Abstract
Am I really sure? This is a question not only scientists ask themselves but practically everybody every day. A recent study provides behavioral evidence supporting the view that one’s subjective confidence in a decision (i.e., feeling sure that a decision is correct) is represented in a task-independent format. Previous neuroimaging studies identified neural correlates of decision confidence but whether or not these are task-dependent remains unclear. Here, combining two perceptual decision tasks with functional magnetic resonance imaging (fMRI), we provide neural evidence for a task-independent representation of degrees of subjective certainty (i.e., a neural representation of subjective certainty that remains constant across two visual tasks). Importantly, due to the constant stimulus-intensity used this result is independent of task-difficulty and stimulus properties. Our data provide strong evidence for a generic mechanism underlying the computation of subjective perceptual certainty in vision.
Highlights
Am I really sure? This is a question scientists ask themselves but practically everybody everyday
We further checked whether relationships between RT, subjective certainty and performance as expected based on the literature were present in our data
We found a negative parametric effect of certainty in the supplementary motor area (SMA) within dorsomedial prefrontal cortex (DMPFC), superior frontal gyrus (SFG), lingual gyrus, inferior frontal gyrus (IFG), Insula, and inferior parietal lobule. (See Table 2 for a full list of activations)
Summary
Am I really sure? This is a question scientists ask themselves but practically everybody everyday. The mechanisms of the emergence of degrees of subjective certainty have been investigated in humans for more than a century (e.g., Peirce and Jastrow, 1884; Vickers, 1979; Fleming et al, 2010, 2012; Pleskac and Busemeyer, 2010; Hebart et al, 2014; Zizlsperger et al, 2014; Gherman and Philiastides, 2015) and more recently in animals (Kepecs et al, 2008; Kiani and Shadlen, 2009; Komura et al, 2013). While process models describe system dynamics and computational mechanisms, representation models describe the processing device itself and the way an entity is represented (such as by a feature list)
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