Bitzer, S. | bioRxiv Science

bioRxiv2018

Human primary motor cortex represents evidence for a perceptual decision before motor response

In perceptual decision making the brain extracts and accumulates decision evidence from a stimulus over time and eventually makes a decision based on the accumulated evidence. Several characteristics of this process have been observed in human electrophysiological experiments, especially an average build-up of motor-related signals supposedly reflecting accumulated evidence, when averaged across trials. A more direct approach to investigate the representation of decision evidence in brain signals is to correlate the trial-to-trial fluctuations of a model-based prediction of evidence with the measured signals. We here report results for an experiment in which we applied this approach to human magnetoencephalographic recordings. These results consolidate a range of previous findings and suggest that decision evidence is processed in three consecutive phases in the human brain: In an early phase around 120 ms after the evidence became visible on the screen, in a transition phase around 180 ms and a plateau phase roughly from 300 to 500 ms. We located sources of evidence representations in these phases in early visual (early), parietal (transition) and motor (plateau) regions of the brain while signals in posterior cingulate cortex represented decision evidence in all three phases. These findings imply that parietal cortex is only transiently involved in the processing of decision evidence, that motor areas represent accumulated evidence throughout decision making and that posterior cingulate cortex may have a central role in processing and maintaining decision evidence.

neuroscience↗

bioRxiv2018

Deterministic response strategies in trial-and-error learning

Trial-and-error learning is a universal strategy for establishing which actions are beneficial or harmful in new environments. However, learning stimulus-response associations solely via trial-and-error is often suboptimal, as in many settings dependencies among stimuli and responses can be exploited to increase learning efficiency. Previous studies have shown that in settings featuring such dependencies, humans typically engage high-level cognitive processes and employ advanced learning strategies to improve their learning efficiency. Here we analyze in detail the initial learning phase of a sample of human subjects (N = 85) performing a trial-and-error learning task with deterministic feedback and hidden stimulus-response dependencies. Using computational modeling, we find that the standard Q-learning model cannot sufficiently explain human learning strategies in this setting. Instead, newly introduced deterministic response models, which are theoretically optimal and transform stimulus sequences unambiguously into response sequences, provide the best explanation for 50.6% of the subjects. Most of the remaining subjects either show a tendency towards generic optimal learning (21.2%) or at least partially exploit stimulus-response dependencies (22.3%), while a few subjects (5.9%) show no clear preference for any of the employed models. After the initial learning phase, asymptotic learning performance during the subsequent practice phase is best explained by the standard Q-learning model. Our results show that human learning strategies in trial-and-error learning go beyond merely associating stimuli and responses via incremental reinforcement. Specifically during initial learning, high-level cognitive processes support sophisticated learning strategies that increase learning efficiency while keeping memory demands and computational efforts bounded. The good asymptotic fit of the Q-learning model indicates that these cognitive processes are successively replaced by the formation of stimulus-response associations over the course of learning.

animal behavior and cognition↗