2,836 research outputs found
Emergent Language Generalization and Acquisition Speed are not tied to Compositionality
Studies of discrete languages emerging when neural agents communicate to
solve a joint task often look for evidence of compositional structure. This
stems for the expectation that such a structure would allow languages to be
acquired faster by the agents and enable them to generalize better. We argue
that these beneficial properties are only loosely connected to
compositionality. In two experiments, we demonstrate that, depending on the
task, non-compositional languages might show equal, or better, generalization
performance and acquisition speed than compositional ones. Further research in
the area should be clearer about what benefits are expected from
compositionality, and how the latter would lead to them
Music adapting to the brain: From diffusion chains to neurophysiology
During the last decade, the use of experimental approaches on cultural evolution
research has provided novel insights, and supported theoretical predictions, on the
principles driving the evolution of human cultural systems. Laboratory simulations of
language evolution showed how general-domain constraints on learning, in addition to
pressures for language to be expressive, may be responsible for the emergence of
linguistic structure. Languages change when culturally transmitted, adapting to fit,
among all, the cognitive abilities of their users. As a result, they become regular and
compressed, easier to acquire and reproduce. Although a similar theory has been
recently extended to the musical domain, the empirical investigation in this field is still
scarce. In addition, no study to our knowledge directly addressed the role of cognitive
constraints in cultural transmission with neurophysiological investigation.
In my thesis I addressed both these issues with a combination of behavioral and
neurophysiological methods, in three experimental studies. In study 1 (Chapter 2), I
examined the evolution of structural regularities in artificial melodic systems while they
were being transmitted across individuals via coordination and alignment. To this
purpose I used a new laboratory model of music transmission: the multi-generational
signaling games (MGSGs), a variant of the signaling games. This model combines
classical aspects of lab-based semiotic models of communication, coordination and
interaction (horizontal transmission), with the vertical transmission across generations
of the iterated learning model (vertical transmission). Here, two-person signaling games
are organized in diffusion chains of several individuals (generations). In each game, the
two players (a sender and a receiver) must agree on a common code - here a miniature
system where melodic riffs refer to emotions. The receiver in one game becomes the
sender in the next game, possibly retransmitting the code previously learned to another
generation of participants, and so on to complete the diffusion chain. I observed the
gradual evolution of several structures features of musical phrases over generations:
proximity, continuity, symmetry, and melodic compression. Crucially, these features
are found in most of musical cultures of the world. I argue that we tapped into universal
processing mechanisms of structured sequence processing, possibly at work in the
evolution of real music. In study 2 (Chapter 3), I explored the link between cultural
adaptation and neural information processing. To this purpose, I combined behavioral
and EEG study on 2 successive days. I show that the latency of the mismatch negativity (MMN) recorded in a pre-attentive auditory sequence processing task on day 1, predicts
how well participants learn and transmit an artificial tone system with affective
semantics in two signaling games on day 2. Notably, MMN latencies also predict which
structural changes are introduced by participants into the artificial tone system. In study
3 (Chapter 4), I replicated and extended behavioral and neurophysiological findings on
the temporal domain of music, with two independent experiments. In the first
experiment, I used MGSGs as a laboratory model of cultural evolution of rhythmic
equitone patterns referring to distinct emotions. As a result of transmission, rhythms
developed a universal property of music structure, namely temporal regularity (or
isochronicity). In the second experiment, I anchored this result with neural predictors. I
showed that neural information processing capabilities of individuals, as measured with
the MMN on day 1, can predict learning, transmission, and regularization of rhythmic
patterns in signaling games on day 2. In agreement with study 2, I observe that MMN
brain timing may reflect the efficiency of sensory systems to process auditory patterns.
Functional differences in those systems, across individuals, may produce a different
sensitivity to pressures for regularities in the cultural system. Finally, I argue that neural
variability can be an important source of variability of cultural traits in a population.
My work is the first to systematically describe the emergence of structural properties of
melodic and rhythmic systems in the laboratory, using an explicit game-theoretic model
of cultural transmission in which agents freely interact and exchange information.
Critically, it provides the first demonstration that social learning, transmission, and
cultural adaptation are constrained and driven by individual differences in the functional
organization of sensory systems
ESCELL: Emergent Symbolic Cellular Language
We present ESCELL, a method for developing an emergent symbolic language of
communication between multiple agents reasoning about cells. We show how agents
are able to cooperate and communicate successfully in the form of symbols
similar to human language to accomplish a task in the form of a referential
game (Lewis' signaling game). In one form of the game, a sender and a receiver
observe a set of cells from 5 different cell phenotypes. The sender is told one
cell is a target and is allowed to send one symbol to the receiver from a fixed
arbitrary vocabulary size. The receiver relies on the information in the symbol
to identify the target cell. We train the sender and receiver networks to
develop an innate emergent language between themselves to accomplish this task.
We observe that the networks are able to successfully identify cells from 5
different phenotypes with an accuracy of 93.2%. We also introduce a new form of
the signaling game where the sender is shown one image instead of all the
images that the receiver sees. The networks successfully develop an emergent
language to get an identification accuracy of 77.8%.Comment: IEEE International Symposium on Biomedical Imaging (IEEE ISBI 2020
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