Retrieval cues and syntactic ambiguity resolution:Speed-accuracy tradeoff evidence

Language comprehension involves coping with ambiguity and recovering from misanalysis. Syntactic ambiguity resolution is associated with increased reading times, a classic finding that has shaped theories of sentence processing. However, reaction times conflate the time it takes a process to complete with the quality of the behavior-related information available to the system. We therefore used the speed-accuracy tradeoff procedure (SAT) to derive orthogonal estimates of processing time and interpretation accuracy, and tested whether stronger retrieval cues (via semantic relatedness: neighed->horse vs. fell->horse) aid interpretation during recovery. On average, ambiguous sentences took 250ms longer (SAT rate) to interpret than unambiguous controls, demonstrating veridical differences in processing time. Retrieval cues more strongly related to the true subject always increased accuracy, regardless of ambiguity. These findings are consistent with a language processing architecture where cue-driven operations give rise to interpretation, and wherein diagnostic cues aid retrieval, regardless of parsing difficulty or structural uncertainty

Neural oscillations and a nascent corticohippocampal theory of reference

The ability to use words to refer to the world is vital to the communicative power of human language. In particular, the anaphoric use of words to refer to previously mentioned concepts (antecedents) allows dialogue to be coherent and meaningful. Psycholinguistic theory posits that anaphor comprehension involves reactivating a memory representation of the antecedent. Whereas this implies the involvement of recognition memory, or the mnemonic sub-routines by which people distinguish old from new, the neural processes for reference resolution are largely unknown. Here, we report time-frequency analysis of four EEG experiments to reveal the increased coupling of functional neural systems associated with referentially coherent expressions compared to referentially problematic expressions. Despite varying in modality, language, and type of referential expression, all experiments showed larger gamma-band power for referentially coherent expressions compared to referentially problematic expressions. Beamformer analysis in high-density Experiment 4 localised the gamma-band increase to posterior parietal cortex around 400-600 ms after anaphor-onset and to frontaltemporal cortex around 500-1000 ms. We argue that the observed gamma-band power increases reflect successful referential binding and resolution, which links incoming information to antecedents through an interaction between the brain’s recognition memory networks and frontal-temporal language network. We integrate these findings with previous results from patient and neuroimaging studies, and we outline a nascent cortico-hippocampal theory of reference

Improvements to 232-thorium, 230-thorium, and 231-protactinium analysis in seawater arising from GEOTRACES intercalibration

The GEOTRACES program requires the analysis of large numbers of seawater samples for ^(232)Th, ^(230)Th, and ^(231)Pa. During the GEOTRACES international intercalibration exercise, we encountered unexpected difficulties with recovery and contamination of these isotopes, ^(232)Th in particular. Experiments were carried out to identify the source of these issues, leading to a more streamlined and efficient procedure. The two particular problems that we identified and corrected were (1) frits in columns supplied by Bio-Rad Laboratories caused loss of Th during column chemistry and (2) new batches of AG1-X8 resin supplied by Bio-Rad Laboratories released more than 100 pg of ^(232)Th during elution of sample. To improve yields and blanks, we implemented a series of changes including switching to Eichrom anion exchange resin (100-200 μm mesh) and Environmental Express columns. All Th and Pa samples were analyzed on a Neptune multi-collector inductively-coupled-plasma mass spectrometer (MC-ICP-MS) using peak hopping of ^(230)Th and ^(229)Th on the central SEM, with either ^(232)Th, ^(236)U (or both) used to monitor for beam intensity. We used in-house laboratory standards to check for machine reproducibility, and the GEOTRACES intercalibration standard to check for accuracy. Over a 1-y period, the 2 s.d. reproducibility on the GEOTRACES SW STD 2010-1 was 2.5% for ^(230)Th, 1.8% for ^(232)Th, and 4% for ^(231)Pa. The lessons learned during this intercalibration process will be of value to those analyzing U-Th-Pa and rare earth elements as part of the GEOTRACES program as well as those using U-series elements in other applications that require high yields and low blanks, such as geochronology

The activation of contextually predictable words in syntactically illegal positions

We present an eye-tracking study testing a hypothesis emerging from several theories of prediction during language processing, whereby predictable words should be skipped more than unpredictable words even in syntactically illegal positions. Participants read sentences in which a target word became predictable by a certain point (e.g. "bone" is 92% predictable given "The dog buried his…"), with the next word actually being an intensifier (e.g. "really"), which a noun cannot follow. The target noun remained predictable to appear later in the sentence. We used the boundary paradigm (Rayner, 1975) to present the predictable noun or an alternative unpredictable noun (e.g. "food") directly after the intensifier, until participants moved beyond the intensifier, at which point the noun changed to a syntactically legal word. Participants also read sentences in which predictable or unpredictable nouns appeared in syntactically legal positions. A Bayesian linear mixed model suggested a 5.7% predictability effect on skipping of nouns in syntactically legal positions, and a 3.1% predictability effect on skipping of nouns in illegal positions. We discuss our findings in relation to theories of lexical prediction during reading

Capitalization interacts with syntactic complexity

We investigated whether readers use the low-level cue of proper noun capitalization in the parafovea to infer syntactic category, and whether this results in an early update of the representation of a sentence’s syntactic structure. Participants read sentences containing either a subject relative or object relative clause, in which the relative clause’s overt argument was a proper noun (e.g., The tall lanky guard who alerted Charlie/Charlie alerted to the danger was young) across three experiments. In Experiment 1 these sentences were presented in normal sentence casing or entirely in upper case. In Experiment 2 participants received either valid or invalid parafoveal previews of the relative clause. In Experiment 3 participants viewed relative clauses in only normal conditions. We hypothesized that we would observe relative clause effects (i.e., inflated fixation times for object relative clauses) while readers were still fixated on the word who, if readers use capitalization to infer a parafoveal word’s syntactic class. This would constitute a syntactic parafoveal-on-foveal effect. Furthermore, we hypothesised that this effect should be influenced by sentence casing in Experiment 1 (with no cue for syntactic category being available in upper case sentences) but not by parafoveal preview validity of the target words. We observed syntactic parafoveal-on-foveal effects in Experiment 1 and 3, and a Bayesian analysis of the combined data from all three experiments. These effects seemed to be influenced more by noun capitalization than lexical processing. We discuss our findings in relation to models of eye movement control and sentence processing theories

The relational processing limits of classic and contemporary neural network models of language processing

Whether neural networks can capture relational knowledge is a matter of long-standing controversy. Recently, some researchers have argued that (1) classic connectionist models can handle relational structure and (2) the success of deep learning approaches to natural language processing suggests that structured representations are unnecessary to model human language. We tested the Story Gestalt model, a classic connectionist model of text comprehension, and a Sequence-to-Sequence with Attention model, a modern deep learning architecture for natural language processing. Both models were trained to answer questions about stories based on abstract thematic roles. Two simulations varied the statistical structure of new stories while keeping their relational structure intact. The performance of each model fell below chance at least under one manipulation. We argue that both models fail our tests because they can't perform dynamic binding. These results cast doubts on the suitability of traditional neural networks for explaining relational reasoning and language processing phenomena