Food and intracranial stimulation responding suppressed with regular-interval shock.

Attenuation of conditioned suppression during intracranial stimulation was compared with that during food reinforcement. Response rates controlled by food and by brain stimulation were equalized on a multiple schedule by adjusting the stimulating current. When foot shock was delivered during timeout periods separating response components, responding for food was significantly more suppressed than responding for brain stimulation. When components were shortened from 10 to 2 minutes, responding maintained by either food or brain stimulation showed a similar temporal pattern of suppression preceding each shock, but responding in the component involving food remained significantly more suppressed. Explanations for the attenuated suppression during brain stimulation based on neural disruption, stimulus blocking, and analgesic properties were questioned. The increased responding during brain stimulation seemed to reflect greater response strength relative to food reinforced responding.     

The sensitivity of response rate to the rate of variable-interval reinforcement for pigeons and rats: a review

The relation between the rate of a response (B) and the rate of its reinforcement (R) is well known to be approximately hyperbolic: B = kR/(R + Ro), where k represents the maximum response rate, and Ro indicates the rate of reinforcers that will engender a response rate equal to half its maximum value. A review of data reported in 17 published papers revealed that, under variable-interval schedules of reinforcement, Ro was usually lower when pigeons were the subjects than when rats were the subjects. The value of k, in contrast, did not differ consistently between pigeons and rats. Some accounts interpret Ro as the rate of alternative, unscheduled reinforcers in the situation, expressed in units of the scheduled reinforcer. So interpreted, the difference in Ro implies that less alternative reinforcement (relative to the scheduled reinforcement) typically is available to pigeons in their operant conditioning chambers than it is to rats in theirs. Whether or not that interpretation of Ro is valid, the pigeon-rat difference in Ro ensures that for reinforcer rates above about 10 per hour, response rate will be noticeably less sensitive to changes in reinforcer rate (and presumably to changes in other incentive and motivational operations) with pigeons than with rats as subjects, at least with the experimental conditions typically employed.     

Tests of behavior momentum in simple and multiple schedules with rats and pigeons.

Four experiments examined the relationship between rate of reinforcement and resistance to change in rats' and pigeons' responses under simple and multiple schedules of reinforcement. In Experiment 1, 28 rats responded under either simple fixed-ratio, variable-ratio, fixed-interval, or variable-interval schedules; in Experiment 2, 3 pigeons responded under simple fixed-ratio schedules. Under each schedule, rate of reinforcement varied across four successive conditions. In Experiment 3, 14 rats responded under either a multiple fixed-ratio schedule or a multiple fixed-interval schedule, each with two components that differed in rate of reinforcement. In Experiment 4, 7 pigeons responded under either a multiple fixed-ratio or a multiple fixed-interval schedule, each with three components that also differed in rate of reinforcement. Under each condition of each experiment, resistance to change was studied by measuring schedule-controlled performance under conditions with prefeeding, response-independent food during the schedule or during timeouts that separated components of the multiple schedules, and by measuring behavior under extinction. There were no consistent differences between rats and pigeons. There was no direct relationship between rates of reinforcement and resistance to change when rates of reinforcement varied across successive conditions in the simple schedules. By comparison, in the multiple schedules there was a direct relationship between rates of reinforcement and resistance to change during most tests of resistance to change. The major exception was delivering response-independent food during the schedule; this disrupted responding, but there was no direct relationship between rates of reinforcement and resistance to change in simple- or multiple-schedule contexts. The data suggest that rate of reinforcement determines resistance to change in multiple schedules, but that this relationship does not hold under simple schedules.     

The Effect of Prominence and Cue Association on Retrieval Processes: A Computational Account

We present a comprehensive empirical evaluation of the ACT‐R–based model of sentence processing developed by Lewis and Vasishth (2005) (LV05). The predictions of the model are compared with the results of a recent meta‐analysis of published reading studies on retrieval interference in reflexive‐/reciprocal‐antecedent and subject–verb dependencies (Jäger, Engelmann, & Vasishth, 2017). The comparison shows that the model has only partial success in explaining the data; and we propose that its prediction space is restricted by oversimplifying assumptions. We then implement a revised model that takes into account differences between individual experimental designs in terms of the prominence of the target and the distractor in memory‐ and context‐dependent cue‐feature associations. The predictions of the original and the revised model are quantitatively compared with the results of the meta‐analysis. Our simulations show that, compared to the original LV05 model, the revised model accounts for the data better. The results suggest that effects of prominence and variable cue‐feature associations need to be considered in the interpretation of existing empirical results and in the design and planning of future experiments. With regard to retrieval interference in sentence processing and to the broader field of psycholinguistic studies, we conclude that well‐specified models in tandem with high‐powered experiments are needed in order to uncover the underlying cognitive processes.     

特征归纳的关联相似性模型

作者提出特征归纳的关联相似性模型,用以描述以归纳特征的关联特征相似性为基础的归纳推理,把归纳推理中相似性和关联知识统一整合起来。该模型认为归纳强度主要取决于归纳特征与关联特征的关联强度和关联特征的相似性程度的乘积,归纳信心主要取决于关联强度,从而分离归纳信心和归纳强度。以大学生为被试的两个实验的主要结果支持关联相似性模型的主要预测。关联相似性模型能够描述解释以关联特征相似性为基础的归纳推理现象,比以往的归纳理论具有更大的解释能力和解释范围     

On Strong Attitudes and Group Deliberation: Relationships,Structure, Changes,and Effects

Drawing on quasi‐experimental data from participants in structured and moderated deliberations on sexual minority rights in Poland, this study tests the relationship between attitude importance, certainty, intensity, and extremity. This study also examines the antecedents of these attributes and their consequences for several democratically important outcomes (i.e., perceived disagreement, negative affect, attitude polarization, self‐reported polarization, argument repertoire, and political participation). This study further asks whether these attributes exert different effects than a composite attitude strength index. Although factor analysis suggests that these attributes represent one construct, they are differently affected by deliberation and exert differential effects. The analyses further reveal that relying on the composite index obscures substantial information and inaccurately represents strength‐related processes and functions. Theoretical, methodological, and practical implications are discussed.     

Neuroscientific Measures of Covert Behavior

In radical behaviorism, the difference between overt and covert responses does not depend on properties of the behavior but on the sensitivity of the measurement tools employed by the experimenter. Current neuroscientific research utilizes technologies that allow measurement of variables that are undetected by the tools typically used by behavior analysts. Data from a specific neuroscientific technique, event-related potential (ERP), suggest that emission of otherwise covert responses can be indexed and that such covert responses are sensitive to stimulus control and selection by consequences. The P3 ERP effect is proposed as indicative of emission. Moreover, ERP results in semantic priming experiments suggest that operants are sensitive to changes in stimulus control even when they are not emitted (latent responses). Changes in response strength of latent responses as a function of stimulus control can in fact be measured by reaction time data and an ERP dependent variable called the N400 effect. If the interpretations provided in this paper are accurate, an index of covertly emitted operants (P3 effect) constitutes experimental evidence suggesting the validity of a Skinnerian radical behaviorist perspective on behavior. Moreover, in a Skinnerian paradigm, measured fluctuations in the response strength of latent operants as a function of environmental changes (N400 effect) would validate Palmer's (2009) concept of the repertoire.     

Components of task switching: A closer look at task switching and cue switching

Research using the diffusion model to decompose task-switching effects has contributed to a better understanding of the processes underlying the observed effect in the explicit task cueing paradigm: Previous findings could be reconciled with multiple component models of task switching or with an account on compound-cue retrieval/repetition priming. In the present study, we used two cues for each task in order to decompose task-switch and cue-switch effects. Response time data support previous findings that comparable parts of the switching effect can be attributed to cue-switching and task-switching. A diffusion model analysis of the data confirmed that non-decision time is increased and drift rates are decreased in unpredicted task-switches. Importantly, it was shown that non-decision time was selectively increased in task-switching trials but not in cue-switching trials. Results of the present study specifically support the notion of additional processes in task-switches and can be reconciled with broader multiple component accounts.     

Mental Health Problems,Recovery, and the Impact of Green Care Services: A Qualitative,Participant-Focused Approach

This study aimed at gaining knowledge of users' experience of green care services (interventions using nature to improve health) for people with mental health or drug problems. Data were obtained from interviews with 20 participants in green care services and were analyzed qualitatively. Findings revealed that work in a social context close to nature and work with animals increased mastery and meaningfulness. Participation resulted in personal changes, new practical skills, improved social networks, and feelings of well-being. There appears to be powerful potential in using green care services as a recovery tool for people with mental health or drug-related problems.     

From intentions via planning and behavior to physical exercise habits

ObjectivesIndividuals who enact a health behavior effortlessly with minimal conscious deliberation can be assumed to have formed a healthy habit. This can be reflected by increases in self-reported habit strength of a behavior. We examined whether physical exercise intentions facilitate changes in exercise habit strength by increasing the use of action planning and exercise.DesignTwo field studies investigated the effect of behavioral intentions on changes in habit strength through a sequential path from action planning to exercise.MethodExercise intentions, action planning, habit strength, and exercise were assessed at two measurement points in time in 231 university students (Study 1), and at four points in time in 134 rehabilitation patients (Study 2).ResultsIn multiple-step mediation models in both samples, there were indirect effects of intentions on habit strength through action planning and behavior.ConclusionAction planning and behavior operated as sequential mediators to bridge the gap between intentions and habit strength. Exercise habit strength may increase as a result of conscious action planning and frequent behavior enactment. Including these constructs jointly into behavior change models may improve the understanding of the mechanisms involved in behavior maintenance.