Really Recursive Rational speech act models (rrrsa) of pragmatic inference

rrrsa is an R package for running RSA models -- Bayesian models of pragmatic inference. rrrsa was created by Ben Peloquin in collaboration with Michael C. Frank and has been optimized for analysis of experimental data such as those presented in Frank, et al. (Under Review) and Peloquin & Frank (2016). For other, more flexible variants of RSA models, please see http://forestdb.org/models/scalar-implicature.html.

Installation

You can install the latest version of rrrsa by installing devtools and running:

install.packages("devtools")
devtools::install_github("benpeloquin7/rrrsa")

What is RSA?

Rational speech act (RSA) models frame language understanding as a special case of social cognition in which speakers and listeners reason about one another recursively. A pragmatic listener $P_{L_n}(m|u)$, reasons about intended meaning $m$ of an utterance $u$ by a rational speaker $P_{s_n}(u|m)$ who chooses an utterance according to the expected utility of an utterance $U(m;u)$. $\alpha$ is a decision noise parameter.

$$P_{L_n}(m|u) \propto P_{S_n}(u|m)P(m)$$ $$P_{S_n} \propto e^{U(m;u)}$$ $$U(m;u) = -\alpha(-\log(P_{L_{n-1}}(m|u)) - C(u))$$

rrrsa includes empirical data

Data from "Rational speech act models of pragmatic reasoning in reference games" (Frank, et al., Under Reivew) and "Determining the alternatives in scalar implicature" (Peloquin & Frank, 2016) are also included in this package. Examples using data from these studies are included below.

rrrsa includes access to all model components

rrrsa provides users with access to all model components. The following sections demonstrate how this functionality can be used.

Calculating the informativity an utterance

rsa.informativity() takes three arguments, literal semantics $P_{L_0}$, alpha level (default 1), and cost (default 0). This function returns the surprisal of an utterance minus cost, multiplied by alpha.

Calculating the utility of an utterance

rsa.utility takes an input vector of literal listener semantics and outputs a normalized vector of speaker likelihoods. If costs are not specified the default 0's vector is used. If alpha is not specified a default value of $1$ is used.

Computing one full recursion

In the RSA framework one full recursion consists of a pragmatic listner $P_{L_1}$ who reasons about a rational speaker $P_{s_1}$ who reason about a literal listener $P_{L_0}$. Expected input is an $m$ matrix of $P_{L_0}$ literal listener values in which columns corresond to items (words) and rows correspond to semantic quantity (stars in Peloquin & Frank, under review). Optional arguments include a costs vector which whould be the same length as ncol and an optional priors vector which should the same length as nrows. rsa.fullRecursion provides safety checking for these cases. Output corresponds with pragmatic listener posterior predictions.

Running multiple recursions

rsa.reason is really a wrapper function for rsa.fullRecursion which provides an additional depth parameter which specifies the recursive depth during reasoning. If depth is not provided, default value is $1$.

Running data frames

Run RSA on a tidied data frame and avoid running individual model components individually with rsa.runDf. An RSA-ready, tidied data frame must contian columns for semantic quantity, item and semantics, where each row corresponds with unique item/quantity combination. A user should specify their naming convention for these items in the quantityVarName, itemVarName and semanticsVarName arguments. The costVarName and priorsVarName args correspond with costs and/or priors data. Users can specify values for alpha and depth hyperparamenters. runDf will return a data frame with a new model predictions preds column appended.

For more specific examples of rrrsa and example analysis using data from Frank, et al. (Under Review) and Peloquin & Frank (2016) please see:

vignette("summary", package="rrrsa")

benpeloquin7 / rrrsa