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Research Article: New Research | Sensory and Motor Systems
White-Matter Pathways for Statistical Learning of Temporal Structures 1
1,2
Vasilis M Karlaftis , Rui Wang
, Yuan Shen
3,4
4
5
1
, Peter Tino , Guy Williams , Andrew Welchman and Zoe
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Kourtzi 1
Department of Psychology, University of Cambridge, Cambridge United Kingdom
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Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing China
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Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou China
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School of Computer Science, University of Birmingham, Birmingham United Kingdom
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Wolfson Brain Imaging Centre, University of Cambridge, Cambridge United Kingdom
DOI: 10.1523/ENEURO.0382-17.2018 Received: 10 November 2017 Revised: 21 April 2018 Accepted: 23 April 2018 Published: 29 June 2018
Author Contributions: Vasilis M Karlaftis: Performed research, Contributed analytic tools, Analyzed data, Wrote the paper; Rui Wang: Performed research, Contributed analytic tools, Analyzed data, Wrote the paper; Yuan Shen: Contributed analytic tools, Analyzed data, Wrote the paper; Peter Tino: Designed research, Contributed analytic tools, Wrote the paper; Guy Williams: Contributed analytic tools, Wrote the paper; Andrew Welchman: Designed research, Wrote the paper; Zoe Kourtzi: Designed research, Wrote the paper. Funding: http://doi.org/10.13039/501100000268Biotechnology and Biological Sciences Research Council (BBSRC) H012508 Funding: http://doi.org/10.13039/501100000275Leverhulme Trust RF-2011-378 Funding: http://doi.org/10.13039/501100004963EC | Seventh Framework Programme (FP7) PITN-GA-2011-290011 PITN-GA-2012-316746 Funding: http://doi.org/10.13039/100010269Wellcome 095183/Z/10/Z Funding: http://doi.org/10.13039/501100000266Engineering and Physical Sciences Research Council (EPSRC) EP/L000296/1 The authors declare no competing financial interests. This work was supported by grants to ZK from the Biotechnology and Biological Sciences Research Council (H012508), the Leverhulme Trust (RF-2011-378) and the [European Community's] Seventh Framework Programme [FP7/2007-2013] under agreement PITN-GA-2011-290011, AEW from the Wellcome Trust (095183/ Z/10/Z) and the [European Community's] Seventh Framework Programme [FP7/2007-2013] under agreement PITN-GA-2012-316746, PT from Engineering and Physical Sciences Research Council (EP/L000296/1). Correspondence should be addressed to Zoe Kourtzi, Department of Psychology, University of Cambridge, Cambridge, UK. Email:
[email protected] Cite as: eNeuro 2018; 10.1523/ENEURO.0382-17.2018
Accepted manuscripts are peer-reviewed but have not been through the copyediting, formatting, or proofreading process. Copyright © 2018 Karlaftis et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
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1. Title: White-matter pathways for statistical learning of temporal structures 2. Abbreviated Title: White-matter connectivity for statistical learning 3. Authors and Affiliations Vasilis M Karlaftis1, Rui Wang1,2, Yuan Shen3,4, Peter Tino4, Guy Williams5, Andrew Welchman1, Zoe Kourtzi1 1 Department of Psychology, University of Cambridge, Cambridge, United Kingdom 2 Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China 3 Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China 4 School of Computer Science, University of Birmingham, Birmingham, United Kingdom 5 Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, United Kingdom 4. Author Contributions: Vasilis M Karlaftis: Performed research, Contributed analytic tools, Analyzed data, Wrote the paper; Rui Wang: Performed research, Contributed analytic tools, Analyzed data, Wrote the paper; Yuan Shen: Contributed analytic tools, Analyzed data, Wrote the paper; Peter Tino: Designed research, Contributed analytic tools, Wrote the paper; Guy Williams: Contributed analytic tools, Wrote the paper; Andrew Welchman: Designed research, Wrote the paper; Zoe Kourtzi: Designed research, Wrote the paper 5. Correspondence should be addressed to Zoe Kourtzi Department of Psychology, University of Cambridge Cambridge, UK Email:
[email protected] 6. Number of Figures: five (5) 7. Number of Tables :three (3) 8. Number of Multimedia: 0 9. Number of words for Abstract: 225 10. Number of words for Significance Statement: 112 11. Number of words for Introduction: 686 12. Number of words for Discussion: 1718 13. Acknowledgements: We would like to thank Caroline di Bernardi Luft for helping with data collection; the CamGrid team; Morten L. Kringelbach, Henrique M. Fernandes and Tim J. Van Hartevelt for help with DTI analyses; Heidi Johansen-Berg for help with optimizing the DTI sequences and helpful discussions. 14. Conflict of Interest: The authors declare no competing financial interests. 15. Funding sources: This work was supported by grants to ZK from the Biotechnology and Biological Sciences Research Council (H012508), the Leverhulme Trust (RF-2011-378) and the [European Community's] Seventh Framework Programme [FP7/2007-2013] under agreement PITNGA-2011-290011, AEW from the Wellcome Trust (095183/Z/10/Z) and the [European Community's] Seventh Framework Programme [FP7/2007-2013] under agreement PITN-GA-2012-316746, PT from Engineering and Physical Sciences Research Council (EP/L000296/1).
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Title: White-matter pathways for statistical learning of temporal structures
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Abstract: Extracting the statistics of event streams in natural environments is critical for interpreting
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current events and predicting future ones. The brain is known to rapidly find structure and meaning in
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unfamiliar streams of sensory experience, often by mere exposure to the environment (i.e. without
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explicit feedback). Yet, we know little about the brain pathways that support this type of statistical
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learning. Here, we test whether changes in white-matter connectivity due to training relate to our
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ability to extract temporal regularities. By combining behavioral training and Diffusion Tensor
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Imaging (DTI), we demonstrate that humans adapt to the environment’s statistics as they change over
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time from simple repetition to probabilistic combinations. In particular, we show that learning relates
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to the decision strategy that individuals adopt when extracting temporal statistics. We next test for
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learning-dependent changes in white-matter connectivity and ask whether they relate to individual
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variability in decision strategy. Our DTI results provide evidence for dissociable white-matter
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pathways that relate to individual strategy: extracting the exact sequence statistics (i.e. matching)
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relates to connectivity changes between caudate and hippocampus, while selecting the most probable
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outcomes in a given context (i.e. maximizing) relates to connectivity changes between prefrontal,
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cingulate and basal ganglia (caudate, putamen) regions. Thus, our findings provide evidence for
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distinct cortico-striatal circuits that show learning-dependent changes of white-matter connectivity
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and support individual ability to learn behaviorally-relevant statistics.
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Significance statement: Training is known to improve performance in a range of sensory-motor tasks
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and alter white-matter connectivity, as measured by Diffusion Tensor imaging (DTI). Yet, learning to
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extract the statistics of event streams in natural environments is thought to often occur without explicit
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feedback (i.e. by mere exposure to the environment). Here, we demonstrate that this type of statistical
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learning of temporal structures without trial-by-trial feedback relates to changes in white-matter
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connectivity in the human brain. Our findings provide evidence for distinct cortico-striatal circuits that
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support individual ability to learn behaviorally-relevant statistics. In particular, individuals engage
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dissociable structural brain networks depending on their decision strategy, suggesting alternate brain
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routes to learning predictive structures. 2
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Key words: vision, brain plasticity, brain imaging, Diffusion Tensor Imaging, statistical learning.
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Introduction
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Interacting successfully in dynamic environments entails that we extract meaningful structure from
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initially incomprehensible streams of events. This ability to extract spatial and temporal regularities
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from the environment, often without explicit feedback, is known as statistical learning (Perruchet and
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Pacton, 2006; Aslin and Newport, 2012). In particular, observers report that stimuli (shapes, tones, or
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syllables) that co-occur spatially or follow in a temporal sequence appear familiar (Saffran et al.,
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1996, 1999; Chun, 2000; Fiser and Aslin, 2002; Turk-Browne et al., 2005). Typically, regularities in
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the natural environment are probabilistic; for instance, combinations of sounds or syllables appear at
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different frequencies in the context of music or language. Learning such sequences entails extracting
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the probabilistic statistics that govern the temporal structure of events. Previous work has highlighted
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the role of strategies in probabilistic learning (Shanks et al., 2002; Erev and Barron, 2005) and
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perceptual decision-making (Eckstein et al., 2013; Acerbi et al., 2014; Murray et al., 2015). That is,
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observers are shown to match their choices stochastically according to the underlying input statistics
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or maximize their success by selecting the most probable outcomes. Despite the fundamental
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importance of statistical learning for making perceptual decisions, we know surprisingly little about
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the brain pathways that support individual ability and strategies for learning temporal regularities.
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Here, we combine behavioral measurements and multi-session DTI (before and after training)
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to investigate the structural (i.e. white-matter) pathways that engage in statistical learning of temporal
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structures. Recent advances in Diffusion Tensor Imaging (DTI) allow us to reliably measure brain
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connectivity as indexed by local water molecule diffusion (Basser and Pierpaoli, 1996; Le Bihan et
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al., 2001) or long-distance brain connections (Basser et al., 2000). DTI work provides accumulating
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evidence for learning-dependent changes in white-matter connectivity (Zatorre et al., 2012) due to
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training in a range of tasks including motor learning (Scholz et al., 2009; Taubert et al., 2010;
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Sampaio-Baptista et al., 2013), spatial navigation (Sagi et al., 2012; Hofstetter et al., 2013), working
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memory (Takeuchi et al., 2010), artificial grammar learning (Flöel et al., 2009) and language
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(Schlegel et al., 2012; Hofstetter et al., 2016). Here, we ask whether mere exposure to streams of
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information (i.e. without trial-by-trial feedback) changes white-matter connectivity in pathways that 4
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support our ability to extract statistical regularities. Further, we test whether these learning-dependent
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changes in white-matter connectivity relate to individual decision strategies when learning temporal
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structures.
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In particular, to investigate the brain pathways involved in learning temporal structures
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unencumbered by past experience, we generated temporal sequences based on Markov models of
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different orders (i.e. context lengths of 0, 1 or 2 previous items) (Figure 1). To simulate event
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structures in the natural environment that typically contain regularities at different scales, from simple
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repetition to probabilistic combinations, we exposed participants to sequences of unfamiliar symbols
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and varied the sequence structure unbeknownst to the participants by increasing the context length.
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We presented participants first with sequences determined by frequency statistics (i.e. occurrence
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probability per symbol), followed by sequences determined by context-based statistics that increased
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in context length (i.e. the probability of a given symbol appearing depends on the n preceding
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symbols). Participants performed a prediction task, indicating which symbol they expected to appear
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next in the sequence. Following previous statistical learning paradigms, participants were exposed to
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the sequences without trial-by-trial feedback.
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Our behavioral results show that individuals adapt to the environment’s statistics; that is they
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are able to extract predictive structures that change over time. Further, we show that individual
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learning of structures relates to decision strategy. In particular, learning context-based statistics relates
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to selecting the most probable outcomes in a given context (i.e. maximizing) rather than the exact
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sequence statistics (i.e. matching). Our DTI results demonstrate that individual strategies for learning
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behaviorally-relevant statistics engage distinct cortico-striatal circuits. In particular, learning-
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dependent changes in white-matter connectivity relate to individual variability in decision strategy:
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matching relates to connectivity changes between caudate and hippocampus, while maximizing relates
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to connectivity changes between prefrontal, cingulate and basal ganglia (caudate, putamen). Thus, our
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findings provide evidence for learning-dependent changes of white-matter connectivity in distinct
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cortico-striatal circuits that support our ability to extract behaviorally-relevant statistics in variable
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environments. 5
Figure 1
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Methods
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Observers: Forty-four healthy volunteers (15 female, 29 male) participated in the experiment; half
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participated in the training group and the rest in the no-training control group. The data from one
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participant per group were excluded from the study due to excessive head movement, resulting in
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twenty-one participants per group (training group: mean age 21.56 years, standard deviation 1.84
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years; no-training group: mean age 25.53 years, standard deviation 2.60 years). All participants were
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naive to the study, had normal or corrected-to-normal vision and signed an informed consent. Human
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subjects were recruited at a location which will be identified if the article is published. All
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experiments were approved by [Author University] Ethics Committees.
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Stimuli: Stimuli comprised four symbols chosen from Ndjuká syllabary (Figure 1a). These symbols
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were highly discriminable from each other and were unfamiliar to the participants. Each symbol
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subtended 8.5o of visual angle and was presented in black on a mid-grey background. Experiments
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were controlled using Matlab and the Psychophysics toolbox 3 (Brainard, 1997; Pelli, 1997). For the
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behavioral training sessions, stimuli were presented on a 21-inch CRT monitor (ViewSonic P225f
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1280 x 1024 pixel, 85 Hz frame rate) at a distance of 45 cm. For the test sessions, stimuli were
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presented inside the MRI scanner using a projector and a mirror set-up (1280 x 1024 pixel, 60 Hz
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frame rate) at a viewing distance of 67.5 cm. The physical size of the stimuli was adjusted so that the
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angular size was constant during training and test sessions.
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Sequence design: We generated probabilistic sequences by using a temporal Markov model and
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varying the memory length (i.e. context length) of the sequence (Anonymous, 2017). The model
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consists of a series of symbols, where the symbol at time i is determined probabilistically by the
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previous ‘k’ symbols. We refer to the symbol presented at time i, s(i), as the target and to the
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preceding k-tuple of symbols (s(i-1), s(i-2), … , s(i-k)) as the context. The value of ‘k’ is the order or
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level of the sequence:
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P (s(i) | s(i-1), s(i-2), … , s(1)) = P (s(i) | s(i-1), s(i-2), … , s(i-k)), k
