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Behavioral data, though has been an influential index oncognitive processes, is under scrutiny for having poorreliability as a result of noise or lacking replications ofreliable effects. Here, we argue that cognitive modeling canbe used to enhance the test-retest reliability of the behavioralmeasures by recovering individual-level parameters frombehavioral data. We tested this empirically with theProbabilistic Stimulus Selection (PSS) task, which is used tomeasure a participant’s sensitivity to positive or negativereinforcement. An analysis of 400,000 simulations from anAdaptive Control of Thought - Rational (ACT-R) model ofthis task showed that the poor reliability of the task is due tothe instability of the end-estimates: because of the way thetask works, the same participants might sometimes end uphaving apparently opposite scores. To recover the underlyinginterpretable parameters and enhance reliability, we used aBayesian Maximum A Posteriori (MAP) procedure. We wereable to obtain reliable parameters across sessions (IntraclassCorrelation Coefficient ~ 0.5), and showed that this approachcan further be used to provide superior measures in terms ofreliability, and bring greater insights into individualdifferences.

The current study aimed to elucidate the contributions of thesubcortical basal ganglia to human language by adopting theview that these structures engage in a basic neurocomputationthat may account for its involvement across a wide range of lin-guistic phenomena. Specifically, we tested the hypothesis thatbasal ganglia reinforcement learning mechanisms may accountfor variability in semantic selection processes necessary forambiguity resolution. To test this, we used a biased homographlexical ambiguity priming task that allowed us to measure au-tomatic processes for resolving ambiguity towards high fre-quency word meanings. Individual differences in task perfor-mance were then related to indices of basal ganglia function-ing and reinforcement learning, which were used to group sub-jects by learning style: primarily from choosing positive feed-back (Choosers), primarily from avoiding negative feedback(Avoiders), and balanced participants who learned equally wellfrom both (Balanced). The pattern of results suggests that bal-anced individuals, whom learn from both positive and negativereward equally well, had significantly lower access to the sub-ordinate homograph word meaning. Choosers and Avoiders,on the other hand, had higher access to the subordinate wordmeaning even after a long delay between prime and target. Ex-perimental findings were then tested using an ACT-R compu-tational model of reinforcement learning that learns from bothpositive and negative feedback. Results from the computa-tional model confirm and extend the pattern of behavioral find-ings, and provide a reinforcement learning account of lexicalpriming processes in human linguistic abilities, where a dual-path reinforcement learning system is necessary for preciselymapping out word co-occurrence probabilities.

Methodological advances have made it possible to generatefMRI predictions for cognitive architectures, such as ACT-R, thus expanding the range of model predictions and mak-ing it possible to distinguish between alternative models thatproduce otherwise identical behavioral patterns. However, fortasks associated with relatively brief response times, fMRI pre-dictions are often not sufficient to compare alternative models.In this paper, we outline a method based on effective connec-tivity, which significantly augments the amount of informationthat can be extracted from fMRI data to distinguish betweenmodels. We show the application of this method in the caseof two competing ACT-R models of the Stroop task. Althoughthe models make, predictably, identical behavioral and BOLDtime-course predictions, patterns of functional connectivity fa-vor one model over the other. Finally, we show that the samedata suggests directions in which both models should be re-vised.

Post‐traumatic stress disorder (PTSD) is a psychiatric disorder often characterized by the unwanted re‐experiencing of a traumatic event through nightmares, flashbacks, and/or intrusive memories. This paper presents a neurocomputational model using the ACT‐R cognitive architecture that simulates intrusive memory retrieval following a potentially traumatic event (PTE) and predicts hippocampal volume changes observed in PTSD. Memory intrusions were captured in the ACT‐R rational analysis framework by weighting the posterior probability of re‐encoding traumatic events into memory with an emotional intensity term I to capture the degree to which an event was perceived as dangerous or traumatic. It is hypothesized that (1) increasing the intensity I of a PTE will increase the odds of memory intrusions, and (2) increased frequency of intrusions will result in a concurrent decrease in hippocampal size. A series of simulations were run and it was found that I had a significant effect on the probability of experiencing traumatic memory intrusions following a PTE. The model also found that I was a significant predictor of hippocampal volume reduction, where the mean and range of simulated volume loss match results of existing meta‐analyses. The authors believe that this is the first model to both describe traumatic memory retrieval and provide a mechanistic account of changes in hippocampal volume, capturing one plausible link between PTSD and hippocampal volume. This paper presents a neurocomputational model using the ACT‐R cognitive architecture that simulates intrusive memory retrieval following a potentially traumatic event and predicts hippocampal volume changes observed in Post‐Traumatic Stress Disorder. [ABSTRACT FROM AUTHOR]

The current study aimed to elucidate the contributions of the subcortical basal ganglia to human language by adopting the view that these structures engage in a basic neurocomputation that may account for its involvement across a wide range of linguistic phenomena. Specifically, we tested the hypothesis that basal ganglia reinforcement learning (RL) mechanisms may account for variability in semantic selection processes necessary for ambiguity resolution. To test this, we used a biased homograph lexical ambiguity priming task that allowed us to measure automatic processes for resolving ambiguity toward high‐frequency word meanings. Individual differences in task performance were then related to indices of basal ganglia RL, which were used to group subjects into three learning styles: (a) Choosers who learn by seeking high reward probability stimuli; (b) Avoiders, who learn by avoiding low reward probability stimuli; and (c) Balanced participants, whose learning reflects equal contributions of choose and avoid processes. The results suggest that balanced individuals had significantly lower access to subordinate, or low‐frequency, homograph word meanings. Choosers and Avoiders, on the other hand, had higher access to the subordinate word meaning even after a long delay between prime and target. Experimental findings were then tested using an ACT‐R computational model of RL that learns from both positive and negative feedback. Results from the computational model simulations confirm and extend the pattern of behavioral findings, providing an RL account of individual differences in lexical ambiguity resolution. Going from cognitive theory to neural data to ACT‐R models, the authors relate brain activity in a lexical ambiguity priming task to brain processes that resolve ambiguity in word meanings. These detailed data were tested and found compatible to the results of an ACT‐R computational model of reinforcement learning (RL). The model confirms and extends the behavioral findings to provide a RL account of individual differences in lexical ambiguity resolution. [ABSTRACT FROM AUTHOR]

In this study, repetitive transcranial magnetic stimulation (rTMS) was applied over left dorsal premotor cortex (PMd) while participants performed a novel task paradigm that required planning of responses in accordance with both instructed rules and present stimuli. rTMS is a noninvasive form of neurostimulation that can interfere with ongoing processing of a targeted cortical region, resulting in a transient "virtual lesion" that can reveal the contribution of the region to ongoing behavior. Increased response times (RTs) were observed specifically when rTMS was applied over PMd while participants were preparing to execute a complex response to an uninstructed stimulus. To further delineate the effect of stimulation, condition‐specific RT distributions were modeled as three‐parameter Weibull distributions through hierarchical Bayesian modeling (HBM). Comparison of the estimated parameters to those of a paired control demonstrated that while PMd‐rTMS slightly decreased nondecision time, it also greatly increased the variability in the RT distribution. This increased variability resulted in an overall increase in predicted mean RT and is consistent with a delay in cognitive processes. In conjunction, an ACT‐R cognitive model of the task was developed in order to systematically test alternative hypotheses on the potential cognitive functions that may be affected by stimulation of PMd. ACT‐R simulations suggested that participant's behavior was due to an effect of TMS on a "re‐planning" process, indicating that PMd may be specifically involved in planning of complex motor responses to specific visual stimuli. In conjunction with the HBM modeling effort, these results suggest that PMd‐rTMS is capable of pausing or slowing the execution of a motor response‐planning process. The Role of Dorsal Premotor Cortex in Resolving Abstract Motor Rules provides alternative hypotheses about the cognitive functions affected by the application of repetitive transcranial magnetic stimulation. Their model simulated the effect of stimulation of the left dorsal premotor cortex right as participants provide a Models were used to demonstrate that the increased variability in observed response times can result from interference in replanning during the process of responding to the uninstructed stimulus. [ABSTRACT FROM AUTHOR]

Translational applications of cognitive science depend on having predictive models at the individual, or idiographic, level. However, idiographic model parameters, such as working memory capacity, often need to be estimated from specific tasks, making them dependent on task-specific assumptions. Here, we explore the possibility that idiographic parameters reflect an individual's biology and can be identified from task-free neuroimaging measures. To test this hypothesis, we correlated a reliable behavioral trait, the individual rate of forgetting in long-term memory, with a readily available task-free neuroimaging measure, the resting-state EEG spectrum. Using an established, adaptive fact-learning procedure, the rate of forgetting for verbal and visual materials was measured in a sample of 50 undergraduates from whom we also collected eyes-closed resting-state EEG data. Statistical analyses revealed that the individual rates of forgetting were significantly correlated across verbal and visual materials. Importantly, both rates correlated with resting-state power levels in the low (13-15 Hz) and upper (15-17 Hz) portion of the beta frequency bands. These correlations were particularly strong for visuospatial materials, were distributed over multiple fronto-parietal locations, and remained significant even after a correction for multiple comparisons (False Discovery Rate) and after robust correlation methods were applied. These results suggest that computational models could be individually tailored for prediction using idiographic parameter values derived from inexpensive, task-free imaging recordings. [ABSTRACT FROM AUTHOR]