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PubMed2000

"Work ethic" in pigeons: reward value is directly related to the effort or time required to obtain the reward.

Stimuli associated with less effort or with shorter delays to reinforcement are generally preferred over those associated with greater effort or longer delays to reinforcement. However, the opposite appears to be true of stimuli that follow greater effort or longer delays. In training, a simple simultaneous discrimination followed a single peck to an initial stimulus (S+FR1 S-FR1) and a different simple simultaneous discrimination followed 20 pecks to the initial stimulus (S+FR20 S-FR20). On test trials, pigeons preferred S+FR20 over S+FR1 and S-FR20 over S-FR1. These data support the view that the state of the animal immediately prior to presentation of the discrimination affects the value of the reinforcement that follows it. This contrast effect is analogous to effects that when they occur in humans have been attributed to more complex cognitive and social factors.

Animals↗
PubMed1998

Predictive reward signal of dopamine neurons.

The effects of lesions, receptor blocking, electrical self-stimulation, and drugs of abuse suggest that midbrain dopamine systems are involved in processing reward information and learning approach behavior. Most dopamine neurons show phasic activations after primary liquid and food rewards and conditioned, reward-predicting visual and auditory stimuli. They show biphasic, activation-depression responses after stimuli that resemble reward-predicting stimuli or are novel or particularly salient. However, only few phasic activations follow aversive stimuli. Thus dopamine neurons label environmental stimuli with appetitive value, predict and detect rewards and signal alerting and motivating events. By failing to discriminate between different rewards, dopamine neurons appear to emit an alerting message about the surprising presence or absence of rewards. All responses to rewards and reward-predicting stimuli depend on event predictability. Dopamine neurons are activated by rewarding events that are better than predicted, remain uninfluenced by events that are as good as predicted, and are depressed by events that are worse than predicted. By signaling rewards according to a prediction error, dopamine responses have the formal characteristics of a teaching signal postulated by reinforcement learning theories. Dopamine responses transfer during learning from primary rewards to reward-predicting stimuli. This may contribute to neuronal mechanisms underlying the retrograde action of rewards, one of the main puzzles in reinforcement learning. The impulse response releases a short pulse of dopamine onto many dendrites, thus broadcasting a rather global reinforcement signal to postsynaptic neurons. This signal may improve approach behavior by providing advance reward information before the behavior occurs, and may contribute to learning by modifying synaptic transmission. The dopamine reward signal is supplemented by activity in neurons in striatum, frontal cortex, and amygdala, which process specific reward information but do not emit a global reward prediction error signal. A cooperation between the different reward signals may assure the use of specific rewards for selectively reinforcing behaviors. Among the other projection systems, noradrenaline neurons predominantly serve attentional mechanisms and nucleus basalis neurons code rewards heterogeneously. Cerebellar climbing fibers signal errors in motor performance or errors in the prediction of aversive events to cerebellar Purkinje cells. Most deficits following dopamine-depleting lesions are not easily explained by a defective reward signal but may reflect the absence of a general enabling function of tonic levels of extracellular dopamine. Thus dopamine systems may have two functions, the phasic transmission of reward information and the tonic enabling of postsynaptic neurons.

Animals↗
PubMed2006

Identifying nurses' rewards: a qualitative categorization study in Belgium.

BACKGROUND: Rewards are important in attracting, motivating and retaining the most qualified employees, and nurses are no exception to this rule. This makes the establishment of an efficient reward system for nurses a true challenge for every hospital manager. A reward does not necessarily have a financial connotation: non-financial rewards may matter too, or may even be more important. Therefore, the present study examines nurses' reward perceptions, in order to identify potential reward options. METHODS: To answer the research question "What do nurses consider a reward and how can these rewards be categorized?", 20 in-depth semi-structured interviews with nurses were conducted and analysed using discourse and content analyses. In addition, the respondents received a list of 34 rewards (derived from the literature) and were asked to indicate the extent to which they perceived each of them to be rewarding. RESULTS: Discourse analysis revealed three major reward categories: financial, non-financial and psychological, each containing different subcategories. In general, nurses more often mentioned financial rewards spontaneously in the interview, compared to non-financial and psychological rewards. The questionnaire results did not, however, indicate a significant difference in the rewarding potential of these three categories. Both the qualitative and quantitative data revealed that a number of psychological and non-financial rewards were important for nurses in addition to their monthly pay and other remunerations. In particular, appreciation for their work by others, compliments from others, presents from others and contact with patients were highly valued. Moreover, some demographical variables influenced the reward perceptions. Younger and less experienced nurses considered promotion possibilities as more rewarding than the older and more senior ones. The latter valued job security and working for a hospital with a good reputation higher than their younger and more junior colleagues. CONCLUSION: When trying to establish an efficient reward system for nurses, hospital managers should not concentrate on the financial reward possibilities alone. They also ought to consider non-financial and psychological rewards (in combination with financial rewards), since nurses value these as well and they may lead to a more personalized reward system.

Journal Article↗
PubMed2001

Modulation of saccadic eye movements by predicted reward outcome.

Reward is a primary goal of behavior and is crucial for survival of animals. To explore the mechanisms underlying such reward-oriented behavior, we devised a memory-guided saccade task in which only one fixed direction out of four was rewarded, which was called the one-direction-rewarded task (1DR). As the rewarded direction was changed in four blocks, saccades in a given direction were rewarded in one block (constituting reward-oriented behavior), but non-rewarded in the other blocks (non-reward-oriented behavior). As a control, an all-directions-rewarded task (ADR) was used. Using these tasks, we found that the parameters of saccades changed depending on whether or not the saccade was followed by reward. (1) The mean saccadic peak velocity was higher and the mean saccade latency was shorter in the rewarded condition than in the non-rewarded condition. (2) The mean saccade amplitude showed no difference in two out of three monkeys. (3) The variations of saccadic velocity, latency and amplitude were smaller in the rewarded condition. (4) Within a block of 1DR, the saccade velocity remained high in the rewarded condition, but decreased gradually in the non-rewarded condition; it decreased only slightly in ADR. The saccade latency showed the opposite pattern of change, but less clearly. (5) The saccades in the non-rewarded condition tended to have slower velocities and longer latencies in the trials shortly after a rewarded trial. (6) The ratio of error trials was much higher in the non-rewarded condition than the rewarded condition. (7) The errors, which were due to premature or incorrect saccades, showed unique spatiotemporal patterns that would reflect the competition between the cognitive and motivational processes. These results provide important constraints to the neuronal mechanism underlying reward-oriented behavior because it must satisfy these rules.

Animals↗
PubMed1996

Neural signals in the monkey ventral striatum related to motivation for juice and cocaine rewards.

1. The results of neuropsychological, neuropharmacological, and neurophysiological experiments have implicated the ventral striatum in reward-related processes. We designed a task to allow us to separate the effects of sensory, motor, and internal signals so that we could study the correlation between the activity of neurons in the ventral striatum and different motivational states. In this task, a visual stimulus was used to cue the monkeys as to their progress toward earning a reward. The monkeys performed more quickly and with fewer mistakes in the rewarded trials. After analyzing the behavioral results from three monkeys, we recorded from 143 neurons from two of the monkeys while they performed the task with either juice or cocaine reward. 2. In this task the monkey was required to release its grip on a bar when a small visual response cue changed colors from red (the wait signal) to green (the go signal). The duration of the wait signal was varied randomly. The cue became blue whenever the monkey successfully responded to the go signal within 1 s of its appearance. A reward was delivered after the monkey successfully completed one, two, or three trials. The schedules were randomly interleaved. A second visual stimulus that progressively brightened or dimmed signaled to the monkeys their progress toward earning a reward. This discriminative cue allowed the monkeys to judge the proportion of work remaining in the current ratio schedule of reinforcement. Data were collected from three monkeys while they performed this task. 3. The average reaction times became faster and error rates declined as the monkeys progressed toward completing the current schedule of reinforcement and thereby earning a reward, whereas the modal reaction time did not change. As the duration of the wait period before the go signal increased, the monkeys reacted more quickly but their error rates scarcely changed. From these results we infer that the effects of motivation and motor readiness in this task are generated by separate mechanisms rather than by a single mechanism subserving generalized arousal. 4. The activity of 138 ventral striatal neurons was sampled in two monkeys while they performed the task to earn juice reward. We saw tonic changes in activity throughout the trials, and we saw phasic activity following the reward. The activity of these neurons was markedly different during juice-rewarded trials than during correctly performed trials when no reward was forthcoming (or expected). The responses also were weakly, but significantly, related to the proximity of the reward in the schedules requiring more than one trial. 5. The monkeys worked to obtain intravenous cocaine while we recorded 62 neurons. For 57 of the neurons, we recorded activity while the monkeys worked in blocks of trials during which they self-administered cocaine after blocks during which they worked for juice. Although fewer neurons responded to cocaine than to juice reward (19 vs. 33%), this difference was not significant. The neuronal response properties to cocaine and juice rewards were independent; that is, the responses when one was the reward one failed to predict the response when the other was the reward. In addition, the neuronal activity lost most of its selectivity for rewarded trials, i.e, the activity did not distinguish nearly as well between cocaine and sham rewards as between juice and sham rewards. 6. Our results show that mechanisms by which cocaine acts do not appear to be the same as the ones activated when the monkeys were presented with an oral juice reward. This finding raises the intriguing possibility that the effects of cocaine could be reduced selectively without blocking the effects of many natural rewards.

Animals↗
PubMed1997

Responses of tonically discharging neurons in the monkey striatum to primary rewards delivered during different behavioral states.

In the primate striatum, the tonically discharging neurons respond to conditioned stimuli associated with reward. We investigated whether these neurons respond to the reward itself and how changes in the behavioral context in which the reward is delivered might influence their responsiveness. A total of 286 neurons in the caudate nucleus and putamen were studied in two awake macaque monkeys while liquid reward was delivered in three behavioral situations: (1) an instrumental task, in which reward was delivered upon execution of a visually triggered arm movement; (2) a classically conditioned task, in which reward was delivered 1 s after a visual signal; (3) a free reward situation, in which reward was delivered at irregular time intervals outside of any conditioning task. The monkeys' uncertainty about the time at which reward will be delivered was assessed by monitoring their mouth movements. A larger proportion of neurons responsive to reward was observed in the free reward situation (86%) than in the classically conditioned (57%) and instrumental tasks (37%). Among the neurons tested in all situations (n = 78), 24% responded to reward regardless of the situation and 65% in only one or two situations. Responses selective for one particular situation occurred exclusively in the free reward situation. When the reward was delivered immediately after the visual signal in the classically conditioned task, most of the neurons reduced or completely lost their responses to reward, and other neurons remained responsive. Conversely, neuronal responses invariably persisted when reward was delivered later than 1 s after the visual signal. This is the first report that tonic striatal neurons might display responses directly to primary rewards. The neuronal responses were strongly influenced by the behavioral context in which the animals received the reward. An important factor appears to be the timing of reward. These neurons might therefore contribute to a general aspect of behavioral reactivity of the subject to relevant stimuli.

Action Potentials↗
PubMedDate not supplied

Reward prediction in primate basal ganglia and frontal cortex.

Reward information is processed in a limited number of brain structures, including fronto-basal ganglia systems. Dopamine neurons respond phasically to primary rewards and reward-predicting stimuli depending on reward unpredictability but without discriminating between rewards. These responses reflect 'errors' in the prediction of rewards in correspondence to learning theories and thus may constitute teaching signals for appetitive learning. Neurons in the striatum (caudate, putamen, ventral striatum) code reward predictions in a different manner. They are activated during several seconds when animals expect predicted rewards. During learning, these activations occur initially in rewarded and unrewarded trials and become subsequently restricted to rewarded trials. This occurs in parallel with the adaptation of reward expectations by the animals, as inferred from their behavioral reactions. Neurons in orbitofrontal cortex respond differentially to stimuli predicting different liquid rewards, without coding spatial or visual features. Thus, different structures process reward information processed in different ways. Whereas dopamine neurons emit a reward teaching signal without indicating the specific reward, striatal neurons adapt expectation activity to new reward situations, and orbitofrontal neurons process the specific nature of rewards. These reward signals need to cooperate in order for reward information to be used for learning and maintaining approach behavior.

Animals↗
PubMed1999

Risk-sensitive foraging: choice behaviour of honeybees in response to variability in volume of reward.

We tested risk sensitivity towards variability in volume of reward with harnessed honeybees, Apis mellifera, in a proboscis-extension conditioning paradigm. We conditioned each subject to turn its head and extend its proboscis towards one of two presented odours; one odour was associated with a constant reward volume and the other with a variable reward volume that was either low or high, with probabilities P=0.75 and (1-P)=0.25, respectively. The volumes of rewards were varied among three experimental conditions. In conditions I and II, the variable reward option included a low reward of zero (i.e. reinforcement was withheld in the low reward value); in condition I, the mean of the variable and of the constant reward options were the same, and in condition II, the variable reward option had a higher mean reward than the constant reward option. The behaviour of subjects did not differ between treatments and the majority of individuals were risk averse. In condition III, the variable reward option did not include a zero reward and the mean reward did not differ between options. Very few of the individuals assigned to condition III developed a preference for either reward option. Thus, honeybees are risk sensitive to variability in volume of reward in some conditions and the degree of risk sensitivity depends on characteristics of the reward distributions. The most salient characteristic may be a relative measure of variability, such as the value of the coefficient of variation of reward. The experimental paradigm that we developed is a powerful tool for studying the mechanism of risk sensitivity in bees, as well as other aspects of learning, decision making, perception and memory. Copyright 1999 The Association for the Study of Animal Behaviour.

Journal Article↗
PubMed1998

Dopamine neurons report an error in the temporal prediction of reward during learning.

Many behaviors are affected by rewards, undergoing long-term changes when rewards are different than predicted but remaining unchanged when rewards occur exactly as predicted. The discrepancy between reward occurrence and reward prediction is termed an 'error in reward prediction'. Dopamine neurons in the substantia nigra and the ventral tegmental area are believed to be involved in reward-dependent behaviors. Consistent with this role, they are activated by rewards, and because they are activated more strongly by unpredicted than by predicted rewards they may play a role in learning. The present study investigated whether monkey dopamine neurons code an error in reward prediction during the course of learning. Dopamine neuron responses reflected the changes in reward prediction during individual learning episodes; dopamine neurons were activated by rewards during early trials, when errors were frequent and rewards unpredictable, but activation was progressively reduced as performance was consolidated and rewards became more predictable. These neurons were also activated when rewards occurred at unpredicted times and were depressed when rewards were omitted at the predicted times. Thus, dopamine neurons code errors in the prediction of both the occurrence and the time of rewards. In this respect, their responses resemble the teaching signals that have been employed in particularly efficient computational learning models.

Animals↗
PubMedDate not supplied

A comparison of economic and social reward in patients with chronic nonmalignant back pain.

OBJECTIVE: Patients with chronic nonmalignant back pain are often exposed to multiple sources of economic and social reward. At issue in the present study was whether these rewards are 1) correlated with similar or dissimilar outcome variables and 2) able to account for unique variance in regression models of illness behavior. METHODS: A 2 x 2 factorial design was used in which patients were retrospectively assigned to one of four independent groups: low economic/low social reward, low economic/high social reward, high economic/low social reward, and high economic/high social reward. Of 265 consecutive patients enrolled at a tertiary pain service, 75 met eligibility criteria and had chronic nonmalignant back pain. RESULTS: Preexisting differences in health status were not associated with differences in illness behavior or pain ratings. With social reward held constant, patients in the high economic reward group missed more days from work (p < .005), had more domestic disability (p < .05), and were more depressed (p < .05) than patients in the low economic reward group. With economic reward held constant, patients in the high social reward group missed more days from work (p < .05), had more domestic disability (p < .01), and were more depressed (p < .01) than patients in the low social reward group. Unlike patients in the high economic reward group, however, patients in the high social reward group had higher levels of pain (p < .05) and more nonspecific medical complaints (p < .01). CONCLUSIONS: Economic and social rewards were both associated with increased disability and depression, but only social rewards were associated with increased symptom reporting. Exposure to economic and social rewards may account for unique variance in illness behavior that cannot be explained by differences in medical diagnosis, symptom duration, pain intensity, depression, or somatization.

Adult↗
PubMed1996

Food reward: brain substrates of wanting and liking.

What are the neural substrates of food reward? Are reward and pleasure identical? Can taste pleasure be assessed in animals? Is reward necessarily conscious? These questions have re-emerged in recent years, and there is now sufficient evidence to prompt re-examination of many preconceptions concerning reward and its relation to brain systems. This paper reviews evidence from many sources regarding both the psychological structure of food reward and the neural systems that mediate it. Special attention is paid to recent evidence from "tasty reactivity" studies of affective reactions to food. I argue that this evidence suggests the following surprising possibilities regarding the functional components and brain substrates of food reward. (1) Reward contains distinguishable psychological or functional components--"liking" (pleasure/palatability) and "wanting" (appetite/incentive motivation). These can be manipulated and measured separately. (2) Liking and wanting have separable neural substrates. Mediation of liking related to food reward involves neurotransmitter systems such as opioid and GABA/benzodiazepine systems, and anatomical structures such as ventral pallidum and brainstem primary gustatory relays. Mediation of wanting related to food reward involves mesotelencephalic dopamine systems, and divisions of nucleus accumbens and amygdala. Both liking and wanting arise from vastly distributed neural systems, but the two systems are separable. (3) Neural processing of food reward is not confined to the limbic forebrain. Aspects of food reward begin to be processed in the brainstem. A neural manipulation can enhance reward or produce aversion but no single lesion or transection is likely abolish all properties of food reward. (4) Both wanting and liking can exist without subjective awareness. Conscious experience can distort or blur the underlying reward process that gave rise to it. Subjective reports may contain false assessments of underlying processes, or even fail at all to register important reward processes. The core processes of liking and wanting that constitute reward are distinct from the subjective report or conscious awareness of those processes.

Behavior↗
PubMed2005

Temporal and probabilistic discounting of rewards in children and adolescents: effects of age and ADHD symptoms.

This study investigated whether age and ADHD symptoms affected choice preferences in children and adolescents when they chose between (1) small immediate rewards and larger delayed rewards and (2) small certain rewards and larger probabilistic uncertain rewards. A temporal discounting (TD) task and a probabilistic discounting (PD) task were used to measure the degree to which the subjective value of a large reward decreased as one had to wait longer for it (TD), and as the probability of obtaining it decreased (PD). Rewards used were small amounts of money. In the TD task, the large reward (10 cents) was delayed by between 0 and 30s, and the immediate reward varied in magnitude (0-10 cents). In the PD task, receipt of the large reward (10 cents) varied in likelihood, with probabilities of 0, 0.25, 0.5, 0.75, and 1.0 used, and the certain reward varied in magnitude (0-10 cents). Age and diagnostic group did not affect the degree of PD of rewards: All participants made choices so that total gains were maximized. As predicted, young children, aged 6-11 years (n = 25) demonstrated steeper TD of rewards than adolescents, aged 12-17 years (n = 21). This effect remained significant even when choosing the immediate reward did not shorten overall task duration. This, together with the lack of interaction between TD task version and age, suggests that steeper discounting in young children is driven by reward immediacy and not by delay aversion. Contrary to our predictions, participants with ADHD (n = 22) did not demonstrate steeper TD of rewards than controls (n = 24). These results raise the possibility that strong preferences for small immediate rewards in ADHD, as found in previous research, depend on factors such as total maximum gain and the use of fixed versus varied delay durations. The decrease in TD as observed in adolescents compared to children may be related to developmental changes in the (dorsolateral) prefrontal cortex. Future research needs to investigate these possibilities.

Adolescent↗