Associative versus predictive processes in Pavlovian conditioning

Relating different perspectives on how outcomes of behavior influence behavior

Many researchers have tackled the question of how behavior is influenced by its outcomes. Some have adopted a nonmechanistic (functional) perspective that attempts to describe the influence of outcomes on behavior. Others have adopted a mechanistic (cognitive) perspective that attempts to explain the influence of outcomes on behavior. Orthogonal to this distinction, some have focused on the influence of outcomes that a behavior had in the past, whereas others also consider the influence of outcomes that a behavior might have in the future. In this article, we relate these different perspectives with the goal of reducing misunderstandings and fostering collaborations between researchers who adopt different perspectives on the common question of how behavior is influenced by its outcomes.

Predictive olfactory learning in Drosophila

Olfactory learning and conditioning in the fruit fly is typically modelled by correlation-based associative synaptic plasticity. It was shown that the conditioning of an odor-evoked response by a shock depends on the connections from Kenyon cells (KC) to mushroom body output neurons (MBONs). Although on the behavioral level conditioning is recognized to be predictive, it remains unclear how MBONs form predictions of aversive or appetitive values (valences) of odors on the circuit level. We present behavioral experiments that are not well explained by associative plasticity between conditioned and unconditioned stimuli, and we suggest two alternative models for how predictions can be formed. In error-driven predictive plasticity, dopaminergic neurons (DANs) represent the error between the predictive odor value and the shock strength. In target-driven predictive plasticity, the DANs represent the target for the predictive MBON activity. Predictive plasticity in KC-to-MBON synapses can also explain trace-conditioning, the valence-dependent sign switch in plasticity, and the observed novelty-familiarity representation. The model offers a framework to dissect MBON circuits and interpret DAN activity during olfactory learning.

Discriminative control of saccade latencies

Recent studies have demonstrated that saccadic reaction times (SRTs) are influenced by the temporal regularities of dynamic environments (Vullings & Madelain, 2018). Here, we ask whether discriminative control (i.e., the possibility to use external stimuli signaling the future state of the environment) of latencies in a search task might be established using reinforcement contingencies. Eight participants made saccades within 80-750 ms toward a target displayed among distractors. We constructed two latency classes, "short" and "long," using the first and last quartiles of the individual baseline distributions. We then used a latency-contingent display paradigm in which finding the visual target among other items was made contingent upon specific SRTs. For a first group, the postsaccadic target was displayed only following short latencies with leftward saccades, and following long latencies with rightward saccades. The opposite was true for a second group. When short- and long-latency saccades were reinforced (i.e., the target was displayed) depending on the saccade direction, median latencies differed by 74 ms on average (all outside the 98% null hypothesis confidence intervals). Posttraining, in the absence of reinforcement, we still observed strong differences in latency distributions, averaging 64 ms for leftward versus rightward saccades. Our results demonstrate the discriminative control of SRTs, further supporting the effects of reinforcement learning for saccade. This study reveals that saccade triggering is finely controlled by learned temporal and spatial properties of the environment using predictive mechanisms.