Guy E. Hawkins

Revisiting the evidence for collapsing boundaries and urgency signals in perceptual decision-making.

For nearly 50 years, the dominant account of decision-making holds that noisy information is accumulated until a fixed threshold is crossed. This account has been tested extensively against behavioral and neurophysiological data for decisions about consumer goods, perceptual stimuli, eyewitness testimony, memories, and dozens of other paradigms, with no systematic misfit between model and data. Recently, the standard model has been challenged by alternative accounts that assume that less evidence is required to trigger a decision as time passes. Such “collapsing boundaries” or “urgency signals” have gained popularity in some theoretical accounts of neurophysiology. Nevertheless, evidence in favor of these models is mixed, with support coming from only a narrow range of decision paradigms compared with a long history of support from dozens of paradigms for the standard theory. We conducted the first large-scale analysis of data from humans and nonhuman primates across three distinct paradigms using powerful model-selection methods to compare evidence for fixed versus collapsing bounds. Overall, we identified evidence in favor of the standard model with fixed decision boundaries. We further found that evidence for static or dynamic response boundaries may depend on specific paradigms or procedures, such as the extent of task practice. We conclude that the difficulty of selecting between collapsing and fixed bounds models has received insufficient attention in previous research, calling into question some previous results.

Transgenerational transmission of anxiety induced by neonatal exposure to lipopolysaccharide: Implications for male and female germ lines

Neonatal lipopolysaccharide (LPS) exposure increases anxiety-like behaviour and alters neuroendocrine responses to stress in adult rats. The current study assessed whether this anxiety-related phenotype observed in rats neonatally exposed to LPS is transferable to subsequent generations. Wistar rats were exposed to LPS (0.05 mg/kg, Salmonella enteritidis) or non-pyrogenic saline (equivolume) on postnatal days 3 and 5. In adulthood, animals were subjected to restraint and isolation stress or no stress, and subsequently evaluated for anxiety-like behaviours on the elevated plus maze, acoustic startle response, and holeboard apparatus. Blood was collected to examine corticosterone responses to stress and behavioural testing in adulthood. Animals from both treatment groups which exhibited the anxiety-like phenotype were bred with untreated partners. Maternal care of the second generation (F2) was monitored over the first week of life. In adulthood, the F2 generation underwent identical testing procedures as the parental (F1) generation. The F2 offspring of females exposed to LPS as neonates exhibited an anxiety-like phenotype in adulthood and a potentiated corticosterone response to stress (p<.05). F2 offspring of males exposed to LPS as neonates also exhibited an anxiety-like phenotype (p<.05), however, no differences in corticosterone responses were observed. To determine the impact of maternal care on the anxiety-like phenotype, a cross-fostering study was conducted in which offspring of LPS-treated females were fostered to saline-treated mothers and vice versa, which was found to reverse the behavioural and endocrine phenotypes of the F2 generation. These data indicate that a neonatally bacterially induced anxiety phenotype is transferable across generations in both sexes. Maternal care is the mediating mechanism along the maternal line. We suggest that transmission may be dependent upon heritable epigenetic phenomena for the paternal line. The implications of this study apply to potential neuroimmune pathways through which psychopathology may be transmitted along filial lines.

Gamelike features might not improve data

Many psychological experiments require participants to complete lots of trials in a monotonous task, which often induces boredom. An increasingly popular approach to alleviate such boredom is to incorporate gamelike features into standard experimental tasks. Games are assumed to be interesting and, hence, motivating, and better motivated participants might produce better data (with fewer lapses in attention and greater accuracy). Despite its apparent prevalence, the assumption that gamelike features improve data is almost completely untested. We test this assumption by presenting a choice task and a change detection task in both gamelike and standard forms. Response latency, accuracy, and overall task performance were unchanged by gamelike features in both experiments. We present a novel cognitive model for the choice task, based on particle filtering, to decorrelate the dependent variables and measure performance in a more psychologically meaningful manner. The model-based analyses are consistent with the hypothesis that gamelike features did not alter cognition. A postexperimental questionnaire indicated that the gamelike version provided a more positive and enjoyable experience for participants than the standard task, even though this subjective experience did not translate into data effects. Although our results hold only for the two experiments examined, the gamelike features we incorporated into both tasks were typical of—and at least as salient and interesting as those usually used by—experimental psychologists. Our results suggest that modifying an experiment to include gamelike features, while leaving the basic task unchanged, may not improve the quality of the data collected, but it may provide participants with a better experimental experience.

A Neural Model of Mind Wandering

The role of the default-mode network (DMN) in the emergence of mind wandering and task-unrelated thought has been studied extensively. In parallel work, mind wandering has been associated with neuromodulation via the locus coeruleus (LC) norepinephrine (LC-NE) system. Here we propose a neural model that links the two systems in an integrative framework. The model attempts to explain how dynamic changes in brain systems give rise to the subjective experience of mind wandering. The model implies a neural and conceptual distinction between an off-focus state and an active mind-wandering state and provides a potential neural grounding for well-known cognitive theories of mind wandering. Finally, the proposed neural model of mind wandering generates precise, testable predictions at neural and behavioral levels.

An optimal adjustment procedure to minimize experiment time in decisions with multiple alternatives

Decisions between multiple alternatives typically conform to Hick’s Law: Mean response time increases log-linearly with the number of choice alternatives. We recently demonstrated context effects in Hick’s Law, showing that patterns of response latency and choice accuracy were different for easy versus difficult blocks. The context effect explained previously observed discrepancies in error rate data and provided a new challenge for theoretical accounts of multialternative choice. In the present article, we propose a novel approach to modeling context effects that can be applied to any account that models the speed–accuracy trade-off. The core element of the approach is “optimality” in the way an experimental participant might define it: minimizing the total time spent in the experiment, without making too many errors. We show how this approach can be included in an existing Bayesian model of choice and highlight its ability to fit previous data as well as to predict novel empirical context effects. The model is shown to provide better quantitative fits than a more flexible heuristic account.

Context effects in multi-alternative decision making: Empirical data and a Bayesian model

For decisions between many alternatives, the benchmark result is Hicks Law: that response time increases log-linearly with the number of choice alternatives. Even when Hicks Law is observed for response times, divergent results have been observed for error rates-sometimes error rates increase with the number of choice alternatives, and sometimes they are constant. We provide evidence from two experiments that error rates are mostly independent of the number of choice alternatives, unless context effects induce participants to trade speed for accuracy across conditions. Error rate data have previously been used to discriminate between competing theoretical accounts of Hicks Law, and our results question the validity of those conclusions. We show that a previously dismissed optimal observer model might provide a parsimonious account of both response time and error rate data. The model suggests that people approximate Bayesian inference in multi-alternative choice, except for some perceptual limitations.

Integrating Cognitive Process and Descriptive Models of Attitudes and Preferences

Discrete choice experiments--selecting the best and/or worst from a set of options--are increasingly used to provide more efficient and valid measurement of attitudes or preferences than conventional methods such as Likert scales. Discrete choice data have traditionally been analyzed with random utility models that have good measurement properties but provide limited insight into cognitive processes. We extend a well-established cognitive model, which has successfully explained both choices and response times for simple decision tasks, to complex, multi-attribute discrete choice data. The fits, and parameters, of the extended model for two sets of choice data (involving patient preferences for dermatology appointments, and consumer attitudes toward mobile phones) agree with those of standard choice models. The extended model also accounts for choice and response time data in a perceptual judgment task designed in a manner analogous to best-worst discrete choice experiments. We conclude that several research fields might benefit from discrete choice experiments, and that the particular accumulator-based models of decision making used in response time research can also provide process-level instantiations for random utility models.

Of monkeys and men: Impatience in perceptual decision-making

For decades sequential sampling models have successfully accounted for human and monkey decision-making, relying on the standard assumption that decision makers maintain a pre-set decision standard throughout the decision process. Based on the theoretical argument of reward rate maximization, some authors have recently suggested that decision makers become increasingly impatient as time passes and therefore lower their decision standard. Indeed, a number of studies show that computational models with an impatience component provide a good fit to human and monkey decision behavior. However, many of these studies lack quantitative model comparisons and systematic manipulations of rewards. Moreover, the often-cited evidence from single-cell recordings is not unequivocal and complimentary data from human subjects is largely missing. We conclude that, despite some enthusiastic calls for the abandonment of the standard model, the idea of an impatience component has yet to be fully established; we suggest a number of recently developed tools that will help bring the debate to a conclusive settlement.

The role of causal models in multiple judgments under uncertainty

Two studies examined a novel prediction of the causal Bayes net approach to judgments under uncertainty, namely that causal knowledge affects the interpretation of statistical evidence obtained over multiple observations. Participants estimated the conditional probability of an uncertain event (breast cancer) given information about the base rate, hit rate (probability of a positive mammogram given cancer) and false positive rate (probability of a positive mammogram in the absence of cancer). Conditional probability estimates were made after observing one or two positive mammograms. Participants exhibited a causal stability effect: there was a smaller increase in estimates of the probability of cancer over multiple positive mammograms when a causal explanation of false positives was provided. This was the case when the judgments were made by different participants (Experiment 1) or by the same participants (Experiment 2). These results show that identical patterns of observed events can lead to different estimates of event probability depending on beliefs about the generative causes of the observations.

Dynamic difficulty balancing for cautious players and risk takers

Dynamic balancing of game difficulty can help cater for different levels of ability in players. However, performance in some game tasks depends on not only the players ability but also their desire to take risk. Taking or avoiding risk can offer players its own reward in a game situation. Furthermore, a game designer may want to adjust the mechanics differently for a risky, high ability player, as opposed to a risky, low ability player. In this work, we describe a novel modelling technique known as particle filtering which can be used to model various levels of player ability while also considering the players risk profile. We demonstrate this technique by developing a game challenge where players are required to make a decision between a number of possible alternatives where only a single alternative is correct. Risky players respond faster but with more likelihood of failure. Cautious players wait longer for more evidence, increasing their likelihood of success, but at the expense of game time. By gathering empirical data for the players response time and accuracy, we develop particle filter models. These models can then be used in real-time to categorise players into different ability and risk-taking levels.