Computational and Neural Mechanisms Underlying Decision-Making in Humans

How do we make economic decisions in everyday life? How do we make decisions in the face of uncertainty regarding the statistics of the environment? These are the questions that played a pivotal role in the formation of the field "Decision Neuroscience". In each chapter of this thesis, we investigated the computational and neural mechanisms to tackle these questions using behavioral and neural data acquired through fMRI experiments. In the first chapter, we investigated the computational and neural basis of economic decision-making in a binary choice task between two food items. We analyzed behavioral and neural data in a task where participants conducted a sequence of binary choices under the manipulation of fixation-based attention. We developed and calibrated a computational model based on evidence sampling and accumulation to show that the model not only accurately captured basic properties such as choice and reaction time (RT) but also the effect of attentional manipulation in participants’ behavior. We found that the evidence accumulation process predicted by the model to drive a decision was implemented in the areas of frontoparietal network including dmPFC and IPS. These regions also exhibited increased functional connectivity with the activity in vmPFC during choice period where sampled evidence was represented. Our results suggest the involvement of these areas in value-based binary choice. In the second chapter, we examined the computations involved in the decision making under uncertainty. In particular, we aimed to pin down the computations related to temporal change detection. Temporal change detection is the capacity to detect change in the statistics that govern the timing of occurrence of events. We analyzed behavioral data from a novel task where participants observed a sequence of images presented at irregular timings and tasked to detect a change in the frequency of image presentations. We developed and compared computational models from Bayesian to heuristic models and found that all the models captured quantitative aspects of participants’ behavior equally well despite the difference in their computational complexity. Thus, we could not distinguish computations involved in temporal change detection solely from the behavioral data. In the third chapter, we aimed to elucidate the computations involved in temporal change detection from the perspective of neural implementation using fMRI data by leveraging the computational models examined in the previous chapter. We found that the key variable to guide a decision derived from a computationally frugal heuristic model correlated with the activity of the frontalparietal network including dlPFC and IPS, while similar variables derived from more computationally taxing Bayesian models did not show significant correlation with any of the brain regions. Our results suggest that humans might be relying on a simple heuristic model to implement temporal change detection.</p

Neural substrates of social facilitation effects on incentive-based performance

Throughout our lives we must perform tasks while being observed by others. Previous studies have shown that the presence of an audience can cause increases in an individual’s performance as compared to when they are not being observed—a phenomenon called ‘social facilitation’. However, the neural mechanisms underlying this effect, in the context of skilled-task performance for monetary incentives, are not well understood. We used functional magnetic resonance imaging to monitor brain activity while healthy human participants performed a skilled-task during conditions in which they were paid based on their performance and observed and not observed by an audience. We found that during social facilitation, social signals represented in the dorsomedial prefrontal cortex (dmPFC) enhanced reward value computations in ventromedial prefrontal cortex (vmPFC). We also found that functional connectivity between dmPFC and ventral striatum was enhanced when participants exhibited social facilitation effects, indicative of a means by which social signals serve to modulate brain regions involved in regulating behavioral motivation. These findings illustrate how neural processing of social judgments gives rise to the enhanced motivational state that results in social facilitation of incentive-based performance

Data-driven Exploration of New Pressure-induced Superconductivity in PbBi2_2Te4_4 with Two Transition Temperatures

Candidates compounds for new thermoelectric and superconducting materials, which have narrow band gap and flat bands near band edges, were exhaustively searched by the high-throughput first-principles calculation from an inorganic materials database named AtomWork. We focused on PbBi$_2$Te$_4$ which has the similar electronic band structure and the same crystal structure with those of a pressure-induced superconductor SnBi2Se4 explored by the same data-driven approach. The PbBi$_2$Te$_4$ was successfully synthesized as single crystals using a melt and slow cooling method. The core level X-ray photoelectron spectroscopy analysis revealed Pb2+, Bi3+ and Te2- valence states in PbBi$_2$Te$_4$. The thermoelectric properties of the PbBi$_2$Te$_4$ sample were measured at ambient pressure and the electrical resistivity was also evaluated under high pressure using a diamond anvil cell with boron-doped diamond electrodes. The resistivity decreased with increase of the pressure, and two pressure-induced superconducting transitions were discovered at 3.4 K under 13.3 GPa and at 8.4 K under 21.7 GPa. The data-driven approach shows promising power to accelerate the discovery of new thermoelectric and superconducting materials

VPP control cannot stabilize the posture during walking for high VPP location

The 11th International Symposium on Adaptive Motion of Animals and Machines. Kobe University, Japan. 2023-06-06/09. Adaptive Motion of Animals and Machines Organizing Committee.Poster Session P7

Vulnerabilities of radiomic features to respiratory motion on four‐dimensional computed tomography‐based average intensity projection images: A phantom study

[Purpose] To evaluate the influence of respiratory motion on the robustness of radiomic features on four-dimensional computed tomography (4DCT)-based average intensity projection (AIP) images by employing an anthropomorphic chest phantom. [Methods] Three spherical objects (φ30 mm), namely, acrylic (100 Hounsfield unit [HU], homogeneous), rubber (−140 HU, homogeneous), and cork (−630 HU, heterogeneous), were moved with motion amplitudes of 0, 1, 2.5, 4, 6, 8, and 10 mm in the phantom, and 4DCT scans were repeated at four different locations. Thereafter, the AIP images were generated considering the average of the 10 respiratory phases of the 4DCT images. Further, the targets were manually delineated on the AIP images in the lung window setting. A total of 851 radiomic features, including 107 unfiltered features and 744 wavelet filter-based features, were extracted from the region of interest for each material. The feature robustness among the different target motion amplitude (ε) was evaluated by normalizing the feature variability of the target motion relative to the variability of data from 573 patients with early-stage non-small cell lung cancer. The features with absolute ε values ≤0.5 were considered highly robust to target motions. [Results] The percentage of robust unfiltered and wavelet filter-based features with a motion amplitude of 1 mm was greater than 83.2% and 93.4%, respectively; however, the percentage decreased by more than 24.3% and 17.6%, respectively, for motion amplitudes greater than 2.5 mm. The movement of cork had a small effect on the feature robustness compared to that of acrylic and rubber, regardless of the target motion amplitudes. [Conclusions] Our phantom study demonstrated that target motion amplitudes ≤1 mm led to the robustness of radiomic features on the 4DCT-based AIP images of thoracic regions. The frequency components and directions of the wavelet filters may be essential factors in 4DCT-based radiomic analysis