Abstract
Models of evidence accumulation have been very successful at describing human decision making behavior. Recent years have also seen the first reports of neural correlates of this accumulation process. However, these studies have mostly focused on perceptual decision making tasks, ignoring the role of additional cognitive processes like memory retrieval that are crucial in real-world decisions. In this study, we tried to find a neural signature of evidence accumulation during a recognition memory task. To do this, we applied a method we have successfully used to localize evidence accumulation in scalp EEG during a perceptual decision making task. This time, however, we applied it to intracranial EEG recordings, which provide a much higher spatial resolution. We identified several brain areas where activity ramps up over time, but these neural patterns do not appear to be modulated by behavioral variables such as the amount of available evidence or response time. This casts doubt on the idea of evidence accumulation as a general decision-making mechanism underlying different types of decisions.
Highlights
The most well-studied component of decisions is the process of evidence accumulation (Heekeren et al, 2008)
In subsequent analyses, we combined face and letter trials because according to the theory, a similar evidence accumulation process should occur for both types of stimuli
As expected (Figure 4), higher levels of decision evidence were associated with higher accuracy and faster response times, except for the lowest level, where participants may have resorted to guessing
Summary
The most well-studied component of decisions is the process of evidence accumulation (Heekeren et al, 2008). Mathematical models that describe this evidence accumulation process, e.g., the drift diffusion model (Ratcliff, 1978), the linear ballistic accumulator (Brown and Heathcote, 2008), and leaky competing accumulators (Usher and McClelland, 2001) have been able to explain a wealth of behavioral data. These models are increasingly used to elucidate the neural mechanisms underlying decision making. We apply this method to two-alternative forced-choice decisions in a recognition memory task
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