What happened (and what did not): Discourse constraints on encoding of plausible alternatives

Abstract

Three experiments investigated how font emphasis influences reading and remembering discourse. Although past work suggests that contrastive pitch contours benefit memory by promoting encoding of salient alternatives, it is unclear both whether this effect generalizes to other forms of linguistic prominence and how the set of alternatives is constrained. Participants read discourses in which some true propositions had salient alternatives (e.g., British scientists found the endangered monkey when the discourse also mentioned French scientists) and completed a recognition memory test. In Experiments 1 and 2, font emphasis in the initial presentation increased participants’ ability to later reject false statements about salient alternatives but not about unmentioned items (e.g., Portuguese scientists). In Experiment 3, font emphasis helped reject false statements about plausible alternatives, but not about less plausible alternatives that were nevertheless established in the discourse. These results suggest readers encode a narrow set of only those alternatives plausible in the particular discourse. They also indicate that multiple manipulations of linguistic prominence, not just prosody, can lead to consideration of alternatives.

The Necessity of the Hippocampus for Statistical Learning

“…Was there evidence of new statistical learning in this patient population? Taken together, we can analyze the data set using mixed effects logistic regression from a signal detection framework to ask whether patients were able to correctly identify which of the two sequentially presented options was the target (Fraundorf, Benjamin, & Watson, 2013; DeCarlo, 2012; Wright, Horry, & Skagerberg, 2009), generalizing across experiments and task versions and explicitly modeling random variability across patients in their ability to identify the target (Snijders & Bosker, 1999).…”

The influences of valence and arousal on judgments of learning and on recall

“…Both of these problems can be solved by linear mixedeffects regression (Baayen, Davidson, & Bates, 2008;Jaeger, 2008; for applications to memory and metamemory, see Fraundorf, Benjamin, & Watson, 2013;Fraundorf, Watson, & Benjamin, 2010;Freeman, Heathcote, Chalmers, & Hockley, 2010;Hourihan, Fraundorf, & Benjamin, 2013;Murayama, Sakaki, Yan, & Smith, 2014). Like all multipleregression models, these models can incorporate multiple predictors of interest (termed fixed effects in the mixed-effects regression context), including continuously varying quantities, such as valence.…”

“…Although it would be possible to collapse such variability into a smaller number of categories (e.g., with a median split) for a factorial analysis of variance (ANOVA), such techniques would greatly reduce statistical power (Cohen, 1983). Second, we now had multiple predictors of interest (arousal, variance, and word frequency) and were interested in assessing the effect of each while holding the others constant.Both of these problems can be solved by linear mixedeffects regression (Baayen, Davidson, & Bates, 2008;Jaeger, 2008; for applications to memory and metamemory, see Fraundorf, Benjamin, & Watson, 2013;Fraundorf, Watson, & Benjamin, 2010;Freeman, Heathcote, Chalmers, & Hockley, 2010;Hourihan, Fraundorf, & Benjamin, 2013;Murayama, Sakaki, Yan, & Smith, 2014). Like all multipleregression models, these models can incorporate multiple predictors of interest (termed fixed effects in the mixed-effects regression context), including continuously varying quantities, such as valence.…”

Production–comprehension asymmetries and the acquisition of evidential morphology

“…Second, unlike traditional analyses of variance (ANOVA) on proportions of categorical outcomes obtained from subject and item means, this approach allows for better treatment of categorical data (Jaeger, 2008, cf. Barr, 2008Fraundorf, Benjamin, & Watson, 2013).…”