Biology subjects

Otto, A. R.

Publications and source records attributed to Otto, A. R..

7 recordsLinked to original sources

bioRxiv2025

Dissecting the contribution of recent reward versus recent performance history on cognitive effort allocation

An extensive body of literature has shown that humans tend to avoid expending cognitive effort, just like for physical effort or financial resources. How then, do we decide whether to put this effort in? Decision-making not only involves choosing our actions, but also the meta-decision of how much cognitive effort to invest in making this choice, weighing the costs of cognitive effort against potential rewards. Popular recent theories, grounded in the field of reinforcement learning, suggest that this cost-benefit trade-off can be informed by the opportunity costs of effort investment, which the brain may approximate by the estimated average reward rate per unit time. It follows from intuition that in a low reward environment, investing cognitive resources in the task at hand will less likely lead to missed opportunities. Recent studies provided support for this idea, showing that people exert more cognitive effort when reward rate is low. Here, we replicate one of the key previous findings but provide an important nuance to this result. Cognitive effort allocation was better explained by participants recent performance history (i.e. accuracy rate) than average reward rate. In combination with the observation that participants were insensitive to the reward currently at stake, this invites a reinterpretation of these previous findings and suggests the need for further studies to assess whether environmental richness may indeed serve as a heuristic to modulate cognitive effort allocation.

neuroscience↗

bioRxiv2024

Non-invasive brain stimulation over the Frontopolar Cortex promotes willingness to exert cognitive effort in a foraging-like sequential choice task

Individuals avoid spending cognitive effort unless expected rewards offset the perceived costs. Recent work employing tasks that provide explicit information about demands and incentives, suggests causal involvement of the Frontopolar Cortex (FPC) in effort-based decision-making. Using transcranial direct current stimulation (tDCS), we examined whether the FPCs role in motivating effort generalizes to sequential choice problems in which task demand and reward rates vary indirectly and as a function of experience. In a double-blind, within- subject design, 46 participants received anodal (i.e., excitatory) or sham stimulation over the right FPC during an Effort Foraging Task, which required choosing between harvesting patches for successively decreasing resources or traveling to replenished patches by performing a cognitive task with environment-specific difficulty. As expected, participants exited patches later (i.e., displayed lower exit thresholds) when travelling required greater (versus less) effort, indicating increased travel costs in high-effort environments. Under anodal tDCS, the difference in exit thresholds between environments was significantly smaller relative to sham. Finally, individual differences analyses hint that participants with lower self-reported motivation to exert effort exhibited greater travel cost reductions following tDCS. Together, these findings support the theorized causal role of the FPC in motivating cognitively effortful behavior, expand its role to more ecologically valid serial decisions and highlight the potential for tDCS as a tool to increase motivation with potential clinical applications. Significance statementUncovering the neural mechanisms regulating engagement in effortful behavior is crucial, as it will improve our understanding and treatment of conditions characterized by reduced motivation, e.g., apathy and anhedonia. The Frontopolar Cortex (FPC) has been implicated in increasing effort exertion in settings that provide explicit information about effort demand and reward. Using transcranial direct current stimulation (tDCS), we investigated whether the FPC retains its motivating capacity in sequential choice problems that vary effort and reward indirectly. We demonstrate that FPC stimulation decreases cognitive effort-based travel costs in an Effort Foraging Task, indicating a causal and general involvement of the FPC in motivating effortful behavior, highlighting the potential of tDCS as a new avenue for increasing motivation with potential clinical applications.

neuroscience↗

bioRxiv2022

The neural basis of cost-benefit trade-offs in effort investment: a quantitative activation likelihood estimation meta-analysis

Influential theories of cognitive effort-based decision-making suggest that a cost-benefit trade-off guides mental effort allocation and that this trade-off may be reflected in shared neural activity in circuits tracking potential reward and task demand, supporting the idea that. While the dorsal medial prefrontal cortex (mPFC) - and particularly the anterior cingulate cortex (dACC) - has been proposed as a candidate region implementing this computation, it remains unclear whether mPFC/dACC activity tracks rewards and task demand independently or integrates them to reflect effort intensity. Recent accounts posit that the dACC plays a key role in mediating cost-benefit trade-offs. However, empirical evidence remains inconsistent. We conducted a systematic meta-analysis of neuroimaging studies, using the activation-likelihood estimation method to quantify brain activity across 45 studies (N = 1273 participants) investigating choices and task performance in reward-guided cognitive control. We observed significant recruitment of the mPFC/dACC, putamen, and anterior insula for processing larger rewards and higher task demands. The mPFC/dACC clusters sensitive to task demands and rewards were anatomically distinct: caudal mPFC/dACC activity tracked increasing task demands, while rostral mPFC/dACC activity tracked increasing reward. Interestingly, caudal mPFC/dACC activity tracked the integration of reward and task-demand, compatible with cost-benefit trade-off theories of dACC function. These findings provide evidence for distinct signals for mental demand and reward in the mPFC/dACC, which are integrated to support the decision to invest mental effort.

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bioRxiv2022

The modulation of acute stress on Model-Free and Model-Based reinforcement learning in Gambling Disorder

Background and aimsExperiencing acute stress is common in behavioral addictions such as gambling disorder. Additionally, like most substance-induced addictions, aberrant decision-making wherein a reactive habit-induced response (conceptualized as a Model-free [MF] in reinforcement learning) suppresses a flexible goal-directed response (conceptualized as a Model-based [MB]) is also common in gambling disorder. In the current study we investigated the influence of acute stress on the balance between habitual response and the goal-directed system. MethodsA sample of N = 116 pathological gamblers (PG) and healthy controls (HC) performed an acute stress task - the Socially Evaluated Cold pressure task (SECPT) - or a control task. Self-reported stress and salivary cortisol were collected as measures of acute stress. Following the SECPT, participants performed the Two-Step Markov Task to account for the relative contribution of MB and MF strategies. Additionally, verbal working-memory and IQ measures were collected to account for their mediating effects on the orchestration between MB/MF and the impact of stress. ResultsBoth groups had comparable baseline and stress-induced cortisol response to the SECPT. Non-stressed PG displayed lower MB learning than HC. MANOVA and regression analyses showed a deleterious effect of stress-induced cortisol response on the orchestration between MB and MF learning in HC but not in PG. Neither working memory nor IQ mediated these effects. Discussion and ConclusionsDespite normal cortisol response to stress, we found an abnormal pattern of modulation of stress on the orchestration between MB and MF learning among PG.

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bioRxiv2022

Context-dependent choice and evaluation in real-world consumer behavior

A body of work spanning neuroscience, economics, and psychology indicates that decisionmaking is context-dependent, which means that the value of an option depends not only on the option in question, but also on the other options in the choice set--or the context. While context effects have been observed primarily in small-scale laboratory studies with tightly constrained, artificially constructed choice sets, it remains to be determined whether these context effects take hold in real-world choice problems, where choice sets are large and decisions driven by rich histories of direct experience. Here, we investigate whether valuations are context-dependent in real-world choice by analyzing a large restaurant rating dataset (Yelp.com) as well as two independent replication datasets which provide complementary operationalizations of restaurant choice. We find that users make fewer ratings-maximizing choices in choice sets with higher-rated options--a hallmark of context-dependent choice-- and that post-choice restaurant ratings also varied systematically with the ratings of unchosen restaurants. Furthermore, in a follow-up laboratory experiment using hypothetical choice sets matched to the real-world data, we find further support for the idea that subjective valuations of restaurants are scaled in accordance with the choice context, providing corroborating evidence for a general mechanistic-level account of these effects. Taken together, our results provide a potent demonstration of context-dependent choice in real-world choice settings, manifesting both in decisions and subjective valuation of options.

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bioRxiv2017

The Opportunity Cost of Time Modulates Cognitive Effort

A spate of recent work demonstrates that humans seek to avoid the expenditure of cognitive effort, much like physical effort or economic resources. Less is clear, however, about the circumstances dictating how and when people decide to expend cognitive effort. Here we adopt a popular theory of opportunity costs and response vigor and to elucidate this question. This account, grounded in Reinforcement Learning, formalizes a trade-off between two costs: the harder work assumed necessary to emit faster actions and the opportunity cost inherent in acting more slowly (i.e., the delay that results to the next reward and subsequent rewards). Recent work reveals that the opportunity cost of time--operationalized as the average reward rate per unit time, theorized to be signaled by tonic dopamine levels, modulates the speed with which a person responds in a simple discrimination tasks. We extend this framework to cognitive effort in a diverse range of cognitive tasks, for which 1) the amount of cognitive effort demanded from the task varies from trial to trial and 2) the putative expenditure of cognitive effort holds measureable consequences in terms of accuracy and response time. In the domains of cognitive control, perceptual decision-making, and task-switching, we found that subjects tuned their level of effort exertion in accordance with the experienced average reward rate: when the opportunity cost of time was high, subjects made more errors and responded more quickly, which we interpret as a withdrawal of cognitive effort. That is, expenditure of cognitive effort appeared to be modulated by the opportunity cost of time. Further, and consistent with our account, the strength of this modulation was predicted by individual differences in efficacy of cognitive control. Taken together, our results elucidate the circumstances dictating how and when people expend cognitive effort.

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bioRxiv2017

Offline Replay Supports Planning: fMRI Evidence from Reward Revaluation

Making decisions in sequentially structured tasks requires integrating distally acquired information. The extensive computational cost of such integration challenges planning methods that integrate online, at decision time. Furthermore, it remains unclear whether \"offline\" integration during replay supports planning, and if so which memories should be replayed. Inspired by machine learning, we propose that (a) offline replay of trajectories facilitates integrating representations that guide decisions, and (b) unsigned prediction errors (uncertainty) trigger such integrative replay. We designed a 2-step revaluation task for fMRI, whereby participants needed to integrate changes in rewards with past knowledge to optimally replan decisions. As predicted, we found that (a) multi-voxel pattern evidence for off-task replay predicts subsequent replanning; (b) neural sensitivity to uncertainty predicts subsequent replay and replanning; (c) off-task hippocampus and anterior cingulate activity increase when revaluation is required. These findings elucidate how the brain leverages offline mechanisms in planning and goal-directed behavior under uncertainty.

neuroscience↗