The retrosplenial cortex as a possible "sensory integration" area: A neural network modeling approach of the differential outcomes effect in negative patterning

Abstract

We explored the hypothesis that learning a Pavlovian negative patterning task would be facilitated when training with differential, as opposed to non-differential, reinforcing outcomes. Two groups of rats received pairings of one visual and one auditory stimulus with food reward when these stimuli were presented on separate training trials, but without reward when both stimuli were presented on simultaneous stimulus compound trials (V+, A+, AV-; similar to an XOR problem). For Group Differential, each stimulus was separately paired with distinctively tasting food rewards, whereas for Group Non-Differential each stimulus was randomly paired with both food reward types across different stimulus element trials. We observed that rats learned the negative patterning task more rapidly and effectively when trained with differential outcomes. These data support a multi-layered connectionist model introduced by Delamater (2012) in which a multi-modal processing structure plays the role of a "sensory integration" area like that hypothesized for the retrosplenial cortex by Dave Bucci and his colleagues (e.g., Todd, Fournier, & Bucci, 2019). We discuss how such a region may develop different "negative occasion setting" and "configural inhibition" mechanisms in solving negative patterning and related tasks.

Keywords: Connectionist modeling; Differential outcome effect; Negative patterning; Pavlovian learning; Retrosplenial cortex.

Figure 1:

A. A feedforward connectionist model (see Delamater, 2012) with three layers: bottom (input or conditioned stimuli), middle (hidden layer), top (output or unconditioned stimuli). A1 and A2 represent two auditory stimuli which are connected to the auditory and multi-modal pathways in the hidden layer. V1 and V2 represent two visual stimuli and are connected to multi-modal and visual pathways in the hidden layer. Lower case letters refer to individual features activated by input stimuli. Panels B, C, D, and E show simplified networks with only multi-modal units in the hidden layer to illustrate how negative patterning tasks can be solved. B and C show different solutions to a single outcome negative patterning task. D and E, show different solutions to negative patterning tasks that use two outcomes. B and D illustrate competitive “negative occasion setting” solutions, and C and E solutions that include a “configural” mechanism.

Figure 2:

Simulated data (n = 32; per group). Negative patterning learning is more successful when training with differential relative to non-differential outcomes. A. Mean output activation (y-axis) as a function of 10-trials blocks (x-axis) for each of the four trial types (F+, N*, FN−, ctx−, for the Differential outcome groups, and F+, N+, FN−, ctx− for the Non-Differential outcome group). B. Same as in panel A but the average of single element trials (F and N). C. Element minus compound discrimination scores for both groups. All simulation codes are available in https://github.com/santiagocdo/NLM_Bucci_Delamater-Castiello.

Figure 3:

Experimental data (n = 32; 16 per group). Negative patterning learning is more successful when training with differential relative to non-differential outcomes. A. Mean magazine response rates expressed as CS – pre CS elevation scores (y-axis) as a function of 28 daily training sessions (x-axis) for each of the three trial types (F+, N*, FN− for the Differential outcome groups, and F+/*, N+/*, FN− for the Non-Differential outcome group). B. Same as in panel A but the average of single element trials (F and N). C. Element minus compound discrimination scores for Differential and Non-Differential groups; black tick horizontal line represent significance between group differences in each session from sessions 16 to 28, p < 0.05.

Figure 4:

Output unit activation at the end of 150 training blocks as a function of the percentage of multimodal units knocked out. The multi-modal (MM) units were knocked out (KO) either Before (panels A and C) or After (panels B and D) training. For each % of KO MM hidden units, we used 100 networks with an α= 0.3 and β = 0.9 (see Appendix). A and B show activations at the end of training for each of the three trial types. C and D show activations comparing elements against compounds. Panel E shows discrimination indexes (element activation – compound activation) for before versus after training KO. All simulation codes are available in https://github.com/santiagocdo/NLM_Bucci_Delamater-Castiello.