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Object-finding skill created by repeated reward experience.

ABSTRACT: For most animals, survival depends on rapid detection of rewarding objects, but search for an object surrounded by many others is known to be difficult and time consuming. However, there is neuronal evidence for robust and rapid differentiation of objects based on their reward history in primates (Hikosaka, Kim, Yasuda, & Yamamoto, 2014). We hypothesized that such robust coding should support efficient search for high-value objects, similar to a pop-out mechanism. To test this hypothesis, we let subjects (n = 4, macaque monkeys) view a large number of complex objects with consistently biased rewards with variable training durations (1, 5, or 30 + days). Following training, subjects searched for a high-value object (Good) among a variable number of low-value objects (Bad). Consistent with our hypothesis, we found that Good objects were accurately and quickly targeted, often by a single and direct saccade with a very short latency (<200 ms). The dependence of search times on display size reduced significantly with longer reward training, giving rise to a more efficient search (40 ms/item to 16 ms/item). This object-finding skill showed a large capacity for value-biased objects and was maintained in the long-term memory with no interference from reward learning with other objects. Such object-finding skill, and in particular its large capacity and long term retention, would be crucial for maximizing rewards and biological fitness throughout life where many objects are experienced continuously and/or intermittently.

SUBMITTER: Ghazizadeh A

PROVIDER: S-EPMC5015994 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Measuring cognitive load is important for surgical education and patient safety. Traditional approaches of measuring cognitive load of surgeons utilise behavioural metrics to measure performance and surveys and questionnaires to collect reports of subjective experience. These have disadvantages such as sporadic data, occasionally intrusive methodologies, subjective or misleading self-reporting. In addition, traditional approaches use subjective metrics that cannot distinguish between skill levels. Functional neuroimaging data was collected using a high density, wireless NIRS device from sixteen surgeons (11 attending surgeons and 5 surgery resident) and 17 students while they performed two laparoscopic tasks (Peg transfer and String pass). Participant's subjective mental load was assessed using the NASA-TLX survey. Machine learning approaches were used for predicting the subjective experience and skill levels. The Prefrontal cortex (PFC) activations were greater in students who reported higher-than-median task load, as measured by the NASA-TLX survey. However in the case of attending surgeons the opposite tendency was observed, namely higher activations in the lower v higher task loaded subjects. We found that response was greater in the left PFC of students particularly near the dorso- and ventrolateral areas. We quantified the ability of PFC activation to predict the differences in skill and task load using machine learning while focussing on the effects of NIRS channel separation distance on the results. Our results showed that the classification of skill level and subjective task load could be predicted based on PFC activation with an accuracy of nearly 90%. Our finding shows that there is sufficient information available in the optical signals to make accurate predictions about the surgeons' subjective experiences and skill levels. The high accuracy of results is encouraging and suggest the integration of the strategy developed in this study as a promising approach to design automated, more accurate and objective evaluation methods.

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Object-finding skill created by repeated reward experience.

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