Biology subjects

Froemer, R.

Publications and source records attributed to Froemer, R..

9 recordsLinked to original sources

bioRxiv2024

Reinforcement learning enhances training and performance in skilled alpine skiers compared to traditional coaching instruction

Skilled athletes need powerful movement strategies to solve tasks effectively. Typically, athletes learn these strategies with instruction-based teaching methods where coaches offer athletes a correct solution. Inspired by recent evidence from decision neuroscience, we asked whether skilled athletes learn strategy choices better with an evaluation-based training strategy (reinforcement learning). To address this question, we conducted a three-day learning experiment with skilled alpine ski racers (n=98) designed to improve their performance on flat slopes on slalom with four strategies at their disposal to achieve this goal. We compared performance and strategy choices of three groups: a reinforcement learning group, that only received feedback about their race times after every run, a supervised (free choice) learning group, that received strategy instructions from their coach, and a supervised (target skill) learning group, being coached to use the theoretically optimal strategy for skiing well on flats. We found that despite making similar strategy choices, the skiers in the reinforcement learning group, showed greater improvements in their race times during the training sessions than their counterparts in the supervised (free choice) learning group and outperformed them during a subsequent retention test. Surprisingly, the skiers in the reinforcement learning group even showed descriptively (but not significantly) better performance than those in the supervised (target skill) learning group. Our findings show that reinforcement learning can be an effective training strategy for improving strategy choices and performance among skilled athletes, even among the best ones.

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bioRxiv2022

Two distinct value-related patterns of EEG activity emerge during value-based choice, neither related to evidence accumulation

Previous work has identified characteristic neural signatures of value-based decision-making, including neural dynamics that closely resemble the ramping evidence accumulation process believed to underpin choice. Here, we test whether these signatures of the choice process can be temporally dissociated from additional, choice-independent value signals. Indeed, EEG activity during value-based choice revealed distinct spatiotemporal clusters, with a stimulus-locked cluster reflecting affective reactions to choice sets and a response-locked cluster reflecting choice difficulty. Surprisingly, neither of these clusters met the criteria for an evidence accumulation signal. Instead, we found that stimulus-locked activity can mimic an evidence accumulation process when aligned to the response. Re-analyzing four previous studies - including three perceptual decision-making studies - we show that response-locked signatures of evidence accumulation disappear when stimulus-locked and response-locked activity are modelled jointly. Collectively, our findings show that neural signatures of value can reflect choice-independent processes and look deceptively like evidence accumulation. Significance StatementTo choose, people must evaluate their options and select between them. Selection is well described by a process of accumulating evidence up to some threshold, with an electrophysiological signature in the centroparietal positivity (CPP). However, decision-making also gives rise to value signals reflecting affective reactions and other selection-unrelated processes. Measuring EEG while participants made value-based choices, we identified two spatiotemporally distinct value signals, neither reflecting evidence accumulation. Instead, we show that evidence accumulation signals found in the CPP can arise artifactually from overlapping stimulus- and response-related activity. These findings call for a significant reexamination of established links between neural and computational mechanisms of choice, while inviting deeper consideration of the array of cognitive and affective processes that occur in parallel.

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bioRxiv2020

Learning when effort matters: Neural dynamics underlying updating and adaptation to changes in performance efficacy

To determine how much cognitive control to invest in a task, people need to consider whether exerting control matters for obtaining rewards. In particular, they need to account for the efficacy of their performance - the degree to which rewards are determined by performance or by independent factors. Yet it remains unclear how people learn about their performance efficacy in an environment. Here we combined computational modeling with measures of task performance and EEG, to provide a mechanistic account of how people (a) learn and update efficacy expectations in a changing environment, and (b) proactively adjust control allocation based on current efficacy expectations. Across two studies subjects performed an incentivized cognitive control task while their performance efficacy (the likelihood that rewards are performance- contingent or random) varied over time. We show that people update their efficacy beliefs based on prediction errors - leveraging similar neural and computational substrates as those that underpin reward learning - and adjust how much control they allocate according to these beliefs. Using computational modeling, we show that these control adjustments reflect changes in information processing, rather than the speed-accuracy tradeoff. These findings demonstrate the neurocomputational mechanism through which people learn how worthwhile their cognitive control is.

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bioRxiv2020

When effort matters: Expectations of reward and efficacy guide cognitive control allocation

The amount of mental effort we invest in a task is influenced by the reward we can expect if we perform that task well. However, some of the rewards that have the greatest potential for driving these efforts (e.g., jobs, grants) are partly determined by factors beyond ones control. In such cases, effort has more limited efficacy for obtaining rewards. We have proposed that people integrate information about the expected reward and efficacy for effort to determine the expected value of control, and then adjust their control allocation (i.e. mental effort) accordingly. Here we test this theorys key behavioral and neural predictions. We show that participants invest more control when this control is more rewarding and more efficacious, and that these incentive components separately modulate EEG signatures of incentive evaluation and proactive control allocation. Our findings support the prediction that people combine worth and worthwhileness to determine how much effort to invest.

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bioRxiv2020

Time course and shared neurocognitive mechanisms of mental imagery and visual perception

When we imagine an object and when we actually see that object, similar brain regions become active. Yet, the time course and mechanisms with which imagery engages perceptual networks remain to be better understood. An emerging view holds that imagery and perception follow distinct dynamics during early visual processing with similarities arising only during later, high-level visual processing. However, confounds of visual stimulation and paradigms favoring observation of high-level processes associated with subjective imagery strength may have precluded evidence of earlier shared mechanisms. We therefore manipulated prior knowledge that informs early-stage top-down predictions and tracked electrophysiological brain responses while fully controlling visual stimulation. Participants saw and imagined objects associated with varying amounts of semantic knowledge. Imagery and perception were equally influenced by knowledge at an early stage (P1 component), revealing shared mechanisms that support low-level visual processing. This finding complements previous research by showing that imagery is not merely perception in reverse. Instead, in line with the predictive processing framework, both, perception and imagery are active and constructive processes that share top-down mechanisms already in early phases of processing.

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bioRxiv2019

Spatiotemporally distinct neural mechanisms underlie our reactions to and comparison between value-based options

Previous research suggests that people evaluate options in at least two ways: (1) appraising their overall value and (2) choosing between them. Here we test whether these processes are temporally dissociable, with appraisal-related processes tied to the time ones options appear and choice-related processes tied to the time a decision is made. We recorded EEG while participants individually rated and subsequently made choices between consumer goods. As predicted, we found appraisal-related neural activity locked to the onset of the stimuli and choice-related activity locked to (and preceding) the response. Patterns of appraisal- and choice-related activity were further associated with distinct topographical profiles. Using a novel neural index of ones certainty about a given items value, we also provide evidence that choices and choice-related activity were further modulated by this option-specific value certainty. Taken together, our results support the hypothesis that spatiotemporally distinct mechanisms underlie appraisal and choice, suggesting that commonly observed neural correlates of choice value may reflect either or both of these processes.

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bioRxiv2019

Age-Related Changes in the Neural Dynamics of Bottom-Up and Top-Down Processing During Visual Object Recognition: An Electrophysiological Investigation

When recognizing objects in our environments, we rely on both what we see and what we know. While elderly adults have been found to display increased sensitivity to top-down influences of contextual information during object recognition, the locus of this increased sensitivity remains unresolved. To address this issue, we examined the effects of aging on the neural dynamics of bottom-up and top-down visual processing during rapid object recognition. Specific EEG ERP components indexing bottom-up and top-down processes along the visual processing stream were assessed while systematically manipulating the degree of object ambiguity and scene context congruity. An increase in early attentional feedback mechanisms (as indexed by N1) as well as a functional reallocation of executive attentional resources (as indexed by P200) prior to object identification were observed in elderly adults, while post-perceptual semantic integration (as indexed by N400) remained intact. These findings suggest that compromised bottom-up perceptual processing of visual input in healthy aging leads to an increased involvement of top-down processes to resolve greater perceptual ambiguity during object recognition.

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bioRxiv2019

Goal congruency dominates reward value in accounting for behavioral and neural correlates of value-based decision-making

How we engage with a set of options (e.g., items on a menu) is affected both by the rewards they promise and our goal in choosing between them. Typically, our goal is to maximize potential reward and minimize potential punishment. Previous research on value-based decision-making has characterized how people make decisions with this goal in mind, identifying consistent behavioral and neural signatures associated with how rewarding a set of choice options are, overall and relative to one another. However, these studies suffer from a common confound: in each case, more rewarding options were also more congruent with ones goal of choosing the best option. Previous findings can therefore be accounted for by the reward value or the goal congruency of ones options. To compare these accounts directly, we had participants make choices while their goal varied between choosing the best or the worst option, resulting in situations where either high-reward or low-reward options were most goal-congruent. We found that goal congruency uniquely accounts for past observations that decision speed varies with the overall value of ones options. Within brain regions commonly associated with choice value, fMRI activity was associated with both relative and overall goal congruency. These goal congruency correlates were dissociable from separate correlates of the overall reward associated with a choice set (independent of ones goal). Our findings call for a reinterpretation of previous research on value-based choice, and offer an approach moving forward for disentangling the roles of rewards and goals in how we evaluate our options. Significance StatementWhether it is between restaurants or career paths, to make adaptive decisions we must evaluate our options and identify those that are most conducive to our current goal. Dysfunctional decision-making can therefore result from aberrant reward processing (e.g., impulse disorders) or from aberrant goal processing (e.g., OCD, ADHD). By focusing only on how people choose their favorite option in a choice set (when rewards and goals are aligned), past research has been unable to distinguish the degree to which behavior and neural activity are determined by reward versus goal processing. We disentangle these processes and show that behavior and fMRI activity are differentially influenced by the promised rewards versus the degree to which those rewards align with ones current goal.

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bioRxiv2018

I knew that! Confidence in outcome prediction and its impact on feedback processing and learning

Influential theories emphasize the importance of predictions in learning: we learn from feedback to the extent that it is surprising, and thus conveys new information. Here we explore the hypothesis that surprise depends not only on comparing current events to past experience, but also on online evaluation of performance via internal monitoring. Specifically, we propose that people leverage insights from response-based performance monitoring - outcome predictions and confidence - to control learning from feedback. In line with predictions from a Bayesian inference model, we find that people who are better at calibrating their confidence to the precision of their outcome predictions learn more quickly. Further in line with our proposal, EEG signatures of feedback processing are sensitive to the accuracy of, and confidence in, post-response outcome predictions. Taken together, our results suggest that online predictions and confidence serve to calibrate neural error signals to improve the efficiency of learning.

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