The latencies of successive two-alternative, forced-choice response times display intricately patterned sequential effects, or dependencies. They vary as a function of particular trial-histories, and in terms of the order and identity of previously presented stimuli and registered responses. This article tests a novel hypothesis that sequential effects are governed by dynamic principles, such as those entailed by a discrete sine-circle map adaptation of the Haken Kelso Bunz (HKB) bimanual coordination model. The model explained the sequential effects expressed in two classic sequential dependency data sets. It explained the rise of a repetition advantage, the acceleration of repeated affirmative responses, in tasks with faster paces. Likewise, the model successfully predicted an alternation advantage, the acceleration of interleaved affirmative and negative responses, when a task’s pace slows and becomes more variable. Detailed analyses of five studies established oscillatory influences on sequential effects in the context of balanced and biased trial presentation rates, variable pacing, progressive and differential cognitive loads, and dyadic performance. Overall, the empirical patterns revealed lawful oscillatory constraints governing sequential effects in the time-course and accuracy of performance across a broad continuum of recognition and decision activities.

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.

No abstract

Introduction: Cognition is assumed to rely on distinct and additive substages such as perceptual encoding, memory, and motor control. Nevertheless, questions surrounding the assumptions of modularity and additivity persist. If a stable cognitive architecture exists, then repeatedly executing the same cognitive act should repeatedly engage the self-same structure. If discreet sub-acts behave in a manner consistent with a sum of independent random variables, then the assumption of additive and modular cognitive processes is reasonable. However, if they develop dependencies, then the assumption of additivity and modularity in cognition should be questioned. Methods: The study required participants (N = 180) to successively execute identical elementary cognitive acts in a stacked 1-word, 2-word, and 4-word lexical decision task. Correct response time was the primary dependent measure. Results: Statistical analyses revealed evidence for additivity in mean response time after a logarithmic transformation (r2 = .81, p < .05 & r2 = .74, p < .05). This pattern is consistent with multiplicative dynamics. Conclusions: The results indicate that variance grows multiplicatively as a function of the number of sub-acts. A straightforward way to generate this pattern of variability growth is to assume the sub-acts develop successive dependencies and combine multiplicatively.

Ultrasound biofeedback therapy (UBT) provides real-time imaging of tongue movements and has demonstrated positive speech remediation outcomes; however, some individuals have limited or no response. UBT outcomes could be further improved by a simplified biofeedback display to enhance motor learning. Such simplification requires automatic processing of ultrasound images to determine biofeedback parameters and targets. We investigate potential biofeedback parameters using TonguePART, a method that automatically tracks the tongue surface on midsagittal ultrasound images to quantify displacement trajectories of the tongue root, dorsum, and blade. Our focus is rhotic syllables (/i/, /u/, /o/, /e/, /ε/, and /a/ with initial or final /r/) from children with residual speech sound disorders and children with typically developing speech. We train support vector machines on measured tongue part displacement trajectories to distinguish between accurate and misarticulated productions as determined from auditory perceptual ratings. Preliminary data indicate that a linear combination of the tongue dorsum and blade displacements, between the vowel and consonant, can distinguish between accurate and misarticulated productions of rhotic syllables. These results suggest a real-time biofeedback parameter based on projections of real-time dorsum and blade displacements, along with potential target values, different for each vowel, for this parameter in simplified UBT for speech remediation.