To investigate impression formation, researchers tend to rely on statements that describe a person's behavior (e.g., "Alex ridicules people behind their backs"). These statements are presented to ...participants who then rate their impressions of the person. However, a corpus of behavior statements is costly to generate, and pre-existing corpora may be outdated and might not measure the dimension(s) of interest. The present study makes available a normed corpus of 160 contemporary behavior statements that were rated on 4 dimensions relevant to impression formation: morality, competence, informativeness, and believability. In addition, we show that the different dimensions are non-independent, exhibiting a range of linear and non-linear relationships, which may present a problem for past research. However, researchers interested in impression formation can control for these relationships (e.g., statistically) using the present corpus of behavior statements.
Many kinds of objects and events in our world have a strongly time-dependent quality. However, most theories about concepts and categories either are insensitive to variation over time or treat it as ...a nuisance factor that produces irrational order effects during learning. In this article, we present two category learning experiments in which we explored peoples’ ability to learn categories whose structure is strongly time-dependent. We suggest that order effects in categorization may in part reflect a sensitivity to changing environments, and that understanding dynamically changing concepts is an important part of developing a full account of human categorization.
Despite robust evidence that misinformation continues to influence event-related reasoning after a clear retraction, evidence for the continued influence of misinformation on person impressions is ...mixed. Across four experiments, we investigated the impact of person-related misinformation and its correction on dynamic (moment-to-moment) impression formation. Participants formed an impression of a protagonist, "John", based on a series of behaviour descriptions, including misinformation that was later retracted. Person impressions were recorded after the presentation of each behaviour description. As predicted, we found a strong effect of information valence on person impressions: negative misinformation had a greater impact on person impressions than positive misinformation (Experiments 1 and 2). Furthermore, in each experiment participants fully discounted the misinformation once retracted, regardless of whether the misinformation was negative or positive. This was true even when the other behaviour descriptions were congruent with (Experiment 2) or causally related to the retracted misinformation (Experiments 3 and 4). To test for generalisation, Experiment 4 used a different misinformation statement; it again showed no evidence for the continued influence of retracted misinformation on person impressions. Our findings indicate that person-related misinformation can be effectively discounted following a clear retraction.
I address two questions that underlie most of the articles in this special issue: 1) What do different levels of explanation in psychology reveal? And 2) how do the dynamics of science affect what ...can be learned? I suggest that understanding hypothesis testing and generation in the abstract can provide a useful framework for understanding how cognitive modelling and neuroscience may interact. I further suggest that the preference for simple explanations and the dynamics of hypothesis testing may play out in different ways within the two fields, and that their overlap may prove most useful in the realm of hypothesis generation.
The future of human behaviour research Box-Steffensmeier, Janet M; Burgess, Jean; Corbetta, Maurizio ...
Nature human behaviour,
01/2022, Letnik:
6, Številka:
1
Journal Article
In response to the COVID-19 pandemic, countries are introducing digital passports that allow citizens to return to normal activities if they were previously infected with (immunity passport) or ...vaccinated against (vaccination passport) SARS-CoV-2. To be effective, policy decision makers must know whether these passports will be widely accepted by the public, and under what conditions? This study focuses attention on immunity passports, as these may prove useful in countries both with and without an existing COVID-19 vaccination program, however, our general findings also extend to vaccination passports.
We aimed to assess attitudes towards the introduction of immunity passports in six countries, and determine what social, personal and contextual factors predicted their support.
We collected 13,678 participants through online representative sampling across six countries - Australia, Japan, Taiwan, Germany, Spain, and the United Kingdom - during April to May of the 2020 COVID-19 pandemic, and assessed attitudes and support for the introduction of immunity passports.
Immunity passport support was moderate-to-low, being highest in Germany (51%; 775 of 1507 participants) and the United Kingdom (51%; 759 of 1484), followed by Taiwan (47%; 2841 of 5989), Australia (46%; 963 of 2086) and Spain (46%; 693 of 1491), and lowest in Japan (22%; 241 of 1081). Bayesian generalized linear mixed-effects modelling assessed predictive factors for immunity passport support across countries. International results showed neoliberal world views (odds ratio, OR = 1.17, CI1.13:1.22), personal concern (OR = 1.07, CI1:1.16) and perceived virus severity (OR = 1.07, CI1.01:1.14), the fairness of immunity passports (OR = 2.51, CI2.36:2.66), liking immunity passports (OR = 2.77, CI2.61:2.94), and a willingness to become infected to gain an immunity passport (OR = 1.6, CI1.51:1.68), were all predictive factors of immunity passport support. By contrast, gender (woman; OR = 0.9, CI0.82:0.98), immunity passport concern (OR = 0.61, CI0.57:0.65), and risk of harm to society (OR = 0.71, CI0.67:0.76) predicted a decrease in support for immunity passports. Minor differences in predictive factors were found between countries and results were modelled separately to provide national accounts of these data.
Our research suggests that support for immunity passports is predicted by the personal benefits and societal risks they confer. These findings generalized across six countries and may also prove informative for the introduction of vaccination passports, helping policy-makers to introduce effective COVID-19 passport policies in these six countries and around the world.
The impressive recent performance of large language models has led many to wonder to what extent they can serve as models of general intelligence or are similar to human cognition. We address this ...issue by applying GPT-3.5 and GPT-4 to a classic problem in human inductive reasoning known as property induction. Over two experiments, we elicit human judgments on a range of property induction tasks spanning multiple domains. Although GPT-3.5 struggles to capture many aspects of human behaviour, GPT-4 is much more successful: for the most part, its performance qualitatively matches that of humans, and the only notable exception is its failure to capture the phenomenon of premise non-monotonicity. Our work demonstrates that property induction allows for interesting comparisons between human and machine intelligence and provides two large datasets that can serve as benchmarks for future work in this vein.
Both adults and children have shown impressive cross-situational word learning in which they leverage the statistics of word usage across many different scenes in order to isolate specific word ...meanings (e.g., Yu & Smith, 2007). However, relatively little is known about how this learning scales to real language. Some theoretical analyses suggest that when words follow a Zipfian distribution, as they do in natural language, it should be more difficult to learn a lexicon because of the many low-frequency words that are only observed a few times (Blythe, Smith, & Smith, 2010; Vogt, 2012). Although this effect can be mitigated somewhat by assuming mutual exclusivity (Reisenauer, Smith, & Blythe, 2013), no mathematical analyses suggest that learning in a Zipfian environment should be easier. In this work, we show the opposite of the predicted effect using cross-situational learning experiments with adults: when the distribution of words and meanings is Zipfian, learning is not impaired and is usually improved. Over a series of experiments, we provide evidence that this is because Zipfian distributions help people to disambiguate the meanings of the other words in the situation.
•Category frequency changes generalization differently in one- and two-category tasks.•Increasing category frequency tightens generalizations for a single category.•Increasing category frequency ...expands generalizations with two categories.•Sampling assumptions moderate the effect of frequency in the two-category case.
Categorization and generalization are fundamentally related inference problems. Yet leading computational models of categorization (as exemplified by, e.g., Nosofsky, 1986) and generalization (as exemplified by, e.g., Tenenbaum and Griffiths, 2001) make qualitatively different predictions about how inference should change as a function of the number of items. Assuming all else is equal, categorization models predict that increasing the number of items in a category increases the chance of assigning a new item to that category; generalization models predict a decrease, or category tightening with additional exemplars. This paper investigates this discrepancy, showing that people do indeed perform qualitatively differently in categorization and generalization tasks even when all superficial elements of the task are kept constant. Furthermore, the effect of category frequency on generalization is moderated by assumptions about how the items are sampled. We show that neither model naturally accounts for the pattern of behavior across both categorization and generalization tasks, and discuss theoretical extensions of these frameworks to account for the importance of category frequency and sampling assumptions.
PDF
