Biology subjects

Koechlin, E.

Publications and source records attributed to Koechlin, E..

3 recordsLinked to original sources

bioRxiv2019

Imprecise neural computations as source of human adaptive behavior in volatile environments

Everyday life features uncertain and ever-changing situations. In such environments, optimal adaptive behavior requires higher-order inferential capabilities to grasp the volatility of external contingencies. These capabilities however involve complex and rapidly intractable computations, so that we poorly understand how humans develop efficient adaptive behaviors in such environments. Here we demonstrate this counterintuitive result: simple, low-level inferential processes involving imprecise computations conforming to the psychophysical Weber Law actually lead to near-optimal adaptive behavior, regardless of the environment volatility. Using volatile experimental settings, we further show that such imprecise, low-level inferential processes accounted for observed human adaptive performances, unlike optimal adaptive models involving higher-order inferential capabilities, their biologically more plausible, algorithmic approximations and non-inferential adaptive models like reinforcement learning. Thus, minimal inferential capabilities may have evolved along with imprecise neural computations as contributing to near-optimal adaptive behavior in real-life environments, while leading humans to make suboptimal choices in canonical decision-making tasks.

neuroscience↗

bioRxiv2019

Temporal Chunking as a Mechanism for Unsupervised Learning of Task-Sets

Depending on environmental demands, humans can learn and exploit multiple concurrent sets of stimulus-response associations. Mechanisms underlying the learning of such task-sets remain unknown. Here we investigate the hypothesis that task-set learning relies on unsupervised chunking of stimulus-response associations that occur in temporal proximity. We examine behavioral and neural data from a task-set learning experiment using a network model. We first show that task-set learning can be achieved provided the timescale of chunking is slower than the timescale of stimulus-response learning. Fitting the model to behavioral data confirmed this expectation and led to specific predictions linking chunking and task-set retrieval that were borne out by behavioral performance and reaction times. Comparing the model activity with BOLD signal allowed us to identify neural correlates of task-set retrieval in a functional network involving ventral and dorsal prefrontal cortex, with the dorsal system preferentially engaged when retrievals are used to improve performance.

neuroscience↗

bioRxiv2018

Believing in one's power: a counterfactual heuristic for goal-directed control

Most people envision themselves as operant agents endowed with the capacity to bring about changes in the outside world. This ability to monitor one's own causal power has long been suggested to rest upon a specific model of causal inference, i.e., a model of how our actions causally relate to their consequences. What this model is and how it may explain departures from optimal inference, e.g., illusory control and self-attribution biases, are still conjecture. To address this question, we designed a series of novel experiments requiring participants to continuously monitor their causal influence over the task environment by discriminating changes that were caused by their own actions from changes that were not. Comparing different models of choice, we found that participants' behaviour was best explained by a model deriving the consequences of the forgone action from the current action that was taken and assuming relative divergence between both. Importantly, this model agrees with the intuitive way of construing causal power as \"difference-making\" in which causally efficacious actions are actions that make a difference to the world. We suggest that our model outperformed all competitors because it closely mirrors people's belief in their causal power - a belief that is well-suited to learning action-outcome associations in controllable environments. We speculate that this belief may be part of the reason why reflecting upon one's own causal power fundamentally differs from reasoning about external causes.

neuroscience↗