Awake canine fMRI predicts dogs' preference for praise vs food - PubMed (original) (raw)
Awake canine fMRI predicts dogs' preference for praise vs food
Peter F Cook et al. Soc Cogn Affect Neurosci. 2016 Dec.
Abstract
Dogs are hypersocial with humans, and their integration into human social ecology makes dogs a unique model for studying cross-species social bonding. However, the proximal neural mechanisms driving dog-human social interaction are unknown. We used functional magnetic resonance imaging in 15 awake dogs to probe the neural basis for their preferences for social interaction and food reward. In a first experiment, we used the ventral caudate as a measure of intrinsic reward value and compared activation to conditioned stimuli that predicted food, praise or nothing. Relative to the control stimulus, the caudate was significantly more active to the reward-predicting stimuli and showed roughly equal or greater activation to praise vs food in 13 of 15 dogs. To confirm that these differences were driven by the intrinsic value of social praise, we performed a second imaging experiment in which the praise was withheld on a subset of trials. The difference in caudate activation to the receipt of praise, relative to its withholding, was strongly correlated with the differential activation to the conditioned stimuli in the first experiment. In a third experiment, we performed an out-of-scanner choice task in which the dog repeatedly selected food or owner in a Y-maze. The relative caudate activation to food- and praise-predicting stimuli in Experiment 1 was a strong predictor of each dog's sequence of choices in the Y-maze. Analogous to similar neuroimaging studies of individual differences in human social reward, our findings demonstrate a neural mechanism for preference in domestic dogs that is stable within, but variable between, individuals. Moreover, the individual differences in the caudate responses indicate the potentially higher value of social than food reward for some dogs and may help to explain the apparent efficacy of social interaction in dog training.
Keywords: dogs; fMRI; reward; social.
© The Author (2016). Published by Oxford University Press.
Figures
Fig. 1.
In-scanner reward expectation task. Subjects were presented with the three experimental stimuli in the mock scanner during training. Subject Kady is shown here viewing the car object during conditioning (see also
Supplementary Movie S1
). Each dog was exposed to each stimulus and its outcome (car-praise, horse-food and brush-nothing) 40 times in a pre-set semi-random schedule during training within the 2 weeks prior to live scanning. They were exposed to each object and paired outcome eight more times immediately prior to live-scanning. During live scanning, objects were presented for 10 s each, followed immediately by outcome.
Fig. 2.
Structural definition of ventral caudate and demonstration of consistent neural valuation. (Left) For each subject, a mask of ventral left and right caudate nucleus was drawn directly on the T2 structural scans, shown here in the transverse (A) and coronal (B) planes. In the transverse plane, the rostral portion of the brain is toward the top of the image where the eyes are visible laterally and the olfactory bulb medially. Caudate masks were drawn to be consistent with those we have used in previous studies and covered the entirety of the head of the caudate nucleus ventral to the ventral-most portion of the genu of the corpus callosum. To obtain condition and contrast-specific measures of BOLD activation in these regions, each individual’s statistical maps in functional space were transformed to structural space, and the relevant beta values were averaged across the caudate ROIs. (C) Regression line fitting differential caudate activation to expectation of praise vs food (Experiment 1, _x_-axis) vs differential caudate activation during receipt of praise vs withholding of praise (Experiment 2, _y_-axis). [_F_1,10 = 9.49, _R_2 = 0.49, P = 0.01, two-sided]. (C—inset) Mean percent signal change in the a priori ventral caudate masks is shown for the contrasts [CSpraise − CSneutral] and [CSfood − CSneutral] for Experiments 1 and 2. Relative response to food cue vs neutral cue and praise cue vs neutral cue were not significantly different within or between Experiments 1 and 2, nor was there an interaction between experiment and contrast (Experiment: _F_1,14.6 = 1.78, P = 0.20; Contrast: _F_1,14.6 = 0.95, P = 0.35; Experiment × Contrast: _F_1,14.6 = 1.61, P = 0.22).
Fig. 3.
Behavioral preference test. (A) schematic representation of Y-maze testing set up. Dogs were released from the control room door at a point equidistant from the owner and food locations (X). (B) Subject Ohana being released from the control room to make her choice between food (yellow bowl at left) and owner (at right) (also see
Supplementary Movies S2 and S3
). On each trial, the dog was allowed to make one selection, resulting in either consuming the food or receiving verbal praise and petting from the owner. Location of owner and food was switched on each trial to control for effect of side biases. Following release from the control room, each dog was allowed 20 s to make one (and only one) choice before being collected and returned to the control room for another trial.
Fig. 4.
Hidden Markov model and logistic regression of neural vs behavioral preference. (A) Y-maze choice sequences for each participant on each of the 20 test trials. (B) A schematic representation of the hidden Markov model used to compute transition probabilities between food bowl and owner in the behavioral Y-maze task (Experiment 3). ‘Food’ and ‘Praise’ represent the internal states associated with selection of the food bowl ‘Bowl’ and owner ‘Owner’, respectively. E represents emission probabilities, the likelihood of moving from an internal state to the matched behavior. P represents probabilities, for transitioning between states or staying in the current state. (C) A logit function was fit to the relationship between the differential caudate activation to expectation of praise vs food in Experiment 1 (_x_-axis) and the differential probability of staying with owner vs food in the Markov model (_y_-axis). To fit the logit model, we first transformed the difference in transition probabilities into an equivalent number of owner/bowl choices, according to: N(_P_PP − _P_FF + 1)/2, where N was the total number of trials in which a dog made a choice. The logit model was then fit in R using a probit link function. [Z(13) = 6.24, P < 0.001, null deviance = 93.08, residual deviance = 50.10, chi-square goodness of fit P < 0.001].
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