Andrew Westbrook’s research while affiliated with Rutgers, The State University of New Jersey and other places

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Publications (46)


(A) An illustration of the color wheel working memory task. Every trial of the task consists of three phases. In the encoding phase (2 s), participants need to memorize colored squares. After a delay of 2 s, during the interference phase (2 s), a letter indicates if it is an ignore (I for ignore) or an updating (U for update) trial. In ignore trials, participants need to maintain colors from the encoding phase and ignore intervening stimuli. In update trials, participants have to let go of their previous representations and update into their memory the stimuli from the interference phase. Another delay separates interference from the response phase. This delay is 2 s for ignore and 6 s for update trials to match the time that the target stimuli are maintained between conditions. During response phase, participants see a color wheel and black frames of the same squares; they have 4 s to click on the target color for the highlighted square. Demand is manipulated by varying the number of squares from one to four. The example displayed here is of the highest demand. B Example trials of the COGED task. Participants perform two versions of choices. In the “task vs no effort” version, participants have to choose between repeating a level of ignore or update and not repeating the color wheel task at all (“No Redo”). The task option offer remains fixed at €2 and the no effort “No Redo” option varies from €0.1 to €2.2. In the “Ignore vs Update” trials, participants must choose between repeating either the ignore or update condition of the same demand. Ignore offers are always fixed at €2 and update offers vary from €0.1 to €4. Trial duration is 6 s. The trials are intermixed.
Performance on the color wheel working memory task. (A) Median deviance for experiment 1 (27 participants). (B) Median deviance for replication experiment 2 (61 participants). Deviance in degrees from the correct color is displayed here as a function of set size for ignore and update trials. (C,D) Median reaction times as a function of set size for ignore and update conditions. (C) Experiment 1 (27 participants). (D) Experiment 2 (61 participants). Error bars indicate within-participant SEM72,73 .
Example participant logistic regression curves. (A,B) Logistic regression curves for “task vs no effort” choices of a representative participant for update (A) and ignore (B) condition. The probability of accepting the “no effort” (i.e. no task) offer (y-axis) is plotted as a function of the amount of money offered for “no effort” (x-axis). Task offer was always €2 for both conditions and all set sizes and the “no effort” offer varied from €0.10 to €2.20. The estimated indifference point is the offer for “no effort” where the probability of choosing to do the task or the “no effort” option is equal (i.e., 0.5). Indifference points decreased with increasing set size. (C,D) Example logistic regression curves for “ignore vs update” indifference points. The probability of choosing the update offer is shown to vary as a function of the amount of money offered for update. Ignore offer was always €2 for all set sizes, while the update offer varied from €0.10 to €4. The indifference point is the update offer for which the acceptance probability is 0.5, i.e., subjective equivalence. (C) Representative participant who discounted rewards in order to avoid ignore trials (preference for update). (D) Example participant who discounted rewards in order to avoid the more demanding levels of update trials (preference for ignore).
(A,B) “Task vs no effort” indifference points as a function of set size. (A) Experiment 1 (24 participants). (B) Experiment 2 (50 participants). The indifference points index the subjective value that participants assign to performing the task. An indifference point of €1.5 would mean that participants were willing to forego €0.50 to avoid performing the task option (the task offer was fixed at €2). (C,D) Logistic regression curves for “task vs no effort” choices per condition across set size. The probability of accepting the “no effort” (i.e., no task) offer (y-axis) is plotted as a function of the value difference (Δvalue) between the effort and no effort offer (x-axis). A value difference of €1.5 reflects that participants sacrifice €1.5 when choosing the no effort offer over the effort offer. (C) Experiment 1 (24 participants). (D) Experiment 2 (50 participants). (E,F) Indifference points for “ignore versus update” choices as a function of set size. (E) Data from experiment 1 (26 participants). (F) Data from experiment 2 (57 participants). Indifference points smaller than 2 indicate a preference for update over ignore (offer for ignore was fixed at €2). Error bars indicate within-participant SEM72,73.
Absence of correlations between condition differences in performance and preference (IP). Ignore minus update performance (median deviance) does not covary with ignore minus update IP. Depicted data pooled from both Experiments (74 participants). Correlation coefficient: Kendall’s tau.

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Shielding working memory from distraction is more effortful than flexible updating
  • Article
  • Full-text available

April 2025

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3 Reads

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Monja I. Froböse

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Andrew Westbrook

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Roshan Cools

Exerting cognitive control is known to carry a subjective effort cost and people are generally biased to avoid it. Recent theorizing suggests that the cost of cognitive effort serves as a motivational signal to bias people away from excessive focusing and towards more cognitive flexibility. We asked whether the effort cost of stable distractor resistance is higher than that of non-selective flexible updating of working memory representations. We tested this prediction by using (1) a delayed response paradigm in which we manipulate demands for distractor resistance and flexible updating, as well as (2) a subsequent cognitive effort discounting paradigm that allows us to quantify subjective effort costs. We demonstrate, in two different samples (28 and 62 participants) that participants discount tasks both high in distractor resistance and flexible updating when comparing with taking a break. As predicted, when directly contrasting distractor resistance and non-selective flexible updating the subjective cost of performing a task requiring distractor resistance is higher than that requiring flexible updating.

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Schematic depiction of the Cognitive Effort Discounting (COGED) paradigm
In Part 1, participants performed two blocks (64 items with 16 targets per block) of each color-coded level of N-back (N = 1–4) to familiarize them with the effort demanded by each load level. In Part 2, participants completed an effort-reward decision-making procedure including 45 choices between repeating performance of higher-load N-back for a fixed larger reward or low load 1-back for a variable lower amount. After each choice (confirmed by a gray box surrounding the chosen option), the reward amount offered for the black task was titrated (increased by 50% if the harder option was chosen and vice versa) until participants were indifferent between the options, thereby enabling quantification of effort costliness and the rate at which the subjective value of an offer is discounted by the subjective effort cost (Methods). In Part 3, during fMRI, participants made an additional 90 decisions about effort-reward trade-offs that were individually calibrated based on the indifference points estimated during Part 2 to systematically vary the subjective value of the higher-load offers and decision difficulty (Methods; Supplementary Information).
Main behavioral results
Decreasing mean subjective value of rewards offered for higher-load N-back estimated from indifference points (averaged across base reward amounts; ±SEM) calculated from choices made during the pre-scan effort-reward decision-making procedure (COGED Part 2) is plotted by diagnostic group; illustrating the increasing subjective cost as a function of increasing cognitive load, but overall comparable cognitive effort discounting between groups. AN anorexia nervosa, HC healthy control.
Group differences in activation associated with subjective value representation during decision-making
Greater chosen offer SV-related activation during the decision-making phase of COGED part 3 in AN > HC (warm colors) in a region of the right inferior frontal gyrus in lateral prefrontal cortex (LPFC; 42, 24, 19; Tmax = 4.20; k = 33) and in AN < HC (cool colors) in a region of left primary sensorimotor cortex (SMC; −66, −26, 27; Tmax = 4.12; k = 71) as revealed by trialwise parametric analysis is shown at on selected slices of the MNI152 template at a voxelwise threshold of p < 0.001 (whole-brain corrected, p < 0.05).
Group differences in activation related to the decision easiness/difficulty
Greater activation in AN > HC during the decision-making phase of COGED part 3 as a function of the difference in SV between the chosen and unchosen options (SVchosen - SVunchosen) in a region of the left superior intraparietal sulcus (IPS; −27, −60, 50; Tmax = 3.90; k = 51) as revealed by trialwise parametric analysis is shown on selected slices of the MNI152 template at a voxelwise threshold of p < 0.001 (whole-brain corrected, p < 0.05). The group difference in this relationship was bilateral at a more lenient voxelwise threshold (Supplementary Fig. S8).
Exaggerated frontoparietal control over cognitive effort-based decision-making in young women with anorexia nervosa

August 2024

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137 Reads

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1 Citation

Molecular Psychiatry

Effortful tasks are generally experienced as costly, but the value of work varies greatly across individuals and populations. While most mental health conditions are characterized by amotivation and effort avoidance, individuals with anorexia nervosa (AN) persistently engage in effortful behaviors that most people find unrewarding (food restriction, excessive exercise). Current models of AN differentially attribute such extreme weight-control behavior to altered reward responding and exaggerated cognitive control. In a novel test of these theoretical accounts, we employed an established cognitive effort discounting paradigm in combination with fMRI in young acutely underweight female patients with AN (n = 48) and age-matched healthy controls (HC; n = 48). Contrary to the hypothesis that individuals with AN would experience cognitive effort (operationalized as N-back task performance) as less costly than HC participants, groups did not differ in the subjective value (SV) of discounted rewards or in SV-related activation of brain regions involved in reward valuation. Rather, all group differences in both behavior (superior N-back performance in AN and associated effort ratings) and fMRI activation (increased SV-related frontoparietal activation during decision-making in AN even for easier choices) were more indicative of increased control. These findings suggest that while effort discounting may be relatively intact in AN, effort investment is high both when performing demanding tasks and during effort-based decision-making; highlighting cognitive overcontrol as an important therapeutic target. Future research should establish whether exaggerated control during effort-based decision-making persists after weight-recovery and explore learning the value of effort in AN with tasks involving disorder-relevant effort demands and rewards.


Design
The experiment has pre-training, training and post-training sections. Rewards are delivered only during the training section, but not the pre-training or post-training sections. a, The pre- and post-training sections consist of two demand selection task blocks each; these two blocks assess effort preferences on two unrelated cognitive tasks (to aid visualization, fewer and larger dots are shown than in the actual dot-motion inhibition task). On each trial, participants choose the version of the task (easy or hard) they prefer and then perform their chosen task. Effort preference is defined as the proportion of choices whereby participants choose the hard task. b, The training section has only one block that consists of two distinct and pseudo-randomly interleaved trial types: rewarded trial (n = 40) and probe trial (n = 20). At the beginning of each trial, participants will see one of two cues (reward trial cue or probe trial cue) explicitly signalling the presence or absence of rewards on that trial. Next, they will be presented with two options (easy or hard); they will choose the version they prefer and then perform their chosen task. If it is a rewarded trial (signalled by the reward-trial cue), participants can earn rewards or points; if it is a probe (that is, unrewarded) trial (signalled by the probe-trial cue), participants will be fully aware that they will not receive any points (see also probe trial feedback panel), no matter their choice and performance. c, Value functions showing how rewards on rewarded trials in the training section differ across the three experimental conditions. Vertical dotted lines reflect median performance (i.e. reaction time) for a given participant; horizontal dotted lines reflect the mean reward (number of points). Choice (easy versus hard) determines rewards in the effort condition. Performance (correct reaction times) determines rewards in the performance condition. Neither choice nor performance determines rewards in the neutral condition. Critically, the expected reward is identical across conditions. No points are given for incorrect or missed responses (reaction time deadlines are participant specific). All conditions have identical task instructions, structure, sequence and cues. The only difference is whether rewards are assigned based on the reward-effort value function (effort condition), reward-performance value function (performance condition) or a uniform distribution (neutral condition).
Source data
Bayesian posterior densities for the effect of condition on effort preferences
For the eight registered hypotheses (top and middle rows), the mass of the posterior distributions was mostly above zero, and five hypotheses had BFs greater than 1 (at least anecdotal evidence for the experimental hypothesis). Bottom rows analyses were not registered and are included here only to facilitate the interpretation of condition contrasts in the top and middle rows. Positive Bayesian posterior estimates are hypothesis-consistent results (that is, higher effort preference in the effort than the other condition). No result is consistent with the null hypothesis because no BF is smaller than the registered criteria of BF ≤ 0.3. Posterior means and HPD intervals (95% and 89%) are shown. Dashed vertical lines indicate the null value where effort preference is the same in both conditions (i.e. beta estimate = 0).
Source data
Effort preference relative to baseline preference in the pre-training section
The effort preference difference computed by subtracting the pre-training baseline effort preference from the effort preference for rewarded trials (training section), probe trials (training section), post-training inhibition task and post-training updating task. Zero indicates no change relative to pre-training baseline. Each dot is one participant. Mean and 95% credible intervals are shown.
Source data
Exploratory analyses and Bayesian posterior densities for the effect of condition on task accuracy
Positive Bayesian posterior estimates indicate higher accuracy in the effort than the other condition. Model specification: accuracy ~ condition + pre-training baseline accuracy + objective task difficulty. Posterior means and HPD intervals (95% and 89%) are shown.
Source data
Effort preference correlations
Effort preferences for different trial types in different study sections correlated strongly with one another. ***P < 0.001.
Source data
An experimental manipulation of the value of effort

March 2024

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335 Reads

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27 Citations

Nature Human Behaviour

People who take on challenges and persevere longer are more likely to succeed in life. But individuals often avoid exerting effort, and there is limited experimental research investigating whether we can learn to value effort. We developed a paradigm to test the hypothesis that people can learn to value effort and will seek effortful challenges if directly incentivized to do so. We also dissociate the effects of rewarding people for choosing effortful challenges and performing well. The results provide limited evidence that rewarding effort increased people’s willingness to choose harder tasks when rewards were no longer offered (near transfer). There was also mixed evidence that rewarding effort increased willingness to choose harder tasks in another unrelated and unrewarded task (far transfer). These heterogeneous results highlight the need for further research to understand when this paradigm may be the most effective for increasing and generalizing the value of effort.


Figure 4. A,B) WM weighting (í µí¼Œ) and C) the RL learning rate (í µí»¼ !" ) parameters from
Figure 5. Accuracy as a function of previous correct iterations for each stimulus and drug. The
Figure 6. Test phase performance (selection of the option rewarded at a higher rate) as a function
Striatal Dopamine Can Enhance Learning, Both Fast and Slow, and Also Make it Cheaper

February 2024

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98 Reads

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3 Citations

Associations can be learned incrementally, via reinforcement learning (RL), or stored instantly in working memory (WM). While WM is fast, it is also capacity-limited and effortful. Striatal dopamine may promote RL plasticity, and WM, by facilitating updating and effort exertion. Yet, prior studies have failed to distinguish between dopamine's effects on RL versus WM. N = 100 participants completed a paradigm isolating these systems in a double-blind study measuring dopamine synthesis with [18F]-FDOPA imaging and manipulating dopamine with methylphenidate and sulpiride. Learning is enhanced among high synthesis capacity individuals and by methylphenidate, but impaired by sulpiride. Methylphenidate also blunts effort cost learning. Computational modeling reveals that individuals with high dopamine synthesis rely more on WM, while methylphenidate boosts their RL rates. The D2 antagonist sulpiride reduces accuracy due to diminished WM involvement and faster WM decay. We conclude that dopamine enhances both slow RL, and fast WM, by promoting plasticity and reducing effort sensitivity. These results also highlight the need to control for dopamine's effects on WM when testing its effects on RL.



Example of a task from the rationality battery
Imagine that there are three inhabitants of a fictitious country, A, B, and C, each of whom is either a knight or a knave. Knights always tell the truth. Knaves always lie. Two people are said to be of the same type if they are both knights or both knaves. A and B make the following statements: A says: “B is a knave.” B says: “A and C are of the same type.” What is C?.
Schematic illustration of the demand selection task
In this trial correct responding is by pressing the right mouse button. Note: Participants saw the rules at the beginning and had to remember them during the test blocks.
Schematic illustration of the choice phase of the cognitive effort discounting task
Note: Participants saw the n-back instructions during training, in the choice phase they were asked to play 1-back vs n-back and the value for 1-back was titrated either up (if n-back chosen) or down (if 1-back chosen).
Descriptive data (box plots) for the main outcome variables per study
Pearson’s Correlations per study and overall effect size including confidence intervals and p-value
Is it cognitive effort you measure? Comparing three task paradigms to the Need for Cognition scale

August 2023

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165 Reads

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14 Citations

Measuring individual differences in cognitive effort can be elusive as effort is a function of motivation and ability. We report six studies (N = 663) investigating the relationship of Need for Cognition and working memory capacity with three cognitive effort measures: demand avoidance in the Demand Selection Task, effort discounting measured as the indifference point in the Cognitive Effort Discounting paradigm, and rational reasoning score with items from the heuristic and bias literature. We measured perceived mental effort with the NASA task load index. The three tasks were not correlated with each other (all r’s < .1, all p’s > .1). Need for Cognition was positively associated with effort discounting (r = .168, p < .001) and rational reasoning (r = .176, p < .001), but not demand avoidance (r = .085, p = .186). Working memory capacity was related to effort discounting (r = .185, p = .004). Higher perceived effort was related to poorer rational reasoning. Our data indicate that two of the tasks are related to Need for Cognition but are also influenced by a participant’s working memory capacity. We discuss whether any of the tasks measure cognitive effort.


Effects of current and past depressive episodes on behavioral performance and subjective experience during an N-back task

February 2023

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23 Reads

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2 Citations

Journal of Behavior Therapy and Experimental Psychiatry

Background and objectives: Depression impairs working memory (WM). And, while many studies have documented impairment in WM during depression remission, those using the N-back task did not find differences between individuals with remitted depression and healthy controls. One reason for these findings may be that certain depression phenotypes, such as the childhood-onset form, which is likely to be associated with persistent WM problems, are underrepresented or unevenly represented in the studies. Because childhood-onset depression (COD) affects individuals while cognitive development is still ongoing, it is more likely to have lasting detrimental effects, as evidenced in residual memory impairment, than depression that onsets later in life. Further, it is unclear if depression episodes have cumulative effects on WM when measured via the N-back. Methods: We examined the effects of depression on WM performance (response time, accuracy, signal detection d') and subjective experience (difficulty, mental effort required) during a four-level N-back task among 112 adults with COD (42 currently depressed; 70 remitted depressed) and 80 never-depressed controls. Results: Compared to never-depressed controls, there was minimal evidence of impaired WM performance among participants with remitted or current depression; the groups also reported overall similar subjective experiences during the N-back. Notably, number of lifetime depressive episodes had a detrimental cumulative effect on response accuracy and d'. Limitations: WM was assessed only in regard to verbal memory. The sample size of currently depressed cases was smaller than that of the other groups. Conclusions: WM remains largely intact among adults with remitted COD, but increased number of depression episodes worsens WM performance.


Expected Costs of Mental Efforts are Updated When People Exert Effort, not by Prospective Information

November 2022

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59 Reads

To understand human behaviour, it is crucial to reveal the mechanisms by which we learn and decide about effort costs and benefits in an uncertain world. Whereas the mechanisms for reward learning are well-understood, the mechanisms for effort cost learning, and especially mental effort, remain elusive. Initially, we hypothesized that cost learning follows temporal-difference learning such that brains update expected costs when informed about upcoming effort levels. However, fMRI data revealed neural correlates of a cost-prediction error of mental effort during task execution and not in response to an effort cue about upcoming effort demands. These results imply that expected costs are updated during actual effort exertion, implying that the adaptive learning of mental effort cost does not follow traditional temporal-difference learning. This study contributes to the understanding of the neural mechanism underlying unwillingness to work, suggesting that humans learn mental effort costs only when they experience effort exertion.


Cognitive effort avoidance in veterans with suicide attempt histories

November 2022

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25 Reads

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1 Citation

Acta Psychologica

Suicide attempts (SA) are increasing in the United States, especially in veterans. Discovering individual cognitive features of the subset of suicide ideators who attempt suicide is critical. Cognitive theories attribute SA to facile schema-based negative interpretations of environmental events. Over-general autobiographical memory and facile solutions in problem solving tasks in SA survivors suggest that aversion to expending cognitive effort may be a neurobehavioral marker of SA risk. In veterans receiving care for mood disorder, we compared cognitive effort discounting and evidence-gathering in a beads task between veterans with (SAHx+; n = 26) versus without (SAHx-; n = 22) a history of SA. Groups did not differ in depressed mood or in a proxy metric of premorbid intelligence. Compared to SAHx- participants, SAHx+ participants self-reported significantly more severe cognitive problems in most domains, and also eschewed choice to earn higher monetary reward if earning it required a slightly increased working memory (WM) demand relative to an easy WM task. There was no group difference, however, in extent of evidence-gathering before declaring a conclusion in a beads task. These preliminary data suggest that aversion to expenditure of cognitive effort, potentially as a component of cognitive difficulties, may be a marker for SA risk.


Striatal dopamine dissociates methylphenidate effects on value-based versus surprise-based reversal learning

August 2022

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192 Reads

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16 Citations

Psychostimulants such as methylphenidate are widely used for their cognitive enhancing effects, but there is large variability in the direction and extent of these effects. We tested the hypothesis that methylphenidate enhances or impairs reward/punishment-based reversal learning depending on baseline striatal dopamine levels and corticostriatal gating of reward/punishment-related representations in stimulus-specific sensory cortex. Young healthy adults (N = 100) were scanned with functional magnetic resonance imaging during a reward/punishment reversal learning task, after intake of methylphenidate or the selective D2/3-receptor antagonist sulpiride. Striatal dopamine synthesis capacity was indexed with [¹⁸F]DOPA positron emission tomography. Methylphenidate improved and sulpiride decreased overall accuracy and response speed. Both drugs boosted reward versus punishment learning signals to a greater degree in participants with higher dopamine synthesis capacity. By contrast, striatal and stimulus-specific sensory surprise signals were boosted in participants with lower dopamine synthesis. These results unravel the mechanisms by which methylphenidate gates both attention and reward learning.


Citations (38)


... In fact, in AN a number of behavioral and neurobiological alterations may co-exist, and often persist, independently of food intake.These include: effortful behaviors (King et al. 2024); aberrant learning (Shott et al. 2012); mild cognitive impairment (Bayless et al. 2002); sleep alterations (Toor et al. 2021;Malcolm et al. 2022;Degasperi et al. 2024); disruption of peripheral and central circadian rhythms due to altered expression of clock genes (Salaün et al. 2024); alterations of reward-driven behaviors (Derissen et al. 2023). These behavioral alterations are key to comprehend whyAN progresses according to a long-lasting disease duration. ...

Reference:

The brainstem reticular formation pivots abnormal neural transmission in the course of Anorexia Nervosa
Exaggerated frontoparietal control over cognitive effort-based decision-making in young women with anorexia nervosa

Molecular Psychiatry

... In contrast, only a few theories focus on the value of physical effort irrespective of its instrumentality studied in animals, empirical investigations in humansparticularly in the physical domain-remain limited. Nevertheless, given the fundamental nature of the underlying learning principle, it is plausible to expect similar results in humans (see e.g., Clay et al., 2022;Lin et al., 2024). ...

An experimental manipulation of the value of effort

Nature Human Behaviour

... It seems that difficulties with integrating negative feedback may be central to these alterations (Mathar et al., 2017;Käenmäki et al., 2010), which could explain a potential insensitivity to the negative consequences associated with (over) eating. Crucially, in humans, a substantial contribution to (reward) learning is mediated by WM processes (Moustafa et al., 2008;Collins and Frank, 2012;Collins and Frank, 2018;Collins et al., 2014;Collins et al., 2017;Westbrook et al., 2024). The observed difficulties in reward learning in obesity may hence partly be rooted in a failure to update WM with new reward information, suggesting cognitive issues that extend beyond mere difficulties in valuation processes. ...

Striatal Dopamine Can Enhance Learning, Both Fast and Slow, and Also Make it Cheaper

... Forty studies were included in the meta-analysis evaluating the relationship between negative symptoms and defeatist performance beliefs (Beck et al., 2013;Bennett, Brown, Fang, & Blanchard, 2023;Berry & Greenwood, 2018;Buchanan et al., 2021;Clay, Raugh, Bartolomeo, & Strauss, 2020;Couture, Blanchard, & Bennett, 2011;Décombe et al., 2021;Ebrahimi et al., 2021;Fisher et al., 2023;Granholm et al., 2022;Granholm, Holden, Link, & McQuaid, 2014;Granholm, Holden, Link, McQuaid, & Jeste, 2013;Grant & Beck, 2009;Green et al., 2022;Green, Hellemann, Horan, Lee, & Wynn, 2012;Kiwanuka, Strauss, McMahon, & Gold, 2014;Lee & Yu, 2023;Lincoln et al., 2010;Luther et al., 2023;McGovern, Reddy, Reavis, & Green, 2020;Park, Bennett, Couture, & Blanchard, 2013;Paul, Strauss, Gates-Woodyatt, Barchard, & Allen, 2023;Pillny, Krkovic, & Lincoln, 2018;Pillny & Lincoln, 2016;Pos et al., 2019;Raugh & Strauss, 2024;Rector, 2004;Reddy et al., 2018;Romanowska & Best, 2023;Saperia et al., 2019;Shaheen & Amin, 2016;Staring, ter Huurne, & van der Gaag, 2013;Strauss & Gold, 2016;Strauss, Morra, Sullivan, & Gold, 2015;Takeda et al., 2019;Thonon, Levaux, Della Libera, & Larøi, 2020;Ventura et al., 2014;Zhang, James, & Strauss, 2023), with 38 studies used in the main analysis with global negative symptoms (Table S2) and 31 studies used in the subdomain analyses (Table S2.1). ...

The role of defeatist performance beliefs on cognitive effort-cost decision-making in schizophrenia
  • Citing Article
  • November 2023

Schizophrenia Research

... Participants followed a link to complete the study on Inquisit Web, an online survey program. The use of Prolific to recruit participants for a study conducted on Inquisit Web is a common research practice (Cummins et al., 2021;Maekelae et al., 2023;O'Connor et al., 2024), particularly for studies containing a dot-probe task, which requires reaction times to the millisecond (Fayyaz et al., 2024;Todd et al., 2022). Data collected on Inquisit is statistically equal in quality compared to data collected in person (Leong et al., 2022). ...

Is it cognitive effort you measure? Comparing three task paradigms to the Need for Cognition scale

... (1,2) Early studies suggested that depressive symptoms can serve as the initial manifestation of cognitive impairment, develop as a psychological effect of perceiving cognitive impairment, (3,4) or be a consequence of cumulative less meaningful cognitive engagement over the years because of depression-related behavioral modifications. (5) Additionally, severe or prolonged depression has been linked to a higher risk of dementia. (6) On the other hand, the causality could be in the other direction: cognitive impairment leading to depression. ...

Effects of current and past depressive episodes on behavioral performance and subjective experience during an N-back task
  • Citing Article
  • February 2023

Journal of Behavior Therapy and Experimental Psychiatry

... Some evidence suggests steeper cognitive-effort discounting is predictive of failing to seek psychotherapy for depression (Trusty & Swift, 2023) and prior suicide attempts among veterans (Bjork et al., 2022). One study found that the likelihood of engaging in physical effort for certain food rewards was related to whether participants were lean or obese (Mathar et al., 2016). ...

Cognitive effort avoidance in veterans with suicide attempt histories
  • Citing Article
  • November 2022

Acta Psychologica

... Here, we apply these network state definitions to investigate how acute administration of catecholaminergic agents affect the temporal properties of large-scale brain networks, with functional relevance, during resting-state fMRI scans in healthy adults. Specifically, in one scan condition, we administered methylphenidate (MPH), a dopamine and norepinephrine transporter (DAT/NET) reuptake inhibitor which acts globally to increase extracellular dopamine and norepinephrine and has been shown to enhance cognition due to MPH's impact on striatal function (Kodama et al. 2017;van den Bosch et al. 2022;Westbrook et al. 2020;Wilens 2008). In a second scan condition, we administered haloperidol (HAL), a selective antagonist of D2/D3 receptors located primarily in the striatum. ...

Striatal dopamine dissociates methylphenidate effects on value-based versus surprise-based reversal learning

... Previous studies have found that individuals with depression exhibit impaired cognitive effort in tasks requiring cognitive control (21). However, in mild depression, sad faces as emotional cues may selectively enhance cognitive engagement, thereby reducing the difference in AB effect between this group and healthy controls (42). This suggests that, while attentional control deficits are more prominent in severe depression, those with mild depression may exhibit less disruption, which could diminish as emotional intensity wanes. ...

Economic Choice and Heart Rate Fractal Scaling Indicate That Cognitive Effort Is Reduced by Depression and Boosted by Sad Mood
  • Citing Article
  • August 2022

Biological Psychiatry Cognitive Neuroscience and Neuroimaging

... In contrast, the stimulus phase mainly required participants to encode the two stimulus dimensions (reward and valence). Thus, the concerted activation of the DA and NA system may be particularly important to select and invigorate responses to behaviourally relevant stimuli rather than mere stimulus evaluation ( Hofmans et al., 2022). As such, this novel paradigmatic distinction between stimulus encoding and response implementation sheds new light on the contributions of the LC and SN/VTA during the processing of reward prospect and valence. ...

Effects of average reward rate on vigor as a function of individual variation in striatal dopamine

Psychopharmacology