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Dietmar Heinke, Joo-Hyun Song; A novel diffusion-based model of choice reaching experiments. Journal of Vision 2020;20(11):776. https://doi.org/10.1167/jov.20.11.776.
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In long-standing research, previous studies have shown that reaching movements can be influenced by attentional selection processes. Critical evidence for this effect stems from reach trajectories in colour-oddity tasks. These experiments showed that the modulation of the reaching curvature is linked to colour priming i.e., colour repetitions lead to smaller curvatures compared to trials where target colour switches (e.g., Song & Nakayama, 2008; Moher & Song, 2016). Following this evidence, Heinke and colleagues developed a neurologically inspired robotics model for colour priming (Strauss et al., 2015). Critically, the model shows that in neural structures the attentional selection process easily leak into the motor system causing the curvature effect. Here, we present an alternative, simpler model which allows us to conduct quantitative investigations (e.g., model fitting) into the leakage effect. This novel model employs two diffusion processes (DP). (Normally DPs are used to model perceptual decision making.) Here one DP is assumed to capture the selection of the odd colour target (“attentional” stage) and “leaks” into the second DP (“motor” stage). This motor DP can be seen to describe the noisy spatial progression of reaching trajectories and only begins when the perceptual stage reaches a threshold allowing us to model reaching latencies. Importantly, our simulation studies also showed that the curvature effect can be easily captured by this new model.
Further simulation studies found that the model tends to exhibit correlation between reaching time and curvature. However, this result contradicts empirical findings. Interestingly, a simple modification of the model, additional feedback from motor DP to perceptual DP, allowed us to fix this problem. This feedback can be interpreted as a novel prediction for an influence of visual attention through reaching trajectories. In addition, we will present results from Bayesian model fitting underlining the potential usefulness of the model.
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