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Schizophrenia is associated with a number of deficits in decision-making, but the scope, nature, and cause of these deficits are not completely understood. Here we focus on a particular type of decision, known as the explore/ exploit dilemma, in which people must choose between exploiting options that yield relatively known rewards and exploring more ambiguous options of uncertain reward probability or magnitude. Previous work has shown that healthy people use two distinct strategies to decide when to explore: directed exploration, which involves choosing options that would reduce uncertainty about the reward values (information seeking), and random exploration (exploring by chance), which describes behavioral variability that is not goal directed. We administered a recently developed gambling task designed to quantify both directed and random exploration to 108 patients with schizophrenia (PSZ) and 33 healthy volunteers (HVs). We found that PSZ patients show reduced directed exploration relative to HVs, but no difference in random exploration. Moreover, patients’ directed exploration behavior clusters into two qualitatively different behavioral phenotypes. In the first phenotype, which accounts for the majority of the patients (79%) and is consistent with previously reported behavior, directed exploration is only marginally (but significantly) reduced, suggesting that these patients can use directed exploration, but at a slightly lower level than community controls. In contrast, the second phenotype, comprising 21% of patients, exhibits a form of “extreme ambiguity aversion,” in which they almost never choose more informative options, even when they are clearly of higher value. Moreover, in PSZ, deficits in directed exploration were related to measures of intellectual function, whereas random exploration was related to positive symptoms. Taken together, our results suggest that schizophrenia has differential effects on directed and random exploration and that investigating the explore/exploit dilemma in psychosis patients may reveal subgroups of patients with qualitatively different patterns of exploration.

Animals, including humans, consistently exhibit myopia in two different contexts: foraging, in which they harvest locally beyond what is predicted by optimal foraging theory, and intertemporal choice, in which they exhibit a preference for immediate vs. delayed rewards beyond what is predicted by rational (exponential) discounting. Despite the similarity in behavior between these two contexts, previous efforts to reconcile these observations in terms of a consistent pattern of time preferences have failed. Here, via extensive behavioral testing and quantitative modeling, we show that rats exhibit similar time preferences in both contexts: they prefer immediate vs. delayed rewards and they are sensitive to opportunity costs of delays to future decisions. Further, a quasi-hyperbolic discounting model, a form of hyperbolic discounting with separate components for short- and long-term rewards, explains individual rats' time preferences across both contexts, providing evidence for a common mechanism for myopic behavior in foraging and intertemporal choice.

Objectives Difficulties with deception detection may leave older adults especially vulnerable to fraud. Interoception, that is, the awareness of one's bodily signals, has been shown to influence deception detection, but this relationship has not been examined in aging yet. The present study investigated effects of interoceptive accuracy on 2 forms of deception detection: detecting interpersonal lies in videos and identifying text-based deception in phishing emails. Methods Younger (18–34 years) and older (53–82 years) adults completed a heartbeat detection task to determine interoceptive accuracy. Deception detection was assessed across 2 distinct, ecologically valid tasks: (i) a lie detection task in which participants made veracity judgments of genuine and deceptive individuals, and (ii) a phishing email detection task to capture online deception detection. Using multilevel logistic regression models, we determined the effect of interoceptive accuracy on lie and phishing detection in younger versus older adults. Results In older, but not younger, adults greater interoceptive accuracy was associated with better accuracy in both detecting deceptive people and phishing emails. Discussion Interoceptive accuracy was associated with both lie detection and phishing detection accuracy among older adults. Our findings identify interoceptive accuracy as a potential protective factor for fraud susceptibility, as measured through difficulty detecting deception. These results support interoceptive accuracy as a relevant factor for consideration in interventions targeted at fraud prevention among older adults. [ABSTRACT FROM AUTHOR]

Computational modeling of behavior has revolutionized psychology and neuroscience. By fitting models to experimental data we can probe the algorithms underlying behavior, find neural correlates of computational variables and better understand the effects of drugs, illness and interventions. But with great power comes great responsibility. Here, we offer ten simple rules to ensure that computational modeling is used with care and yields meaningful insights. In particular, we present a beginner-friendly, pragmatic and details-oriented introduction on how to relate models to data. What, exactly, can a model tell us about the mind? To answer this, we apply our rules to the simplest modeling techniques most accessible to beginning modelers and illustrate them with examples and code available online. However, most rules apply to more advanced techniques. Our hope is that by following our guidelines, researchers will avoid many pitfalls and unleash the power of computational modeling on their own data.

Adults use both bottom-up sensory inputs and top-down signals to generate predictions about future sensory inputs. Infants have also been shown to make predictions with simple stimuli and recent work has suggested top-down processing is available early in infancy. However, it is unknown whether this indicates that top-down prediction is an ability that is continuous across the lifespan or whether an infant's ability to predict is different from an adult's, qualitatively or quantitatively. We employed pupillometry to provide a direct comparison of prediction abilities across these disparate age groups. Pupil dilation response (PDR) was measured in 6-month olds and adults as they completed an identical implicit learning task designed to help learn associations between sounds and pictures. We found significantly larger PDR for visual omission trials (i.e. trials that violated participants' predictions without the presentation of new stimuli to control for bottom-up signals) compared to visual present trials (i.e. trials that confirmed participants' predictions) in both age groups. Furthermore, a computational learning model that is closely linked to prediction error (Rescorla-Wagner model) demonstrated similar learning trajectories suggesting a continuity of predictive capacity and learning across the two age groups.

Serial stay-or-search problems are ubiquitous across manydomains, including employment, internet search, mate search,and animal foraging. For instance, in patch foragingproblems, animals must decide whether to stick with adepleting reward vs search for a new source. The optimalstrategy in patch foraging problems, described by theMarginal Value Theorem (MVT; Charnov, 1976), is to leavethe depleting patch when the local reward rate within a patchmatches the overall long-run reward rate. Many species ofanimals, ranging from birds to rodents, monkeys, andhumans, adhere to this policy in important respects, but tendto overharvest, or stick with the depleting resource too long.Here we attempt to determine the cognitive biases thatunderlie overharvesting in one of these species (the rat). Wecharacterized rat behavior in response to two basicmanipulations in patch foraging tasks: to travel time betweenpatches and depletion rate, and two novel manipulations tothe foraging environment: the size of reward and length ofdelays, and placement of delays (pre- vs. post-reward). Inresponse to the basic manipulations, rats qualitativelyfollowed predictions of MVT, but stayed in patches for longerthan is predicted. In the latter two manipulations, rats deviatedfrom predictions of MVT, exhibiting changes in behavior notpredicted by MVT. We formally tested whether four separatecognitive biases – subjective costs, decreasing marginal utilityfor reward discounting of future reward, and ignoring post-reward delays – could explain overharvesting in the formertwo manipulations and deviations from MVT in the latter two.All of the biases tested explained overharvesting behavior inthe former contexts, but only one bias – in which rats ignorepost-reward delays – also explained deviations from MVTdue to larger rewards with longer delays and due tointroduction of a pre-reward delay. Our results show thatmultiple biases can explain certain aspects of overharvestingbehavior, and, while foraging behavior may be the result ofthe use of multiple biases, inaccurate estimation of post-reward delays likely contributes to overharvesting.

Context (such as our location or current goal) informs everyday decisions, both by predicting stimuli and determiningrelevant responses. How do we develop priors that are general enough to apply in various contexts yet specific enough tomaximize reward in a given context? We investigated this using the AX-CPT, a task in which a cue determines which buttonto press for a probe that appears seconds later. We manipulated the frequency of the probe given the cue across participantsand built a diffusion model to estimate how the cue informs participants’ priors for the decision. We found that participants’context-dependent priors were closer to each other and less extreme than those predicted by a model that maximizes rewardrate given the true stimulus frequencies. However, participants’ priors were optimal given their subjective frequency estimates,which showed that they averaged response probabilities across cues when the cues made sufficiently similar predictions.

Any adaptive organism faces the choice between takingactions with known benefits (exploitation), and sampling newactions to check for other, more valuable opportunitiesavailable (exploration). The latter involves information-seeking, a drive so fundamental to learning and long-termreward that it can reasonably be considered, through evolutionor development, to have acquired its own value, independentof immediate reward. Similarly, behaviors that fail to yieldinformation may have come to be associated with aversiveexperiences such as boredom, demotivation, and taskdisengagement. In accord with these suppositions, we proposethat boredom reflects an adaptive signal for managing theexploration-exploitation tradeoff, in the service of optimizinginformation acquisition and long-term reward. We testedparticipants in three experiments, manipulating theinformation content in their immediate task environment, andshowed that increased perceptions of boredom arise inenvironments in which there is little useful information, andthat higher boredom correlates with higher exploration. Thesefindings are the first step toward a model formalizing therelationship between exploration, exploitation and boredom.

Daily life often makes us decide between two goals:maximizing immediate rewards (exploitation) and learningabout the environment so as to improve our options for futurerewards (exploration). An adaptive organism therefore shouldplace value on information independent of immediate reward,and affective states may signal such value (e.g., curiosity vs.boredom: Hill & Perkins, 1985; Eastwood et al. 2012). Thistradeoff has been well studied in “bandit” tasks involvingchoice among a fixed number of options, but is equallypertinent in situations such as foraging, hunting, or job search,where one encounters a series of new options sequentially.Here, we augment the classic serial foraging scenario to moreexplicitly reward the development of knowledge. We developa formal model that quantifies the value of information in thissetting and how it should impact decision making, parallelingthe treatment of reward by the marginal value theorem (MVT)in the foraging literature. We then present the results of anexperiment designed to provide an initial test of this model,and discuss the implications of this information-foragingframework on boredom and task disengagement.