Towards a neurocomputational model of the actor-strategy in language comprehension Phillip Alday Matthias Schlesewsky Ina Bornkessel-Schlesewsky Live code at https://bitbucket.org/palday/ginnungagap-code Motivation: The Actor Heuristic Neurophysiological data from typologically diverse languages provide evidence for an actor-based interpretation strategy in language comprehension[1]. The search for an actor depends on a number of prominence scales: person, case, animacy, position, number and deﬁniteness. Actor Space The two arguments in a transitive construction are “pulled” toward or “pushed” away from actorhood by the weight of the individual features. Distinctness between arguments corresponds to distance in the actor space. Argument Prominence prominence distorts the space into attractor basins prominence distorts the space into respulsor hills actorhood follows prominence Weighting Inﬂuences Each prominence scale is subject to a language-speciﬁc weighting. This weighting serves to distort the actor space, increasing or decreasing the relative inﬂuence of speciﬁc prominence features. For example, case is stronger in German than word order, while in English the weight of word order is so extreme that the other scales are largely irrelevant. Aims Here, we sought to quantify the relative weights of the prominence scales and compare this against various proposed metrics in actor space. We used linear mixed models to examine the predictive power of each metric for ﬁxed factors (prominence scales) for human EEG while compensating for subject and item (lexical) variation. Computational Model We implemented an initial quantiﬁcation of the actor heuristic. Weights were estimated with the help of existing research[2]. There are three metrics for measuring distinctness, or diﬀerence in prominence. metric formula description dist P i |NP2i −NP1i| Manhattan distance (feature overlap) signdist P i (NP2i −NP1i) pairwise diﬀerence (signed, unweighted diﬀerence) sdiﬀ ⃗w · ⃗ NP2 −⃗w · ⃗ NP1 scalar diﬀerence of weighted prominences Prominence Hierarchies Feature Hierarchy Person: First = 1 > Other = 0 Case: Nominative = 1 > Accusative = -1 Animacy: Animate = 1 > Inanimate = 0 Position: Early = 1 > Late = 0 Number: Singular = 1 > Plural = 0 Deﬁniteness: Deﬁnite = 1 > Indeﬁnite = 0 Materials and Design 2x2x2x2 design with 60 lexical items; every condition appeared for each lexical item. Word Order Ambiguity NP1-Type NP2-Type Subject ﬁrst × Ambiguous × Noun × Noun Object ﬁrst Unambiguous Pronoun Pronoun 25 Ag/AgCl electrodes @ 250 Hz 37 German native speakers (20 women) 400ms for single words, 500ms for phrases, 100 ms ISI EEG Data NP2 noun NP2 pronoun FCZ CPZ PZ N400 P600 FCZ CPZ PZ N400 P600 0.5 1.0 −4 4 s µV Grand average ERPs triggered at the on- set of NP2 for the ambiguous condition. Blue = subject (actor) initial, Red = object ini- tial; solid = noun, dashed = pronoun for NP1. Model Data dist signdist sdiﬀ N400 P600 Mean EEG (restricted to the left posterior ROI) in each time window as a function of the diﬀerent metrics (centered) and its interaction with ambiguity. The colors indicate the “height”, i.e., a range of (predicted) values of the mean EEG. More color indicates more variation. Metric Performance Likelihood ratio test for models in the N400 time window with NP1 ambiguous. A linear mixed model of the form mean ~ c.(metric) + (1 | item) + (1 | subj), was calculated with lme4[3]. Df AIC logLik Chisq Chi Df Pr(Chisq) dist: 5 197812 -98901.48 signdist: 5 197740 -98865.21 0.00 0 1 sdiﬀ: 5 197596 -98793.06 216.84 0 <2.2e-16*** Discussion All three metrics show some potential; however, sdiff performs signiﬁcantly better than signdist (which performs better than dist). The signedness of signdist allows for conditional prediction — hence the diﬀerent color scale in the ambiguity condition above. The gradience of sdiff allows for much more precise predictions, which is reﬂected in the subtlety present in the respective color scale. Sample