Reward boosts neural coding of task rules to optimise cognitive flexibility

Cognitive flexibility is critical for intelligent behaviour. However, its execution is effortful and often suboptimal. Recent work indicates that flexible behaviour can be improved by the prospect of reward, suggesting that rewards optimise flexible control processes. Here we investigated how different reward prospects influence neural encoding of task rule information to optimise cognitive flexibility. We applied representational similarity analysis (RSA) to human electroencephalograms, recorded while participants performed a rule-guided decision-making task. During the task, the prospect of reward varied from trial to trial. Participants made faster, more accurate judgements on high reward trials. Critically, high reward boosted neural coding of the active task rule and the extent of this increase was associated with improvements in task performance. These results suggest that reward motivation can improve cognitive performance by strengthening neural coding of task rule information, improving cognitive flexibility during complex behaviour. Significance Statement The importance of motivation is evident in the ubiquity with which reward prospect guides adaptive behaviour and the striking number of neurological conditions where motivation is impaired. In this study, we investigated how dynamic changes in motivation, as manipulated through reward, shape neural coding for task rules during a flexible decision-making task. The results of this work suggest that motivation to obtain reward modulates encoding of task rules needed for flexible behaviour. The extent to which reward increased task rule coding also tracked improvements in behavioural performance under high reward conditions. These findings help inform how motivation shapes neural processing in the healthy human brain. Introduction Flexible cognitive control is critical to human intelligence. When vying to win a card game, we can use arbitrary rules to play the best hand. When navigating a new city, we can apply navigation rules to sensory input from the world around us to arrive at the next tourist attraction. Cognitive flexibility is suboptimal compared to behaviours that require less flexible processing (Kleinsorge & Rinkenauer, 2012; Shen & Chun, 2011) but can be improved by motivational factors, such as the prospect of reward for fast and accurate performance (Braem & Egner, 2018). When the prospect of reward is high, performance improves on flexible rule-based tasks (Etzel et al., 2016; Kleinsorge & Rinkenauer, 2012), suggesting that reward might optimise cognitive flexibility by tuning rule-based neural coding patterns. The existing neuroimaging literature provides support for this perspective, indicating that reward prospect leads to stronger recruitment of frontoparietal brain regions implicated in cognitive control (Parro, Dixon, & Christoff, 2018). When reward cues are presented at the beginning of each trial, activity in these regions is typically enhanced prior to the onset of a target stimulus, suggesting that proactive control mechanisms contribute to reward-induced performance benefits (Engelmann et al., 2009; Krebs et al., 2011; Padmala & Pessoa, 2011). Critically, a recent study combining fMRI with pattern classification methods found that frontoparietal activity prior to target onset encoded abstract task rules with greater fidelity on reward trials than on no-reward trials (Etzel et al., 2016). The extent to which reward prospect enhanced the decodability of task rules also mediated behavioural benefits, suggesting that performance improvements may result from reward-driven tuning in cognitive control processes that prioritise task-relevant processing. This proposal has considerable theoretical appeal. Increased prioritisation of task-relevant information would provide a general explanation for the beneficial effects of reward on flexible cognitive control that is grounded in theories of prefrontal cortex function (Duncan, 2001; Miller & Cohen, 2001) and extends across a wide array of cognitive demands. Nonetheless, constraints on the temporal resolution of fMRI make it challenging to isolate task rule coding from subsequent perceptual processing because the slow hemodynamic response can make it difficult to pinpoint effects in time and distinguish sustained anticipatory activity from transient stimulus-evoked responses. To address this, high temporal resolution methods such as magnetoencephalography and electroencephalography are needed because their ability to distinguish rapid stimulus-evoked dynamics makes them ideal for isolating the effects of reward on task rule coding from subsequent neural coding patterns. The present study aimed to characterise how trial-by-trial changes in reward motivation influence neural coding for task rule information in the service of flexible behaviour. To do so, we developed a behavioural task that manipulated reward motivation dynamically by varying the magnitude of reward for fast and accurate performance. We then applied representational similarity analysis (RSA; Kriegeskorte, Mur, & Bandettini 2008) to human electroencephalograms to examine changes in neural coding as a function of reward prospect. This high temporal resolution, multivariate approach allowed us to track both task rule and sensory variables while separating task preparation from target-evoked neural processing in the lead up to adaptive behavioural responses. To summarise the main results, we found that high reward prospect produced significant performance improvements in accuracy and reaction time (RT). Consistent with the view that reward prospect increases proactive cognitive control, we found a significant increase in neural coding for task rules under high reward conditions. High reward prospect also modulated downstream sensorimotor coding, significantly increasing neural coding for task-relevant perceptual features of target stimuli and accurate motor responses. However, the only variables that tracked improvements in performance were neural coding of reward prospect and the increase in task coding on high reward trials. Method Participants We set a target sample size of 30 participants. During recruitment three participants were excluded, one due to a corrupt EEG recording and two due to excessive artefacts that led to the rejection of more than 120/650 trials. We therefore collected three more participants to reach the 30 participant target. The final sample were between 18 and 35 years of age (mean age = 23, 11 female), with normal or corrected-to-normal vision, who reported no history of neurological or psychiatric illness. Participants received £8 per hour or course credit for taking part and could earn up to £10 extra for their performance. This study was approved by the Central University Research Ethics Committee at the University of Oxford and all participants signed informed consent before taking part. Materials Stimuli were presented on a 22-inch screen with a spatial resolution of 1280 x 1024 and refresh rate of 60Hz. Stimulus presentation was controlled using Psychophysics Toolbox-3 (Kleiner, Brainard & Pelli, 2007) in MATLAB (MathWorks, version R2015b). Reward cues and feedback shown during the task were presented in size 30 Arial font. Task cues and target stimuli had approximate visual angles of 2.52° (100x100 pixels) and 1.26° (50x50 pixels) respectively, with visual angles calculated based on an approximate viewing distance of 60cm. F and J keys on a standard QWERTY keyboard were used to record left and right hand responses. EEG data were recorded with 61 Ag/AgCl sintered electrodes (EasyCap, Herrsching, Germany), a NeuroScan SynAmps RT amplifier, and Curry 7 acquisition software (Compumedics NeuroScan, Charlotte, NC). EEG data were preprocessed in EEGLAB (Delorme & Makeig, 2004, version 14.1.1b), behavioural analyses were performed using JASP (https://jasp-stats.org, version 0.8.1.3) and EEG analyses were performed in MATLAB (MathWorks, version R2015b) using the EEGlab and Fieldtrip toolboxes as well as custom scripts. Code Accessibility Task and analysis code will be made available prior to publication. Experimental Design and Statistical Analysis In this task (Figure 1), participants' overarching goal was to gain as many points as possible. To do so, participants categorized bi-dimensional target stimuli based on their colour (yellow vs blue) or shape (square vs circle). On each trial, only one feature dimension of the target (colour or shape) was relevant to gaining points, while the other feature served as an irrelevant distractor. The relevant feature dimension was signalled through a visual task cue prior to target onset. In addition to a single relevant feature dimension, each trial offered a high or low reward magnitude for making a correct response. This was signalled to participants at the beginning of each trial by a single pound sign (low reward: 5-10 points) or three pound signs (high reward: 50-100 points). The experimental sequence consisted of a reward cue, task cue, target, feedback screen and an inter-trial interval (ITI). The reward cue (£ or £££) was first presented for 800ms, followed by a 400ms delay. The task cue (one of four possible abstract shapes) was then presented for 200ms. The mapping of cues to tasks was counterbalanced between participants. Task cue offset was followed by a 400ms delay. The target (a yellow square, blue square, yellow circle or blue circle) was then presented and remained on screen until a response was given or for a maximum of 1400ms. If the active task rule was colour, the correct response mapping was ‘f’ or ‘j’ for yellow or blue targets respectively. If the active rule was shape, the correct response mapping was ‘f’ or ‘j’ for square or circle targets respectively. The response phase was followed by feedback lasting 200ms. An incorrect response or omission resulted in feedback showing ’+0’ points. A correct response resulted in feedback showing ‘+X’,