Interacting with volatile environments stabilizes hidden-state inference and its brain signatures

Abstract

Making accurate decisions in uncertain environments requires identifying the generative cause of sensory cues, but also the expected outcomes of possible actions. Although both cognitive processes can be formalized as Bayesian inference, they are commonly studied using different experimental frameworks, making their formal comparison difficult. Here, by framing a reversal learning task either as cue-based or outcome-based inference, we found that humans perceive the same volatile environment as more stable when inferring its hidden state by interaction with uncertain outcomes than by observation of equally uncertain cues. Multivariate patterns of magnetoencephalographic (MEG) activity reflected this behavioral difference in the neural interaction between inferred beliefs and incoming evidence, an effect originating from associative regions in the temporal lobe. Together, these findings indicate that the degree of control over the sampling of volatile environments shapes human learning and decision-making under uncertainty.

Controllability boosts neural and cognitive signatures of changes-of-mind in uncertain environments

“…4 supplement 1). We found a significant negative correlation between inference noise and hazard rate (rho=-0.51, p =.0025), in line with a previously reported trade-off between these two sources of noise (Weiss et al, 2021). We also found a borderline correlation between confidence threshold and hazard rate (rho=-0.36, p =.0409).…”

“…Participants also reached a higher asymptotic reversal rate in the C+ condition ( t 32 =6.3, p =4.0×10 -7 ; Fig. 2B), replicating earlier findings (Weiss et al, 2021).…”

“…Nevertheless, participants are more likely to probe again their new behavioral strategy on the next decision, even in the absence of objective evidence supporting this new strategy – a form of active ‘hypothesis testing’ (Markant and Gureckis, 2014). Using computational modelling (Glaze et al, 2015; Weiss et al, 2021), we show that controllability increases the stability of participants’ beliefs, a mechanism that explains the observed behavioral correlates of information seeking. At the physiological level, changes-of-mind occurring in the controllable condition are preceded by stronger suppression of neuromagnetic alpha-band activity in the dorsal attention network, and followed by increased pupil-linked arousal during the presentation of decision outcomes.…”

Flexible Sensory-Motor Mapping Rules Manifest in Correlated Variability of Stimulus and Action Codes Across the Brain

“…4c). This feature rendered the model adaptive, sensitive to different levels of environmental volatility, in line with observations of human behavior across different experimental settings (Glaze et al, 2015;Murphy et al, 2021;Weiss et al, 2021). In our version of the model, this adaptive inference process for the higher-order decision (rule selection) was coupled with a lower-order decision about the grating orientation that governed the behavioral choice (Fig.…”

Agency rescues competition for credit assignment among predictive cues from adverse learning conditions

“…The beneficial effects of agency—often referred to as free (vs. forced) or self-determined (vs. imposed) choice—on performance has been documented in domains such as education 38 and creativity 39 . In the human cognitive literature, agency over what task 40 , 41 or what feature of a task to engage with 42 has been shown to enhance performance, and the neural bases of this phenomenon are receiving increasing attention 42 – 45 . Our data adds to this literature by showing that the way predictive credit is negotiated among environmental cues is dramatically altered by whether the individual has control over the occurrence of those cues, even when there is no knowledge of the specific cue being presented.…”