I am a postdoctoral fellow in Marc Feldman's lab in the Stanford University Department of Biology. I received my bachelor’s degree from the Hebrew University, in Physics and Humanities, my MSc from the Hebrew University, in Ecology and Evolution, studying molecular evolution in Guy Sella's lab, and my PhD from Tel Aviv University, working in Arnon Lotem's lab in the department of Zoology.
Doctor of Philosophy, Tel-Aviv University (2014)
PhD, Tel Aviv University, Zoology (2015)
MSc, Hebrew University, Evolution (2010)
BSc, Hebrew University, Physics Humanities ('Amirim' program) (2007)
Marcus Feldman, Postdoctoral Faculty Sponsor
Current Research and Scholarly Interests
I study a wide range of evolutionary questions, from molecular evolution, through the selection of behavioral strategies and the evolution of cognitive learning mechanisms, to the impact that the microbiome has on its host's reaction to selective pressures that act on it.
In a recent collaboration in Marc Feldman's lab at Stanford I have also been working on cultural evolution and the way that creativity shapes the trajectory of change of human technology.
The effects of the microbiome on evolution of the host species, Stanford (1/1/2015)
- Cultural evolutionary theory: how culture evolves and why it matters. Proceedings of the National Academy of Sciences 2017; In Press
- Culture and the evolution of cognition: a process-level approach Proceedings of the National Academy of Sciences 2017; In Press
- A parsimonious neutral model suggests Neanderthal replacement was determined by migration and random species drift Nature Communications 2017; In Press
Evolution in leaps: The punctuated accumulation and loss of cultural innovations.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES
2015; 112 (49)
View details for DOI 10.1073/pnas.1520492112
Greater than the sum of its parts? Modelling population contact and interaction of cultural repertoires.
Journal of the Royal Society, Interface
2017; 14 (130)
Evidence for interactions between populations plays a prominent role in the reconstruction of historical and prehistoric human dynamics; these interactions are usually interpreted to reflect cultural practices or demographic processes. The sharp increase in long-distance transportation of lithic material between the Middle and Upper Palaeolithic, for example, is seen as a manifestation of the cultural revolution that defined the transition between these epochs. Here, we propose that population interaction is not only a reflection of cultural change but also a potential driver of it. We explore the possible effects of inter-population migration on cultural evolution when migrating individuals possess core technological knowledge from their original population. Using a computational framework of cultural evolution that incorporates realistic aspects of human innovation processes, we show that migration can lead to a range of outcomes, including punctuated but transient increases in cultural complexity, an increase of cultural complexity to an elevated steady state and the emergence of a positive feedback loop that drives ongoing acceleration in cultural accumulation. Our findings suggest that population contact may have played a crucial role in the evolution of hominin cultures and propose explanations for observations of Palaeolithic cultural change whose interpretations have been hotly debated.
View details for DOI 10.1098/rsif.2017.0171
View details for PubMedID 28468920
- Not Only at your own risk: The dependency of the evolution of risk-taking on population dynamics, species’ life history, and the environment Scientific Reports 2017; In Press
Game-Changing Innovations: How Culture Can Change the Parameters of Its Own Evolution and Induce Abrupt Cultural Shifts
PLOS COMPUTATIONAL BIOLOGY
2016; 12 (12)
One of the most puzzling features of the prehistoric record of hominid stone tools is its apparent punctuation: it consists of abrupt bursts of dramatic change that separate long periods of largely unchanging technology. Within each such period, small punctuated cultural modifications take place. Punctuation on multiple timescales and magnitudes is also found in cultural trajectories from historical times. To explain these sharp cultural bursts, researchers invoke such external factors as sudden environmental change, rapid cognitive or morphological change in the hominids that created the tools, or replacement of one species or population by another. Here we propose a dynamic model of cultural evolution that accommodates empirical observations: without invoking external factors, it gives rise to a pattern of rare, dramatic cultural bursts, interspersed by more frequent, smaller, punctuated cultural modifications. Our model includes interdependent innovation processes that occur at different rates. It also incorporates a realistic aspect of cultural evolution: cultural innovations, such as those that increase food availability or that affect cultural transmission, can change the parameters that affect cultural evolution, thereby altering the population's cultural dynamics and steady state. This steady state can be regarded as a cultural carrying capacity. These parameter-changing cultural innovations occur very rarely, but whenever one occurs, it triggers a dramatic shift towards a new cultural steady state. The smaller and more frequent punctuated cultural changes, on the other hand, are brought about by innovations that spur the invention of further, related, technology, and which occur regardless of whether the population is near its cultural steady state. Our model suggests that common interpretations of cultural shifts as evidence of biological change, for example the appearance of behaviorally modern humans, may be unwarranted.
View details for DOI 10.1371/journal.pcbi.1005302
View details for Web of Science ID 000392126000057
View details for PubMedID 28036346
View details for PubMedCentralID PMC5241012
- Land snail populations in abandoned water cisterns in Israel: a model system of artificial niche colonization Journal of Conchology 2016; In Press
The bottleneck may be the solution, not the problem
BEHAVIORAL AND BRAIN SCIENCES
As a highly consequential biological trait, a memory "bottleneck" cannot escape selection pressures. It must therefore co-evolve with other cognitive mechanisms rather than act as an independent constraint. Recent theory and an implemented model of language acquisition suggest that a limit on working memory may evolve to help learning. Furthermore, it need not hamper the use of language for communication.
View details for DOI 10.1017/S0140525X15000886
View details for Web of Science ID 000377938000022
View details for PubMedID 27562516
The problem of multimodal concurrent serial order in behavior
NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS
2015; 56: 252-265
The "problem of serial order in behavior," as formulated and discussed by Lashley (1951), is arguably more pervasive and more profound both than originally stated and than currently appreciated. We spell out two complementary aspects of what we term the generalized problem of behavior: (i) multimodality, stemming from the disparate nature of the sensorimotor variables and processes that underlie behavior, and (ii) concurrency, which reflects the parallel unfolding in time of these processes and of their asynchronous interactions. We illustrate these on a number of examples, with a special focus on language, briefly survey the computational approaches to multimodal concurrency, offer some hypotheses regarding the manner in which brains address it, and discuss some of the broader implications of these as yet unresolved issues for cognitive science.
View details for DOI 10.1016/j.neubiorev.2015.07.009
View details for Web of Science ID 000361578500019
View details for PubMedID 26209088
Evolution of protolinguistic abilities as a by-product of learning to forage in structured environments.
Proceedings. Biological sciences / The Royal Society
2015; 282 (1811)
The skills required for the learning and use of language are the focus of extensive research, and their evolutionary origins are widely debated. Using agent-based simulations in a range of virtual environments, we demonstrate that challenges of foraging for food can select for cognitive mechanisms supporting complex, hierarchical, sequential learning, the need for which arises in language acquisition. Building on previous work, where we explored the conditions under which reinforcement learning is out-competed by seldom-reinforced continuous learning that constructs a network model of the environment, we now show that realistic features of the foraging environment can select for two critical advances: (i) chunking of meaningful sequences found in the data, leading to representations composed of units that better fit the prevalent statistical patterns in the environment; and (ii) generalization across units based on their contextual similarity. Importantly, these learning processes, which in our framework evolved for making better foraging decisions, had been earlier shown to reproduce a range of findings in language learning in humans. Thus, our results suggest a possible evolutionary trajectory that may have led from basic learning mechanisms to complex hierarchical sequential learning that can support advanced cognitive abilities of the kind needed for language acquisition.
View details for DOI 10.1098/rspb.2015.0353
View details for PubMedID 26156764
Evolved to adapt: A computational approach to animal innovation and creativity
2015; 61 (2): 350-U179
View details for Web of Science ID 000351612300013
Learning a Generative Probabilistic Grammar of Experience: A Process-Level Model of Language Acquisition
2015; 39 (2): 227-267
We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural-language corpora. Given a stream of linguistic input, our model incrementally learns a grammar that captures its statistical patterns, which can then be used to parse or generate new data. The grammar constructed in this manner takes the form of a directed weighted graph, whose nodes are recursively (hierarchically) defined patterns over the elements of the input stream. We evaluated the model in seventeen experiments, grouped into five studies, which examined, respectively, (a) the generative ability of grammar learned from a corpus of natural language, (b) the characteristics of the learned representation, (c) sequence segmentation and chunking, (d) artificial grammar learning, and (e) certain types of structure dependence. The model's performance largely vindicates our design choices, suggesting that progress in modeling language acquisition can be made on a broad front-ranging from issues of generativity to the replication of human experimental findings-by bringing biological and computational considerations, as well as lessons from prior efforts, to bear on the modeling approach.
View details for DOI 10.1111/cogs.12140
View details for Web of Science ID 000351281700001
View details for PubMedID 24977647
Juvenile zebra finches learn the underlying structural regularities of their fathers' song.
Frontiers in psychology
2015; 6: 571-?
Natural behaviors, such as foraging, tool use, social interaction, birdsong, and language, exhibit branching sequential structure. Such structure should be learnable if it can be inferred from the statistics of early experience. We report that juvenile zebra finches learn such sequential structure in song. Song learning in finches has been extensively studied, and it is generally believed that young males acquire song by imitating tutors (Zann, 1996). Variability in the order of elements in an individual's mature song occurs, but the degree to which variation in a zebra finch's song follows statistical regularities has not been quantified, as it has typically been dismissed as production error (Sturdy et al., 1999). Allowing for the possibility that such variation in song is non-random and learnable, we applied a novel analytical approach, based on graph-structured finite-state grammars, to each individual's full corpus of renditions of songs. This method does not assume syllable-level correspondence between individuals. We find that song variation can be described by probabilistic finite-state graph grammars that are individually distinct, and that the graphs of juveniles are more similar to those of their fathers than to those of other adult males. This grammatical learning is a new parallel between birdsong and language. Our method can be applied across species and contexts to analyze complex variable learned behaviors, as distinct as foraging, tool use, and language.
View details for DOI 10.3389/fpsyg.2015.00571
View details for PubMedID 26005428
View details for PubMedCentralID PMC4424812
Reconciling genetic evolution and the associative learning account of mirror neurons through data-acquisition mechanisms
BEHAVIORAL AND BRAIN SCIENCES
2014; 37 (2): 210-211
An associative learning account of mirror neurons should not preclude genetic evolution of its underlying mechanisms. On the contrary, an associative learning framework for cognitive development should seek heritable variation in the learning rules and in the data-acquisition mechanisms that construct associative networks, demonstrating how small genetic modifications of associative elements can give rise to the evolution of complex cognition.
View details for DOI 10.1017/S0140525X13002392
View details for Web of Science ID 000335826100048
View details for PubMedID 24775167
The evolution of continuous learning of the structure of the environment
JOURNAL OF THE ROYAL SOCIETY INTERFACE
2014; 11 (92)
Continuous, 'always on', learning of structure from a stream of data is studied mainly in the fields of machine learning or language acquisition, but its evolutionary roots may go back to the first organisms that were internally motivated to learn and represent their environment. Here, we study under what conditions such continuous learning (CL) may be more adaptive than simple reinforcement learning and examine how it could have evolved from the same basic associative elements. We use agent-based computer simulations to compare three learning strategies: simple reinforcement learning; reinforcement learning with chaining (RL-chain) and CL that applies the same associative mechanisms used by the other strategies, but also seeks statistical regularities in the relations among all items in the environment, regardless of the initial association with food. We show that a sufficiently structured environment favours the evolution of both RL-chain and CL and that CL outperforms the other strategies when food is relatively rare and the time for learning is limited. This advantage of internally motivated CL stems from its ability to capture statistical patterns in the environment even before they are associated with food, at which point they immediately become useful for planning.
View details for DOI 10.1098/rsif.2013.1091
View details for Web of Science ID 000332384700024
View details for PubMedID 24402920
- Option generation in decision-making research: why just talk? FRONTIERS IN PSYCHOLOGY 2013; 4
Pervasive Adaptive Protein Evolution Apparent in Diversity Patterns around Amino Acid Substitutions in Drosophila simulans
2011; 7 (2)
In Drosophila, multiple lines of evidence converge in suggesting that beneficial substitutions to the genome may be common. All suffer from confounding factors, however, such that the interpretation of the evidence-in particular, conclusions about the rate and strength of beneficial substitutions-remains tentative. Here, we use genome-wide polymorphism data in D. simulans and sequenced genomes of its close relatives to construct a readily interpretable characterization of the effects of positive selection: the shape of average neutral diversity around amino acid substitutions. As expected under recurrent selective sweeps, we find a trough in diversity levels around amino acid but not around synonymous substitutions, a distinctive pattern that is not expected under alternative models. This characterization is richer than previous approaches, which relied on limited summaries of the data (e.g., the slope of a scatter plot), and relates to underlying selection parameters in a straightforward way, allowing us to make more reliable inferences about the prevalence and strength of adaptation. Specifically, we develop a coalescent-based model for the shape of the entire curve and use it to infer adaptive parameters by maximum likelihood. Our inference suggests that ∼13% of amino acid substitutions cause selective sweeps. Interestingly, it reveals two classes of beneficial fixations: a minority (approximately 3%) that appears to have had large selective effects and accounts for most of the reduction in diversity, and the remaining 10%, which seem to have had very weak selective effects. These estimates therefore help to reconcile the apparent conflict among previously published estimates of the strength of selection. More generally, our findings provide unequivocal evidence for strongly beneficial substitutions in Drosophila and illustrate how the rapidly accumulating genome-wide data can be leveraged to address enduring questions about the genetic basis of adaptation.
View details for DOI 10.1371/journal.pgen.1001302
View details for Web of Science ID 000287697300017
View details for PubMedID 21347283
Genomic structure and sequence of the gilthead seabream (Sparus aurata) growth hormone-encoding gene: Identification of minisatellite polymorphism in intron I
2000; 43 (5): 836-845
The growth hormone (GH) gene of the gilthead seabream (Sparus aurata) (saGH) has been cloned, sequenced, and characterized. The saGH gene spans approximately 4.3 kb and consists of six exons and five introns, as found for all cloned teleost GH genes with the exception of carps and catfish. The first and third introns contain long stretches of repetitive tandem repeats. The second intron, which is unusually long compared with that in other teleosts (and other vertebrates) spans 1747 nucleotides (nt) and contains several inverted repeats. Intron-targeted polymerase chain reaction (PCR) analysis identified length polymorphism of the first intron. Sequence analysis of four variants (405, 424, 636, and 720 nt) out of many variants found revealed that the variation in length is due to differences in the number of repeat monomers (17-mer or 15-mer) as well as minor changes in their length. This repeat unit contains the consensus half-site motif of the thyroid hormone response element (TRE) and estrogen response element (ERE). Polymorphism was found also in the third intron. This is the first report of such high polymorphism of the first intron of GH gene in a vertebrate.
View details for Web of Science ID 000089534000014
View details for PubMedID 11081974