SVR results for arousal ratings.

<p>A) Spatio-temporal decoding: Multivariate support vector regression (SVR) was used to regress <i>arousal</i> ratings (in increments: low/medium/high) from distributed patterns of CSD-ERPs within analysis time windows of 40 ms that were moved through the first 500 ms of each trial in steps of 20 ms. Ratings could be regressed significantly above chance between 180–200 ms (denoting the central time points of the analysis windows) and again around 380 ms after stimulus presentation (black line). Additionally shown are the results of the same SVR analysis using shuffled labels to obtain an empirical chance distribution for statistical testing (grey line). B) Temporal decoding: Multivariate SVR was used for each channel separately to regress <i>arousal</i> ratings (in increments: low/medium/high) from purely temporal patterns of CSD-ERPs within the first significant time period of the spatio-temporal analysis (combined time bins 180 ms and 200 ms). The heat map illustrates predictive channels, with electrodes P9 (<i>t</i>(14) = 2.59*), PO3 (<i>t</i>(14) = 2.24*), O1 (<i>t</i>(14) = 2.46*), P6 (<i>t</i>(14) = 2.46*), PO4 (<i>t</i>(14) = 2.87*) reaching significance. *<i>P</i><.05; **<i>P</i><.01 (uncorrected); error bars  =  standard errors.</p

Scalp distributions of differences in CSD-ERPs.

<p>The left column displays scalp maps for the average distribution of voltage for the N1 (50 to 150 ms), the N2 (150–250 ms), the EPN (280 to 320 ms) and the P3 (300 to 700 ms) time windows. The middle column visualises the difference maps for <i>arousal</i> (high minus low), and the right column displays the difference maps for <i>time reference</i> (future minus present) for the same ERP components.</p

Correlation coefficients of Low-level Image Features and Ratings.

<p>Note: <i>r</i> =  Pearson correlation coefficients; <i>P</i> =  uncorrected significance level; SD  =  Standard Deviation of the Mean; ‘Mean ratings’ refers to tests between the average ratings (across all participants) and the low-level image feature parameters; ‘Individual ratings’ refers to the averaged correlation coefficients between the individual participants' ratings and the low-level image features (SE  =  Standard Error of the Mean; Mean <i>r</i>_z  =  Fisher-Z-transformed <i>r</i>; *the critical value for <i>P</i><.05, df  = 23) was <i>r</i>_crit  = .553.</p><p>Correlation coefficients of Low-level Image Features and Ratings.</p

CSD-ERP results.

<p>Grand-average event-related potentials time-locked to stimulus onset, after current source density analysis (CSD-ERPs) sorted by rating increments for A) <i>Arousal</i> and B) <i>Time reference</i>. The classical CSD-ERP components were tested for significant differences between post-experimental rating increments at their typical sites: the CSD-N1 at FCz, the CSD-N2, the CSD-P3 at Pz, and the CSD-EPN as an average of P9, P10, PO7, PO8, O1, O2. None of the differences in amplitudes and latencies was significant for any comparison (see text for statistics).</p

Means, standard errors of mean, and ANOVA results for the components of the event-related potentials for <i>arousal</i> ratings.

<p>Note: <i>df</i>s Greenhouse-Geisser corrected if required; SE  =  standard error of mean; **<i>P</i><.01.</p><p>Means, standard errors of mean, and ANOVA results for the components of the event-related potentials for <i>arousal</i> ratings.</p

Paradigm.

<p>On each trial, one of 24 IAPS images was presented in the background for 3.2 s while participants engaged in an attention-demanding foreground task. They continuously monitored the random opening of a box (alternating between 400 ms closed, 400 ms opened) and responded to the side of each opening with a left or right button press using the left and right index finger. This task phase was followed by a jittered delay of 3 s (25%), 4 s (50%) or 5 s (25%) in which only a white fixation cross was shown. Each image was shown 3 times per block (total of 72 trials per block), and participants finished 6 blocks (total of 432 trials) with an individually randomised order of images for each participant and each block.</p

Means, standard errors of mean, and ANOVA results for the components of the event-related potentials for <i>time reference</i> ratings.

<p>Note: <i>df</i>s Greenhouse-Geisser corrected if required; SEM  =  standard error of mean, **<i>P</i><.01.</p><p>Means, standard errors of mean, and ANOVA results for the components of the event-related potentials for <i>time reference</i> ratings.</p

SVR results for time reference ratings.

<p>A) Spatio-temporal decoding: Multivariate support vector regression (SVR) was used to regress <i>time reference</i> ratings (in increments: present/intermediate/future) from distributed patterns of CSD-ERPs within analysis time windows of 40 ms that were moved through the first 500 ms of each trial in steps of 20 ms. Ratings could be regressed significantly above chance between 100–120 ms, 160–200 ms (denoting the central time points of the analysis windows) and again around 280 ms and 420 ms after stimulus presentation (black line). Additionally shown are the results of the same SVR analysis using shuffled labels to obtain an empirical chance distribution for statistical testing (grey line). B) Temporal decoding: SVR was used for each channel separately to regress <i>time reference</i> ratings (in increments: present/intermediate/future) from purely temporal patterns of CSD-ERPs within the first significant time periods of the spatio-temporal analysis (top panel: combined time bins 100 ms, 120ms; bottom panel: combined time bins 160 ms, 180 ms, 200 ms). Top panel: The heat map illustrates predictive channels, with electrodes FC5 (<i>t</i>(14) = 2.60*), CP5 (<i>t</i>(14) = 2.22*), Oz (<i>t</i>(14) = 2.63*), F4 (<i>t</i>(14) = 2.86*), O3 (<i>t</i>(14) = 3.97**) reaching significance. Bottom panel: Prediction reached significance for electrodes P9 (<i>t</i>(14) = 2.66*), PO7 (<i>t</i>(14) = 3.08**), CP2 (<i>t</i>(14) = 2.23*), PO4 (<i>t</i>(14) = 2.27*), O2 (<i>t</i>(14) = 3.46**); *<i>P</i><.05, **<i>P</i><.01 (uncorrected); error bars  =  standard errors.</p

Intrinsic Valuation of Information in Decision Making under Uncertainty

<div><p>In a dynamic world, an accurate model of the environment is vital for survival, and agents ought regularly to seek out new information with which to update their world models. This aspect of behaviour is not captured well by classical theories of decision making, and the cognitive mechanisms of information seeking are poorly understood. In particular, it is not known whether information is valued only for its instrumental use, or whether humans also assign it a non-instrumental intrinsic value. To address this question, the present study assessed preference for non-instrumental information among 80 healthy participants in two experiments. Participants performed a novel information preference task in which they could choose to pay a monetary cost to receive advance information about the outcome of a monetary lottery. Importantly, acquiring information did not alter lottery outcome probabilities. We found that participants were willing to incur considerable monetary costs to acquire payoff-irrelevant information about the lottery outcome. This behaviour was well explained by a computational cognitive model in which information preference resulted from aversion to temporally prolonged uncertainty. These results strongly suggest that humans assign an intrinsic value to information in a manner inconsistent with normative accounts of decision making under uncertainty. This intrinsic value may be associated with adaptive behaviour in real-world environments by producing a bias towards exploratory and information-seeking behaviour.</p></div

Model fit.

<p> Actual (blue) and UP-predicted (grey) group-level mean proportions of information-seeking choices as a function of information cost across participants. Error bars represent SEM. N = 40.</p