The anterior cingulate cortex (ACC) has been implicated across multiple highly specialized cognitive functions-including task engagement, motivation, error detection, attention allocation, value processing, and action selection. Here, we ask if ACC lesions disrupt task performance and firing in dorsomedial striatum (DMS) during the performance of a reward-guided decision-making task that engages many of these cognitive functions. We found that ACC lesions impacted several facets of task performance-including decreasing the initiation and completion of trials, slowing reaction times, and resulting in suboptimal and inaccurate action selection. Reductions in movement times towards the end of behavioral sessions further suggested attenuations in motivation, which paralleled reductions in directional action selection signals in the DMS that were observed later in recording sessions. Surprisingly, however, beyond altered action signals late in sessions-neural correlates in the DMS were largely unaffected, even though behavior was disrupted at multiple levels. We conclude that ACC lesions result in overall deficits in task engagement that impact multiple facets of task performance during our reward-guided decision-making task, which-beyond impacting motivated action signals-arise from dysregulated attentional signals in the ACC and are mediated via downstream targets other than DMS.

Adolescents experience significant developmental changes during a time of heightened sensitivity to social cues, particularly rejection by peers, which can be especially overwhelming for those with elevated levels of social anxiety. Social evaluative decision-making tasks have been useful in uncovering the neural correlates of information processing biases; however, linking youths\' task-based performance to individual differences in psychopathology (e.g., anxiety symptoms) has proven more elusive. Here, we address this weakness with drift diffusion modeling to decompose youths\' performance on the social judgment paradigm (SJP) to determine if this approach is useful in discovering individual differences in anxiety symptoms, as well as puberty, age, and sex. A sample of 103 adolescents (55 males, Mage = 14.49, SD = 1.69) completed the SJP and self-report measures of anxiety, as well as self- and parent-reported measures of puberty. The decision threshold parameter, reflecting the amount of evidence needed to make a social evaluative decision, predicted youth self-reported anxiety, above and beyond typical metrics of SJP performance. Our results highlight the potential advantage of parsing task performance according to the underlying cognitive processes. Future research would likely benefit from applying computational modeling approaches to social judgment tasks when attempting to uncover performance-based individual differences in psychopathology.

Current theories of decision-making propose that decisions arise through competition between choice options. Computational models of the decision process estimate how quickly information about choice options is integrated and how much information is needed to trigger a choice. Experiments using this approach typically report data from well-trained participants. As such, we do not know how the decision process evolves as a decision-making task is learned for the first time. To address this gap, we used a behavioral design separating learning the value of choice options from learning to make choices. We trained male rats to respond to single visual stimuli with different reward values. Then, we trained them to make choices between pairs of stimuli. Initially, the rats responded more slowly when presented with choices. However, as they gained experience in making choices, this slowing reduced. Response slowing on choice trials persisted throughout the testing period. We found that it was specifically associated with increased exponential variability when the rats chose the higher value stimulus. Additionally, our analysis using drift diffusion modeling revealed that the rats required less information to make choices over time. These reductions in the decision threshold occurred after just a single session of choice learning. These findings provide new insights into the learning process of decision-making tasks. They suggest that the value of choice options and the ability to make choices are learned separately and that experience plays a crucial role in improving decision-making performance.

Our prior research has identified neural correlates of cognitive control in the anterior cingulate cortex (ACC), leading us to hypothesize that the ACC is necessary for increasing attention as rats flexibly learn new contingencies during a complex reward-guided decision-making task. Here, we tested this hypothesis by using optogenetics to transiently inhibit the ACC, while rats of either sex performed the same two-choice task. ACC inhibition had a profound impact on behavior that extended beyond deficits in attention during learning when expected outcomes were uncertain. We found that ACC inactivation slowed and reduced the number of trials rats initiated and impaired both their accuracy and their ability to complete sessions. Furthermore, drift-diffusion model analysis suggested that free-choice performance and evidence accumulation (i.e., reduced drift rates) were degraded during initial learning-leading to weaker associations that were more easily overridden in later trial blocks (i.e., stronger bias). Together, these results suggest that in addition to attention-related functions, the ACC contributes to the ability to initiate trials and generally stay on task.

Variability in decision-making capacity and reward responsiveness may underlie differences in the ability to abstain from smoking. Computational modeling of choice behavior, as with the Hierarchical Drift Diffusion Model (HDDM), can help dissociate reward responsiveness from underlying components of decision-making. Here we used the HDDM to identify which decision-making or reward-related parameters, extracted from data acquired in a reward processing task, contributed to the ability of people who smoke that are not seeking treatment to abstain from cigarettes during a laboratory task.
80 adults who smoke cigarettes completed the Probabilistic Reward Task (PRT) - a signal detection task with a differential reinforcement schedule - following smoking as usual, and the Relapse Analogue Task (RAT) - a task in which participants could earn money for delaying smoking up to 50min - after a period of overnight abstinence. Two cohorts were defined by the RAT; those who waited either 0-min (n=36) or the full 50-min (n=44) before smoking.
PRT signal detection metrics indicated all subjects learned the task contingencies, with no differences in response bias or discriminability between the two groups. However, HDDM analyses indicated faster drift rates in 50-min vs. 0-min waiters.
Relative to those who did not abstain, computational modeling indicated that people who abstained from smoking for 50min showed faster evidence accumulation during reward-based decision-making. These results highlight the importance of decision-making mechanisms to smoking abstinence, and suggest that focusing on the evidence accumulation process may yield new targets for treatment.

UNASSIGNED: Adolescence is a time of heightened risk for developing depression and also a critical period for the development and integration of self-identity. Despite this, the relation between the neurophysiological correlates of self-referential processing and major depressive symptoms in youth is not well understood. Here, we leverage computational modeling of the self-referential encoding task (SRET) to identify behavioral moderators of the association between the posterior late positive potential (LPP), an event-related potential associated with emotion regulation, and youth self-reported symptoms of depression. Specifically, within a drift-diffusion framework, we evaluated whether the association between the posterior LPP and youth symptoms of major depression was moderated by drift rate, a parameter reflecting processing efficiency during self-evaluative decisions.
UNASSIGNED: A sample of 106 adolescents, aged 12 to 17 (53% male; Mage = 14.49, SD = 1.70), completed the SRET with concurrent high-density electroencephalography and self-report measures of depression and anxiety.
UNASSIGNED: Findings indicated a significant moderation: for youth showing greater processing efficiency (drift rate) when responding to negative compared to positive words, larger posterior LPPs predicted greater depressive symptom severity.
UNASSIGNED: We relied on a community sample and our study was cross-sectional in nature. Future longitudinal work with clinically depressed youth would be beneficial.
UNASSIGNED: Our results suggest a neurobehavioral model of adolescent depression wherein efficient processing of negative information co-occurs with increased demands on affective self-regulation. Our findings also have clinical relevance; youth\'s neurophysiological response (posterior LPP) and performance during the SRET may serve as a novel target for tracking treatment-related changes in one\'s self-identity.

Special Operations organizations have recently demonstrated their commitment to enhanced cognitive functioning and improving brain health through the development of a Cognitive Domain. However, as this new enterprise becomes supported by more resources and personnel, a critical question involves what cognitive assessments should be conducted to evaluate cognitive functions. The assessment itself forms a crux in the Cognitive Domain that could mislead cognitive practitioners if not properly applied. Here, the discussion addresses the most important criteria to satisfy in the development of a Special Operations cognitive assessment, including operational relevance, optimization, and speed. Cognitive assessments in this domain must incorporate the following: (1) a task with clear operational relevance to ensure meaningful results, (2) no ceiling effects so that performance can support cognitive enhancement initiatives, and (3) the task itself should impose a minimal time requirement to avoid creating a substantial logistical burden. A dynamic threat assessment task supported by drift diffusion modeling can meet all requisite criteria, while also providing more insight into decision parameters of Special Operations personnel than any currently used test. The discussion concludes with a detailed description of this recommended cognitive assessment task, as well as the research and development steps needed to support its application.

When it comes to the selection of adequate movements, people may apply varying strategies. Explicit if-then rules, compared to implicit prospective action planning, can facilitate action selection in young healthy adults. But aging alters cognitive processes. It is unknown whether older adults may similarly, profit from a rule-based approach to action selection. To investigate the potential effects of aging, the Rule/Plan Motor Cognition (RPMC) paradigm was applied to three different age groups between 31 and 90 years of age. Participants selected grips either instructed by a rule or by prospective planning. As a function of age, we found a general increase in a strategy-specific advantage as quantified by the difference in reaction time between plan- and rule-based action selection. However, in older age groups, these differences went in both directions: some participants initiated rule-based action selection faster, while for others, plan-based action selection seemed more efficient. The decomposition of reaction times into speed of the decision process, action encoding, and response caution components suggests that rule-based action selection may reduce action encoding demands in all age groups. There appears a tendency for the younger and middle age groups to have a speed advantage in the rule task when it comes to information accumulation for action selection. Thus, one influential factor determining the robustness of the rule-based efficiency effect across the lifespan may be presented by the reduced speed of information uptake. Future studies need to further specify the role of these parameters for efficient action selection.

Electric-double-layer (EDL) gating can induce large capacitance densities (∼1-10 μF cm-2) in two-dimensional (2D) semiconductors; however, several properties of the electrolyte limit performance. One property is the electrochemical activity which limits the gate voltage (VG) that can be applied and therefore the maximum extent to which carriers can be modulated. A second property is electrolyte thickness, which sets the response speed of the EDL gate and therefore the time scale over which the channel can be doped. Typical thicknesses are on the order of micrometers, but thinner electrolytes (nanometers) are needed for very-large-scale-integration (VLSI) in terms of both physical thickness and the speed that accompanies scaling. In this study, finite element modeling of an EDL-gated field-effect transistor (FET) is used to self-consistently couple ion transport in the electrolyte to carrier transport in the semiconductor, in which density of states, and therefore quantum capacitance, is included. The model reveals that 50 to 65% of the applied potential drops across the semiconductor, leaving 35 to 50% to drop across the two EDLs. Accounting for the potential drop in the channel suggests that higher carrier densities can be achieved at larger applied VG without concern for inducing electrochemical reactions. This insight is tested experimentally via Hall measurements of graphene FETs for which VG is extended from ±3 to ±6 V. Doubling the gate voltage increases the sheet carrier density by an additional 2.3 × 1013 cm-2 for electrons and 1.4 × 1013 cm-2 for holes without inducing electrochemistry. To address the need for thickness scaling, the thickness of the solid polymer electrolyte, poly(ethylene oxide) (PEO):CsClO4, is decreased from 1 μm to 10 nm and used to EDL gate graphene FETs. Sheet carrier density measurements on graphene Hall bars prove that the carrier densities remain constant throughout the measured thickness range (10 nm-1 μm). The results indicate promise for overcoming the physical and electrical limitations to VLSI while taking advantage of the ultrahigh carrier densities induced by EDL gating.

What an adolescent thinks about themselves, commonly termed self-referential processing, has significant implications for youth long-term psychological well-being. Self-referential processing plays an important role in anticipatory and reactive processing in social contexts and contributes to symptoms of social anxiety. Previous work examining self-referential processing largely focuses on child and adolescent depression, relying on endorsement and reaction time for positive and negative self-describing adjectives in a self-referential encoding task (SRET). Here, we employ computational methods to interrogate the latent processes underlying choice reaction times to evaluate the fit of several drift-diffusion models of youth SRET performance. A sample of 106 adolescent, aged 12-17 (53% male; Mage = 14.49, SD = 1.70) completed the SRET and self-report measures of anxiety and depression. Our results support the utility of modeling the SRET, where the rate of evidence accumulation (i.e., drift rate) during negative self-referential processing was related to social anxiety above-and-beyond mean task performance. Our regression analyses indicated that youth efficiency in processing of self-referential views was domain general to anxiety, highlighting the importance of assessing both social and physiological anxiety symptoms when predicting SRET performance. The computational modeling results revealed that self-referential views are not uniquely related to depression-related constructs but also facets of anxiety.