The Winter Workshop 2018

Theme: "Body control and self representation"

January 9 (Tue) to 11 (Thrs), 2018
Rusutsu Resort Hotel , Hokkaido, Japan
The closest airport is Sapporo-Chitose Airport. It takes two hours by bus (reservation needed) from the airport.

Registration fee : 2,000 yen ( Student : free )


Doctor H.Henrik Ehrsson's talk and Doctor Jose M. Carmena's talk have been canceled.
"Body control and self representation"
January 9
Special Session 18:10 - 21:00
18:10-19:00 Hiroaki Gomi (NTT Communication Science Laboratories)
19:10-20:00 Jun Tani (Okinawa Institute of Science and Technology)
20:10-21:00 Ken Takiyama (Tokyo University of Agriculture and Technology)
21:00-23:00 Poster session
January 10
Topic Session 15:30 - 18:20
15:30-16:20 Antonia Hamilton (University College London)
16:30-17:20 Tatsuya Kameda(The University of Tokyo)
17:30-18:20 Atsushi Yokoi(Center for Information and Neural Networks)
20:00-22:00 Poster session
January 11
Topic Session 9:00 - 11:50
9:00-9:50 Masafumi Oizumi (ARAYA inc.)
10:00-10:50 Aurelio Cortese (ATR Institute International Computational Neuroscience Laboratories)
11:00-11:50 Hiroshi Nomura (Hokkaido University)

Abstracts and References:

Hiroaki Gomi
NTT Communication Science Laboratories

Implicit visuomotor control and its effect on self-awareness

In our daily life, our sophisticated movements would consist not only of explicit aspects, such as complex motor planning using kinematics and dynamics calculations for multilink body system, but also of implicit and hidden aspects, such as complex reflexes. Over the last 10 years, we have explored several aspects of implicit visuomotor control induced by visual motion. I will first demonstrates an impact of visual motion on a dynamic manual reaching, and examines implicit visuomotor mechanisms by showing behavioral and brain imaging data. Surprisingly, different specificities of visual motion analyses for the hand and eye controls suggest multiple motion integration mechanisms linking to each motor function, and distorted conscious attribution of action generated by implicit process would suggest a lack of motor prediction/estimation.

References :
1. Abekawa N, Gomi H (2015) Online gain update for manual following response accompanied by gaze shift during arm reaching. Journal of neurophysiology 113:1206-1216.
2. Gomi H, Abekawa N, Shimojo S (2013) The hand sees visual periphery better than the eye: motor-dependent visual motion analyses. The Journal of Neuroscience 33:16502-16509.
3. Abekawa N, Gomi H (2010) Spatial coincidence of intentional actions modulates an implicit visuomotor control. J Neurophysiol 103:2717-2727.
4. Kadota K, Gomi H (2010) Implicit visuomotor processing for quick online reactions is robust against aging. J Neurosci 30:205-209.
5. Amano K, Kimura T, Nishida S, Takeda T, Gomi H (2009) Close similarity between spatiotemporal frequency tunings of human cortical responses and involuntary manual following responses to visual motion. J Neurophysiol 101:888-897.
6. Gomi H, Abekawa N, Nishida S (2006) Spatiotemporal tuning of rapid interactions between visual-motion analysis and reaching movement. The Journal of Neuroscience 26:5301-5308.

Jun Tani
Okinawa Institute of Science and Technology

Emergentist Account for Non-Reductive Consciousness: From Neurorobotics Study

This talk proposes that the mind is comprised of emergent phenomena, which appear via intricate and often conflictive interactions between top-down intentional processes involved in proactively acting on the external world and bottom-up recognition processes involved in receiving the resultant perceptual reality. This view has been tested via a series of neurorobotics experiments employing predictive coding principles implemented in “deep” recurrent neural network (RNN) models conducted in my lab for more than 20 years. These experimental results suggest that phenomena of consciousness as well as free will can be structurally accounted by circular causality inevitably emerged in the enactment loop. Finally, I discuss whether consciousness is still a hard problem or not.

References :
1. J. Tani: “Exploring Robotic Minds: Actions, Symbols, and Consciousness as Self-Organizing Dynamic Phenomena.”, New York: Oxford University Press, 2016.
2. J. Tani: Exploring Robotic Minds by Predictive Coding Principle. The Newsletter of the Technical Committee on Cognitive and Developmental Systems. 14(1) , 4-5. 2017. (

Ken Takiyama
Tokyo University of Agriculture and Technology,Department of Engineering

Prospective coding in human motor learning and decision making

In our daily life, we make predictions in various situations, e.g., we predict tomorrow's weather, outcomes of soccer matches, or stock price. In those predictions, our neural system receives some inputs (e.g., sky scene in predicting tomorrow's weather) and represent future states (e.g., tomorrow's weather). This representation of future states is referred to as prospective coding (ref. Komura et al., 2001). Here
First, I explain about our computational model of motor learning [1]. Diverse features of motor learning have been reported in numerous studies, but no single theoretical framework concurrently accounts for these features. We propose models for motor learning to explain these features in a unified way by extending a motor primitive framework (ref. Thorough man & Shadmehr, 2000, Nature). Our model assumes that the recruitment pattern of motor primitives is determined by the predicted movement error of an upcoming movement (prospective error). I demonstrate that this model has a strong explanatory power to reproduce a wide variety of motor-learning-related phenomena that have been separately explained by different computational models.
Second, I explain about motor decision making in a competitive game [2]. Although risk-seeking behavior in human motor decision making has been reported in several studies (e.g., Wu et al., 2009), those studies focused on an experiment with a single subject. In our daily life (especially in music or sports), our decision making (action selection) can be influenced by opponents in competitive games and partners in collaborative games; however, how decision making is affected by others remains unclear. Our experimental results demonstrate that subjects show risk-averse behavior at the onset of a competitive game, in contrast to risk-seeking behavior when they performed the same movement without any opponent. To understand the risk-averse behavior in a competitive game, we propose a computational model. Our computational model suggests that the risk-averse behavior is a result of optimization when our decision making is influenced by the predicted actions and results of ourselves and opponents (prospective outcome).

References :
1. K. Takiyama, M. Hirashima, D. Nozaki, Prospective errors determine motor learning, Nature Communications, 6, 5925: 1-12 (2015)
2. K. Ota, K. Takiyama, Competitive game influences risk-sensitivity in motor decision-making, Program No. 316.2. 2017 Washington, DC: Society for Neuroscience, 2017.

Antonia Hamilton
Institute of Cognitive Neuroscience, University College London

Mechanisms of social interaction

Imitating other people and being imitated is a visuomotor process with important consequences for our social relationships. The mechanisms by which basic visuomotor functioning is employed in the service of social interaction remains unknown. This talk will describe work from my lab over the last few years which uses virtual reality and near-infrared spectroscopy to probe the neural and cognitive mechanisms of imitating and being imitated. We find evidence that imitation can act as a communicative process, sending social signals to others, but that these signals are not always received in a clear fashion. This work emphasises the importance of using new ecologically valid methods, and outlines future directions for the field.

References :

Tatsuya Kameda
Department of Social Psychology, The University of Tokyo

Does “Ought” have Empirical Grounds in “Be”? Adaptive/Neural Bases of Distributive Justice

Distributive justice concerns the moral principles by which we seek to allocate resources fairly among diverse members of a society. Although the concept of fair allocation is one of the fundamental building blocks for societies, there is no clear consensus on how to achieve socially just allocations. I first consider evolutionary/adaptive bases of egalitarian social-allocation, which is found robustly across many hunter-gatherer societies as well as in some sectors of modern societies. Through anthropological data, evolutionary computer simulations, and behavioral experiments, I argue that the egalitarian social-allocation may be an adaptive device to reduce statistical risk involved in resource acquisition in natural environments collectively. I then extend this reasoning toward a more micro direction, investigating neuro-cognitive commonalities of distributive judgments and risky decisions. We explore the hypothesis that people’s allocation decisions for others are closely related to economic decisions for oneself at behavioral, cognitive and neural levels, via a concern about the minimum, worst-off position. In a series of experiments using attention-monitoring and brain-imaging techniques, we investigated this “maximin” concern (maximizing the minimum possible payoff) via responses in two seemingly disparate tasks: third-party distribution of rewards for others, and choosing gambles for self. The experiments revealed three robust results: (1) participants’ distributive choices closely matched their risk preferences — “Rawlsians” who maximized the worst-off position in distributions for others avoided riskier gambles for themselves, while “utilitarians” who favored the largest-total distributions preferred riskier but more profitable gambles; (2) across such individual choice-preferences, however, participants generally showed the greatest spontaneous attention to information about the worst possible outcomes in both tasks; and (3) this robust concern about the minimum outcomes was correlated with activation of the right temporo-parietal junction (RTPJ), the region associated with perspective-taking. The results provide convergent evidence that social distribution for others is psychologically linked to risky decision-making for self, drawing on common cognitive-neural processes with spontaneous perspective-taking of the worst-off position. I conclude my talk by discussing how social neuroscience and social sciences can create better collaborations toward mutually more beneficial, reciprocal ends.

References :
1. Kameda, T., Takezawa, M., & Hastie, R. (2005). Where do norms come from? The example of communal-sharing. Current Directions in Psychological Science, 14, 331-334.
2. Kameda, T., Takezawa, M., Ohtsubo, Y., & Hastie, R. (2010). Are our minds fundamentally egalitarian? Adaptive bases of different socio-cultural models about distributive justice. In M. Schaller, S. J., Heine, A. Norenzayan, T. Yamagishi, & T. Kameda (Eds.), Evolution, culture, and the human mind (pp. 151-163). New York: Psychology Press.
3. Kameda, T., Inukai, K., Higuchi, S., Ogawa, A., Kim, H., Matsuda, T., & Sakagami, M. (2016). Rawlsian maximin rule operates as a common cognitive anchor in distributive justice and risky decisions. Proceedings of the National Academy of Sciences of the USA, 113(42), 11817-11822.
4. 亀田達也(著)(2017). 『モラルの起源―実験社会科学からの問い』岩波新書.

Atsushi Yokoi
Center for Information and Neural Networks, NICT, Japan

Hierarchical representation of sequences of finger movements in human neocortex

The hierarchical view of action sequencing, first proposed by Lashley (1951), has been supported by a large body of behavioural evidence. Yet its neuronal underpinnings remain unclear. In this talk, I will present the results of two functional magnetic resonance imaging (fMRI) experiments in which we used novel multivariate fMRI analysis techniques to ask whether and how complex sequences of finger movements are represented over the cortical motor areas in the human brain. The first experiment addresses the question whether there is genuine sequence encoding in primary motor cortex (M1). In the second experiment, we asked how complex sequences (8 sequences, 11 digits each) are hierarchically encoded in higher motor areas, such as premotor, supplementary motor areas, or parietal regions. Finally, I will also discuss possible coexistence of hierarchical and non-hierarchical sequence representation.

References :
1. Lashley, K. S. The problem of serial order in behavior. In L. A. Jeffress (Ed.), Cerebral mechanisms in be- havior. New York: Wiley, 1951.
2. Yokoi A., Arbuckle S.A., and Diedrichsen J. Does human primary motor cortex represent sequences of finger movements? bioRxiv. 2017. DOI:
3. Diedrichsen J., Yokoi A., and Arbuckle S.A. Pattern component modeling: A flexible approach for understanding the representational structure of brain activity patterns. Neuroimage. (in press), DOI:

Masafumi Oizumi
ARAYA inc.

An information theoretic approach to the boundary problem of consciousness

When two people, say Alice and Bob, are talking, Alice cannot be conscious of what Bob is conscious of. Alice's consciousness does not include Bob's consciousness, and vice versa. There seems to be a clear boundary between Alice’s and Bob's consciousness. That is, two independent conscious entities, namely Alice and Bob, exist even though they are "connected" by talking with each other. On the other hand, inside Alice's (or Bob’s) brain, the opposite situation happens. In Alice’s brain, there are two brains, i.e., left brain and right brain, which potentially could have their own independent consciousness if they were not connected by corpus callosum. This is actually the case in split brain patients whose corpus callosum is cut. However, in the normal brain, two independent consciousness of left brain and right brain do not exist. What exists is one unified consciousness, namely Alice (or Bob), which includes both left brain and right brain. There is no clear boundary between the left brain and right brain's consciousness. What is the critical difference between the two cases? In one case, there is a boundary but in the other case, there is no boundary. What physical mechanisms determine the boundaries of consciousness? Can we predict the position of such boundaries in a complex brain network? In this talk, I will discuss the boundary problem of consciousness based on Integrated Information Theory (IIT). IIT is an attempt to mathematically quantify consciousness from the viewpoint of information and integration, which are considered to be the essential properties of consciousness. I will explain the theoretical predictions of IIT on the boundary problem and discuss how we should test them by experiments.

References :
1. Oizumi M, Tsuchiya N, Amari S (2016) Unified Framework for Information Integration Based on Information Geometry. PNAS, 113, 14817-14822.
2. Oizumi M, Albantakis L, Tononi G (2014). From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0. PLoS Comp Biol, 10, e1003588.
3. 大泉匡史 (2014) 意識の統合情報理論.Clinical Neuroscience, 32, 905-912, 2014.
4. 大泉匡史, 土谷尚嗣 (2012) 温度計に意識はあるか? -- 意識レベルの定量化へ向けた理論と実践.LiSA, 19, 352-359.

Aurelio Cortese
ATR Institute International Computational Neuroscience Laboratories

Manipulating metacognition and inducing learning dynamics with real-time fMRI

Studies using real-time functional magnetic resonance imaging (rt-fMRI) have recently incorporated the decoding approach, allowing for fMRI to be used as a tool to manipulate fine-grained neural activity. In a first set of experiments we bidirectionally modulated perceptual confidence by systematically manipulating multivoxel correlates of confidence in a frontoparietal network. The intervention was highly specific since task accuracy did not change, hence showing that confidence judgements are unlikely to simply reflect the strength of internal sensory evidence. Rather, confidence is a late-stage estimation, a metacognitive computation dissociable from sensory processes. Capitalizing on these results, in a new study we developed a novel design whereby participants’ own brain activity was used in real time to define the states of a two-armed bandit machine. Crucially, this information was withheld from the participants. This approach allowed us to intrinsically study reinforcement learning in the brain by emulating what the brain faces at all times: an enormous number of possible states related to a given action and outcome. We show that even in the absence of any physical cue, participants were able to implicitly access information embedded in spontaneous brain activity to make choices maximizing their reward. Furthermore, metacognition seemed to play a significant role in directing reward choices. Learning also resulted in changes in brain dynamics, with increased connectivity between basal ganglia and prefrontal regions, as well as voxel activities better representing task space. These results thus provide insights into the neural processes underlying metacognition and the computational roles of consciousness in decision-making, specifically in reinforcement learning.

Hiroshi Nomura
Department of Pharmacology, Graduate School of Pharmaceutical Sciences, Hokkaido University

Central histamine reactivates weak memory engrams and restores forgotten memories

Even after memories fade over long time, the lost memories may persist latently in the brain and sometimes reappear spontaneously. Reinforcement of positive modulators for retrieval of long-term memory may recover the forgotten items. However, how the retrieval of long-term memory is modulated is less understood than short-term memory. Thus, a method that promotes the retrieval of forgotten long-term memories has not been well established. Histamine in the central nervous system is implicated in learning and memory. Histamine H3 receptors inhibit the presynaptic release of histamine and other neurotransmitters and negatively regulate histamine synthesis. Because histamine H3 receptors are constitutively active, their inverse agonists upregulate histamine release. Therefore, histamine H3 receptor inverse agonists may enhance learning and memory. In this study, we show that histamine H3 receptor inverse agonists enhance retrieval of long-term memory and recover the forgotten memory in mice and humans. These findings indicate that activation of histamine receptor signaling in the PRh boosts reactivation of weak memory engrams and restores the apparently forgotten memories.

References :
1. Nomura H, Hara K, Abe R, Hitora-Imamura N, Nakayama R, Sasaki T, Matsuki N, Ikegaya Y. Memory formation and retrieval of neuronal silencing in the auditory cortex. Proc Natl Acad Sci U S A. 2015 Aug 4;112(31):9740-4.
2. Nakayama D, Iwata H, Teshirogi C, Ikegaya Y, Matsuki N, Nomura H. Long-delayed expression of the immediate early gene Arc/Arg3.1 refines neuronal circuits to perpetuate fear memory. J Neurosci. 2015 Jan 14;35(2):819-30.
3. Nakayama D, Baraki Z, Onoue K, Ikegaya Y, Matsuki N, Nomura H. Frontal association cortex is engaged in stimulus integration during associative learning. Curr Biol. 2015 Jan 5;25(1):117-23.