Christian C. Luhmann

Neural Dissociation of Delay and Uncertainty in Intertemporal Choice

Decision makers often face choices whose consequences unfold over time. To explore the neural basis of such intertemporal choice behavior, we devised a novel two-alternative choice task with probabilistic reward delivery and contrasted two conditions that differed only in whether the outcome was revealed immediately or after some delay. In the immediate condition, we simply varied the reward probability of each option and the outcome was revealed immediately. In the delay condition, the outcome was revealed after a delay during which the reward probability was governed by a constant hazard rate. Functional imaging revealed a set of brain regions, such as the posterior cingulate cortex, parahippocampal gyri, and frontal pole, that exhibited activity uniquely associated with the temporal aspects of the task. This engagement of the so-called “default network” suggests that during intertemporal choice, decision makers simulate the impending delay via a process of prospection.

Why essences are essential in the psychology of concepts

Woo-kyoung Ahn*, Charles Kalish, Susan A. Gelman, Douglas L. Medin, Christian Luhmann, Scott Atran, John D. Coley, Patrick Shafto Department of Psychology, Vanderbilt University, Nashville, TN 37203, USA Department of Educational Psychology, University of Wisconsin, Madison, WI 53706, USA Department of Psychology, University of Michigan, Ann Arbor, MI 48109-1109, USA Department of Psychology, Northwestern University, Evanston, IL 60208-2710, USA Centre National de la Recherche Scientifique, Marseilles, France Department of Psychology, Northeastern University, Boston, MA 02115, USA

Effect of theory-based feature correlations on typicality judgments.

In the present study, we examine what types of feature correlations are salient in our conceptual representations. It was hypothesized that of all possible feature pairs, those that are explicitly recognized as correlated (i.e., explicit pairs) and affect typicality judgments are the ones that are more likely theory based than are those that are not explicitly recognized (i.e., implicit pairs). Real-world categories and their properties, taken from Malt and Smith (1984), were examined. We found that explicit pairs had a greater number of asymmetric dependency relations (i.e., one feature depends on the other feature, but not vice versa) and stronger dependency relations than did implicit pairs, which were statistically correlated in the environment but were not recognized as such. In addition, people more often provided specific relation labels for explicit pairs than for implicit pairs; these labels were most often causal relations. Finally, typicality judgments were more affected when explicit correlations were broken than when implicit correlations were broken. It is concluded that in natural categories, feature correlations that are explicitly represented and affect typicality judgments are the ones about which people have theories.

Temporal decision-making: insights from cognitive neuroscience

Decisions frequently have consequences that play out over time and these temporal factors can exert strong influences on behavior. For example, decision-makers exhibit delay discounting, behaving as though immediately consumable goods are more valuable than those available only after some delay. With the use of functional magnetic resonance imaging, we are now beginning to characterize the physiological bases of such behavior in humans and to link work on this topic from neuroscience, psychology, and economics. Here we review recent neurocognitive investigations of temporal decision-making and outline the theoretical picture that is beginning to take shape. Taken as a whole, this body of work illustrates the progress made in understanding temporal choice behavior. However, we also note several questions that remain unresolved and areas where future work is needed.

BUCKLE: A Model of Unobserved Cause Learning

Dealing with alternative causes is necessary to avoid making inaccurate causal inferences from covariation data. However, information about alternative causes is frequently unavailable, rendering them unobserved. The current article reviews the way in which current learning models deal, or could deal, with unobserved causes. A new model of causal learning, BUCKLE (bidirectional unobserved cause learning) extends existing models of causal learning by dynamically inferring information about unobserved, alternative causes. During the course of causal learning, BUCKLE continually computes the probability that an unobserved cause is present during a given observation and then uses the results of these inferences to learn the causal strengths of the unobserved as well as observed causes. The current results demonstrate that BUCKLE provides a better explanation of peoples causal learning than the existing models.

The feedback negativity reflects favorable compared to nonfavorable outcomes based on global, not local, alternatives

The feedback negativity (FN) has been shown to reflect the binary evaluation of possible outcomes in a context-dependent manner, but it is unclear whether context dependence is based on global or local alternatives. A cued gambling task was used to examine whether the FN is sensitive to possible outcomes on a given trial, or the range of outcomes across trials. On 50% of trials, participants could break even or lose money; on remaining trials, participants could win or break even. Breaking even was an unfavorable outcome relative to all possibilities in the current task, but the best possible outcome on 50% of trials. Results indicated that breaking even elicited an FN in both contexts, and reward feedback was uniquely associated with an enhanced positivity. Results suggest that the magnitude of the FN depends on all possible outcomes within the current task and are consistent with the view that the FN reflects reward-related neural activity.

Theory-based categorization under speeded conditions

It is widely accepted that similarity influences rapid categorization, whereas theories can influence only more leisurely category judgments. In contrast, we argue that it is not the type of knowledge used that determines categorization speed, but rather the complexity of the categorization processes. In two experiments, participants learned four categories of items, each consisting of three causally related features. Participants gave more weight to cause features than to effect features, even under speeded response conditions. Furthermore, the time required to make judgments was equivalent, regardless of whether participants were using causal knowledge or base-rate information. We argue that both causal knowledge and base-rate information, once precompiled during learning, can be used at roughly the same speeds during categorization, thus demonstrating an important parallel between these two types of knowledge.

Memory Transmission in Small Groups and Large Networks: An Agent-Based Model

The spread of social influence in large social networks has long been an interest of social scientists. In the domain of memory, collaborative memory experiments have illuminated cognitive mechanisms that allow information to be transmitted between interacting individuals, but these experiments have focused on small-scale social contexts. In the current study, we took a computational approach, circumventing the practical constraints of laboratory paradigms and providing novel results at scales unreachable by laboratory methodologies. Our model embodied theoretical knowledge derived from small-group experiments and replicated foundational results regarding collaborative inhibition and memory convergence in small groups. Ultimately, we investigated large-scale, realistic social networks and found that agents are influenced by the agents with which they interact, but we also found that agents are influenced by nonneighbors (i.e., the neighbors of their neighbors). The similarity between these results and the reports of behavioral transmission in large networks offers a major theoretical insight by linking behavioral transmission to the spread of information.

The conceptual centrality of causal cycles

How do causal cycles affect judgments of conceptual centrality? Generally, a feature is central to a concept to the extent that other features in the concept depend on it, thereby rendering it immutable from the concept (Sloman, Love, & Ahn, 1998). Previous research on conceptual centrality has focused primarily on features involved in four major types of dependency structures: simple cause-effect relations, causal chains, common-cause structures, and common-effect structures. Causal cycles are a fifth type of dependency structure commonly reported in people’s real-life concepts, yet to date, they have been relatively ignored in research on conceptual centrality. The results of six experiments suggest that previously held assumptions about the conceptual representation of cycles are incorrect. We discuss the implications of these findings for our understanding of theory-based concepts.

Adaptive intertemporal preferences in foraging-style environments

Decision makers often face choices between smaller more immediate rewards and larger more delayed rewards. For example, when foraging for food, animals must choose between actions that have varying costs (e.g., effort, duration, energy expenditure) and varying benefits (e.g., amount of food intake). The combination of these costs and benefits determine what optimal behavior is. In the present study, we employ a foraging-style task to study how humans make reward-based choices in response to the real-time constraints of a dynamic environment. On each trial participants were presented with two rewards that differed in magnitude and in the delay until their receipt. Because the experiment was of a fixed duration, maximizing earnings required decision makers to determine how to trade off the magnitude and the delay associated with the two rewards on each trial. To evaluate the extent to which participants could adapt to the decision environment, specific task characteristics were manipulated, including reward magnitudes (Experiment 1) and the delay between trials (Experiment 2). Each of these manipulations was designed to alter the pattern of choices made by an optimal decision maker. Several findings are of note. First, different choice strategies were observed with the manipulated environmental constraints. Second, despite contextually-appropriate shifts in behavior between conditions in each experiment, choice patterns deviated from theoretical optimality. In particular, the delays associated with the rewards did not exert a consistent influence on choices as required by exponential discounting. Third, decision makers nevertheless performed surprisingly well in all task environments with any deviations from strict optimality not having particularly deleterious effects on earnings. Taken together, these results suggest that human decision makers are capable of exhibiting intertemporal preferences that reflect a variety of environmental constraints.