Lesions of dorsal striatum eliminate lose‐switch responding but not mixed‐response strategies in rats

Abstract

We used focal brain lesions in rats to examine how dorsomedial (DMS) and dorsolateral (DLS) regions of the striatum differently contribute to response adaptation driven by the delivery or omission of rewards. Rats performed a binary choice task under two modes: one in which responses were rewarded on half of the trials regardless of choice; and another 'competitive' one in which only unpredictable choices were rewarded. In both modes, control animals were more likely to use a predictable lose-switch strategy than animals with lesions of either DMS or DLS. Animals with lesions of DMS presumably relied more on DLS for behavioural control, and generated repetitive responses in the first mode. These animals then shifted to a random response strategy in the competitive mode, thereby performing better than controls or animals with DLS lesions. Analysis using computational models of reinforcement learning indicated that animals with striatal lesions, particularly of the DLS, had blunted reward sensitivity and less stochasticity in the choice mechanism. These results provide further evidence that the rodent DLS is involved in rapid response adaptation that is more sophisticated than that embodied by the classic notion of habit formation driven by gradual stimulus-response learning.

Feeder Approach between Trials Is Increased by Uncertainty and Affects Subsequent Choices

“…Consistent with our previous finding (Skelin et al, 2014), the DLS lesion group made significantly fewer lose-shift responses than the control or NACc-lesion groups ( F 2,16 = 15.83, p = 1.00 × 10 –6 ; Fig. 6 A ), and this reduction was irrespective of the ITI (Fig.…”

“…The location and extent of the lesions (Fig. 5 A , B ) are similar to previous reports from our group and others (Hall et al, 2001; Skelin et al, 2014). …”

“…However, the EFS responses persisted at substantial levels (mean = 0.230 ± 0.106) even after extended training (8 sessions after training was complete). We next sought correlational evidence whether the neural systems promoting EFS are associated with those promoting either win-stay or lose-shift responding, which have distinct properties and neural dependencies (Skelin et al, 2014; Gruber and Thapa, 2016). We excluded all trials after EFS in the subsequent analysis of win-stay and lose-shift responding to avoid the immediate effect of EFS on choice.…”

“…A computer program served as an opponent for the rats and was implemented as in previous studies (Algorithm 2; Barraclough et al, 2004; Lee et al, 2004; Skelin et al, 2014; Gruber and Thapa, 2016). The algorithm attempts to predict the rat’s next choice by comparing the pattern of choice sequences in the preceding trials (1–4 back) with the choice history of the current session.…”

Interactions among attention-deficit hyperactivity disorder (ADHD) and problem gambling in a probabilistic reward-learning task

“…In addition to attention effects, the firing of dopamine neurons appears to encode a reward prediction error signal that provide a neurobiological learning signal analogous to that in computational models of reinforcement learning [8,14,15]. Such learning models can account for gradual adaptation of animal choice behavior through trial-and-error [16][17][18]. Alterations of dopamine would thus be expected to impair learning from wins and losses, as has been shown in Parkinsonian patients [19].…”