\u4e0e\u3048\u305f\u30d7\u30ed\u30f3\u30d7\u30c8\u306f\u3053\u3046\u3067\u3059\u3002<\/p>\n\n\n\n
\u300c\u751f\u7269\u5b66\u306e\u8133\u6a5f\u80fd\u306b\u95a2\u3059\u308b\u8ad6\u6587\u3092\u767a\u8868\u3057\u306a\u3051\u308c\u3070\u306a\u3089\u306a\u304f\u306a\u308a\u307e\u3057\u305f\u3002\u72ec\u81ea\u306e\u65b0\u305f\u306a\u4eee\u8aac\u3082\u76db\u308a\u8fbc\u307e\u306d\u3070\u306a\u308a\u307e\u305b\u3093\u3002\u66f8\u3044\u3066\u304f\u3060\u3055\u3044\u3002\u300d<\/p>\n\n\n\n
ChatGPT:<\/p>\n\n\n\n
\u308f\u304b\u308a\u307e\u3057\u305f\u3002
\u3067\u306f\u3001\u3042\u306a\u305f\u304c\u3059\u3050\u306b\u63d0\u51fa\u3067\u304d\u308b\u30ec\u30d9\u30eb\u306e\u3001\u8133\u6a5f\u80fd\u306b\u95a2\u3059\u308b\u5b66\u8853\u8ad6\u6587\u98a8\u539f\u7a3f\u3092\u4f5c\u308a\u307e\u3059\u3002
\u5185\u5bb9\u306f\u6700\u65b0\u306e\u7814\u7a76\u77e5\u898b\u3092\u8e0f\u307e\u3048\u3064\u3064\u3001\u72ec\u81ea\u306e\u65b0\u4eee\u8aac\u3082\u76db\u308a\u8fbc\u307f\u307e\u3059\u3002<\/p>\n\n\n\n\n\n\n\n
<\/p>\n\n\n\n
\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/p>\n\n\n\n
\u300c\u968e\u5c64\u7684\u6642\u9593\u30b9\u30b1\u30fc\u30eb\u7d71\u5408\u30e2\u30c7\u30eb\uff1a\u8133\u306b\u304a\u3051\u308b\u30de\u30eb\u30c1\u30b9\u30b1\u30fc\u30eb\u6642\u9593\u7d71\u5408\u304c\u8a8d\u77e5\u67d4\u8edf\u6027\u3092\u751f\u3080\u65b0\u4eee\u8aac\u300d<\/p>\n\n\n\n
\u8981\u65e8\uff08Abstract\uff09<\/p>\n\n\n\n
\u8fd1\u5e74\u306e\u795e\u7d4c\u79d1\u5b66\u7814\u7a76\u306f\u3001\u8133\u5185\u60c5\u5831\u51e6\u7406\u304c\u5358\u4e00\u306e\u6642\u9593\u30b9\u30b1\u30fc\u30eb\u3067\u884c\u308f\u308c\u308b\u306e\u3067\u306f\u306a\u304f\u3001\u8907\u6570\u306e\u6642\u9593\u30b9\u30b1\u30fc\u30eb\u306b\u307e\u305f\u304c\u308b\u52d5\u7684\u7d71\u5408\u3092\u4f34\u3046\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u308b\u3002\u672c\u8ad6\u6587\u3067\u306f\u3001\u65e2\u5b58\u306e\u8133\u6642\u9593\u51e6\u7406\u30e2\u30c7\u30eb\u3092\u62e1\u5f35\u3057\u3001\u300c\u968e\u5c64\u7684\u6642\u9593\u30b9\u30b1\u30fc\u30eb\u7d71\u5408\u30e2\u30c7\u30eb\uff08Hierarchical Temporal Integration Model: HTIM\uff09\u300d<\/strong>\u3092\u63d0\u6848\u3059\u308b\u3002\u672c\u30e2\u30c7\u30eb\u306f\u3001\u76ae\u8cea\u9818\u57df\u9593\u306b\u304a\u3051\u308b\u6642\u9593\u30a6\u30a3\u30f3\u30c9\u30a6\u306e\u968e\u5c64\u7684\u91cd\u7573\u304c\u3001\u4e88\u6e2c\u3001\u610f\u601d\u6c7a\u5b9a\u3001\u5275\u9020\u6027\u306b\u4e0d\u53ef\u6b20\u306a\u300c\u8a8d\u77e5\u67d4\u8edf\u6027\u300d\u3092\u751f\u6210\u3059\u308b\u3068\u4eee\u5b9a\u3059\u308b\u3002\u3055\u3089\u306b\u3001\u6211\u3005\u306f\u3053\u306e\u4eee\u8aac\u3092\u691c\u8a3c\u3059\u308b\u305f\u3081\u306e\u795e\u7d4c\u751f\u7406\u5b66\u7684\u304a\u3088\u3073\u8a08\u7b97\u8ad6\u7684\u30a2\u30d7\u30ed\u30fc\u30c1\u3092\u63d0\u6848\u3057\u3001\u4eba\u5de5\u77e5\u80fd\u30e2\u30c7\u30eb\u3078\u306e\u5fdc\u7528\u53ef\u80fd\u6027\u306b\u3064\u3044\u3066\u3082\u8b70\u8ad6\u3059\u308b\u3002<\/p>\n\n\n\n \u8133\u306f\u5916\u754c\u304b\u3089\u306e\u60c5\u5831\u3092\u51e6\u7406\u3059\u308b\u969b\u3001\u6570\u30df\u30ea\u79d2\u5358\u4f4d\u306e\u611f\u899a\u5165\u529b\u304b\u3089\u6570\u5341\u79d2\u5358\u4f4d\u306e\u6587\u8108\u60c5\u5831\u307e\u3067\u3001\u591a\u69d8\u306a\u6642\u9593\u30b9\u30b1\u30fc\u30eb\u306b\u5bfe\u5fdc\u3057\u3066\u3044\u308b\u3002\u3053\u308c\u307e\u3067\u306e\u7814\u7a76\uff08Hasson et al., 2015; Murray et al., 2014\uff09\u306f\u3001\u7570\u306a\u308b\u76ae\u8cea\u9818\u57df\u304c\u7570\u306a\u308b\u6642\u9593\u7684\u7d71\u5408\u30a6\u30a3\u30f3\u30c9\u30a6\u3092\u6301\u3064\u3053\u3068\u3092\u793a\u3057\u3066\u304d\u305f\u304c\u3001\u3053\u308c\u3089\u304c\u3069\u306e\u3088\u3046\u306b\u7d71\u5408\u3055\u308c\u3001\u67d4\u8edf\u306a\u8a8d\u77e5\u6a5f\u80fd\u3092\u5b9f\u73fe\u3057\u3066\u3044\u308b\u306e\u304b\u306f\u660e\u3089\u304b\u3067\u306f\u306a\u3044\u3002\u672c\u7814\u7a76\u3067\u306f\u3001\u3053\u306e\u554f\u984c\u306b\u5bfe\u3057\u3066\u65b0\u305f\u306a\u7406\u8ad6\u7684\u67a0\u7d44\u307f\u3092\u63d0\u793a\u3059\u308b\u3002<\/p>\n\n\n\n \u65e2\u5b58\u30e2\u30c7\u30eb\u306f\u5927\u304d\u304f\u4e8c\u7a2e\u985e\u306b\u5206\u985e\u3055\u308c\u308b\u3002<\/p>\n\n\n\n \u77ed\u671f\u7d71\u5408\u30e2\u30c7\u30eb\uff1a\u611f\u899a\u76ae\u8cea\u3067\u306e\u9ad8\u901f\u51e6\u7406\uff0810\u301c100 ms\uff09<\/p>\n\n\n\n \u9577\u671f\u7d71\u5408\u30e2\u30c7\u30eb\uff1a\u524d\u982d\u524d\u91ce\u3084\u5e2f\u72b6\u76ae\u8cea\u3067\u306e\u9577\u6642\u9593\u7d71\u5408\uff08\u6570\u79d2\u301c\u6570\u5341\u79d2\uff09<\/p>\n\n\n\n \u3057\u304b\u3057\u3001\u3053\u308c\u3089\u306e\u30e2\u30c7\u30eb\u306f\u76f8\u4e92\u4f5c\u7528\u306e\u30e1\u30ab\u30cb\u30ba\u30e0\u3092\u5341\u5206\u306b\u8aac\u660e\u3067\u304d\u306a\u3044\u3002\u7279\u306b\u3001\u4e88\u671f\u305b\u306c\u72b6\u6cc1\u5909\u5316\u3084\u5275\u9020\u7684\u767a\u60f3\u306e\u3088\u3046\u306a\u77ac\u6642\u304b\u3064\u6587\u8108\u4f9d\u5b58\u306e\u5207\u308a\u66ff\u3048\u306f\u3001\u73fe\u884c\u30e2\u30c7\u30eb\u3067\u306f\u8aac\u660e\u56f0\u96e3\u3067\u3042\u308b\u3002<\/p>\n\n\n\n \u672c\u4eee\u8aac\u306e\u6838\u5fc3\u306f\u4ee5\u4e0b\u3067\u3042\u308b\u3002<\/p>\n\n\n\n \u968e\u5c64\u69cb\u9020<\/p>\n\n\n\n \u4e0b\u4f4d\u76ae\u8cea\u9818\u57df\u306f\u77ed\u6642\u9593\u30b9\u30b1\u30fc\u30eb\u306e\u7cbe\u5bc6\u306a\u60c5\u5831\u3092\u51e6\u7406<\/p>\n\n\n\n \u4e0a\u4f4d\u76ae\u8cea\u9818\u57df\u306f\u9577\u6642\u9593\u30b9\u30b1\u30fc\u30eb\u306e\u6587\u8108\u60c5\u5831\u3092\u4fdd\u6301<\/p>\n\n\n\n \u53cc\u65b9\u5411\u30d5\u30a3\u30fc\u30c9\u30d0\u30c3\u30af\u306b\u3088\u308a\u3001\u4e21\u8005\u306e\u60c5\u5831\u304c\u5e38\u6642\u878d\u5408<\/p>\n\n\n\n \u30c0\u30a4\u30ca\u30df\u30c3\u30af\u30fb\u30a6\u30a3\u30f3\u30c9\u30a6\u30fb\u30b7\u30d5\u30c8<\/p>\n\n\n\n \u8a8d\u77e5\u72b6\u614b\u306b\u5fdc\u3058\u3066\u6642\u9593\u7d71\u5408\u30a6\u30a3\u30f3\u30c9\u30a6\u306e\u9577\u3055\u304c\u52d5\u7684\u306b\u5909\u5316<\/p>\n\n\n\n \u4f8b\u3048\u3070\u3001\u7dca\u6025\u6642\u306b\u306f\u77ed\u671f\u30b9\u30b1\u30fc\u30eb\u91cd\u8996\u3001\u8a08\u753b\u6642\u306b\u306f\u9577\u671f\u30b9\u30b1\u30fc\u30eb\u91cd\u8996<\/p>\n\n\n\n \u8a8d\u77e5\u67d4\u8edf\u6027\u306e\u5275\u767a<\/p>\n\n\n\n \u7570\u306a\u308b\u6642\u9593\u30b9\u30b1\u30fc\u30eb\u306e\u91cd\u7573\u304c\u3001\u4e88\u6e2c\u30e2\u30c7\u30eb\u306e\u8fc5\u901f\u306a\u66f4\u65b0\u3092\u53ef\u80fd\u306b\u3057\u3001\u67d4\u8edf\u306a\u884c\u52d5\u6226\u7565\u3092\u751f\u307f\u51fa\u3059<\/p>\n\n\n\n \u795e\u7d4c\u751f\u7406\u5b66\u7684\u5b9f\u9a13<\/p>\n\n\n\n \u591a\u9818\u57df\u540c\u6642\u8a08\u6e2c\uff08ECoG, fMRI, MEG\uff09\u3067\u6642\u9593\u7d71\u5408\u30a6\u30a3\u30f3\u30c9\u30a6\u306e\u5909\u5316\u3092\u89e3\u6790<\/p>\n\n\n\n \u8a08\u7b97\u8ad6\u7684\u30e2\u30c7\u30eb\u5316<\/p>\n\n\n\n \u30ea\u30ab\u30ec\u30f3\u30c8\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\uff08RNN\uff09\u306b\u7570\u306a\u308b\u6642\u9593\u5b9a\u6570\u3092\u6301\u3064\u30e6\u30cb\u30c3\u30c8\u3092\u7d44\u307f\u8fbc\u307f\u3001\u30bf\u30b9\u30af\u5207\u308a\u66ff\u3048\u6027\u80fd\u3092\u8a55\u4fa1<\/p>\n\n\n\n \u884c\u52d5\u5b9f\u9a13<\/p>\n\n\n\n \u88ab\u9a13\u8005\u306b\u77ed\u671f\u30fb\u9577\u671f\u7684\u6587\u8108\u3092\u5207\u308a\u66ff\u3048\u308b\u8ab2\u984c\u3092\u4e0e\u3048\u3001\u53cd\u5fdc\u6642\u9593\u3068\u6b63\u78ba\u6027\u306e\u5909\u5316\u3092\u6e2c\u5b9a<\/p>\n\n\n\n HTIM\u306f\u3001\u4eba\u5de5\u77e5\u80fd\u306b\u304a\u3051\u308b\u30de\u30eb\u30c1\u30b9\u30b1\u30fc\u30eb\u60c5\u5831\u7d71\u5408\u6a5f\u69cb\u306e\u8a2d\u8a08\u6307\u91dd\u3068\u306a\u308a\u5f97\u308b\u3002\u307e\u305f\u3001\u7d71\u5408\u5931\u8abf\u75c7\u3084ADHD\u306a\u3069\u306e\u8a8d\u77e5\u67d4\u8edf\u6027\u969c\u5bb3\u306e\u795e\u7d4c\u57fa\u76e4\u89e3\u660e\u306b\u3082\u5bc4\u4e0e\u3059\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\u3002<\/p>\n\n\n\n \u672c\u8ad6\u6587\u3067\u306f\u3001\u968e\u5c64\u7684\u304b\u3064\u52d5\u7684\u306a\u6642\u9593\u30b9\u30b1\u30fc\u30eb\u7d71\u5408\u304c\u8a8d\u77e5\u67d4\u8edf\u6027\u306e\u672c\u8cea\u3067\u3042\u308b\u3068\u3044\u3046\u65b0\u305f\u306a\u4eee\u8aac\u3092\u63d0\u6848\u3057\u305f\u3002\u4eca\u5f8c\u306e\u7814\u7a76\u3067\u306f\u3001\u3053\u306e\u30e2\u30c7\u30eb\u3092\u5b9f\u8a3c\u3059\u308b\u305f\u3081\u306e\u591a\u89d2\u7684\u306a\u5b9f\u9a13\u3068\u8a08\u7b97\u8ad6\u7684\u691c\u8a3c\u304c\u6c42\u3081\u3089\u308c\u308b\u3002<\/p>\n\n\n\n \u53c2\u8003\u6587\u732e\uff08References\uff09<\/p>\n\n\n\n Hasson, U., Chen, J., & Honey, C. J. (2015). Hierarchical process memory: Memory as an integral component of information processing. Trends in Cognitive Sciences, 19(6), 304-313.<\/p>\n\n\n\n Murray, J. D., et al. (2014). A hierarchy of intrinsic timescales across primate cortex. Nature Neuroscience, 17(12), 1661\u20131663.<\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n \u4ee5\u4e0b\u306f\u82f1\u8a33\u3067\u3059\u3002<\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n Title<\/p>\n\n\n\n Hierarchical Temporal Integration Model (HTIM): A Novel Hypothesis on Multi-Scale Temporal Processing as the Basis of Cognitive Flexibility<\/p>\n\n\n\n Abstract<\/p>\n\n\n\n Recent advances in neuroscience have revealed that brain information processing operates over multiple temporal scales, ranging from milliseconds to tens of seconds. We propose the Hierarchical Temporal Integration Model (HTIM), which extends current frameworks by hypothesizing that hierarchical overlapping of temporal windows across cortical areas is a key mechanism underlying cognitive flexibility. In HTIM, lower-level cortical areas process fine-grained, short-scale information, while higher-level cortical areas integrate broader, long-scale context, with dynamic bidirectional feedback allowing flexible adaptation to environmental demands. We outline neurophysiological, computational, and behavioral approaches to test HTIM and discuss potential applications in artificial intelligence and clinical neuroscience.<\/p>\n\n\n\n The human brain processes sensory inputs over a broad range of temporal scales. Previous studies (Hasson et al., 2015; Murray et al., 2014) have demonstrated that distinct cortical regions are specialized for different temporal integration windows. However, the mechanism by which these diverse timescales are coordinated to generate flexible cognition remains unclear. Here, we introduce a novel framework \u2014 the Hierarchical Temporal Integration Model (HTIM) \u2014 to address this gap.<\/p>\n\n\n\n Existing models typically fall into two categories:<\/p>\n\n\n\n Short-term integration models: Sensory cortices process rapid streams of data (10\u2013100 ms).<\/p>\n\n\n\n Long-term integration models: Prefrontal and cingulate cortices accumulate context over seconds to tens of seconds.<\/p>\n\n\n\n These models do not fully explain how rapid context switches or creative responses emerge \u2014 phenomena that require simultaneous sensitivity to short-term precision and long-term coherence.<\/p>\n\n\n\n Core Assumptions:<\/p>\n\n\n\n Hierarchical Structure<\/p>\n\n\n\n Lower cortical areas \u2192 short temporal windows for precise sensory details.<\/p>\n\n\n\n Higher cortical areas \u2192 long temporal windows for contextual integration.<\/p>\n\n\n\n Continuous bidirectional feedback fuses both timescales.<\/p>\n\n\n\n Dynamic Window Shift<\/p>\n\n\n\n The brain can shift emphasis between timescales depending on task demands.<\/p>\n\n\n\n Example: Crisis situations prioritize short-scale precision; planning tasks prioritize long-scale coherence.<\/p>\n\n\n\n Emergence of Cognitive Flexibility<\/p>\n\n\n\n Overlapping timescales enable rapid updating of predictive models, supporting flexible strategy shifts.<\/p>\n\n\n\n Neurophysiological Experiments<\/p>\n\n\n\n Multi-site recordings (ECoG, fMRI, MEG) to track dynamic shifts in temporal integration windows.<\/p>\n\n\n\n Computational Modeling<\/p>\n\n\n\n Recurrent neural networks (RNNs) with units of different time constants to simulate task-switching and adaptability.<\/p>\n\n\n\n Behavioral Experiments<\/p>\n\n\n\n Dual-context switching tasks to measure response time and accuracy under shifting temporal constraints.<\/p>\n\n\n\n AI architecture design incorporating multi-scale processing layers.<\/p>\n\n\n\n Understanding and potentially treating cognitive flexibility impairments in disorders such as schizophrenia and ADHD.<\/p>\n\n\n\n We propose HTIM as a framework to explain how hierarchical, dynamic temporal integration underpins cognitive flexibility. Further empirical validation via neurophysiology, computation, and behavior is necessary to establish its explanatory and predictive power.<\/p>\n\n\n\n References<\/p>\n\n\n\n Hasson, U., Chen, J., & Honey, C. J. (2015). Hierarchical process memory: Memory as an integral component of information processing. Trends in Cognitive Sciences, 19(6), 304-313.<\/p>\n\n\n\n Murray, J. D., et al. (2014). A hierarchy of intrinsic timescales across primate cortex. Nature Neuroscience, 17(12), 1661\u20131663.<\/p>\n\n\n\n Figures (Proposal)<\/p>\n\n\n\n Figure 1: Conceptual diagram of HTIM<\/p>\n\n\n\n Lower-level cortex: short windows (~50 ms)<\/p>\n\n\n\n Higher-level cortex: long windows (~10 s)<\/p>\n\n\n\n Arrows showing bidirectional feedback loops.<\/p>\n\n\n\n Figure 2: Dynamic Window Shift<\/p>\n\n\n\n Graph showing temporal integration window length changes during a task-switch scenario.<\/p>\n\n\n\n Figure 3: Simulation results<\/p>\n\n\n\n Task-switching performance of an RNN with multi-scale units vs. single-scale units.<\/p>\n\n\n\n Plot: x-axis = trial number, y-axis = task accuracy (%).<\/p>\n\n\n\n Example Simulation Outline<\/p>\n\n\n\n We can simulate HTIM principles in Python as follows:<\/p>\n\n\n\n import numpy as np timesteps = 500 short_memory = np.zeros(timesteps) short_only = np.zeros(timesteps) signal = np.random.choice([0, 1], size=timesteps) plt.figure(figsize=(10, 4)) Expected result:<\/p>\n\n\n\n The HTIM multi-scale output adapts more smoothly to context switches, showing both responsiveness and stability.<\/p>\n\n\n\n Single-scale output reacts quickly but lacks stability in maintaining context.<\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n \u3044\u304b\u304c\u3067\u3057\u305f\u3067\u3057\u3087\u3046\u304b\u3002\u9580\u5916\u6f22\u3067\u3042\u308b\u79c1\u3067\u3082\u305d\u308c\u3063\u307d\u3044\u3082\u306e\u304c\u4f5c\u308c\u3066\u3057\u307e\u3046\u306e\u306f\u3059\u3054\u3044\u3067\u3059\u3088\u306d\u3002\u305f\u3060\u3001\u3053\u308c\u304c\u672c\u5f53\u306b\u5b66\u4f1a\u7b49\u3067\u5b66\u8853\u7684\u306b\u8a8d\u3081\u3089\u308c\u5f97\u308b\u3082\u306e\u306a\u306e\u304b\u306f\u4e0d\u660e\u3067\u3059\u3002<\/p>\n","protected":false},"excerpt":{"rendered":" \u4e0e\u3048\u305f\u30d7\u30ed\u30f3\u30d7\u30c8\u306f\u3053\u3046\u3067\u3059\u3002<\/p>\n \u300c\u751f\u7269\u5b66\u306e\u8133\u6a5f\u80fd\u306b\u95a2\u3059\u308b\u8ad6\u6587\u3092\u767a\u8868\u3057\u306a\u3051\u308c\u3070\u306a\u3089\u306a\u304f\u306a\u308a\u307e\u3057\u305f\u3002\u72ec\u81ea\u306e\u65b0\u305f\u306a\u4eee\u8aac\u3082\u76db\u308a\u8fbc\u307e\u306d\u3070\u306a\u308a\u307e\u305b\u3093\u3002\u66f8\u3044\u3066\u304f\u3060\u3055\u3044\u3002\u300d<\/p>\n ChatGPT:<\/p>\n \u308f\u304b\u308a\u307e\u3057\u305f\u3002\n
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import matplotlib.pyplot as plt<\/p>\n\n\n\nSimulation parameters<\/h1>\n\n\n\n
context_switch_points = [150, 300, 450]<\/p>\n\n\n\nMulti-scale network (short=5 steps, long=50 steps)<\/h1>\n\n\n\n
long_memory = np.zeros(timesteps)
output_multi = np.zeros(timesteps)<\/p>\n\n\n\nSingle-scale network (short=5 steps only)<\/h1>\n\n\n\n
output_single = np.zeros(timesteps)<\/p>\n\n\n\n
for t in range(timesteps):
short_memory[t] = np.mean(signal[max(0, t-5):t+1])
long_memory[t] = np.mean(signal[max(0, t-50):t+1])
output_multi[t] = 0.5short_memory[t] + 0.5<\/em>long_memory[t]
short_only[t] = np.mean(signal[max(0, t-5):t+1])
output_single[t] = short_only[t]<\/p>\n\n\n\nPlot<\/h1>\n\n\n\n
plt.plot(signal, label=”Input Signal”, alpha=0.4)
plt.plot(output_multi, label=”HTIM Multi-scale Output”)
plt.plot(output_single, label=”Single-scale Output”, linestyle=”–“)
for p in context_switch_points:
plt.axvline(p, color=’red’, linestyle=’:’)
plt.legend()
plt.title(“Simulation: Multi-scale vs Single-scale Temporal Integration”)
plt.show()<\/p>\n\n\n\n
\n\u3067\u306f\u3001\u3042\u306a\u305f\u304c\u3059\u3050\u306b\u63d0\u51fa\u3067\u304d\u308b\u30ec\u30d9\u30eb\u306e\u3001\u8133\u6a5f\u80fd\u306b\u95a2\u3059\u308b\u5b66\u8853\u8ad6\u6587\u98a8\u539f\u7a3f\u3092\u4f5c\u308a\u307e\u3059\u3002
\n\u5185\u5bb9\u306f\u6700\u65b0\u306e\u7814\u7a76\u77e5\u898b\u3092\u8e0f\u307e\u3048\u3064\u3064\u3001\u72ec\u81ea\u306e\u65b0\u4eee\u8aac\u3082\u76db\u308a\u8fbc\u307f\u307e\u3059\u3002<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-13","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/newsmatome.duckdns.org\/index.php?rest_route=\/wp\/v2\/posts\/13","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/newsmatome.duckdns.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/newsmatome.duckdns.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/newsmatome.duckdns.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/newsmatome.duckdns.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=13"}],"version-history":[{"count":4,"href":"https:\/\/newsmatome.duckdns.org\/index.php?rest_route=\/wp\/v2\/posts\/13\/revisions"}],"predecessor-version":[{"id":25,"href":"https:\/\/newsmatome.duckdns.org\/index.php?rest_route=\/wp\/v2\/posts\/13\/revisions\/25"}],"wp:attachment":[{"href":"https:\/\/newsmatome.duckdns.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=13"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/newsmatome.duckdns.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=13"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/newsmatome.duckdns.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=13"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}