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Dotov, Nie and Chemero (2010) conducted a set of exper-iments to demonstrate how phenomenology, particularly thework of Martin Heidegger, interfaces with experimental re-search in embodied cognitive science. Specifically, they drew aparallel between Heidegger’s notion of readiness-to-hand andthe concept of an extended cognitive system (Clark 2008) bylooking for the presence or absence of interaction-dominantdynamics (Holden, van Orden, and Turvey 2009; Ihlen andVereijken 2010) in a hand/mouse system. We share Dotov,Nie and Chemero’s optimism about the potential for cross-pollination between phenomenology and cognitive science, butwe think that it can be better advanced through a shift in fo-cus. First, we argue in favor of using Maurice Merleau-Ponty’sphenomenological theory as the philosophical foundation forexperimental research in embodied cognitive science. Sec-ond, we describe an audio-visual tracking task in virtual realitythat we designed and used to empirically investigate human-environment coupling and interactivity. In addition to provid-ing further support for phenomenologically-inspired empiricalcognitive science, our research also offers a more generaliz-able scientific treatment of the interaction between humans andtheir environments.

Study participants are typically unable to generate binary button-press sequences that pass as classically random sequences, such as from successive "fair coin" flips. Instead, their sequences repeat or alternate between responses too often. These deviations from randomness are commonly explained in terms of limitations or idiosyncrasies in cognitive processing. This article tests a novel hypothesis that randomness departures in participant-generated binary sequences are driven by coordination dynamics; alternating and repeating sequences are related to bimanual coordination attractors. Participants (N = 128) were asked to generate sequences that were representative of a random sequence, by successively pressing either of two buttons across 1,600 trials. Statistical analyses identify the binary button-press dynamics with a discrete sine-circle version of the Haken, Kelso, Bunz bimanual coordination model. Permutation analyses revealed the most common one- to five-trial subsequences were identified with the most dynamically stable coordinative relationships, consistent with bimanual coordination predictions. The sequences were consistent with scaling noises. Thus, participants' sequences departed from classical randomness by virtue of membership in a more inclusive category of variability that subsumes classical randomness. Recurrence quantification analysis revealed the mixture of stochasticity and determinism in the sequences was better approximated by the sine-circle model than by phase-randomized surrogate data sets that preserved both the power spectral densities and distributions of each participant's sequence. A relationship between randomness production and two-alternative forced-choice performance is established that constrains response time distribution models. The article's organization illustrates a nonreductive approach to inference for cognitive systems, inspired by statistical physics concepts such as renormalization group theory and universality. [ABSTRACT FROM AUTHOR]

Ultrasound biofeedback therapy (UBT), which incorporates real-time imaging of tongue articulation, has demonstrated generally positive speech remediation outcomes for individuals with residual speech sound disorder (RSSD). However, UBT requires high attentional demands and may therefore benefit from a simplified display of articulation targets that are easily interpretable and can be compared to real-time articulation. Identifying such targets requires automatic quantification and analysis of movement features relevant to accurate speech production. Our image-analysis program TonguePART automatically quantifies tongue movement as tongue part displacement trajectories from midsagittal ultrasound videos of the tongue, with real-time capability. The present study uses such displacement trajectories to compare accurate and misarticulated American-English rhotic /ɑr/ productions from 40 children, with degree of accuracy determined by auditory perceptual ratings. To identify relevant features of accurate articulation, support vector machine (SVM) classifiers were trained and evaluated on several candidate data representations. Classification accuracy was up to 85%, indicating that quantification of tongue part displacement trajectories captured tongue articulation characteristics that distinguish accurate from misarticulated production of /ɑr/. Regression models for perceptual ratings were also compared. The simplest data representation that retained high predictive ability, demonstrated by high classification accuracy and strong correlation between observed and predicted ratings, was displacements at the midpoint of /r/ relative to /ɑ/ for the tongue dorsum and blade. This indicates that movements of the dorsum and blade are especially relevant to accurate production of /r/, suggesting that a predictive parameter and biofeedback target based on this data representation may be usable for simplified UBT. [ABSTRACT FROM AUTHOR]