditterich et al 2003; Bogacz et al 2006; Pirrone et al 2018) to value-based decisions It has been shown that difference-based accumulation of evidence is fundamental in perceptual and in value-based decisions (Basten et al 2010, but see Pirrone et al 2014)

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Conclusion