The human task-evoked pupillary response provides a sensitive physiological index of the intensity and online resource demands of numerous cognitive processes (e.g., memory retrieval, problem solving, or target detection). Cognitive pupillometry is a wellestablished technique that relies upon precise measurement of these subtle response functions. Baseline variability of pupil diameter is a complex artifact that typically necessitates mathematical correction. A methodological paradox within pupillometry is that linear and nonlinear forms of baseline scaling both remain accepted baseline correction techniques, despite yielding highly disparate results. The task-evoked pupillary response (TEPR) could potentially scale nonlinearly, similar to autonomic functions such as heart rate, in which the amplitude of an evoked response diminishes as the baseline rises. Alternatively, the TEPR could scale similarly to the cortical hemodynamic response, as a linear function that is independent of its baseline. However, the TEPR cannot scale both linearly and nonlinearly. Our aim was to adjudicate between linear and nonlinear scaling of human TEPR. We manipulated baseline pupil size by modulating the illuminance in the testing room as participants heard abrupt pure-tone transitions (Exp. 1) or visually monitored word lists (Exp. 2). Phasic pupillary responses scaled according to a linear function across all lighting (dark, mid, bright) and task (tones, words) conditions, demonstrating that the TEPR is independent of its baseline amplitude. We discuss methodological implications and identify a need to reevaluate past pupillometry studies.

Transcranial direct current stimulation (tDCS) was paired with eye tracking to elucidate contributions of frontal, temporoparietal and anterior temporal cortex to early visual search patterns during picture naming (e.g., rapid visual scanning to diagnostic semantic features). Neurotypical adults named line drawings of objects prior to and following tDCS in three separate sessions, each employing a unique electrode montage. The gaze data revealed montage by stimulation (pre/post) interaction effects characterized by longer initial visual fixations (mean difference = 89 ms; Cohen's d = .8) and cumulative fixation durations (mean difference = 98 ms; Cohen's d = .9) on key semantic features (e.g., the head of an animal) after cathodal frontotemporal stimulation relative to the pre-stimulation baseline. We interpret these findings as reflecting a tDCS-induced modulation of semantic contributions of the anterior temporal lobe(s) to top-down influences on object recognition. Further, we discuss implications for the optimization of tDCS for the treatment of anomia in aphasia.

In a verbal fluency task, a person is required to produce as many exemplars of a given category (e.g., 'animals', or words starting with 'f') as possible within a fixed duration. Successful verbal fluency performance relies both on the depth of search within semantic/phonological neighborhoods ('clustering') and the ability to flexibly disengage between exhausted clusters ('switching'). Convergent evidence from functional imaging and neuropsychology suggests that cluster-switch behaviors engage dissociable brain regions. Switching has been linked to a frontoparietal network dedicated to executive functioning and controlled lexical retrieval, whereas clustering is more commonly associated with temporal lobe regions dedicated to semantic and phonological processing. Here we attempted to modulate cluster-switch dynamics among neurotypical adults (N = 24) using transcranial direct current stimulation (tDCS) delivered at three sites: a) anterior temporal cortex; b) frontal cortex; and c) temporoparietal cortex. Participants completed letter-guided and semantic category verbal fluency tasks pre/post stimulation. Cathodal stimulation of anterior temporal cortex facilitated the total number of words generated and the number of words generated within clusters during semantic category verbal fluency. These neuromodulatory effects were specific to stimulation of the one anatomical site. Our findings highlight the role of the anterior temporal lobes in representing semantic category structure and support the claim that clustering and switching behaviors have distinct substrates. We discuss implications both for theory and application to neurorehabilitation.

Cognitive science has seen a rapid evolution of the tools available for studying conceptual knowledge. Historically, much of our understanding of semantic memory has been informed through studies using language. Natural language processing (NLP) has offered groundbreaking techniques for elucidating relationships between concepts and language at unprecedented scales. One of the most popular applications has involved mathematically representing concepts using high dimensional feature spaces. Here we describe the nature of such semantic spaces and review ways in which human- and machine-generated semantic distance metrics differ in capturing taxonomic (e.g., dog-wolf) versus thematic (e.g., dog-leash) semantic relationships. We propose a novel method and open-source algorithm for deriving semantic distances between adjacent content words in connected language samples. This R package transforms a user-specified language transcript into a vector of pairwise semantic distances spanning all adjacent bigrams (e.g., The cat drank the milk → cat-drink, drink-milk, etc.). These distances constitute a continuous time series reflecting word-by-word level changes in meaning across a language sample of any length. We derive semantic distance norms and apply the proposed technique to a classic work of short fiction, To Build a Fire (Jack London, 1908). We discuss extensions of this time series approach, including the potential for forecasting, causal modeling, topic cohesion, and as an implicit measure of semantic impairment in spoken and/or written narratives.

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