Ariane Bigenwald and Valerian Chambon (2019), Criminal Responsibility and Neuroscience: No Revolution Yet, Frontiers in Psychology, 10, 1406, 10.3389/fpsyg.2019.01406

Résumé :
Since the 90’s, neurolaw is on the rise. At the heart of heated debates lies the recurrent theme of a neuro-revolution of criminal responsibility. However, caution should be observed: the alleged foundations of criminal responsibility (amongst which free will) are often inaccurate and the relative imperviousness of its real foundations to scientific facts often underestimated. Neuroscientific findings may impact on social institutions, but only insofar as they also engage in a political justification of the changes being called for, convince populations, and take into consideration the ensuing consequences. Moreover, the many limits of neuroscientific tools call for increased vigilance when, if ever, using neuroscientific evidence in a courtroom. In this article, we aim at setting the basis for future sound debates on the contribution of neuroscience to criminal law, and in particular to the assessment of criminal responsibility. As such, we provide analytical tools to grasp the political and normative nature of criminal responsibility and review the current or projected use of neuroscience in the law, all the while bearing in mind the highly-publicized question: can neuroscience revolutionize criminal responsibility? Answering this question implicitly requires answering a second question: should neuroscience revolutionize the institution of criminal responsibility? Answering both, in turn, requires drawing the line between science and normativity, revolution and dialogue, fantasies and legitimate hopes

Guillaume Dezecache, Aude Bourgeois, Christophe Bazin, Philippe Schlenker, Emmanuel Chemla and Audrey Maille (2019), Orangutans’ Comprehension of Zoo Keepers’ Communicative Signals, Open Access Animals, 9(6), 300; https://doi.org/10.3390/ani9060300

Résumé:
Zoological institutions often encourage cooperative interactions between keepers and animals so as to promote animals’ welfare. One useful technique has been conditioning training, whereby animals learn to respond to keepers’ requests, which facilitates a number of, otherwise sensitive, daily routines. As various media have been used to convey keepers’ instructions, the question remains of which modality is best to promote mutual understanding. Here, we explored this question with two captive female orangutans. In the first experiment, we compared orangutans’ understanding of previously acquired instructions when those were performed with verbal signals only, gazes only, gestures only, and when all those modalities were combined. Our results showed that gestures were sufficient for successful comprehension by these two apes. In the second experiment, we asked whether this preference could be driven by the non-arbitrary relationship that gestures bear to what they refer to, through iconicity or pointing. Our results revealed that neither iconicity nor pointing helped the subjects comprehend the keepers’ instructions. Our results indicate a preference for instructions given through gestural signals in two captive female orangutans, although its cause remains elusive. Future practice may encourage the use of gestures in communication between keepers and orangutans in general or potentially other animals.

Fontanesi L, Palminteri S, Lebreton M. (2019), Decomposing the effects of context valence and feedback information on speed and accuracy during reinforcement learning: A meta-analytical approach using diffusion decision modeling. Cogn Affect Behav Neurosci, 19(3):490-502. doi: 10.3758/s13415-019-00723-1.

Résumé :
Reinforcement learning (RL) models describe how humans and animals learn by trial-and-error to select actions that maximize rewards and minimize punishments. Traditional RL models focus exclusively on choices, thereby ignoring the interactions between choice preference and response time (RT), or how these interactions are influenced by contextual factors. However, in the field of perceptual decision-making, such interactions have proven to be important to dissociate between different underlying cognitive processes. Here, we investigated such interactions to shed new light on overlooked differences between learning to seek rewards and learning to avoid losses. We leveraged behavioral data from four RL experiments, which feature manipulations of two factors: outcome valence (gains vs. losses) and feedback information (partial vs. complete feedback). A Bayesian meta-analysis revealed that these contextual factors differently affect RTs and accuracy: While valence only affects RTs, feedback information affects both RTs and accuracy. To dissociate between the latent cognitive processes, we jointly fitted choices and RTs across all experiments with a Bayesian, hierarchical diffusion decision model (DDM). We found that the feedback manipulation affected drift rate, threshold, and non-decision time, suggesting that it was not a mere difficulty effect. Moreover, valence affected non-decision time and threshold, suggesting a motor inhibition in punishing contexts. To better understand the learning dynamics, we finally fitted a combination of RL and DDM (RLDDM). We found that while the threshold was modulated by trial-specific decision conflict, the non-decision time was modulated by the learned context valence. Overall, our results illustrate the benefits of jointly modeling RTs and choice data during RL, to reveal subtle mechanistic differences underlying decisions in different learning contexts.

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